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Marketing Intelligence Report
AmazonFresh
1516 12th
Ave.
Seattle, WA 98101
July 5th
, 2015
Neil Lindsay
AmazonFresh
1516 12th
Ave.
Seattle, WA 98101
Dear Mr. Lindsay:
Per your letter of authorization dated May 19, 2015, our team has completed the Marketing
Intelligence Project for AmazonFresh. Enclosed please find a report titled “Marketing
Intelligence Report for AmazonFresh” The report presents the study’s objectives, data analysis,
strategic analysis, marketing metrics, database and marketing dashboard, key findings,
limitations, and implications.
My team used standard marketing intelligence practices throughout the project and we believe
that the results address the stated project objectives. I trust that the findings will help you in your
decisions for AmazonFresh marketing decisions.
If you have any questions, please do not hesitate to call me at (123)-456-7890 or email me at
(Marketingintelligence@amazonfresh.edu). I enjoyed working with you on this project and I
look forward to working with you again in the future.
Sincerely,
ABC
ABC
Director of Marketing Intelligence Project
Marketing Intelligence Report
2015
Chu Wang
Dahai Liu
Sagar Chadderwala
2015/7/5
Marketing Intelligence Project
AmazonFresh
Marketing Intelligence Report
Marketing Intelligence Report
Content
EXECUTIVE SUMMARY .......................................................................................................... 1
PHASE 1 ........................................................................................................................................ 7
SUMMARY..........................................................................................................................................7
MANAGERIAL REPORT .................................................................................................................10
TECHNICAL REPORT......................................................................................................................19
REFERENCES ...................................................................................................................................23
PHASE 2 STRATEGIC ANALYSIS ........................................................................................ 25
COMPANY OVERVIEW ..................................................................................................................25
MARKET OVERVIEW .....................................................................................................................25
TARGET CUSTOMERS....................................................................................................................27
VALUE PROPOSITIONS..................................................................................................................27
GOALS AND OBJECTIVES.............................................................................................................28
REFERENCES ...................................................................................................................................28
PHASE 3 - MARKETING METRICS...................................................................................... 29
PART 1 - METRICS...........................................................................................................................29
PART 2 - BIG PICTURE OF MARKETING METRICS ..................................................................39
PART 3 - METRIC CALCULATIONS FOR PROPOSAL ...............................................................41
PART 4 - MARKETING DASHBOARD ..........................................................................................47
PART 5 - FUNNEL AND INSIGHTS ...............................................................................................48
REFERENCES ...................................................................................................................................49
PHASE 4 – DATABASE, SQL AND MARKETING DASHBOARD.................................... 50
INTRODUCTION ..............................................................................................................................50
DATABASE DESIGN........................................................................................................................50
Marketing Intelligence Report
TABLE SCHEMES ............................................................................................................................50
SQL CODES.......................................................................................................................................51
MARKETING DASHBOARD...........................................................................................................65
LIMITATIONS........................................................................................................................... 66
TECHNICAL APPENDIX......................................................................................................... 67
SAS CODES.......................................................................................................................................67
Marketing Intelligence Report
1
Executive Summary
AmazonFresh, a subsidiary of Amazon.com, the largest Internet retailer in United States,
succeeds in the business model of online grocery ordering and home delivery. Recently, faced by
a large number of challenges, AmazonFresh is putting an effort into figuring out how to enlarge
their business and increase profitability.
As the marketing consultant of AmazonFresh, our marketing department applied a rigorous
marketing intelligence project in support of the objective. There are four phases in the entire
marketing decision process: At first, in Phase 1, we explored external data, internal data and
secondary information to generate the related marketing intelligence, and then identified the
business opportunity -- geographic market expansion. In Phase 2, the situation analysis was
conducted to identify the goals for expansion into new markets. In Phase 3, we demonstrated
how our proposal could generate value for AmazonFresh by applying multiple marketing metrics
and evaluating the performance of the new business line. At last, in Phase 4, we designed a
database for our proposal and created a dashboard to present key metrics effectively.
The entire process is shown as the following flowchart:
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Marketing Decision Model Process
Phase1
DataAnalysis
Phase2
StrategicAnalysis
Phase3
MarketingMetrics
Phase4
Databases,SQL,and
MarketingDashboard
MARKETING INTELLIGENCE PROJECT
AMAZONFRESH
MARKETING DECISION MODELS
MA
MMAm
INTERNAL DATASET EXTERNAL DATASET SECONDARY RESEARCH
OPPORTUNITY IDENTIFIED– MKT EXPANSION
SITUATION ANALYSIS
DEFINE GOALS
IDENTIFY THE TARGET MARKET DEVELOP A VALUE PROPOSITION
DEFINE PERFORMANCE
METRICS
IDENTIFY COMPARISON
BENCHMARKS
CONDUCT PERFORMANCE
ANALYSIS
VALUE CREATION
MODEL
DESIGN MARKETING
DASHBOARD
DESIGN AND IMPLEMENT A
DATABASE
IMPLEMENT THE
MARKETING DASHBOARD
Marketing Intelligence Report
3
Phase 1 Data Analysis
In Phase 1, data analysis was carefully performed including data aggregation, data cleaning, and
data configuration. Furthermore, we integrally combined and investigated three aspects of data
source: secondary reports analysis, internal data analysis and external data analysis.
 Secondary reports analysis
As per secondary research, online grocery business which currently accounts for 4 percent of the
total grocery business is poised to grow at a rate of 9.6% by 2018.
This can be explained with the shopper readiness of online grocery purchase, the easy and
comfortable shopping experience, and time-saving grocery shopping channel.
 Internal and external data analysis
Internal data and external data were used to identify new business opportunities for
AmazonFresh.
1. Market Expansion
The analysis indicated the opportunities for AmazonFresh to expand the business beyond its
current locations in order to generate more profits.
The model for expansion was based on two criteria:
1) High income households
2) Population density of 600 per square mile
We analyzed the internal customer profiles by aggregating the revenue at a zip code level and
merging this aggregated data with external data files which contain median household income,
density per square mile and corresponding city and state names. There were 34 cities identified
for expansion which lied across five states including New York, New Jersey, Pennsylvania,
Texas and California.
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Figure 1: Potential market for AmazonFresh at state wise
2. Consumer Behavior Analysis
Customer Behavior Analysis was conducted to identify the trend in support of customer retention.
Two main findings are listed as below:
1) The most common channel of payment is Credit Card
2) Gift Card has very minimal effect on increasing overall sales
The following three suggested strategies were proposed based on the two findings:
1) Optimize the user experience with credit card payment in terms of information
restoring and transaction processing in both Website and App interaction interface
design
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2) Build a beneficial partnership with credit card banks which enables AmazonFresh to
offer promotion and deal to its own customers and then boost credit card payment
3) Improve the Gift Card program by increasing its awareness, adopting effective
promotion strategies and better understanding the customer demands
Phase 2 Strategic Analysis
In Phase 1, we had identified the areas of opportunities to introduce a new value proposition. At
this phase, we conducted situation analysis to identify the AmazonFresh’s goals and objectives.
1. Expanding market to 34 cities in the United States by 2017
2. Market share to be increased to 10% of online grocery business by 2017
3. Increasing awareness of AmazonFresh by 50% over all markets by massive advertising
and promotions
Phase 3 Marketing Metrics
In this phase, we identified metrics that could demonstrate how our proposal would lead the
value to AmazonFresh. In total, we conducted 15 metrics fundamentally focusing on two areas:
1. Metrics that measure the value of brand
2. Metrics that measure the value of customers
We have mapped the process by leveraging Dupont Model of how various marketing metrics
lead to value for the firm, and then a marketing dashboard was carefully created to provide a big
picture of all the key performance indicators which could help the CMO making critical
decisions. All the metrics calculated for the proposal were equipped with reasonable estimation
and real industry numbers. Finally, a funnel was generated which described the theoretical
customer journey to conversion from the moment of contact to the ultimate purchase.
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Phase 4 Database, SQL and a Marketing Dashboard
A database was designed in the proposal which consisted of 4 tables, each having one primary
key and foreign key to connect the tables with each other. The database was connected to the
Excel file which enabled the marketing dashboard to be timely reflective of any update in the
tables.
The marketing dashboard mainly reflected three key details:
1. The City which gives the maximum revenue
2. The product which is the most profitable
3. The product giving the maximum sales in different cities which can be used for effective
marketing campaign
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PHASE 1
SUMMARY
The data analysis report aims at exploring business opportunities for AmazonFresh which is a
subsidiary of Amazon.com and provides online grocery home delivery. The report is based on
two areas of study: 1) A summary of all the secondary reports that comprehensively demonstrate
the industry landscape, trend development, and predictions; 2) An analysis of the dataset
combining internal data provided by the company and external datasets gathered from outside
resources to generate our marketing strategy.
The goal for the secondary report analysis is to point us to seek the new line of business. Two
interesting insights are drawn from the research: First, the online grocery shopping has a huge
potential in the market, and AmazonFresh works well in some areas comparing to its competitors.
Second, great opportunities exist in market expansion for AmazonFresh from Seattle, San
Francisco to other areas of the nation. Further research on market expansion and consumer
behavior are demanded in order to better attract and satisfy the target market.
The next step is to define the criteria for the potential markets where the firm is going to expand.
We see the areas with high density of population and consumers from the same areas with
higher-than-average income but lower-than-average purchasing are good to target. Therefore, we
analyzed the customer profiles from internal dataset by aggregating the revenue at a zip code
level and merging this aggregated data with external data files which contain median household
income, density per square mile and corresponding city and state names. Thus, we identified 34
cities in 5 states where AmzonFresh would like to expand the market.
Furthermore, we also conducted consumer behavior analysis on payment method and
effectiveness of the gift card program. We found out that credit card was the most common
payment method through the channels of Website and App. In addition, gift card program did
have some effect on accelerating the sale but has a great space for improvement
Overall, based on the findings of our analysis, the marketing strategies can be proposed as below:
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1. Expand the market to 34 cities within 5 states including New York, New Jersey,
Pennsylvania, Texas and California
2. Optimize user experience of credit card transaction payment across all channels
3. Build partnership with credit card banks to offer promotion and deal to consumers
4. Advertise on AmazonFresh gift card program and improve distribution system of gift
card
The complete data analysis process can be illustrated as the following flow chart:
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Data Analysis Process in Phase 1
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MANAGERIAL REPORT
To determine the current and future demands and preferences, we assessed the potential
opportunities where are sensible for the company to go further in the future, and examined
consumer behavior of the market. The three areas explored are as follows:
1. Secondary data was acquired to gain a complete understanding of the industry landscape
and trend development in the online grocery business
2. Three external data files were combined with the internal data to further identify potential
markets
3. The internal customer database was analyzed thoroughly to explore opportunities and
understand customer behavior
Part 1 Company Background
AmazonFresh is a subsidiary of Amazon.com, an American e-commerce company headquartered
in Seattle, Washington. AmazonFresh first ventured into the business of delivering home grocery
in August 2007. It first offered delivery service to Seattle suburb of Mercer Island. Gradually, it
expanded its market to other cities of California.
AmazonFresh offers fresh grocery, prepared meals, quality meat, seafood, baked goods, unique
ingredients for a special recipe and much more. In addition, it sells a subset of items from the
main Amazon.com storefront, such as electronic items. Products ordered from AmazonFresh are
available for home delivery on the same or the next day basically depending on the time of the
order. Also, it allows customers to select time slot convenient to them for delivery.
Part 2 Secondary Report
 Online Grocery Industry
According to a report from IBIS World, a market research firm, online grocery sales increased at
a rate of 14.1% annually over the last five years. It estimated the online grocery business
collectively brought in $10.9 billion in sales in 2014.1
Additionally, Brick Meets Click, a retail
research firm, claimed that about 1 in 10 are buying some groceries online, and forecasted that
online grocery shopping would reach between 11% and 17% by 2023.2
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Figure 2: Expected growth for online grocery sales (2014-2023)
Grocery sales through an online platform are poised to grow tremendously in coming years.
However, there is a major challenge in this industry. IBIS World estimates that the profit of
online grocery business was just $927.1 million in 2014, only 8.5 percent of total revenue. By
2018, research projects that profit margins will slip to 6.9 percent of sales.3
Therefore, why does Amazon enter into online groceries in the first place even though it’s a low
margin business? The main reasons are:
1. It is an influential way to increase the frequency of customer interaction.
2. It will help in putting Treasure Truck (a new way for Amazon customers to order and
pickup highly desirable, limited-quantity products, food and more) 4
in high density
neighborhoods and potentially will help change the economies of same day delivery.
 Reasons for online grocery shopping
Market Level analysis done by Bricks Meets Click suggests that the trend of online grocery will
be driven by:
1. Shopper readiness and availability of platform for shopping online
2. Customer ease in online shopping and the overall experience
Also, one Nielsen research indicates the primary factors that drive online grocery shopping. The
result shows convenience is the main reason that people choose for online grocery shopping.
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Figure 3: Reasons for online grocery shopping5
AmazonFresh is clearly making moves towards offering convenience to its customers. The firm
is trying all means that offers its customers flexibility and is removing all those barriers that can
prevent the future growth. It has introduced new platforms for shopping to make it easily:
-Online Website
-Amazon App
-Amazon Dash (new product launched)
-Amazon Dash Button
 Barriers to online grocery shopping
Nielsen also conducted analysis about factors that have become barriers to the growth of this
industry. The result shows that shipping cost and shipping time are the most critical problems for
customers.
Means of
convenience
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Figure 4: Barriers to online grocery shopping6
AmazonFresh is working on the barriers that are preventing consumer to shop online: it has
introduced annual prime membership of $299, which includes all the benefits of prime
membership. Also, the company is working significantly on reducing the delivery time. It
provides same day or early morning free delivery service for orders up to $35.
 AmazonFresh direct competitive analysis
Table 1: AmazonFresh and the direct competitors
AmazonFresh Peapod FreshDirect Safeway Walmart
Launch Date 2007 1989 2002 2002 2011
Geographic
Coverage
Some cities
in California
PA, MD, DC,
MA, CT, IL,
NY. NJ
NYC,
Philadelphia
NC, OR,
AZ,NV,
DC
SJ, SF,
Philadelphia
Target
Customer
High Income Family High Income Family Family
Same-Day
Delivery
Yes No No Yes No
General
Merchandise
Yes No No No Yes
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Minimum
Order
None $60 $30 $49 None
No of SKUS 115,000 10,000 8,500 30,000 10,000
Pricing High Medium High Medium Medium
Delivery Fee $9.99 $9.95 $5.99 $12.95 $8.00
 AmazonFresh’s Market Share and Expansion Strategy:
Currently, AmazonFresh market share in online grocery business is only about 1% because of
the limited areas that it is serving. However, survey conducted by Bricks Meet Click revealed
that almost 40 percent of shoppers preferred AmazonFresh to any other food retailers.7
The study reveals that AmazonFresh business strategy is to enter the mass market with big force.
AmazonFresh is an important element of Amazon to address the largely untouched opportunity.
Its incremental margins could be as high as 16%. 8
AmazonFresh first started with operating in pilot mode in Seattle, San Francisco and Los
Angeles to test if online grocery shopping is a viable business. Today, the firm is planning to roll
out in more cities.
AmazonFresh business model for expansion:
1. High Income Households
2. Population of at least 2 million people at a density 600 per square mile
Part 3 Data Analysis
 Market Expansion: Identifying the opportunities
1. Internal Dataset
Historical customer purchase data is provided by Amazon to find opportunities for its subsidiary
AmazonFresh. It is a twelve-year period data, from September 19th
, 1997 to April 23rd
, 2009 with
total 14449 records, which provides information on order source, quantity of items purchased,
payment information and zip code of the purchasers.
