Approaches for a data driver attribution model

1. Advanced Attribution Model,
Analytics Summit, November 13th , 2014
Aspa Lekka
a.lekka@foodpanda.com

2. Foodpanda is a global online food delivery marketplace
Company:
 www.foodpanda.com
 Founded in 2012
 Present in more than 45 countries
Business Intelligence & Analytics:
 Google Analytics and Google Analytics Premium
 Team of 8 people

3. TV
Mobile
App Ad
SEM Conversion
Customer Journey

4. Multiple channels:
•SEM
•Display
•CRM
•Affiliate
•Radio
•Price Comparison
•…..
Marketing budget allocation
Get new visitors
AWARENESS
Maximize orders
ACTION
0 50 100 150 200 250
Social
Direct
Display
Affiliate
Price comparison
Search
Last click- orders TOTAL: 503
0 20 40 60 80 100 120 140 160 180
Social
Direct
Display
Affiliate
Price comparison
Search
Last click - orders TOTAL: 438
Awareness
Interest
Desire
Action
Why attribution model?

5. Every channel gets attributed the correct orders

6. Calculating the CAC for Marketing Campaigns
NEW CUSTOMERS ACQUIRED
Campaign 1 BUDGET Monday Tuesday Wednesday Thursday Friday Saturday Sunday TOTAL
Monday 1,000 € 13 13
Tuesday 1,100 € 21 21
Wednesday 900 € 22 22
Thuerday 1,000 € 25 25
Friday 850 € 23 23
Saturday 1,200 € 24 24
Sunday 1,000 € 26 26
CAC
77 €
52 €
41 €
40 €
37 €
50 €
38 €

7. How efficient was the Marketing Budget?
NEW CUSTOMERS ACQUIRED
Campaign 1 BUDGET Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday Tuesday Wednesday Thursday Friday Saturday TOTAL
Monday 1,000 € 10.5 2.9 14.5 10.8 12.3 19.0 9.0 79.0
Tuesday 1,100 € 10.5 6.6 3.9 13.3 3.1 6.2 13.9 57.4
Wednesday 900 € 5.2 4.1 8.0 9.4 8.1 18.1 17.8 70.6
Thuerday 1,000 € 12.7 20.2 17.3 5.8 3.9 4.0 9.7 73.6
Friday 850 € 4.7 7.8 12.0 6.6 6.0 20.5 2.6 60.2
Saturday 1,200 € 19.4 16.7 7.6 17.6 19.0 6.1 16.1 102.4
Sunday 1,000 € 7.4 2.2 5.9 15.2 13.4 16.4 11.7 72.1
CAC*
13 €
17 €
14 €
14 €
17 €
10 €
14 €

8. Data Driven Models for Attribution
1. SHAPLEY VALUE
2. SURVIVAL ANALYSIS
PATH ANALYSIS
NETWORKS
STRUCTURAL EQUATION MODELING 3.

9. The Shapley Value
SHAPLEY VALUE

10. The Shapley Value
The Shapley value is a way to assign credit among a group of “players” who cooperate for a certain end
An example:
• 3 players
(2 with right glove and 1 with left glove)
• Goal: Form a pair
• Assign credit to each player after forming a pair
There are two possible pairs that we can form and in both of them, Player 1 needs to be involved. Therefore, Player 1, is of more importance compared to Player 2 or
Player 3. Consequently, when sharing the profits, he should get a bigger part compared to Player 2 (if we case 1 is true) or Player 3 (if case 2 is true)

11. • 3 channels
• A click chain that consists of these 3 channels and led to 500 transactions
• Evaluate the contribution of each channel to these 500 transactions
100
125
50
270
375
350
500
The Shapley Value

12. SEM
100
DISPLAY
270-100=170
SEO
100 500-270=230
125
50
270
375
350
500
SEM
100
SEO
375-100=275
DISPLAY
500-375=125
DISPLAY
125
SEM
270-125=145
SEO
500-270=230
DISPLAY
125
SEO
350-125=225
SEM
500-350=150
SEO
50
SEM
375-50=325
DISPLAY
500-375=125
SEO
50
DISPLAY
350-50=300
SEM
500-350=150
Calculating the Shapley Value

13. Calculating the Shapley Value
SEM
100
DISPLAY
270-100=170
SEO
500-270=230
SEM
100
SEO
375-100=275
DISPLAY
500-375=125
DISPLAY
125
SEM
270-125=145
SEO
500-270=230
DISPLAY
125
SEO
350-125=225
SEM
500-350=150
SEO
50
SEM
375-50=325
DISPLAY
500-375=125
SEO
50
DISPLAY
350-50=300
SEM
500-350=150
SEM’s expected marginal contribution is:
DISPLAY’s expected marginal contribution is:
SEO’s expected marginal contribution is:
SEM
162
(0.32)
DISPLAY
162
(0.32)
SEO
176
(0.36)
500 orders attributed to the three channels :

14. Survival Analysis
SURVIVAL ANALYSIS

15. Target
population
Treatment 1
Dead
Alive
Treatment 2
Dead
Alive
Event
Event
TIME
What is the patient’s
probability to be still
alive after 20 years?
Survival Analysis

16. Visitors
Channel 1
Event
Conversion
NO
Conversion
Channel 2
Conversion
NO
Conversion
Event
TIME
What is the visitors’
probability to convert
after 30 days or 5 visits?
Survival Analysis

17. Day1
Day2 Day5 Day2
Day3 Day7 Day8
DAY 1 / VISIT 1
DAY 3 / VISIT 3
0 1 0 1
DAY 6 / VISIT 6
0 1
DAY 9 / VISIT 9
0 1
Survival Analysis

18. VISITORS
TIME
START END
: censored observation
: event (conversion)
Censored observation:
There is not “time to
event” recorded because:
•Loss of follow up
 Drop out
 Conversion due to a
cause that is out of
our interest
•End of the study
Survival Analysis

19. Survival Analysis
Estimate time-to-event for a group of
individuals, such as time until a visitor purchases
To compare time-to-event between two or
more groups, such as visitors that have clicked
on a Display ad compared to visitors that have
not clicked on a Display ad.
To assess the relationship of co-variables to
time-to-event, such as: does number of clicks,
pages viewed, or time on site effect the decision
to purchase?

20. DATA DRIVEN MODELS
ADJUSTED TO EACH CASE
LIMITED TUTORIALS
ADJUST FORMULAS TO
YOUR DATA
(QUITE) EASY TO SET UP
LINK ADS WITH ONSITE BEHAVOR
FIND COST EFFICIENT CLICK CHAINS
MERGE OFFLINE AND ONLINE DATA
Evaluation & Suggestions

21. Thank you!
Aspa Lekka
a.lekka@foodpanda.com