Understanding Customer Purchase Behaviour | Angoss KnowledgeSTUDIO

Understanding Customer Purchase Behavior
Market Basket Analysis

Angoss Software Corporation. All rights reserved.

Predict. Act. Perform.

PREDICT the best or most attractive,
profitable opportunities by discovering
valuable insight and intelligence
from your data

Clear and detailed recommendations –
whether for sales and marketing, or risk –
are provided for businesses to ACT upon

PERFORM and deliver measureable
ROI to improve business performance



 Business analytics provides organizations with
valuable insights from data
 Leading organizations are applying predictive
modeling to growing volumes of data
 Uncover and action new opportunities to increase
revenue and profitability



 Discover and apply a deeper level of insight into
customer purchase patterns
 Better understanding of your customers’ buying
behaviour
 More informed decisions about how and where to
deploy efforts and resources
 Optimize sales and marketing efforts



 Advanced model in KnowledgeSTUDIO
 Discover associations between items or events
 Derive rules that indicate whether certain events
do or do not happen together
 Use for cross-sell / upsell, product promotions
and placement, next-best offer strategies


Association Rules

 Association rule:
{chequing_savings, insurance} => {housing loans}

 Classic example: Amazon.com recommender
system
 “Customer who bought this item also bought item A”



 Visualize the degree of
attraction or repellence
between items
 View charts representing
item associations, rank
association rules
 Apply association rules
to new data to produce
recommendations



 Leverage large amounts of customer data to
determine which products are purchased together
 Resulting association rules can be used to build
strategies around:
-Product recommendations and promotions
-Cross-sell and Upsell strategies
-Product placement
-Product and service bundling
-Next Best Offer strategies
-Affinity marketing programs
-Inventory selection and optimization



 Understand customer purchasing patterns
 Explore associations between items and events
 Predict the likelihood of a customer response
 Optimize your marketing and sales operations


Market Basket Analysis Use Cases


Use Case #1- Financial Services Cross Sell
(Recommender system, next best offer)

• Financial Services
Industry

• Bank has low cross sell rates of its products
Opportunity

• Build a market basket model at the customer level to understand which products
“go together” (are often selected together)
Solution • Score each customer to get their next best product recommendation

• More relevant and personalized offers yield higher response rates
• Instead of pushing products out, the bank is suggesting the best product for
Benefits each customer (pulling products in)


Use Case #1- Financial Services Cross Sell
(Recommender system, next best offer)

 Extensions of the cross sell application include:
 Telecommunications
 Insurance,
 Retail & E-Commerce
 Online: Website Navigation

 Differential marketing basket analysis is very
powerful as multiple models at the same time.
 Retail example: model store by store, by customer
segment, or across channels.


Use Case #2 – Grocery Promotion Design

• Grocery Retail
Industry

• Grocer wants to design smart promotions for weekly flyers
Opportunity

• Build a market basket model to understand which products go together:
• Transaction Level: Identify products with high “pull through”, products
Solution found in the same baskets as many high margin products
• Customer Loyalty Card ID Level : Identify which are unlikely to co-occur
• Discounted products are chosen to build basket size
• Discounted products are not substitutes and do not cannibalize other
Benefits products. They serve different segments of customers and appeal to
the greatest number of customers, thereby increasing store traffic


Use Case #3 – Insurance Anomaly Detection

• Insurance
Industry

• Medical insurance provider wants to reduce losses from fraud by
Opportunity detecting more anomalies in claims data

• Build a market basket model to understand what claim attributes rarely
coincide
Solution • Score each claim and identify unusual claims

• Automated anomaly detection means that claims can be screened
upon receipt, enabling prompt payment of the majority of claims
Benefit • Claims can be ranked by the severity and queued for investigation


Use Case #4 – Retail Product Placement

• Retail
Industry

• Specialty retailer wants to increase the number of items purchased per
transaction by reorganizing its store to encourage “impulse shopping”
Opportunity • Currently average basket size is close to one (one item purchased)

• Build a market basket model at the transaction level to understand
what items are being purchased together
Solution • Put impulse items closer to items they are more likely to be bought with

• Increased basket size
Benefit


Use Case #5 – Manufacturing Logistics &
Operations

• Manufacturing
Industry

• A just-in-time (JIT) supplier needs to fulfill orders from its manufacturing
Opportunity partner

• Build a market basket model at the order level to understand which
items are being ordered together
Solution
• Reorganize to bring those items physically closer to each other

• Reduced transportation costs
• Reduced time-in-motion
Benefit
• Reduced damage and loss of stock


Market Basket Analysis in
KnowledgeSTUDIO
 Data and scenario overview
• Big box retailer with store loyalty program
• Encourage shoppers to buy from other departments

 Market basket model build:
• Wizards
• Charts and visualizations
• Association rules filtering and deployment

 Scoring the model:
• Within KnowledgeSTUDIOTM and via code generation

 Sample campaign strategy tree


Thank You
Angoss Software Corporation

www.angoss.com
info@angoss.com


Understanding Customer Purchase Behaviour | Angoss KnowledgeSTUDIO

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Understanding Customer Purchase Behaviour | Angoss KnowledgeSTUDIO