2. External Datasets
To identify the potential markets, we focused on two factors based on our secondary research:
1) Zip codes which have less revenue share but higher income than the average
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2) Zip codes which have density higher than 600 per square mile
To conduct the analysis, we searched for three external datasets:
1) U.S. nationwide zip codes with median household income
2) U.S. nationwide zip codes with density per square mile
3) U.S. nationwide zip codes with city and state information
3. Analysis & Findings
First, we combined our internal dataset and median household income dataset together, and
found out 1495 zip codes with higher income and lower revenue. Next, we applied the
population density dataset to the former combined dataset in order to further explore the potential
market. Finally, there were 1001 potential zip codes selected for market expansion.
To see clearly where our markets are, we took the 1001 zip codes on state level, and figured out
following top five potential states: California, New York, New Jersey, Pennsylvania, and
Texas.
Figure 5: AmazonFresh’s potential market at state wise
The potential cities where can be expanded are:
 California (5) – Long Beach, Orange, Sacramento, San Diego, San Jose
 New Jersey (9) – Caldwell, Demarest, Dunellen, Hackettstown, Short Hills, Shrewsbury,
Tenafly, Trenton, West Orange
 New York (5) – Floral Park, New York, Pittsford, Rochester, Staten Island
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 Pennsylvania (6) – Feasterville Trevose, Flourtown, Gilbertsville, Harrison City, Huntington
Valley, Jenkintown
 Texas (9) – Arlington, Cypress, Dallas, Missouri City, Plano, San Antonio, spring, Sugar
land, Tomball
Figure 6: AmazonFresh’s potential market at city wise
4. Recommendations
Our analysis suggests that AmazonFresh should expand its market beyond its local test markets:
Seattle, Los Angeles and San Francisco. The potential states to boom the current business are
California, New York, New Jersey, Pennsylvania, and Texas. At the beginning, we recommend
AmazonFresh to start by going into San Diego, San Jose, New York, and the other 31 cities.
 Customer Behavior Analysis
1. Dataset and Analysis
The customer behavior is analyzed to identify trends among the customers which could be
promoted intensively for customer retention. In this part, we used the internal customer data only.
At the beginning, we focused on the payment methods between two different purchase channels:
Website and App. Nowadays, website is still the main purchase platform for online grocery.
However, mobile purchasing is on the rise: 13% of U.S. online adults used a purchased through
smartphone in 2011 and mobile commerce is supposed to grow at 39% a year over the next five
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years, reaching $31 billion by 20169
. For this reason, we conducted the analysis to see the
customer’s different payment behaviors.
AmazonFresh launches Gift Card to promote the business. In our analysis, we also examined
how effectively Gift Card works to generate sales.
2. Findings
1) Payment Methods: Website vs. App users
- App Users
The main payment method in both “Yes” (app users) group and “No” (non-app users) group is
Credit Card. Also, around 15% of customers who use App to make a purchase pay by
Cash/Check.
Figure 7: Payment methods between App users and non-App users
-Web Users
Almost all (99.8%) the customers who purchase online use Credit Card to pay the order.
However, for customers who do not use website, nearly 18% of them pay by Cash/Check. Also,
there are also 6% of customers whose money stay account receivable (A/R).
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Figure 8: Payment methods between Web users and non-Web users
2) Gift Card Effectiveness
Our analysis showed that the Gift Card did have some effect. However, it didn’t work
appropriately to generate sales. Therefore, there is a good space to improve the Gift Card
performance.
Figure 9: AmazonFresh Gift Card
3. Recommendations
1) Since credit card is the most common payment method through both website channel and
App channel, we recommend to optimize the customer experience by making their payment
easier. For example, the payment information can be securely saved under the customer’s
account, and next time the customer does not have to type into the credit card information again.
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2) We recommend to build a cooperative relationship with selected banks to offer related
promotions, such as earning extra credits, to the customers who pay by credit card.
3) In order to increase the Gift Card performance, we suggest AmzonFresh to do advertising
on its homepage to increase the awareness of Gift Card. Also, it would be better for the
AmazonFresh to leverage on the local market channels. For example, sell the Gift Card at local
stores, pharmacies, etc., where the firm is determined to expand the market.
4) We suggest providing Gift Card free to all the new customers for their first purchase on
AmazonFresh. For example, the customers can enjoy $15 Gift Card when they complete their
first order on AmazonFresh.
TECHNICAL REPORT
Firstly, we cleaned the raw data to generate a new internal dataset. Then, we found out our
potential zip codes by combining and analyzing the internal dataset and external datasets
together. Finally, we analyzed the internal data thoroughly including all the zip codes to explore
customer behavior and opportunities for AmazonFresh.
 Data Cleaning
Import the original data in SAS and clean the data file.
- We modified the original revenue for each order by using the formulas below and
generated one new variable “TotalRevenue”
- We considered the Shipping and handling as one of our revenue source because
AmazonFresh has its own delivery service.
Pre 01/25/2007, TotalRevenue = GrossProductRevenueAmount+ShippingHandling-
CancelAmount-RefundAmount-ReturnedAmount;
Since 01/25/2007, TotalRevenue = GrossProductRevenueAmount-SalesTax-CancelAmount-
RefundAmount-ReturnedAmount.
 Finding the potential market (zip codes, cities and states)
1. Aggregate the total revenue at zip code level.
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We selected CustomerZipcode and the sum of total revenue and then did the aggregation by
using SQL language in SAS.
2. Merge external data file with income information and the aggregate data in SAS and
clean the combined dataset.
- We first sorted the aggregated data by zip code, took the external dataset which gave us
the median income for individual zip code and merged both the datasets in SAS.
- After that, we cleaned the data file by deleting the zip codes which we did not cover in
our original internal data file.
- Now, we had the combined dataset providing us total revenue and median income for
each zip code.
3. Set the standard for our potential market and select the zip codes.
- We believed our potential market was where the income was higher than the average
income but the revenue was less than the average revenue generated. Besides, the
population density was higher than 600 per square mile.
- We calculated the mean total revenue ($142.5) and mean income ($64156) in SAS.
- We selected the zip codes where the income was higher than the mean income and
revenue was lower than the mean revenue. After that, we further selected our zip codes
where the density was higher than 600.
- Now we had 1001 zip codes where we could potentially venture our business.
- We looked up where those 1001 zip codes were located and selected the top performance
states and cities. Also, we draw the distribution map of the states and cities.
 Analyzing the customer behavior
1. Modify certain variables.
In our case, AmazonFresh didn’t have “subscription” and “catalog” purchase channel.
Instead, it had “Dash” and “App”. According to the internal data we had, we changed the
variables’ names from “SubscriptionIndicator” and “SubscriptionQuantity” to
“DashIndicator” and “DashQuantity”. Also, we changed “CatalogItemIndicator” and
“CatalogItemQuantity” to” AppIndicator” and “AppQuantity” accordingly.
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2. Based on the internal data file with all zip codes, we aggregated the number of orders by
Appindicator and PaymentCategoryCode to see the different payment methods people
were using when they purchased on App and they did not. The SAS result shows as
below:
Table2: Aggregation result of AppIndicator
AppIndicator PaymentCategoryCode NumberofOrders
N 1 103
N 2 4535
N 3 567
N 5 3
Y . 1
Y 1 1416
Y 2 7804
Y 3 5
Y 5 14
Figure 10: Aggregation result of AppIndicator
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3. Based on the internal data file with all zip codes, we aggregated the number of orders by
Webindicator and PaymentCategoryCode to see the different payment methods people
were using when they purchased at website and they do not. The SAS result shows as
below:
Table3: Aggregation result of WebItemIndicator
WebItemIndicator PaymentCategoryCode NumberofOrders
N . 1
N 1 1516
N 2 7829
N 3 572
N 5 14
Y 1 3
Y 2 4510
Y 5 3
Figure 11: Aggregation result of WebItemIndicator
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4. We selected GiftCard Indicator as independent variable and Total Revenue as dependent
revenue to run two-independent T-test. The result shows as below:
Table4: Two-independent T-test result
Equality of Variances
Method Num DF Den DF F Value Pr > F
Folded F 14363 83 1.58 0.0077
REFERENCES
1. Halzack, Sarah. (2015, January 20). The staggering challenges of the online grocery
business. Retrieved from https://www.washingtonpost.com/news/the-
switch/wp/2015/01/20/the-staggering-challenges-of-the-online-grocery-business/
2. Bishop, Steve. (2014, October). What’s ahead for online grocery? Growth forecast and
implications. Retrieved from
http://www.brickmeetsclick.com/stuff/contentmgr/files/0/7e878db844896290579646d25
8136534/pdf/bmc_what__s_ahead_for_online_grocery_3_16b.pdf
3. Halzack, Sarah. (2015, January 20). The staggering challenges of the online grocery
business. Retrieved from https://www.washingtonpost.com/news/the-
switch/wp/2015/01/20/the-staggering-challenges-of-the-online-grocery-business/
4. Amazon official website. Seattle, Meet Treasure Truck. Retrieved from
https://www.amazon.com/treasuretruck?tag=googhydr-
20&hvadid=63572766542&hvpos=1t1&hvexid=&hvnetw=g&hvrand=30074201772272
58398&hvpone=&hvptwo=&hvqmt=b&hvdev=c&ref=pd_sl_3zuuazu94q_e
5. Swedowsky, Maya. (2009, September). Online Grocery Shopping: Ripe Timing for
Resurgence. Retrieved from
Method Variances DF t Value Pr > |t|
Pooled Equal 14446 1.48 0.1388
Satterthwaite Unequal 84.537 1.85 0.0673
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http://www.nielsen.com/content/dam/corporate/us/en/newswire/uploads/2009/10/Nielsen-
OnlineGroceryReport_909.pdf
6. Swedowsky, Maya. (2009, September). Online Grocery Shopping: Ripe Timing for
Resurgence. Retrieved from
http://www.nielsen.com/content/dam/corporate/us/en/newswire/uploads/2009/10/Nielsen-
OnlineGroceryReport_909.pdf
7. Rudarakanchana, Nat. (2014, January 29). The Future for E-Grocery: AmazonFresh Retains Lead,
But Startups Jump in. Retrieved from http://www.ibtimes.com/future-e-grocery-amazon-
amzn-fresh-retains-lead-startups-jump-1549969
8. Krause, Reinhardt. (2013, October 2). AmazonFresh Seen 'Highly Profitable' If
Expanded. Retrieved from http://www.investors.com/news/technology/amazon-online-
grocery-business-viewed-as-highly-profitable/
9. Urken, Ross, K. (2012, May 17). Bargain Shopping Simplified: Is This App the Answer?
Retrieved from http://www.dailyfinance.com/2012/05/17/bargain-shopping-simplified-is-
this-app-the-answer/
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PHASE 2 STRATEGIC ANALYSIS
COMPANY OVERVIEW
1. Subsidiary of Amazon.com American Ecommerce company
2. AmazonFresh had an estimate of about $15.4 billion of sales in 2013.1
3. Have a competitive advantage because of its already set infrastructure
4. Key areas of growth are expansion into other markets, pricing strategies and CRM
MARKET OVERVIEW
 Market
1. Grocery sale through online platform only represents 4% of online grocery retail market,
but it is among the fastest growing segment.
2. At the rate its going online grocery is poised to grow at an annual rate of 9.5%, with the
potential to become a $9.4 billion industry by 2017.2
3. Major challenge of online grocery shopping is the profit margin only accounting for
about 8.5% of total revenue mainly because of high distribution cost.
 Customers
1. Customers have begun to transform their shopping behaviors to online platforms.
2. Increasingly time-pressed customers appreciate flexibility and convenience of online
grocery shopping.
3. As e-commerce technologies are becoming more user friendly, the line between physical
and digital world is vanishing.
Collaborators
1. AmazonFresh has collaborated with USPS for delivery of fresh groceries.3
2. AgLocal partners with AmazonFresh to provide on-demand meat delivery.4
3. Bon Appétit announces new partnership with AmazonFresh.5
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 Competitors
AmazonFresh differentiates itself with direct and indirect competitors by 3 core competencies:
marketing, merchandising and logistics.
 Perceptual Map
All of the attributes depicted in the perceptual map are closely related to the overall customer
experience. From the axis, we observe that Walmart and Instacart are the strong and direct
competitors to AmazonFresh.
Figure 12: Perceptual Map (1)
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Figure 13: Perceptual Map (2)
 Technological Advancements
1. AmazonFresh introduces new shopping platforms, Amazon App, Amazon Dash, Amazon
Dash Button, to improve the convenience of online shopping for its loyal customers.
2. Amazon also introduces Amazon Prime Air for delivery in 60 minutes.
TARGET CUSTOMERS
Because of the relative higher price compared to its competitors, AmazonFresh should mainly
target on:
1. Big families which spend more than $10,000 annually at supermarkets
2. High income singles that are technologically savvy and too busy to take out time.
VALUE PROPOSITIONS
1. Convenience and efficiency (24/7 ordering online)
2. Excellent operations – same day delivery
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3. Broad selection – saving time with one-stop shopping
GOALS AND OBJECTIVES
1. Market expansion to 34 cities within 5 states by 2017.
2. Market Share to be increased to 10% of online grocery business by 2017.
3. Awareness of AmazonFresh to be increased by 50% by massive advertising and
promotions.
REFERENCES
1. Rudarakanchana, Nat. (2014, January 29). The Future for E-Grocery: AmazonFresh Retains Lead, But
Startups Jump In. Retrieved from http://www.ibtimes.com/future-e-grocery-amazon-amzn-fresh-
retains-lead-startups-jump-1549969
2. Frojo, Renée. (2013, January 9). Online Grocery Shopping: Boom or Bust? Retrieved from
http://www.bizjournals.com/sanfrancisco/blog/2013/01/online-grocery-shopping-boom-or-
bust.html?page=all
3. Suciu, Peter. (2014, September 25). AmazonFresh, USPS Could Be Marriage Made in Heaven.
Retrieved from http://www.ecommercetimes.com/story/81099.html
4. Sciacca, Annie. (2014, September 29). AgLocal partners with AmazonFresh to provide on-
demand meat delivery. Retrieved from
http://www.bizjournals.com/sanfrancisco/blog/2014/09/aglocal-partners-amazonfresh-meat-
delivery.html
5. Steigrad, Alexandra. (2014, March 31). Bon Appétit to Announce New Partnership with
AmazonFresh. Retrieved from http://wwd.com/globe-news/fashion-memopad/bon-apps-secret-
partner-7624932/
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PHASE 3 - MARKETING METRICS
Marketing Metrics were applied to evaluate performance and operation situation. Metrics are key
performance indicators that allow firms to track performance over time and enable to executive
reasonable business activities.
To quantify our proposal for expansion to the company, we have identified key metrics that
could help the management at different levels from marketing managers to CMO to gauge the
performance of the business. The metrics we calculated cover Customer Perception, Market
Share, Competitive Analysis, Revenues and Cost, Customer Relationships, Financial Evaluation
and Price Sensitivity.
In this session, we first listed all the KPIs (Key Performance Indicators) that would be examined,
coupled with numeric examples, appropriate users, and limitation correspondingly. Then, we
illustrated the whole philosophy of how each marketing metric was connected and organized to
calculate the value for the firm. After that, we calculated the marketing metrics by using the real
industry and business number, and designed a marketing dashboard to show the key findings. In
part 5, we generated a Service Adoption Funnel to describe the customer journey from the first
involvement with the brand to the ultimate goal of purchase.
PART 1 - METRICS
A. Selected Marketing Metrics
Name Definition Importance
Metrics for Customer Perception, Market Share and Competitive Analysis
Revenue Market Sales revenue as percentage of Measure of competitiveness
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Share market sales revenue
Brand
Penetration
Purchasers of a brand category
as a percentage of total
population
Measure brand acceptance by a defined
population.
Awareness Percentage of total population
that is aware of brand
Consideration of who has heard of brand
Ad Awareness Percentage of total population
that is aware of brands
advertising
A measure of effectiveness of advertising.
How powerful it is having an influence on its
target audience
Net Promoter Percentage of customers
willing to recommend to others
less the percentage unwilling to
recommend
Likeliness to recommend, good metric for
marketers.
Share of Wallet It tells the proportion of dollars
that the customer is spending
on your brand
It is not only measures the satisfaction level,
but it measures the degree to where the brand
stands in customer choice and allows to
measure the number of brand customer uses
Metrics for Revenues, Cost Structures and Profitability
Percentage
Margin
Unit margin as a percentage of
Unit Price
Compare margins across different products.
Guide pricing and promotion.
Break Even Sales For revenue break even, divide
fixed costs by contribution
Rough indicator of project attractiveness and
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Level margin (%) ability
Metrics for calculating the value of Individual Customers and Relationships
Customer
Lifetime Value
The present value of the future
cash flows attributed to the
customer relationship.
Customer Relationship Management
decisions should be made with the objective
of improving CLV. Acquisition budgeting
should be based on CLV
Average
Acquisition Cost
The ration of acquisition
spending to the number of new
customers acquired.
Track the cost of acquiring new customers
and compare that cost to the value of the
newly acquired customers.
Average
Retention Cost
The ratio of retention spending
to the number of customers
retained.
Monitor retention spending on a per
customer basis.
Metric for Financial Evaluation of Marketing Program
Net Present
Value
The present value of future
cash flows
Summarize the value of cash flows over
multiple periods
Internal Rate of
Return
The discount rate at which
NPV of an investment is zero
Determine whether to undertake an
investment only by comparing with a
company’s hurdle rate
ROMI The incremental revenue
attributable to marketing
divided by marketing spending
An accurate baseline stating what revenue is
attributable to marketing
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Metric on Price Sensitivity
Price Premium It is the percentage by which
the price of brand exceeds a
benchmark price
Measures how a brand’s price compares to
that of its competition
B. Metrics Details and Numeric Examples
I. Metric Calculations
Estimated sales in year 2014: 600 million
1. Revenue Market Share - The percentage of market account for specific activity.
Revenue Market Share (%) =
Revenue market share differs from unit market share in that it reflects the prices at which goods
are sold. Marketers need to be able to translate sales targets into market.
Example - Revenue by company X is $50,000 and total market sales revenue is $200,000.
Then revenue market share of X is = 50,000/ 200,000 = 25%
2. Brand Penetration - Penetration is a measure of brand or category popularity. It is defined as
the number of people who buy a specific brand or category of goods at least once in a given
period, divided by the size of the relevant market population.
Brand Penetration (%) =
Example - If over a period of six months, in a market of 10,000 household, 23000
households purchase at least one product of Amazon Fresh.
Brand Penetration could be calculated as 2,300/10,000 = 23%
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3. Brand Awareness - The percentage of potential customers who recognize the name of a given
brand. These could be recorded on an aided level to see if customers have heard of AmazonFresh.
The metric could be measured by calling out participant and calculate the percentage of
people who could recall the brand.
4. Ad Awareness - The percentage of target customers who demonstrate awareness of one brand
advertisement. This can be a campaign, media specific, or can cover all advertising.
This is to measure the success rate of advertising. It is a good check if there is a good return on
money invested in advertising.
5. Net Promoter – It is created by subtracting the percentage of detractors among current
customers from the percentage of promoters among current customers.
Promoters - willing to recommend (rating 9 or 10)
Passives - satisfied but unenthusiastic customer (rating 7 or 8)
Detractors - customers willing to recommend the company - (rating 0 to 6)
Net Promoter Score =
Example - if a customer survey of a company reports that there were 20% promoters, 70%
passives and 10% detractors the company would have Net Promoter Score of 20 - 10 = 10.
6. Share of Wallet - The wallet allocation rule is based on brands rank among the number of
brands a customer uses for a particular product or service. These two are important factors to
calculate the share of wallet.
Share of Wallet =
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Example - Estimate the number of brands a customer uses for product category is 3.
Survey on what rank does the brand stand among other brands - let's say the brand is the third
preference.
Calculating the share of wallet: (1- ¾) (2/3) = 0.167
Then the share of customers X is 16.7%
7. Percentage Margin - This could be calculated using total sales revenue and total costs
Margin (%) =
Margin is important for all marketing decisions. It is a key factor in pricing, return on marketing
spending, earnings, forecasts and analysis of customer profitability.
8. Break Even Sales Level - It represents the sales amount in revenue terms that is required to
cover the total costs. Total profit at breakeven is zero.
Contribution per Unit ($) =
Contribution Margin (%) =
Break-Even Volume (#) =
Break-Even Revenue ($) =
Example - with a fixed cost of $30,000, selling price of $40, cost price of $10 per unit
Contribution per Unit = 40-10=$30,
Break Even Volume = $30,000 / $30 = 1000 units,
Break Even Revenue = 1000 * $40 = $40,000
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9. Customer lifetime Value - It is the dollar value of a customer relationship based on the
present value of the projected future cash flows from the customer relationship.
Customer Lifetime Value ($) = Margin ($)
CLV is important concept as its helps to focus on long term relationship with the customers.
Example - AmazonFresh charges $299 per year for prime membership and provides free
delivery for orders above $35. Variable cost is $40. With marketing spending of $100, the
attrition is 6%. The yearly discount is 12%.
Contribution Margin = $299 - $35 - $100 = $164
Retention Rate = 0.94
Discount Rate = 0.12
CLV = $164 * (0.94 / (1+0.12 -0.94) = $856
10. Average Acquisition Cost - This represents the average cost to acquire a customer and is the
total acquisition spending divided by the number of new customers acquired.
Average Acquisition Cost ($) =
11. Average Retention Cost - This represents the average cost to retain an existing customer
and is the total retention spending divided by the number of customers retained.
Average Retention Cost ($) =
Example - if AmazonFresh spent $1.4 million and acquired 64,800 customers, Acquisition
Cost is $21 per customer. If AmazonFresh spent $500,000 to retain 154,980 customers of
which 87,957, then Average Retention Cost is $6 per customer.
12. Net Present Value - is the discounted value of the cash flow associated with the project.
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It is the present value of the dollar received in a given number of periods in the future.
Discounted Value ($) =
13. Internal Rate of Return - The discount rate for which the net present value of the
investment is zero.
It is an important metric to justify the project because it compares the company's hurdle rate.
14. Return on Marketing Investment - This is metric used to calculate the contribution
attributable to marketing divided by the marketing invested or risked.
Return on Marketing Investment (ROMI) =
15. Price Premium - It is the percentage by which products selling price exceeds a benchmark
price.
Price Premium (%) =
It is important to monitor as it is indicative of competitive pricing.
Example – Estimate the price of one certain AmazonFresh product (like mangoes) is $5
while the benchmark price is $4.
Then premium pricing is by 25%.
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II. Information for each of the metric
Name Diagnostic /
Evaluative
Short Term /
Long Term
Users (Level of
Organization)
Cross Sectional
/ Longitudinal
Revenue Market
Share
Evaluative Long Term CMO Longitudinal
Brand
Penetration
Evaluative Long Term CMO Longitudinal
Awareness Evaluative Long Term CMO Longitudinal
Ad Awareness Evaluative Short Term Marketing Manager Cross Sectional
Net Promoter Diagnostic Short Term Marketing Manager Cross Sectional
Share of Wallet Evaluative Long Term CMO, marketing
manager
Cross Sectional
Percentage
Margin
Diagnostic Short Term Marketing Manager Cross Sectional
Break Even
Sales Level
Diagnostic Short Term Marketing
Manager, Finance
Manager
Cross Sectional
Customer
Lifetime Value
Diagnostic Long Term CMO Longitudinal
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Average
Acquisition Cost
Diagnostic Short Term Marketing Manager Cross Sectional
Average
Retention Cost
Diagnostic Short Term Marketing Manager Cross Sectional
Net Present
Value
Diagnostic Short Term Marketing Manager Cross Sectional
Internal Rate of
Return
Diagnostic Short Term Marketing Manager Cross Sectional
ROMI Evaluative Short Term Marketing Manager Longitudinal
Price Premium Diagnostic Short Term Marketing Manager Cross Sectional
III. Users of Marketing Metrics
Marketing Metrics is used at all levels in a company depending on the metric that has to be
studied.
Upper Management Level: CMO, EVP
Middle Management Level: Marketing Manager, Finance Manager
Lower Management Level: Team Leader, Marketing Assistant / Coordinator
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IV. Limitations to Marketing Metrics
All companies recognize the importance of marketing metrics and its function to better manage
and report marketing performance, but there are several limitations coming along with.
1. Metrics are complex and difficult to use
2. Metrics are indicators. They do not solve business problems and do not address
marketing challenges.
3. Many get confused to start with as there are too many metrics which could be used and
selected.
4. It has limited into real business because of insufficient data or certain amount of budget
required achieving those numbers.
5. It is a very time-consuming process and requires a good deal of efforts.
PART 2 - BIG PICTURE OF MARKETING METRICS
Choosing the right metrics is very critical to calculate the overall firm value. As a consulting
team, it is very important for us to provide all the calculated marketing metrics that could lead to
future customer value and firm performance before giving any suggestion which involves huge
investments. Subsequently, a framework was developed to identify key metrics which the firm
should focus on and to give a big picture of how the firm would get to where they were at present
and provide with meaningful insights towards how they could continue to grow in future.
The metrics that have been used are split into two main categories:
1. Metrics that measure the value of brand
2. Metrics that measure the value of customers
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Figure 14: Metric model to calculate firm value
The model is based on the consideration that overall firm value is formed by the customer value
and brand value.
Customer Value is a critical parameter and could be measured in marketing metric - customer
lifetime value, which is a prediction of net profit attributed to the entire future relationship with
the customer.
Earlier the CLV was measured in terms of past performance, but today possible future
performance is also taken into account. It predicts the future cash flows from a customer whose
net present value could be evaluated.
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Brand Value is measured to see how brand is perceived by the consumers and how much
customers are willing to purchase from the brand. We believe the share of wallet is a very helpful
metric to see what percent of customer total purchase is shared by AmazonFresh.
PART 3 - METRIC CALCULATIONS FOR PROPOSAL
In order to justify the proposal of new market expansion, it is reasonable that we quantify the
performance evaluation and the potential value that could be brought to the firm before any
significant investments are made. Therefore, some related marketing metrics were carefully
calculated to justify the proposal.
1. Market Share
Today in United States, total grocery business reaches to $600 billion in which online business
accounts for 4 percent making $24 billion business.
AmazonFresh currently operates in 3 cities and accounts for 1% of total business which is $240
million.
Proposal of expansion into 40 cities by 2017 could drive the sales revenue as high as 10 times
which is $2.4 billion.
In this way, total market sales revenue will reach to $26 billion.
Hence, market share for Amazon in coming years = $2.4 / $26 = 9.23%
2. Margins and Percentage Margin
AmazonFresh sells thousands of Amazon Items, fresh grocery and local products.
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On average, the grocery business is a very low marginal business, approximately 3 percent
margin.
Consider a simple product for calculation - Tuscan Dairy Whole Vitamin D milk 64 Oz.
AmazonFresh sells at a price for $2.19 while the price at store is $1.99.
Unit Margin is selling price minus cost price per unit which is equal to $0.2.
Percentage margin is given by ration of unit percentage per selling price which accounts to 9.13%
3. Price Premium
At a market-average level, a 1-¾ inch thick, 2 Count, 30 Ounce beef package usually sells at $75,
and AmazonFresh price is $84.
Clearly, AmazonFresh is pricing its product at a premium compared to its benchmark for
providing home delivery. Hence, we calculate the price premium to measure how much
percentage the firm is charging.
Price premium = [Brand A Price ($) - Benchmark Price] / Benchmark Price
= [84-75] / 75 = 12%
It is essential that price premium should be monitored for every product the firm offers.
4. Break-Even Analysis and Target Volume Analysis
A break-even, representing the sales amount in either unit or revenue terms, is required to cover
both fixed and variable costs. It is very important for a company to achieve break even sales.
As we put proposal for expansion into 34 cities in 5 states, we need to calculate metrics to
quantify the minimum sales required to arrive at break even. Let’s take New York City for
example to quantify the sales required to arrive at break even.
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Contribution per Unit ($) =
Contribution Margin (%) =
Break-Even Volume (#) =
Break-Even Revenue ($) =
AmazonFresh offers a large number of products and has different contribution per unit ($) for
different products. Assuming that on average the contribution per unit is $3 which includes
variable cost of inventory, distribution cost, and margin% which accounts for 4%.
Let the fixed cost for setting up the infrastructure be $300,000 (Warehouse + Employee Salary).
Thus, Break Even Volume (#) = Fixed Costs / Contribution Per Unit = $300, 00 / $3 = 100,000
Break Even Revenue ($) = Fixed Costs / Contribution Margin (%) = $300,000 / 4% = $7,500,000
Target Volume Analysis
The Manager expects to generate volumes that meet target profits for which sales have to be
made beyond break even. To achieve at target profits, level of sales or revenue has to be
determined which is more than sales to cover the firm’s cost.
To determine the target volume to achieve the yearly profit objective of $300,000, AmazonFresh
has to sell far beyond what it used to sell at breakeven.
Target Volume (#) =
Target Volume (#) = (Fixed Costs + Target Profit) / Contribution per Unit
= ($300,000 + $300,000) / 3 = 200,000 units.
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5. Financial Analysis
Payback:
For AmazonFresh to enter New York market, it requires significant investment to set up the
infrastructure which requires a few years to be covered.
Assume that the firm spends $1,500,000 to set up its infrastructure initially, and continues to
produce a net income of $600,000 a year for at least 5 years.
The payback is 3 years meaning that in three years AmazonFresh will cover the investment it
made in taking its market in New York City.
NPV:
Next, to calculate the dollar value of this new line of expansion, estimates are made for the
current bank loan interest rate. Assuming 7% as discount rate, the first cash outflow is at the 0
point of time line. Hence, the NPV is equal to:
PV of all the cash inflows - PV of all the cash outflows = 600,000 / (1+7%) + 600 / (1+7%)^2 +
600/(1+7%)^3 - $1,500,000 = 560,747 + 524,063 + 489,795 - 1,500,000 = $74,605
6. Return on Marketing Investment Analysis
AmazonFresh is expanding into the markets of East Coast as per our proposal where it may face
fierce competition from FreshDirect and Peapod. So, to bring awareness and promote the brand
among customers, it needs to make significant investment in distributing the brand through
events or advertising platform. A great deal will be spent in digital space to better acquire new
customers.
Assume that AmazonFresh spends $20 million in advertising over digital and traditional media
platforms, and as per the Forrester report, there is an increase in revenue by $600 million.
Baseline Revenue from its marketing efforts is $240.
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Contribution margin as calculated per unit is approx 9%.
Return on Marketing Investment (ROMI) =
ROMI = [($600 million - $240 million)* 9% - $20 million] / $20 million = 62%
7. Customer Acquisition versus Retention Costs
The company has already started to offer its services into certain zip codes in Brooklyn, New
York. This year, AmazonFresh plans to spend $2 million in market to expand to other zip codes
of New York City and it is estimated to acquire a customer base of 40,000 in the next year.
At the same time, another $500,000 is spent for retention of its current customer base with
100,000 of which 25% retained at the end of year.
Average Acquisition Cost ($) = 2,000,000 / 80,000 = $25
Average Retention Cost ($) = 500,000 / 25,000 = $20
8. Price Elasticity of Sales Analysis
It measures the responsiveness of the quantity demanded of a good or service to a change in
price.
Assuming that the organic fruit gift box follows a linear demand function, at the current price of
$40 per unit, AmazonFresh sells 40,000 units with an elasticity of -2.
A proposal is floated to lower the price to $35 per unit in order to increase market share. To
calculate the number of unit sold at $35 will be given by:
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-2 = Change in quantity (%) / Change in price (%) = Change in quantity (%) / -12.5%,
Hence, Change in quantity equals to 25%
Thus, the total number of units that would be sold is equal to 50,000.
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PART 4 - MARKETING DASHBOARD
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PART 5 – THE FUNNEL AND INSIGHTS
The Service Adoption Funnel is a model that describes the theoretical customer journey from the
first involvement with the brand to the ultimate goal of purchase. This model is critical from
marketing standpoint as it helps to understand and track the customer behavior throughout the
sales process.
A funnel shape which focuses on consumer’s decision process is used as it shows how company
loses potential customer at each level and track at which level the company losses it maximum
customers. Numbers demonstrated as below are estimated for the expansion in New York City of
the company.
Figure 15: The Service Adoption Funnel
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Thus, with this model of evaluation, 53000 customers could be reached out. Estimate that each
customer buys minimum of $70 grocery per month giving an annual revenue of ($70 x 12) +
$299 for prime membership = $1139
Therefore, it will be a good start for the company with revenue of $60.4 million.
REFERENCES
1. Rudarakanchana, Nat. (2014, January 29). The Future for E-Grocery: AmazonFresh Retains Lead, But
Startups Jump in. Retrieved from http://www.ibtimes.com/future-e-grocery-amazon-amzn-fresh-
retains-lead-startups-jump-1549969
2. McEnery, Thornton. (2014, October 26). Amazon squeezes FreshDirect. Retrieved from
http://www.crainsnewyork.com/article/20141026/TECHNOLOGY/141029885/amazon-
squeezes-freshdirect
3. Barr, Alistair. (2013, October 2). Broader AmazonFresh launch may be ‘highly profitable’.
Retrieved from http://www.usatoday.com/story/tech/2013/10/02/amazon-fresh-
groceries/2907641/
4. Bishop, Todd. (2014, December 13). Tough to swallow: Longtime AmazonFresh customers
leaving over new $299/year subscription. Retrieved from http://www.geekwire.com/2014/tough-
swallow-longtime-amazon-fresh-customers-bolting-new-299year-subscription/
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PHASE 4 – DATABASE, SQL AND MARKETING DASHBOARD
INTRODUCTION
A database was designed in line with our proposal for expansion into new cities. AmazonFresh
is offering multiple products and expanding beyond its current market. Database that could help
retrieve day-to-day sales for different markets will help to design marketing campaigns more
efficiently.
Database was designed to give all key information of individual customers ranging from their
purchase to their personal information. Database can be used for creating a marketing dashboard
that could provide with at-a-glance view of KPIs (Key Information Indicators) that is relevant to
our objective, such as total sales and production. It is a very efficient way to sum up all key
details, trends, and comparison. Furthermore, it is relatively simple to communicate and support
business with meaningful and useful data.
DATABASE DESIGN
Our database was composed of 4 tables that gave information about the customers, orders, order
details and products that customers purchased.
TABLE SCHEMES
In listing each of the columns while creating tables, the fields, type and length were taken in
consideration.
The primary key that was used to uniquely identify each of the rows was listed.
Primary Key for each of the table worked as ID, and a foreign key was introduced to connect the
records in two tables with each other, which was shown in the coding for respective table.
The SQL Server Management Studio was used to create the database and run the query.
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Figure 16: The Database Design
SQL CODES
------------------------------------------------------First Set-----------------------------------------------------
Creating Database and developing query
1. Creating tables, columns and primary and secondary key
Query for customer table
CREATE TABLE Customers (
ID INT NOT NULL,
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LASTNAME VARCHAR (20) NOT NULL,
FIRSTNAME VARCHAR (20) NOT NULL,
PHONE INT,
ADDRESS CHAR (25),
CITY CHAR(25),
STATE CHAR(20),
ZIPPOSTAL INT,
COUNTRY CHAR(20),
PRIMARY KEY (ID)
);
go
Query for Orders Table
CREATE TABLE ORDERS_1(
ID INT,
OrdersDate Date,
ShippingDate Date,
ShipCity CHAR(25),
ShipZipPostal INT,
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ShippingFee INT,
Tax INT,
PaymentType CHAR(25),
OrderMonth VARCHAR(25),
OrderYear INT,
OrderTotal INT,
CustomersID INT,
PRIMARY KEY (ID),
FOREIGN KEY (CustomersID) REFERENCES CUSTOMERS(ID)
);
go
Query for Products Table
CREATE TABLE PRODUCTS(
ID INT,
ProductCode VARCHAR(25),
ProductName CHAR(25),
StandardCost VARCHAR(25),
ListPrice VARCHAR(25),
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QuantityPerUnit VARCHAR(25),
PRIMARY KEY (ID)
);
go
Query for OrderDetails Table
CREATE TABLE OrderDetails(
ID INT,
OrderID INT,
ProductID INT,
Quantity INT,
UnitPrice VARCHAR(25),
GiftCard VARCHAR(25),
PRIMARY KEY (ID),
FOREIGN KEY (OrderID) REFERENCES ORDERS(ID),
FOREIGN KEY (ProductID) REFERENCES PRODUCTS(ID)
);
Go
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2. Inserting data in each table
Inserting Data into customers table
INSERT INTO Customers
VALUES('1','WANG','CHU','1234567890','456 3rd STREET','NEW YORK
CITY','NY','11368','US');
INSERT INTO Customers
VALUES('2','CHADDERWALA','SAGAR','1234567890','123 1rd
STREET','SEATTLE','WA','98052','US');
INSERT INTO Customers
VALUES('3','AXEN','THOMAS','1567891230','125 3rd STREET','LOS
ANGELES','CA','78956','US');
INSERT INTO Customers
VALUES('4','LEE','CHRISTINA','1894561230','127 8th STREET','ORANGE','CA','95432','US');
INSERT INTO Customers
VALUES('5','LUDICK','ANDRE','1985462130','129 9th STREET','SAN
FRANCISCO','CA','99999','US');
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INSERT INTO Customers
VALUES('6','LI','GEORGE','1895432615','115 5th STREET','FLORAL PARK
','NY','78954','US');
Inserting Data into Orders table
INSERT INTO ORDERS_1
VALUES('1','5/4/2015','5/4/2010','SEATTLE','98052','5','7','VISA','MAY','2015','55','1');
INSERT INTO ORDERS_1
VALUES('2','6/5/2015','6/6/2015','NEW YORK CITY','98052','5','7','VISA','MAY','2015','44','2');
INSERT INTO ORDERS_1
VALUES('3','7/5/2015','8/5/2015','LOS ANGELES','98052','5','7','VISA','MAY','2015','33','3');
INSERT INTO ORDERS_1
VALUES('4','3/7/2015','4/7/2015','ORANGE','98052','5','7','VISA','MAY','2015','56','4');
INSERT INTO ORDERS_1
VALUES('5','3/4/2015','4/4/2015','SAN FRANCISCO','98052','5','7','VISA','MAY','2015','12','5');
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INSERT INTO ORDERS_1
VALUES('6','10/3/2015','11/3/2015','FLORAL PARK','98052','5','7','VISA','MAY','2015','45','6');
Inserting Data into Products Table
INSERT INTO PRODUCTS
VALUES ('1','56','Nk','67','7','2'),
('2','66','JK','88','8','6'),
('3','23','YY','89','3','9'),
('4','12','UU','45','4','4'),
('5','77','IK','78','9','2');
Inserting Data into OrderDetails
INSERT INTO OrderDetails
VALUES('1','2','3','5','5','KK'),
('2','2','2','6','4','LL'),
('3','3','3','7','8','PJ'),
('4','4','4','3','1','YH'),
('5','5','5','9','2','IK');
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3. Updating one line of data
Updateding one line of data for customers table
Update Customers
Set
LASTNAME ='Just',
FIRSTNAME ='IN',
PHONE ='77789',
ADDRESS ='225 Saint Jersey Avenue',
CITY ='Jersey',
STATE ='NJ',
ZIPPOSTAL='78906',
COUNTRY ='USA'
WHERE id='3';
/* Updates only when id is 3*/
4. Deleting one line of data
Deleting one line of data for customers table
Delete from customers WHERE lastname='Just';
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59
------------------------------------------------- Second Set -----------------------------------------------------
The query was developed mainly to see which of the city would give max revenue and where the
best market would be. Also, it could check what kind of the brands and products would be
preferred by customers in online grocery shopping.
1. Two queries to retrieve data
Query 1: retrieve data from customers table – ID and City
Select ID, City from Customers;
/* this gives only ID and City; if one wants all columns then just “select * from customers”*/
Managerial Implication - It allows us to study where our customers mostly come from.
Query 2: select top performing products from products table
Select top 2 * from products;
/* Will retrieve top 2 rows*/
Managerial Implication – Allows us to study top 2 performing products
Marketing Intelligence Report
60
2. Query to retrieve data from more than 1 table. (Inner Joins & Outer joins)
Outer Joins
select * from customers a
full join ORDERS_1 b
on a.id=b.CustomersID
/* gives data from both tables whether they match or not*/
Inner Joins
select * from customers a
inner join customers b
on a.id=b.id;
3. Query to retrieve data from two or three tables such that data from one table is
aggregated
Query1
select * from orders
select * from agg_1
select count(a.id) as TotalIDs,
Marketing Intelligence Report
61
sum(b.shippingfee) as Fee,sum(b.tax) as TotalTax,
b.paymenttype,
count(distinct b.customersid) as Customers,
sum(b.ordertotal) as Order_total,b.ShipCity
into Agg_1
--drop table Agg_1
from customers a
join orders_1 b
on a.id=b.CustomersID
where OrderYear='2015'
group by b.paymenttype,b.ShipCity
/* join gives only what is common between the 2*/
/* basically there is a column that matches each table with another table*/
Managerial Implication – This was used to get total revenue generation across different cities.
This will be used to find out which of the city gives max revenue.
Figure 16: Result of Query 1
Marketing Intelligence Report
62
Query 2
select sum(b.OrderTotal) as OrderTotal,sum(b.tax) as TotalTax,
count(distinct b.customersid) as Customers,a.ProductName
into Agg_2
--drop table agg_2
from PRODUCTS a
join ORDERS_1 b
on a.id=b.CustomersID
group by a.productname
having count(b.customersid) !='0';
Managerial Implication – This was used to study which of the products gives max revenue and
hence more in demand among their customers.
Figure 17: Result of Query 2
Marketing Intelligence Report
63
Query 3
select sum(cast(a.QuantityPerUnit as int)) as OrderTotal,sum(cast(a.listprice as int)) as ListPrice,
count(distinct b.ID) as Customers,a.ProductName,b.CITY
into Agg_3
--drop table agg_3
from PRODUCTS a
join Customers b
on a.id=b.ID
group by a.productname,b.CITY
having count(b.ID) !='0';
Managerial Implication – This gives information which of the product is in demand in which of
the cities. This is useful for running an effective advertisement campaign.
Figure 18: Result of Query 3
Note: Difference between HAVING and WHERE Clause in SQL
Marketing Intelligence Report
64
Clause “WHERE” is used to filter the requirements and is used to filter the non-aggregates. It
does not work with aggregation like sum, avg, max etc. Instead, in that case, having statement
will be used. This clause was added to SQL so as to compare the aggregation.
4. Creating a view for query that is prepared
Query
Create view customerview
as select * from Customers;
Marketing Intelligence Report
65
MARKETING DASHBOARD
The database was connected to excel, and charts were created to study different metrics.
There are three charts presented in the dashboard
Chart1 – It shows us the products that give max sales in respective cities
Chart2 – It shows which of the product brings in more revenue to the company. Thus, the chart
gives sales performance for each of the products
Chart 3 – It shows us total revenue generated across various cities
Figure 19: DashBoard
Marketing Intelligence Report
66
LIMITATIONS
Thorough analysis was conducted to identify new opportunities for AmazonFresh using the
Internal Data and External Data; however, there are few limitations to our research:
1. The data set we had provided us the customer behavior of parent company, but not it
whole subsidiary AmazonFresh for which we had to extrapolate our result using our
estimates.
2. The data set provided ranged from 1997 to 2009 which was not updated to study the
current rapidly changing scenario.
3. The data should have incorporated more variables to understand customer demands.
Future Research Project:
The future research project is to study the customer needs by conducted surveys to better serve
their needs and a model has to be developed for metrics which could lead to firm value.
Marketing Intelligence Report
67
TECHNICAL APPENDIX
SAS CODES
PROC IMPORT datafile='C:SASDataSet8DMEF0509-2.xlsx' OUT=mydata
Replace;
RUN;
DATA mydata2;
Set mydata;
If Inputdate < '25JAN2007'd then TotalRevenue =
GrossProductRevenueAmount+ShippingHandling-CancelAmount-
RefundAmount-ReturnedAmount;
Else TotalRevenue = GrossProductRevenueAmount-SalesTax-
CancelAmount-RefundAmount-ReturnedAmount;
RUN;
PROC EXPORT data=mydata2 dbms=excel
outfile = 'c:sasmydata2-newrevenue.xlsx' replace;
RUN;
PROC SQL;
Select CustomerZipCode,
sum(TotalRevenue) as Netrevenue
From mydata2
Group by CustomerZipCode
Order by CustomerZipCode;
QUIT;
PROC IMPORT datafile='C:SASAggZipcode.xlsx' OUT=mydataA
Replace;
RUN;
PROC IMPORT datafile='C:SASIncomeZip.xlsx' OUT=mydataB Replace;
RUN;
DATA combined;
merge mydataA mydataB;
by CustomerZipcode;
RUN;
Marketing Intelligence Report
68
PROC SORT Data = Combined
OUT = Bonus;
By Descending NetRevenue;
RUN;
DATA Cleaning;
set Bonus;
If NetRevenue = "" then delete;
RUN;
PROC MEANS mean median data=cleaning;
Var NetRevenue Income;
RUN;
DATA potentialmkt;
set cleaning;
If (NetRevenue > 142.5 | Income < 64156) then delete;
RUN;
PROC SQL;
Select Webitemindicator, paymentcategorycode,
COUNT (Ordernumber) as NumberofOrders
From mydata2
Group by Webitemindicator, paymentcategorycode;
QUIT;
PROC SQL;
Select appindicator, paymentcategorycode,
COUNT (Ordernumber) as NumberofOrders
From mydata2
Group by appindicator, paymentcategorycode;
QUIT;
DATA mydata3;
set mydata2;
If GiftCertificateRedeemedInd = "Y" then Giftcard = "Y";
Else Giftcard = "N";
RUN;
PROC TTEST data=mydata3;
class Giftcard;
Var Totalrevenue;
RUN;

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Marketing Decision Models Project - AMAZONFRESH

  • 1. Marketing Intelligence Report AmazonFresh 1516 12th Ave. Seattle, WA 98101 July 5th , 2015 Neil Lindsay AmazonFresh 1516 12th Ave. Seattle, WA 98101 Dear Mr. Lindsay: Per your letter of authorization dated May 19, 2015, our team has completed the Marketing Intelligence Project for AmazonFresh. Enclosed please find a report titled “Marketing Intelligence Report for AmazonFresh” The report presents the study’s objectives, data analysis, strategic analysis, marketing metrics, database and marketing dashboard, key findings, limitations, and implications. My team used standard marketing intelligence practices throughout the project and we believe that the results address the stated project objectives. I trust that the findings will help you in your decisions for AmazonFresh marketing decisions. If you have any questions, please do not hesitate to call me at (123)-456-7890 or email me at (Marketingintelligence@amazonfresh.edu). I enjoyed working with you on this project and I look forward to working with you again in the future. Sincerely, ABC ABC Director of Marketing Intelligence Project
  • 2. Marketing Intelligence Report 2015 Chu Wang Dahai Liu Sagar Chadderwala 2015/7/5 Marketing Intelligence Project AmazonFresh
  • 4. Marketing Intelligence Report Content EXECUTIVE SUMMARY .......................................................................................................... 1 PHASE 1 ........................................................................................................................................ 7 SUMMARY..........................................................................................................................................7 MANAGERIAL REPORT .................................................................................................................10 TECHNICAL REPORT......................................................................................................................19 REFERENCES ...................................................................................................................................23 PHASE 2 STRATEGIC ANALYSIS ........................................................................................ 25 COMPANY OVERVIEW ..................................................................................................................25 MARKET OVERVIEW .....................................................................................................................25 TARGET CUSTOMERS....................................................................................................................27 VALUE PROPOSITIONS..................................................................................................................27 GOALS AND OBJECTIVES.............................................................................................................28 REFERENCES ...................................................................................................................................28 PHASE 3 - MARKETING METRICS...................................................................................... 29 PART 1 - METRICS...........................................................................................................................29 PART 2 - BIG PICTURE OF MARKETING METRICS ..................................................................39 PART 3 - METRIC CALCULATIONS FOR PROPOSAL ...............................................................41 PART 4 - MARKETING DASHBOARD ..........................................................................................47 PART 5 - FUNNEL AND INSIGHTS ...............................................................................................48 REFERENCES ...................................................................................................................................49 PHASE 4 – DATABASE, SQL AND MARKETING DASHBOARD.................................... 50 INTRODUCTION ..............................................................................................................................50 DATABASE DESIGN........................................................................................................................50
  • 5. Marketing Intelligence Report TABLE SCHEMES ............................................................................................................................50 SQL CODES.......................................................................................................................................51 MARKETING DASHBOARD...........................................................................................................65 LIMITATIONS........................................................................................................................... 66 TECHNICAL APPENDIX......................................................................................................... 67 SAS CODES.......................................................................................................................................67
  • 6. Marketing Intelligence Report 1 Executive Summary AmazonFresh, a subsidiary of Amazon.com, the largest Internet retailer in United States, succeeds in the business model of online grocery ordering and home delivery. Recently, faced by a large number of challenges, AmazonFresh is putting an effort into figuring out how to enlarge their business and increase profitability. As the marketing consultant of AmazonFresh, our marketing department applied a rigorous marketing intelligence project in support of the objective. There are four phases in the entire marketing decision process: At first, in Phase 1, we explored external data, internal data and secondary information to generate the related marketing intelligence, and then identified the business opportunity -- geographic market expansion. In Phase 2, the situation analysis was conducted to identify the goals for expansion into new markets. In Phase 3, we demonstrated how our proposal could generate value for AmazonFresh by applying multiple marketing metrics and evaluating the performance of the new business line. At last, in Phase 4, we designed a database for our proposal and created a dashboard to present key metrics effectively. The entire process is shown as the following flowchart:
  • 7. Marketing Intelligence Report 2 Marketing Decision Model Process Phase1 DataAnalysis Phase2 StrategicAnalysis Phase3 MarketingMetrics Phase4 Databases,SQL,and MarketingDashboard MARKETING INTELLIGENCE PROJECT AMAZONFRESH MARKETING DECISION MODELS MA MMAm INTERNAL DATASET EXTERNAL DATASET SECONDARY RESEARCH OPPORTUNITY IDENTIFIED– MKT EXPANSION SITUATION ANALYSIS DEFINE GOALS IDENTIFY THE TARGET MARKET DEVELOP A VALUE PROPOSITION DEFINE PERFORMANCE METRICS IDENTIFY COMPARISON BENCHMARKS CONDUCT PERFORMANCE ANALYSIS VALUE CREATION MODEL DESIGN MARKETING DASHBOARD DESIGN AND IMPLEMENT A DATABASE IMPLEMENT THE MARKETING DASHBOARD
  • 8. Marketing Intelligence Report 3 Phase 1 Data Analysis In Phase 1, data analysis was carefully performed including data aggregation, data cleaning, and data configuration. Furthermore, we integrally combined and investigated three aspects of data source: secondary reports analysis, internal data analysis and external data analysis.  Secondary reports analysis As per secondary research, online grocery business which currently accounts for 4 percent of the total grocery business is poised to grow at a rate of 9.6% by 2018. This can be explained with the shopper readiness of online grocery purchase, the easy and comfortable shopping experience, and time-saving grocery shopping channel.  Internal and external data analysis Internal data and external data were used to identify new business opportunities for AmazonFresh. 1. Market Expansion The analysis indicated the opportunities for AmazonFresh to expand the business beyond its current locations in order to generate more profits. The model for expansion was based on two criteria: 1) High income households 2) Population density of 600 per square mile We analyzed the internal customer profiles by aggregating the revenue at a zip code level and merging this aggregated data with external data files which contain median household income, density per square mile and corresponding city and state names. There were 34 cities identified for expansion which lied across five states including New York, New Jersey, Pennsylvania, Texas and California.
  • 9. Marketing Intelligence Report 4 Figure 1: Potential market for AmazonFresh at state wise 2. Consumer Behavior Analysis Customer Behavior Analysis was conducted to identify the trend in support of customer retention. Two main findings are listed as below: 1) The most common channel of payment is Credit Card 2) Gift Card has very minimal effect on increasing overall sales The following three suggested strategies were proposed based on the two findings: 1) Optimize the user experience with credit card payment in terms of information restoring and transaction processing in both Website and App interaction interface design
  • 10. Marketing Intelligence Report 5 2) Build a beneficial partnership with credit card banks which enables AmazonFresh to offer promotion and deal to its own customers and then boost credit card payment 3) Improve the Gift Card program by increasing its awareness, adopting effective promotion strategies and better understanding the customer demands Phase 2 Strategic Analysis In Phase 1, we had identified the areas of opportunities to introduce a new value proposition. At this phase, we conducted situation analysis to identify the AmazonFresh’s goals and objectives. 1. Expanding market to 34 cities in the United States by 2017 2. Market share to be increased to 10% of online grocery business by 2017 3. Increasing awareness of AmazonFresh by 50% over all markets by massive advertising and promotions Phase 3 Marketing Metrics In this phase, we identified metrics that could demonstrate how our proposal would lead the value to AmazonFresh. In total, we conducted 15 metrics fundamentally focusing on two areas: 1. Metrics that measure the value of brand 2. Metrics that measure the value of customers We have mapped the process by leveraging Dupont Model of how various marketing metrics lead to value for the firm, and then a marketing dashboard was carefully created to provide a big picture of all the key performance indicators which could help the CMO making critical decisions. All the metrics calculated for the proposal were equipped with reasonable estimation and real industry numbers. Finally, a funnel was generated which described the theoretical customer journey to conversion from the moment of contact to the ultimate purchase.
  • 11. Marketing Intelligence Report 6 Phase 4 Database, SQL and a Marketing Dashboard A database was designed in the proposal which consisted of 4 tables, each having one primary key and foreign key to connect the tables with each other. The database was connected to the Excel file which enabled the marketing dashboard to be timely reflective of any update in the tables. The marketing dashboard mainly reflected three key details: 1. The City which gives the maximum revenue 2. The product which is the most profitable 3. The product giving the maximum sales in different cities which can be used for effective marketing campaign
  • 12. Marketing Intelligence Report 7 PHASE 1 SUMMARY The data analysis report aims at exploring business opportunities for AmazonFresh which is a subsidiary of Amazon.com and provides online grocery home delivery. The report is based on two areas of study: 1) A summary of all the secondary reports that comprehensively demonstrate the industry landscape, trend development, and predictions; 2) An analysis of the dataset combining internal data provided by the company and external datasets gathered from outside resources to generate our marketing strategy. The goal for the secondary report analysis is to point us to seek the new line of business. Two interesting insights are drawn from the research: First, the online grocery shopping has a huge potential in the market, and AmazonFresh works well in some areas comparing to its competitors. Second, great opportunities exist in market expansion for AmazonFresh from Seattle, San Francisco to other areas of the nation. Further research on market expansion and consumer behavior are demanded in order to better attract and satisfy the target market. The next step is to define the criteria for the potential markets where the firm is going to expand. We see the areas with high density of population and consumers from the same areas with higher-than-average income but lower-than-average purchasing are good to target. Therefore, we analyzed the customer profiles from internal dataset by aggregating the revenue at a zip code level and merging this aggregated data with external data files which contain median household income, density per square mile and corresponding city and state names. Thus, we identified 34 cities in 5 states where AmzonFresh would like to expand the market. Furthermore, we also conducted consumer behavior analysis on payment method and effectiveness of the gift card program. We found out that credit card was the most common payment method through the channels of Website and App. In addition, gift card program did have some effect on accelerating the sale but has a great space for improvement Overall, based on the findings of our analysis, the marketing strategies can be proposed as below:
  • 13. Marketing Intelligence Report 8 1. Expand the market to 34 cities within 5 states including New York, New Jersey, Pennsylvania, Texas and California 2. Optimize user experience of credit card transaction payment across all channels 3. Build partnership with credit card banks to offer promotion and deal to consumers 4. Advertise on AmazonFresh gift card program and improve distribution system of gift card The complete data analysis process can be illustrated as the following flow chart:
  • 14. Marketing Intelligence Report 9 Data Analysis Process in Phase 1
  • 15. Marketing Intelligence Report 10 MANAGERIAL REPORT To determine the current and future demands and preferences, we assessed the potential opportunities where are sensible for the company to go further in the future, and examined consumer behavior of the market. The three areas explored are as follows: 1. Secondary data was acquired to gain a complete understanding of the industry landscape and trend development in the online grocery business 2. Three external data files were combined with the internal data to further identify potential markets 3. The internal customer database was analyzed thoroughly to explore opportunities and understand customer behavior Part 1 Company Background AmazonFresh is a subsidiary of Amazon.com, an American e-commerce company headquartered in Seattle, Washington. AmazonFresh first ventured into the business of delivering home grocery in August 2007. It first offered delivery service to Seattle suburb of Mercer Island. Gradually, it expanded its market to other cities of California. AmazonFresh offers fresh grocery, prepared meals, quality meat, seafood, baked goods, unique ingredients for a special recipe and much more. In addition, it sells a subset of items from the main Amazon.com storefront, such as electronic items. Products ordered from AmazonFresh are available for home delivery on the same or the next day basically depending on the time of the order. Also, it allows customers to select time slot convenient to them for delivery. Part 2 Secondary Report  Online Grocery Industry According to a report from IBIS World, a market research firm, online grocery sales increased at a rate of 14.1% annually over the last five years. It estimated the online grocery business collectively brought in $10.9 billion in sales in 2014.1 Additionally, Brick Meets Click, a retail research firm, claimed that about 1 in 10 are buying some groceries online, and forecasted that online grocery shopping would reach between 11% and 17% by 2023.2
  • 16. Marketing Intelligence Report 11 Figure 2: Expected growth for online grocery sales (2014-2023) Grocery sales through an online platform are poised to grow tremendously in coming years. However, there is a major challenge in this industry. IBIS World estimates that the profit of online grocery business was just $927.1 million in 2014, only 8.5 percent of total revenue. By 2018, research projects that profit margins will slip to 6.9 percent of sales.3 Therefore, why does Amazon enter into online groceries in the first place even though it’s a low margin business? The main reasons are: 1. It is an influential way to increase the frequency of customer interaction. 2. It will help in putting Treasure Truck (a new way for Amazon customers to order and pickup highly desirable, limited-quantity products, food and more) 4 in high density neighborhoods and potentially will help change the economies of same day delivery.  Reasons for online grocery shopping Market Level analysis done by Bricks Meets Click suggests that the trend of online grocery will be driven by: 1. Shopper readiness and availability of platform for shopping online 2. Customer ease in online shopping and the overall experience Also, one Nielsen research indicates the primary factors that drive online grocery shopping. The result shows convenience is the main reason that people choose for online grocery shopping.
  • 17. Marketing Intelligence Report 12 Figure 3: Reasons for online grocery shopping5 AmazonFresh is clearly making moves towards offering convenience to its customers. The firm is trying all means that offers its customers flexibility and is removing all those barriers that can prevent the future growth. It has introduced new platforms for shopping to make it easily: -Online Website -Amazon App -Amazon Dash (new product launched) -Amazon Dash Button  Barriers to online grocery shopping Nielsen also conducted analysis about factors that have become barriers to the growth of this industry. The result shows that shipping cost and shipping time are the most critical problems for customers. Means of convenience
  • 18. Marketing Intelligence Report 13 Figure 4: Barriers to online grocery shopping6 AmazonFresh is working on the barriers that are preventing consumer to shop online: it has introduced annual prime membership of $299, which includes all the benefits of prime membership. Also, the company is working significantly on reducing the delivery time. It provides same day or early morning free delivery service for orders up to $35.  AmazonFresh direct competitive analysis Table 1: AmazonFresh and the direct competitors AmazonFresh Peapod FreshDirect Safeway Walmart Launch Date 2007 1989 2002 2002 2011 Geographic Coverage Some cities in California PA, MD, DC, MA, CT, IL, NY. NJ NYC, Philadelphia NC, OR, AZ,NV, DC SJ, SF, Philadelphia Target Customer High Income Family High Income Family Family Same-Day Delivery Yes No No Yes No General Merchandise Yes No No No Yes
  • 19. Marketing Intelligence Report 14 Minimum Order None $60 $30 $49 None No of SKUS 115,000 10,000 8,500 30,000 10,000 Pricing High Medium High Medium Medium Delivery Fee $9.99 $9.95 $5.99 $12.95 $8.00  AmazonFresh’s Market Share and Expansion Strategy: Currently, AmazonFresh market share in online grocery business is only about 1% because of the limited areas that it is serving. However, survey conducted by Bricks Meet Click revealed that almost 40 percent of shoppers preferred AmazonFresh to any other food retailers.7 The study reveals that AmazonFresh business strategy is to enter the mass market with big force. AmazonFresh is an important element of Amazon to address the largely untouched opportunity. Its incremental margins could be as high as 16%. 8 AmazonFresh first started with operating in pilot mode in Seattle, San Francisco and Los Angeles to test if online grocery shopping is a viable business. Today, the firm is planning to roll out in more cities. AmazonFresh business model for expansion: 1. High Income Households 2. Population of at least 2 million people at a density 600 per square mile Part 3 Data Analysis  Market Expansion: Identifying the opportunities 1. Internal Dataset Historical customer purchase data is provided by Amazon to find opportunities for its subsidiary AmazonFresh. It is a twelve-year period data, from September 19th , 1997 to April 23rd , 2009 with total 14449 records, which provides information on order source, quantity of items purchased, payment information and zip code of the purchasers. 2. External Datasets To identify the potential markets, we focused on two factors based on our secondary research: 1) Zip codes which have less revenue share but higher income than the average
  • 20. Marketing Intelligence Report 15 2) Zip codes which have density higher than 600 per square mile To conduct the analysis, we searched for three external datasets: 1) U.S. nationwide zip codes with median household income 2) U.S. nationwide zip codes with density per square mile 3) U.S. nationwide zip codes with city and state information 3. Analysis & Findings First, we combined our internal dataset and median household income dataset together, and found out 1495 zip codes with higher income and lower revenue. Next, we applied the population density dataset to the former combined dataset in order to further explore the potential market. Finally, there were 1001 potential zip codes selected for market expansion. To see clearly where our markets are, we took the 1001 zip codes on state level, and figured out following top five potential states: California, New York, New Jersey, Pennsylvania, and Texas. Figure 5: AmazonFresh’s potential market at state wise The potential cities where can be expanded are:  California (5) – Long Beach, Orange, Sacramento, San Diego, San Jose  New Jersey (9) – Caldwell, Demarest, Dunellen, Hackettstown, Short Hills, Shrewsbury, Tenafly, Trenton, West Orange  New York (5) – Floral Park, New York, Pittsford, Rochester, Staten Island
  • 21. Marketing Intelligence Report 16  Pennsylvania (6) – Feasterville Trevose, Flourtown, Gilbertsville, Harrison City, Huntington Valley, Jenkintown  Texas (9) – Arlington, Cypress, Dallas, Missouri City, Plano, San Antonio, spring, Sugar land, Tomball Figure 6: AmazonFresh’s potential market at city wise 4. Recommendations Our analysis suggests that AmazonFresh should expand its market beyond its local test markets: Seattle, Los Angeles and San Francisco. The potential states to boom the current business are California, New York, New Jersey, Pennsylvania, and Texas. At the beginning, we recommend AmazonFresh to start by going into San Diego, San Jose, New York, and the other 31 cities.  Customer Behavior Analysis 1. Dataset and Analysis The customer behavior is analyzed to identify trends among the customers which could be promoted intensively for customer retention. In this part, we used the internal customer data only. At the beginning, we focused on the payment methods between two different purchase channels: Website and App. Nowadays, website is still the main purchase platform for online grocery. However, mobile purchasing is on the rise: 13% of U.S. online adults used a purchased through smartphone in 2011 and mobile commerce is supposed to grow at 39% a year over the next five
  • 22. Marketing Intelligence Report 17 years, reaching $31 billion by 20169 . For this reason, we conducted the analysis to see the customer’s different payment behaviors. AmazonFresh launches Gift Card to promote the business. In our analysis, we also examined how effectively Gift Card works to generate sales. 2. Findings 1) Payment Methods: Website vs. App users - App Users The main payment method in both “Yes” (app users) group and “No” (non-app users) group is Credit Card. Also, around 15% of customers who use App to make a purchase pay by Cash/Check. Figure 7: Payment methods between App users and non-App users -Web Users Almost all (99.8%) the customers who purchase online use Credit Card to pay the order. However, for customers who do not use website, nearly 18% of them pay by Cash/Check. Also, there are also 6% of customers whose money stay account receivable (A/R).
  • 23. Marketing Intelligence Report 18 Figure 8: Payment methods between Web users and non-Web users 2) Gift Card Effectiveness Our analysis showed that the Gift Card did have some effect. However, it didn’t work appropriately to generate sales. Therefore, there is a good space to improve the Gift Card performance. Figure 9: AmazonFresh Gift Card 3. Recommendations 1) Since credit card is the most common payment method through both website channel and App channel, we recommend to optimize the customer experience by making their payment easier. For example, the payment information can be securely saved under the customer’s account, and next time the customer does not have to type into the credit card information again.
  • 24. Marketing Intelligence Report 19 2) We recommend to build a cooperative relationship with selected banks to offer related promotions, such as earning extra credits, to the customers who pay by credit card. 3) In order to increase the Gift Card performance, we suggest AmzonFresh to do advertising on its homepage to increase the awareness of Gift Card. Also, it would be better for the AmazonFresh to leverage on the local market channels. For example, sell the Gift Card at local stores, pharmacies, etc., where the firm is determined to expand the market. 4) We suggest providing Gift Card free to all the new customers for their first purchase on AmazonFresh. For example, the customers can enjoy $15 Gift Card when they complete their first order on AmazonFresh. TECHNICAL REPORT Firstly, we cleaned the raw data to generate a new internal dataset. Then, we found out our potential zip codes by combining and analyzing the internal dataset and external datasets together. Finally, we analyzed the internal data thoroughly including all the zip codes to explore customer behavior and opportunities for AmazonFresh.  Data Cleaning Import the original data in SAS and clean the data file. - We modified the original revenue for each order by using the formulas below and generated one new variable “TotalRevenue” - We considered the Shipping and handling as one of our revenue source because AmazonFresh has its own delivery service. Pre 01/25/2007, TotalRevenue = GrossProductRevenueAmount+ShippingHandling- CancelAmount-RefundAmount-ReturnedAmount; Since 01/25/2007, TotalRevenue = GrossProductRevenueAmount-SalesTax-CancelAmount- RefundAmount-ReturnedAmount.  Finding the potential market (zip codes, cities and states) 1. Aggregate the total revenue at zip code level.
  • 25. Marketing Intelligence Report 20 We selected CustomerZipcode and the sum of total revenue and then did the aggregation by using SQL language in SAS. 2. Merge external data file with income information and the aggregate data in SAS and clean the combined dataset. - We first sorted the aggregated data by zip code, took the external dataset which gave us the median income for individual zip code and merged both the datasets in SAS. - After that, we cleaned the data file by deleting the zip codes which we did not cover in our original internal data file. - Now, we had the combined dataset providing us total revenue and median income for each zip code. 3. Set the standard for our potential market and select the zip codes. - We believed our potential market was where the income was higher than the average income but the revenue was less than the average revenue generated. Besides, the population density was higher than 600 per square mile. - We calculated the mean total revenue ($142.5) and mean income ($64156) in SAS. - We selected the zip codes where the income was higher than the mean income and revenue was lower than the mean revenue. After that, we further selected our zip codes where the density was higher than 600. - Now we had 1001 zip codes where we could potentially venture our business. - We looked up where those 1001 zip codes were located and selected the top performance states and cities. Also, we draw the distribution map of the states and cities.  Analyzing the customer behavior 1. Modify certain variables. In our case, AmazonFresh didn’t have “subscription” and “catalog” purchase channel. Instead, it had “Dash” and “App”. According to the internal data we had, we changed the variables’ names from “SubscriptionIndicator” and “SubscriptionQuantity” to “DashIndicator” and “DashQuantity”. Also, we changed “CatalogItemIndicator” and “CatalogItemQuantity” to” AppIndicator” and “AppQuantity” accordingly.
  • 26. Marketing Intelligence Report 21 2. Based on the internal data file with all zip codes, we aggregated the number of orders by Appindicator and PaymentCategoryCode to see the different payment methods people were using when they purchased on App and they did not. The SAS result shows as below: Table2: Aggregation result of AppIndicator AppIndicator PaymentCategoryCode NumberofOrders N 1 103 N 2 4535 N 3 567 N 5 3 Y . 1 Y 1 1416 Y 2 7804 Y 3 5 Y 5 14 Figure 10: Aggregation result of AppIndicator
  • 27. Marketing Intelligence Report 22 3. Based on the internal data file with all zip codes, we aggregated the number of orders by Webindicator and PaymentCategoryCode to see the different payment methods people were using when they purchased at website and they do not. The SAS result shows as below: Table3: Aggregation result of WebItemIndicator WebItemIndicator PaymentCategoryCode NumberofOrders N . 1 N 1 1516 N 2 7829 N 3 572 N 5 14 Y 1 3 Y 2 4510 Y 5 3 Figure 11: Aggregation result of WebItemIndicator
  • 28. Marketing Intelligence Report 23 4. We selected GiftCard Indicator as independent variable and Total Revenue as dependent revenue to run two-independent T-test. The result shows as below: Table4: Two-independent T-test result Equality of Variances Method Num DF Den DF F Value Pr > F Folded F 14363 83 1.58 0.0077 REFERENCES 1. Halzack, Sarah. (2015, January 20). The staggering challenges of the online grocery business. Retrieved from https://www.washingtonpost.com/news/the- switch/wp/2015/01/20/the-staggering-challenges-of-the-online-grocery-business/ 2. Bishop, Steve. (2014, October). What’s ahead for online grocery? Growth forecast and implications. Retrieved from http://www.brickmeetsclick.com/stuff/contentmgr/files/0/7e878db844896290579646d25 8136534/pdf/bmc_what__s_ahead_for_online_grocery_3_16b.pdf 3. Halzack, Sarah. (2015, January 20). The staggering challenges of the online grocery business. Retrieved from https://www.washingtonpost.com/news/the- switch/wp/2015/01/20/the-staggering-challenges-of-the-online-grocery-business/ 4. Amazon official website. Seattle, Meet Treasure Truck. Retrieved from https://www.amazon.com/treasuretruck?tag=googhydr- 20&hvadid=63572766542&hvpos=1t1&hvexid=&hvnetw=g&hvrand=30074201772272 58398&hvpone=&hvptwo=&hvqmt=b&hvdev=c&ref=pd_sl_3zuuazu94q_e 5. Swedowsky, Maya. (2009, September). Online Grocery Shopping: Ripe Timing for Resurgence. Retrieved from Method Variances DF t Value Pr > |t| Pooled Equal 14446 1.48 0.1388 Satterthwaite Unequal 84.537 1.85 0.0673
  • 29. Marketing Intelligence Report 24 http://www.nielsen.com/content/dam/corporate/us/en/newswire/uploads/2009/10/Nielsen- OnlineGroceryReport_909.pdf 6. Swedowsky, Maya. (2009, September). Online Grocery Shopping: Ripe Timing for Resurgence. Retrieved from http://www.nielsen.com/content/dam/corporate/us/en/newswire/uploads/2009/10/Nielsen- OnlineGroceryReport_909.pdf 7. Rudarakanchana, Nat. (2014, January 29). The Future for E-Grocery: AmazonFresh Retains Lead, But Startups Jump in. Retrieved from http://www.ibtimes.com/future-e-grocery-amazon- amzn-fresh-retains-lead-startups-jump-1549969 8. Krause, Reinhardt. (2013, October 2). AmazonFresh Seen 'Highly Profitable' If Expanded. Retrieved from http://www.investors.com/news/technology/amazon-online- grocery-business-viewed-as-highly-profitable/ 9. Urken, Ross, K. (2012, May 17). Bargain Shopping Simplified: Is This App the Answer? Retrieved from http://www.dailyfinance.com/2012/05/17/bargain-shopping-simplified-is- this-app-the-answer/
  • 30. Marketing Intelligence Report 25 PHASE 2 STRATEGIC ANALYSIS COMPANY OVERVIEW 1. Subsidiary of Amazon.com American Ecommerce company 2. AmazonFresh had an estimate of about $15.4 billion of sales in 2013.1 3. Have a competitive advantage because of its already set infrastructure 4. Key areas of growth are expansion into other markets, pricing strategies and CRM MARKET OVERVIEW  Market 1. Grocery sale through online platform only represents 4% of online grocery retail market, but it is among the fastest growing segment. 2. At the rate its going online grocery is poised to grow at an annual rate of 9.5%, with the potential to become a $9.4 billion industry by 2017.2 3. Major challenge of online grocery shopping is the profit margin only accounting for about 8.5% of total revenue mainly because of high distribution cost.  Customers 1. Customers have begun to transform their shopping behaviors to online platforms. 2. Increasingly time-pressed customers appreciate flexibility and convenience of online grocery shopping. 3. As e-commerce technologies are becoming more user friendly, the line between physical and digital world is vanishing. Collaborators 1. AmazonFresh has collaborated with USPS for delivery of fresh groceries.3 2. AgLocal partners with AmazonFresh to provide on-demand meat delivery.4 3. Bon Appétit announces new partnership with AmazonFresh.5
  • 31. Marketing Intelligence Report 26  Competitors AmazonFresh differentiates itself with direct and indirect competitors by 3 core competencies: marketing, merchandising and logistics.  Perceptual Map All of the attributes depicted in the perceptual map are closely related to the overall customer experience. From the axis, we observe that Walmart and Instacart are the strong and direct competitors to AmazonFresh. Figure 12: Perceptual Map (1)
  • 32. Marketing Intelligence Report 27 Figure 13: Perceptual Map (2)  Technological Advancements 1. AmazonFresh introduces new shopping platforms, Amazon App, Amazon Dash, Amazon Dash Button, to improve the convenience of online shopping for its loyal customers. 2. Amazon also introduces Amazon Prime Air for delivery in 60 minutes. TARGET CUSTOMERS Because of the relative higher price compared to its competitors, AmazonFresh should mainly target on: 1. Big families which spend more than $10,000 annually at supermarkets 2. High income singles that are technologically savvy and too busy to take out time. VALUE PROPOSITIONS 1. Convenience and efficiency (24/7 ordering online) 2. Excellent operations – same day delivery
  • 33. Marketing Intelligence Report 28 3. Broad selection – saving time with one-stop shopping GOALS AND OBJECTIVES 1. Market expansion to 34 cities within 5 states by 2017. 2. Market Share to be increased to 10% of online grocery business by 2017. 3. Awareness of AmazonFresh to be increased by 50% by massive advertising and promotions. REFERENCES 1. Rudarakanchana, Nat. (2014, January 29). The Future for E-Grocery: AmazonFresh Retains Lead, But Startups Jump In. Retrieved from http://www.ibtimes.com/future-e-grocery-amazon-amzn-fresh- retains-lead-startups-jump-1549969 2. Frojo, Renée. (2013, January 9). Online Grocery Shopping: Boom or Bust? Retrieved from http://www.bizjournals.com/sanfrancisco/blog/2013/01/online-grocery-shopping-boom-or- bust.html?page=all 3. Suciu, Peter. (2014, September 25). AmazonFresh, USPS Could Be Marriage Made in Heaven. Retrieved from http://www.ecommercetimes.com/story/81099.html 4. Sciacca, Annie. (2014, September 29). AgLocal partners with AmazonFresh to provide on- demand meat delivery. Retrieved from http://www.bizjournals.com/sanfrancisco/blog/2014/09/aglocal-partners-amazonfresh-meat- delivery.html 5. Steigrad, Alexandra. (2014, March 31). Bon Appétit to Announce New Partnership with AmazonFresh. Retrieved from http://wwd.com/globe-news/fashion-memopad/bon-apps-secret- partner-7624932/
  • 34. Marketing Intelligence Report 29 PHASE 3 - MARKETING METRICS Marketing Metrics were applied to evaluate performance and operation situation. Metrics are key performance indicators that allow firms to track performance over time and enable to executive reasonable business activities. To quantify our proposal for expansion to the company, we have identified key metrics that could help the management at different levels from marketing managers to CMO to gauge the performance of the business. The metrics we calculated cover Customer Perception, Market Share, Competitive Analysis, Revenues and Cost, Customer Relationships, Financial Evaluation and Price Sensitivity. In this session, we first listed all the KPIs (Key Performance Indicators) that would be examined, coupled with numeric examples, appropriate users, and limitation correspondingly. Then, we illustrated the whole philosophy of how each marketing metric was connected and organized to calculate the value for the firm. After that, we calculated the marketing metrics by using the real industry and business number, and designed a marketing dashboard to show the key findings. In part 5, we generated a Service Adoption Funnel to describe the customer journey from the first involvement with the brand to the ultimate goal of purchase. PART 1 - METRICS A. Selected Marketing Metrics Name Definition Importance Metrics for Customer Perception, Market Share and Competitive Analysis Revenue Market Sales revenue as percentage of Measure of competitiveness
  • 35. Marketing Intelligence Report 30 Share market sales revenue Brand Penetration Purchasers of a brand category as a percentage of total population Measure brand acceptance by a defined population. Awareness Percentage of total population that is aware of brand Consideration of who has heard of brand Ad Awareness Percentage of total population that is aware of brands advertising A measure of effectiveness of advertising. How powerful it is having an influence on its target audience Net Promoter Percentage of customers willing to recommend to others less the percentage unwilling to recommend Likeliness to recommend, good metric for marketers. Share of Wallet It tells the proportion of dollars that the customer is spending on your brand It is not only measures the satisfaction level, but it measures the degree to where the brand stands in customer choice and allows to measure the number of brand customer uses Metrics for Revenues, Cost Structures and Profitability Percentage Margin Unit margin as a percentage of Unit Price Compare margins across different products. Guide pricing and promotion. Break Even Sales For revenue break even, divide fixed costs by contribution Rough indicator of project attractiveness and
  • 36. Marketing Intelligence Report 31 Level margin (%) ability Metrics for calculating the value of Individual Customers and Relationships Customer Lifetime Value The present value of the future cash flows attributed to the customer relationship. Customer Relationship Management decisions should be made with the objective of improving CLV. Acquisition budgeting should be based on CLV Average Acquisition Cost The ration of acquisition spending to the number of new customers acquired. Track the cost of acquiring new customers and compare that cost to the value of the newly acquired customers. Average Retention Cost The ratio of retention spending to the number of customers retained. Monitor retention spending on a per customer basis. Metric for Financial Evaluation of Marketing Program Net Present Value The present value of future cash flows Summarize the value of cash flows over multiple periods Internal Rate of Return The discount rate at which NPV of an investment is zero Determine whether to undertake an investment only by comparing with a company’s hurdle rate ROMI The incremental revenue attributable to marketing divided by marketing spending An accurate baseline stating what revenue is attributable to marketing
  • 37. Marketing Intelligence Report 32 Metric on Price Sensitivity Price Premium It is the percentage by which the price of brand exceeds a benchmark price Measures how a brand’s price compares to that of its competition B. Metrics Details and Numeric Examples I. Metric Calculations Estimated sales in year 2014: 600 million 1. Revenue Market Share - The percentage of market account for specific activity. Revenue Market Share (%) = Revenue market share differs from unit market share in that it reflects the prices at which goods are sold. Marketers need to be able to translate sales targets into market. Example - Revenue by company X is $50,000 and total market sales revenue is $200,000. Then revenue market share of X is = 50,000/ 200,000 = 25% 2. Brand Penetration - Penetration is a measure of brand or category popularity. It is defined as the number of people who buy a specific brand or category of goods at least once in a given period, divided by the size of the relevant market population. Brand Penetration (%) = Example - If over a period of six months, in a market of 10,000 household, 23000 households purchase at least one product of Amazon Fresh. Brand Penetration could be calculated as 2,300/10,000 = 23%
  • 38. Marketing Intelligence Report 33 3. Brand Awareness - The percentage of potential customers who recognize the name of a given brand. These could be recorded on an aided level to see if customers have heard of AmazonFresh. The metric could be measured by calling out participant and calculate the percentage of people who could recall the brand. 4. Ad Awareness - The percentage of target customers who demonstrate awareness of one brand advertisement. This can be a campaign, media specific, or can cover all advertising. This is to measure the success rate of advertising. It is a good check if there is a good return on money invested in advertising. 5. Net Promoter – It is created by subtracting the percentage of detractors among current customers from the percentage of promoters among current customers. Promoters - willing to recommend (rating 9 or 10) Passives - satisfied but unenthusiastic customer (rating 7 or 8) Detractors - customers willing to recommend the company - (rating 0 to 6) Net Promoter Score = Example - if a customer survey of a company reports that there were 20% promoters, 70% passives and 10% detractors the company would have Net Promoter Score of 20 - 10 = 10. 6. Share of Wallet - The wallet allocation rule is based on brands rank among the number of brands a customer uses for a particular product or service. These two are important factors to calculate the share of wallet. Share of Wallet =
  • 39. Marketing Intelligence Report 34 Example - Estimate the number of brands a customer uses for product category is 3. Survey on what rank does the brand stand among other brands - let's say the brand is the third preference. Calculating the share of wallet: (1- ¾) (2/3) = 0.167 Then the share of customers X is 16.7% 7. Percentage Margin - This could be calculated using total sales revenue and total costs Margin (%) = Margin is important for all marketing decisions. It is a key factor in pricing, return on marketing spending, earnings, forecasts and analysis of customer profitability. 8. Break Even Sales Level - It represents the sales amount in revenue terms that is required to cover the total costs. Total profit at breakeven is zero. Contribution per Unit ($) = Contribution Margin (%) = Break-Even Volume (#) = Break-Even Revenue ($) = Example - with a fixed cost of $30,000, selling price of $40, cost price of $10 per unit Contribution per Unit = 40-10=$30, Break Even Volume = $30,000 / $30 = 1000 units, Break Even Revenue = 1000 * $40 = $40,000
  • 40. Marketing Intelligence Report 35 9. Customer lifetime Value - It is the dollar value of a customer relationship based on the present value of the projected future cash flows from the customer relationship. Customer Lifetime Value ($) = Margin ($) CLV is important concept as its helps to focus on long term relationship with the customers. Example - AmazonFresh charges $299 per year for prime membership and provides free delivery for orders above $35. Variable cost is $40. With marketing spending of $100, the attrition is 6%. The yearly discount is 12%. Contribution Margin = $299 - $35 - $100 = $164 Retention Rate = 0.94 Discount Rate = 0.12 CLV = $164 * (0.94 / (1+0.12 -0.94) = $856 10. Average Acquisition Cost - This represents the average cost to acquire a customer and is the total acquisition spending divided by the number of new customers acquired. Average Acquisition Cost ($) = 11. Average Retention Cost - This represents the average cost to retain an existing customer and is the total retention spending divided by the number of customers retained. Average Retention Cost ($) = Example - if AmazonFresh spent $1.4 million and acquired 64,800 customers, Acquisition Cost is $21 per customer. If AmazonFresh spent $500,000 to retain 154,980 customers of which 87,957, then Average Retention Cost is $6 per customer. 12. Net Present Value - is the discounted value of the cash flow associated with the project.
  • 41. Marketing Intelligence Report 36 It is the present value of the dollar received in a given number of periods in the future. Discounted Value ($) = 13. Internal Rate of Return - The discount rate for which the net present value of the investment is zero. It is an important metric to justify the project because it compares the company's hurdle rate. 14. Return on Marketing Investment - This is metric used to calculate the contribution attributable to marketing divided by the marketing invested or risked. Return on Marketing Investment (ROMI) = 15. Price Premium - It is the percentage by which products selling price exceeds a benchmark price. Price Premium (%) = It is important to monitor as it is indicative of competitive pricing. Example – Estimate the price of one certain AmazonFresh product (like mangoes) is $5 while the benchmark price is $4. Then premium pricing is by 25%.
  • 42. Marketing Intelligence Report 37 II. Information for each of the metric Name Diagnostic / Evaluative Short Term / Long Term Users (Level of Organization) Cross Sectional / Longitudinal Revenue Market Share Evaluative Long Term CMO Longitudinal Brand Penetration Evaluative Long Term CMO Longitudinal Awareness Evaluative Long Term CMO Longitudinal Ad Awareness Evaluative Short Term Marketing Manager Cross Sectional Net Promoter Diagnostic Short Term Marketing Manager Cross Sectional Share of Wallet Evaluative Long Term CMO, marketing manager Cross Sectional Percentage Margin Diagnostic Short Term Marketing Manager Cross Sectional Break Even Sales Level Diagnostic Short Term Marketing Manager, Finance Manager Cross Sectional Customer Lifetime Value Diagnostic Long Term CMO Longitudinal
  • 43. Marketing Intelligence Report 38 Average Acquisition Cost Diagnostic Short Term Marketing Manager Cross Sectional Average Retention Cost Diagnostic Short Term Marketing Manager Cross Sectional Net Present Value Diagnostic Short Term Marketing Manager Cross Sectional Internal Rate of Return Diagnostic Short Term Marketing Manager Cross Sectional ROMI Evaluative Short Term Marketing Manager Longitudinal Price Premium Diagnostic Short Term Marketing Manager Cross Sectional III. Users of Marketing Metrics Marketing Metrics is used at all levels in a company depending on the metric that has to be studied. Upper Management Level: CMO, EVP Middle Management Level: Marketing Manager, Finance Manager Lower Management Level: Team Leader, Marketing Assistant / Coordinator
  • 44. Marketing Intelligence Report 39 IV. Limitations to Marketing Metrics All companies recognize the importance of marketing metrics and its function to better manage and report marketing performance, but there are several limitations coming along with. 1. Metrics are complex and difficult to use 2. Metrics are indicators. They do not solve business problems and do not address marketing challenges. 3. Many get confused to start with as there are too many metrics which could be used and selected. 4. It has limited into real business because of insufficient data or certain amount of budget required achieving those numbers. 5. It is a very time-consuming process and requires a good deal of efforts. PART 2 - BIG PICTURE OF MARKETING METRICS Choosing the right metrics is very critical to calculate the overall firm value. As a consulting team, it is very important for us to provide all the calculated marketing metrics that could lead to future customer value and firm performance before giving any suggestion which involves huge investments. Subsequently, a framework was developed to identify key metrics which the firm should focus on and to give a big picture of how the firm would get to where they were at present and provide with meaningful insights towards how they could continue to grow in future. The metrics that have been used are split into two main categories: 1. Metrics that measure the value of brand 2. Metrics that measure the value of customers
  • 45. Marketing Intelligence Report 40 Figure 14: Metric model to calculate firm value The model is based on the consideration that overall firm value is formed by the customer value and brand value. Customer Value is a critical parameter and could be measured in marketing metric - customer lifetime value, which is a prediction of net profit attributed to the entire future relationship with the customer. Earlier the CLV was measured in terms of past performance, but today possible future performance is also taken into account. It predicts the future cash flows from a customer whose net present value could be evaluated.
  • 46. Marketing Intelligence Report 41 Brand Value is measured to see how brand is perceived by the consumers and how much customers are willing to purchase from the brand. We believe the share of wallet is a very helpful metric to see what percent of customer total purchase is shared by AmazonFresh. PART 3 - METRIC CALCULATIONS FOR PROPOSAL In order to justify the proposal of new market expansion, it is reasonable that we quantify the performance evaluation and the potential value that could be brought to the firm before any significant investments are made. Therefore, some related marketing metrics were carefully calculated to justify the proposal. 1. Market Share Today in United States, total grocery business reaches to $600 billion in which online business accounts for 4 percent making $24 billion business. AmazonFresh currently operates in 3 cities and accounts for 1% of total business which is $240 million. Proposal of expansion into 40 cities by 2017 could drive the sales revenue as high as 10 times which is $2.4 billion. In this way, total market sales revenue will reach to $26 billion. Hence, market share for Amazon in coming years = $2.4 / $26 = 9.23% 2. Margins and Percentage Margin AmazonFresh sells thousands of Amazon Items, fresh grocery and local products.
  • 47. Marketing Intelligence Report 42 On average, the grocery business is a very low marginal business, approximately 3 percent margin. Consider a simple product for calculation - Tuscan Dairy Whole Vitamin D milk 64 Oz. AmazonFresh sells at a price for $2.19 while the price at store is $1.99. Unit Margin is selling price minus cost price per unit which is equal to $0.2. Percentage margin is given by ration of unit percentage per selling price which accounts to 9.13% 3. Price Premium At a market-average level, a 1-¾ inch thick, 2 Count, 30 Ounce beef package usually sells at $75, and AmazonFresh price is $84. Clearly, AmazonFresh is pricing its product at a premium compared to its benchmark for providing home delivery. Hence, we calculate the price premium to measure how much percentage the firm is charging. Price premium = [Brand A Price ($) - Benchmark Price] / Benchmark Price = [84-75] / 75 = 12% It is essential that price premium should be monitored for every product the firm offers. 4. Break-Even Analysis and Target Volume Analysis A break-even, representing the sales amount in either unit or revenue terms, is required to cover both fixed and variable costs. It is very important for a company to achieve break even sales. As we put proposal for expansion into 34 cities in 5 states, we need to calculate metrics to quantify the minimum sales required to arrive at break even. Let’s take New York City for example to quantify the sales required to arrive at break even.
  • 48. Marketing Intelligence Report 43 Contribution per Unit ($) = Contribution Margin (%) = Break-Even Volume (#) = Break-Even Revenue ($) = AmazonFresh offers a large number of products and has different contribution per unit ($) for different products. Assuming that on average the contribution per unit is $3 which includes variable cost of inventory, distribution cost, and margin% which accounts for 4%. Let the fixed cost for setting up the infrastructure be $300,000 (Warehouse + Employee Salary). Thus, Break Even Volume (#) = Fixed Costs / Contribution Per Unit = $300, 00 / $3 = 100,000 Break Even Revenue ($) = Fixed Costs / Contribution Margin (%) = $300,000 / 4% = $7,500,000 Target Volume Analysis The Manager expects to generate volumes that meet target profits for which sales have to be made beyond break even. To achieve at target profits, level of sales or revenue has to be determined which is more than sales to cover the firm’s cost. To determine the target volume to achieve the yearly profit objective of $300,000, AmazonFresh has to sell far beyond what it used to sell at breakeven. Target Volume (#) = Target Volume (#) = (Fixed Costs + Target Profit) / Contribution per Unit = ($300,000 + $300,000) / 3 = 200,000 units.
  • 49. Marketing Intelligence Report 44 5. Financial Analysis Payback: For AmazonFresh to enter New York market, it requires significant investment to set up the infrastructure which requires a few years to be covered. Assume that the firm spends $1,500,000 to set up its infrastructure initially, and continues to produce a net income of $600,000 a year for at least 5 years. The payback is 3 years meaning that in three years AmazonFresh will cover the investment it made in taking its market in New York City. NPV: Next, to calculate the dollar value of this new line of expansion, estimates are made for the current bank loan interest rate. Assuming 7% as discount rate, the first cash outflow is at the 0 point of time line. Hence, the NPV is equal to: PV of all the cash inflows - PV of all the cash outflows = 600,000 / (1+7%) + 600 / (1+7%)^2 + 600/(1+7%)^3 - $1,500,000 = 560,747 + 524,063 + 489,795 - 1,500,000 = $74,605 6. Return on Marketing Investment Analysis AmazonFresh is expanding into the markets of East Coast as per our proposal where it may face fierce competition from FreshDirect and Peapod. So, to bring awareness and promote the brand among customers, it needs to make significant investment in distributing the brand through events or advertising platform. A great deal will be spent in digital space to better acquire new customers. Assume that AmazonFresh spends $20 million in advertising over digital and traditional media platforms, and as per the Forrester report, there is an increase in revenue by $600 million. Baseline Revenue from its marketing efforts is $240.
  • 50. Marketing Intelligence Report 45 Contribution margin as calculated per unit is approx 9%. Return on Marketing Investment (ROMI) = ROMI = [($600 million - $240 million)* 9% - $20 million] / $20 million = 62% 7. Customer Acquisition versus Retention Costs The company has already started to offer its services into certain zip codes in Brooklyn, New York. This year, AmazonFresh plans to spend $2 million in market to expand to other zip codes of New York City and it is estimated to acquire a customer base of 40,000 in the next year. At the same time, another $500,000 is spent for retention of its current customer base with 100,000 of which 25% retained at the end of year. Average Acquisition Cost ($) = 2,000,000 / 80,000 = $25 Average Retention Cost ($) = 500,000 / 25,000 = $20 8. Price Elasticity of Sales Analysis It measures the responsiveness of the quantity demanded of a good or service to a change in price. Assuming that the organic fruit gift box follows a linear demand function, at the current price of $40 per unit, AmazonFresh sells 40,000 units with an elasticity of -2. A proposal is floated to lower the price to $35 per unit in order to increase market share. To calculate the number of unit sold at $35 will be given by:
  • 51. Marketing Intelligence Report 46 -2 = Change in quantity (%) / Change in price (%) = Change in quantity (%) / -12.5%, Hence, Change in quantity equals to 25% Thus, the total number of units that would be sold is equal to 50,000.
  • 52. Marketing Intelligence Report 47 PART 4 - MARKETING DASHBOARD
  • 53. Marketing Intelligence Report 48 PART 5 – THE FUNNEL AND INSIGHTS The Service Adoption Funnel is a model that describes the theoretical customer journey from the first involvement with the brand to the ultimate goal of purchase. This model is critical from marketing standpoint as it helps to understand and track the customer behavior throughout the sales process. A funnel shape which focuses on consumer’s decision process is used as it shows how company loses potential customer at each level and track at which level the company losses it maximum customers. Numbers demonstrated as below are estimated for the expansion in New York City of the company. Figure 15: The Service Adoption Funnel
  • 54. Marketing Intelligence Report 49 Thus, with this model of evaluation, 53000 customers could be reached out. Estimate that each customer buys minimum of $70 grocery per month giving an annual revenue of ($70 x 12) + $299 for prime membership = $1139 Therefore, it will be a good start for the company with revenue of $60.4 million. REFERENCES 1. Rudarakanchana, Nat. (2014, January 29). The Future for E-Grocery: AmazonFresh Retains Lead, But Startups Jump in. Retrieved from http://www.ibtimes.com/future-e-grocery-amazon-amzn-fresh- retains-lead-startups-jump-1549969 2. McEnery, Thornton. (2014, October 26). Amazon squeezes FreshDirect. Retrieved from http://www.crainsnewyork.com/article/20141026/TECHNOLOGY/141029885/amazon- squeezes-freshdirect 3. Barr, Alistair. (2013, October 2). Broader AmazonFresh launch may be ‘highly profitable’. Retrieved from http://www.usatoday.com/story/tech/2013/10/02/amazon-fresh- groceries/2907641/ 4. Bishop, Todd. (2014, December 13). Tough to swallow: Longtime AmazonFresh customers leaving over new $299/year subscription. Retrieved from http://www.geekwire.com/2014/tough- swallow-longtime-amazon-fresh-customers-bolting-new-299year-subscription/
  • 55. Marketing Intelligence Report 50 PHASE 4 – DATABASE, SQL AND MARKETING DASHBOARD INTRODUCTION A database was designed in line with our proposal for expansion into new cities. AmazonFresh is offering multiple products and expanding beyond its current market. Database that could help retrieve day-to-day sales for different markets will help to design marketing campaigns more efficiently. Database was designed to give all key information of individual customers ranging from their purchase to their personal information. Database can be used for creating a marketing dashboard that could provide with at-a-glance view of KPIs (Key Information Indicators) that is relevant to our objective, such as total sales and production. It is a very efficient way to sum up all key details, trends, and comparison. Furthermore, it is relatively simple to communicate and support business with meaningful and useful data. DATABASE DESIGN Our database was composed of 4 tables that gave information about the customers, orders, order details and products that customers purchased. TABLE SCHEMES In listing each of the columns while creating tables, the fields, type and length were taken in consideration. The primary key that was used to uniquely identify each of the rows was listed. Primary Key for each of the table worked as ID, and a foreign key was introduced to connect the records in two tables with each other, which was shown in the coding for respective table. The SQL Server Management Studio was used to create the database and run the query.
  • 56. Marketing Intelligence Report 51 Figure 16: The Database Design SQL CODES ------------------------------------------------------First Set----------------------------------------------------- Creating Database and developing query 1. Creating tables, columns and primary and secondary key Query for customer table CREATE TABLE Customers ( ID INT NOT NULL,
  • 57. Marketing Intelligence Report 52 LASTNAME VARCHAR (20) NOT NULL, FIRSTNAME VARCHAR (20) NOT NULL, PHONE INT, ADDRESS CHAR (25), CITY CHAR(25), STATE CHAR(20), ZIPPOSTAL INT, COUNTRY CHAR(20), PRIMARY KEY (ID) ); go Query for Orders Table CREATE TABLE ORDERS_1( ID INT, OrdersDate Date, ShippingDate Date, ShipCity CHAR(25), ShipZipPostal INT,
  • 58. Marketing Intelligence Report 53 ShippingFee INT, Tax INT, PaymentType CHAR(25), OrderMonth VARCHAR(25), OrderYear INT, OrderTotal INT, CustomersID INT, PRIMARY KEY (ID), FOREIGN KEY (CustomersID) REFERENCES CUSTOMERS(ID) ); go Query for Products Table CREATE TABLE PRODUCTS( ID INT, ProductCode VARCHAR(25), ProductName CHAR(25), StandardCost VARCHAR(25), ListPrice VARCHAR(25),
  • 59. Marketing Intelligence Report 54 QuantityPerUnit VARCHAR(25), PRIMARY KEY (ID) ); go Query for OrderDetails Table CREATE TABLE OrderDetails( ID INT, OrderID INT, ProductID INT, Quantity INT, UnitPrice VARCHAR(25), GiftCard VARCHAR(25), PRIMARY KEY (ID), FOREIGN KEY (OrderID) REFERENCES ORDERS(ID), FOREIGN KEY (ProductID) REFERENCES PRODUCTS(ID) ); Go
  • 60. Marketing Intelligence Report 55 2. Inserting data in each table Inserting Data into customers table INSERT INTO Customers VALUES('1','WANG','CHU','1234567890','456 3rd STREET','NEW YORK CITY','NY','11368','US'); INSERT INTO Customers VALUES('2','CHADDERWALA','SAGAR','1234567890','123 1rd STREET','SEATTLE','WA','98052','US'); INSERT INTO Customers VALUES('3','AXEN','THOMAS','1567891230','125 3rd STREET','LOS ANGELES','CA','78956','US'); INSERT INTO Customers VALUES('4','LEE','CHRISTINA','1894561230','127 8th STREET','ORANGE','CA','95432','US'); INSERT INTO Customers VALUES('5','LUDICK','ANDRE','1985462130','129 9th STREET','SAN FRANCISCO','CA','99999','US');
  • 61. Marketing Intelligence Report 56 INSERT INTO Customers VALUES('6','LI','GEORGE','1895432615','115 5th STREET','FLORAL PARK ','NY','78954','US'); Inserting Data into Orders table INSERT INTO ORDERS_1 VALUES('1','5/4/2015','5/4/2010','SEATTLE','98052','5','7','VISA','MAY','2015','55','1'); INSERT INTO ORDERS_1 VALUES('2','6/5/2015','6/6/2015','NEW YORK CITY','98052','5','7','VISA','MAY','2015','44','2'); INSERT INTO ORDERS_1 VALUES('3','7/5/2015','8/5/2015','LOS ANGELES','98052','5','7','VISA','MAY','2015','33','3'); INSERT INTO ORDERS_1 VALUES('4','3/7/2015','4/7/2015','ORANGE','98052','5','7','VISA','MAY','2015','56','4'); INSERT INTO ORDERS_1 VALUES('5','3/4/2015','4/4/2015','SAN FRANCISCO','98052','5','7','VISA','MAY','2015','12','5');
  • 62. Marketing Intelligence Report 57 INSERT INTO ORDERS_1 VALUES('6','10/3/2015','11/3/2015','FLORAL PARK','98052','5','7','VISA','MAY','2015','45','6'); Inserting Data into Products Table INSERT INTO PRODUCTS VALUES ('1','56','Nk','67','7','2'), ('2','66','JK','88','8','6'), ('3','23','YY','89','3','9'), ('4','12','UU','45','4','4'), ('5','77','IK','78','9','2'); Inserting Data into OrderDetails INSERT INTO OrderDetails VALUES('1','2','3','5','5','KK'), ('2','2','2','6','4','LL'), ('3','3','3','7','8','PJ'), ('4','4','4','3','1','YH'), ('5','5','5','9','2','IK');
  • 63. Marketing Intelligence Report 58 3. Updating one line of data Updateding one line of data for customers table Update Customers Set LASTNAME ='Just', FIRSTNAME ='IN', PHONE ='77789', ADDRESS ='225 Saint Jersey Avenue', CITY ='Jersey', STATE ='NJ', ZIPPOSTAL='78906', COUNTRY ='USA' WHERE id='3'; /* Updates only when id is 3*/ 4. Deleting one line of data Deleting one line of data for customers table Delete from customers WHERE lastname='Just';
  • 64. Marketing Intelligence Report 59 ------------------------------------------------- Second Set ----------------------------------------------------- The query was developed mainly to see which of the city would give max revenue and where the best market would be. Also, it could check what kind of the brands and products would be preferred by customers in online grocery shopping. 1. Two queries to retrieve data Query 1: retrieve data from customers table – ID and City Select ID, City from Customers; /* this gives only ID and City; if one wants all columns then just “select * from customers”*/ Managerial Implication - It allows us to study where our customers mostly come from. Query 2: select top performing products from products table Select top 2 * from products; /* Will retrieve top 2 rows*/ Managerial Implication – Allows us to study top 2 performing products
  • 65. Marketing Intelligence Report 60 2. Query to retrieve data from more than 1 table. (Inner Joins & Outer joins) Outer Joins select * from customers a full join ORDERS_1 b on a.id=b.CustomersID /* gives data from both tables whether they match or not*/ Inner Joins select * from customers a inner join customers b on a.id=b.id; 3. Query to retrieve data from two or three tables such that data from one table is aggregated Query1 select * from orders select * from agg_1 select count(a.id) as TotalIDs,
  • 66. Marketing Intelligence Report 61 sum(b.shippingfee) as Fee,sum(b.tax) as TotalTax, b.paymenttype, count(distinct b.customersid) as Customers, sum(b.ordertotal) as Order_total,b.ShipCity into Agg_1 --drop table Agg_1 from customers a join orders_1 b on a.id=b.CustomersID where OrderYear='2015' group by b.paymenttype,b.ShipCity /* join gives only what is common between the 2*/ /* basically there is a column that matches each table with another table*/ Managerial Implication – This was used to get total revenue generation across different cities. This will be used to find out which of the city gives max revenue. Figure 16: Result of Query 1
  • 67. Marketing Intelligence Report 62 Query 2 select sum(b.OrderTotal) as OrderTotal,sum(b.tax) as TotalTax, count(distinct b.customersid) as Customers,a.ProductName into Agg_2 --drop table agg_2 from PRODUCTS a join ORDERS_1 b on a.id=b.CustomersID group by a.productname having count(b.customersid) !='0'; Managerial Implication – This was used to study which of the products gives max revenue and hence more in demand among their customers. Figure 17: Result of Query 2
  • 68. Marketing Intelligence Report 63 Query 3 select sum(cast(a.QuantityPerUnit as int)) as OrderTotal,sum(cast(a.listprice as int)) as ListPrice, count(distinct b.ID) as Customers,a.ProductName,b.CITY into Agg_3 --drop table agg_3 from PRODUCTS a join Customers b on a.id=b.ID group by a.productname,b.CITY having count(b.ID) !='0'; Managerial Implication – This gives information which of the product is in demand in which of the cities. This is useful for running an effective advertisement campaign. Figure 18: Result of Query 3 Note: Difference between HAVING and WHERE Clause in SQL
  • 69. Marketing Intelligence Report 64 Clause “WHERE” is used to filter the requirements and is used to filter the non-aggregates. It does not work with aggregation like sum, avg, max etc. Instead, in that case, having statement will be used. This clause was added to SQL so as to compare the aggregation. 4. Creating a view for query that is prepared Query Create view customerview as select * from Customers;
  • 70. Marketing Intelligence Report 65 MARKETING DASHBOARD The database was connected to excel, and charts were created to study different metrics. There are three charts presented in the dashboard Chart1 – It shows us the products that give max sales in respective cities Chart2 – It shows which of the product brings in more revenue to the company. Thus, the chart gives sales performance for each of the products Chart 3 – It shows us total revenue generated across various cities Figure 19: DashBoard
  • 71. Marketing Intelligence Report 66 LIMITATIONS Thorough analysis was conducted to identify new opportunities for AmazonFresh using the Internal Data and External Data; however, there are few limitations to our research: 1. The data set we had provided us the customer behavior of parent company, but not it whole subsidiary AmazonFresh for which we had to extrapolate our result using our estimates. 2. The data set provided ranged from 1997 to 2009 which was not updated to study the current rapidly changing scenario. 3. The data should have incorporated more variables to understand customer demands. Future Research Project: The future research project is to study the customer needs by conducted surveys to better serve their needs and a model has to be developed for metrics which could lead to firm value.
  • 72. Marketing Intelligence Report 67 TECHNICAL APPENDIX SAS CODES PROC IMPORT datafile='C:SASDataSet8DMEF0509-2.xlsx' OUT=mydata Replace; RUN; DATA mydata2; Set mydata; If Inputdate < '25JAN2007'd then TotalRevenue = GrossProductRevenueAmount+ShippingHandling-CancelAmount- RefundAmount-ReturnedAmount; Else TotalRevenue = GrossProductRevenueAmount-SalesTax- CancelAmount-RefundAmount-ReturnedAmount; RUN; PROC EXPORT data=mydata2 dbms=excel outfile = 'c:sasmydata2-newrevenue.xlsx' replace; RUN; PROC SQL; Select CustomerZipCode, sum(TotalRevenue) as Netrevenue From mydata2 Group by CustomerZipCode Order by CustomerZipCode; QUIT; PROC IMPORT datafile='C:SASAggZipcode.xlsx' OUT=mydataA Replace; RUN; PROC IMPORT datafile='C:SASIncomeZip.xlsx' OUT=mydataB Replace; RUN; DATA combined; merge mydataA mydataB; by CustomerZipcode; RUN;
  • 73. Marketing Intelligence Report 68 PROC SORT Data = Combined OUT = Bonus; By Descending NetRevenue; RUN; DATA Cleaning; set Bonus; If NetRevenue = "" then delete; RUN; PROC MEANS mean median data=cleaning; Var NetRevenue Income; RUN; DATA potentialmkt; set cleaning; If (NetRevenue > 142.5 | Income < 64156) then delete; RUN; PROC SQL; Select Webitemindicator, paymentcategorycode, COUNT (Ordernumber) as NumberofOrders From mydata2 Group by Webitemindicator, paymentcategorycode; QUIT; PROC SQL; Select appindicator, paymentcategorycode, COUNT (Ordernumber) as NumberofOrders From mydata2 Group by appindicator, paymentcategorycode; QUIT; DATA mydata3; set mydata2; If GiftCertificateRedeemedInd = "Y" then Giftcard = "Y"; Else Giftcard = "N"; RUN; PROC TTEST data=mydata3; class Giftcard; Var Totalrevenue; RUN;