Bij OTTO vormt data science hét fundament voor alle marketingbeslissingen. Om haar klanten beter te begrijpen en te voorzien van een persoonlijk relevant aanbod, begint elke procesverbetering met data-analyse. Dirk neemt je mee in OTTO’s data science visie en zal middels concrete cases uitleggen hoe deze in praktijk wordt gebracht.

1. Combining Recommendations
and Dynamic Pricing:
An offer you can’t refuse
Dirk Verzijden
Duy Ha Ly
Big Data Expo 2018
Utrecht, September 20th

2. Dirk Verzijden
4 years at OTTO:
 Started as business analyst
 Set up BI project team
 Business owner Lascana NL
 Lead SEA OTTO NL
Business Intelligence

3. Duy Ha Ly
1 year at OTTO:
 Data Scientist in BI team
 Product owner of Google 360 Suite
 Responsible for OTTO experimentation
platform
 Responsible for data science projects
from marketing to finance
Data Science

4. Agenda
 Introduction OTTO
 BI and Data Science landscape at OTTO
 Two use cases
 The future: An offer you can’t refuse

5. Agenda
 Introduction OTTO
 BI and Data Science landscape at OTTO
 Two use cases
 The future: An offer you can’t refuse

6. Founded by:
Werner OTTO
1949
Turned over to:
Dr. Michael OTTO
1981
Today’s magazine:
OTTO.nl
2018
A very brief history of OTTO

7. OTTO NL total revenue:
€ 120M (online/offline: 80% / 20%)
Focus on service for diverse assortment:
 Fashion
 Home & Living
 Electronics
Focus OTTO upcoming years:
Change of Business Model into Platform Strategy
OTTO group total revenue:
€ 13.7 Billion
Of which online retail:
€ 7.9 Billion (58%)
123 webshops worldwide
* 51,785 employees
Active in 30 countries
Some facts

8. Our commercial strategy:
Maximize revenue with fixed EBT
But how to do this?
Two options:
 Branding: More budget in advertisement
 Smarter: Spend existing budget better
(with BI and data science)
Some facts

9. Our commercial strategy:
Maximize revenue with fixed EBT
But how to do this?
Two options:
 Branding: More budget in
advertisement
 Smarter: Spend existing
budget better (with BI and
data science)
The digital marketing game we play every day

10. Our commercial strategy:
Maximize revenue with fixed EBT
But how to do this?
Two options:
 Branding: More budget in
advertisement
 Smarter: Spend existing
budget better (with BI and
data science)
The digital marketing game we play every day

11. Agenda
 Introduction OTTO
 BI and Data Science landscape at OTTO
 Two use cases
 The future: An offer you can’t refuse

12. Some years we outsmarted the market, some years we needed to take measures
2012 2013 2014 2015 2016 2017 2018 2019
Initial plan vs realization
Initial target Realized New measures
Five decisive moments:
2012-2014: 8% steady growth
2015: setback I
2016-2017: focus on data and BI team
End 2017: setback II
2018: start with Data Science

13. Some years we outsmarted the market, some years we needed to take measures
2012 2013 2014 2015 2016 2017 2018 2019
Initial plan vs realization
Initial target Realized New measures
Five decisive moments:
2012-2014: 8% steady growth
2015: setback I
2016-2017: focus on data and BI team
End 2017: setback II
2018: start with Data Science

14. Some years we outsmarted the market, some years we needed to take measures
2012 2013 2014 2015 2016 2017 2018 2019
Initial plan vs realization
Initial target Realized New measures
Five decisive moments:
2012-2014: 8% steady growth
2015: setback I
2016-2017: focus on data and BI team
End 2017: setback II
2018: start with Data Science

15. Some years we outsmarted the market, some years we needed to take measures
2012 2013 2014 2015 2016 2017 2018 2019
Initial plan vs realization
Initial target Realized New measures
Five decisive moments:
2012-2014: 8% steady growth
2015: setback I
2016-2017: focus on data and BI team
End 2017: setback II
2018: start with Data Science

16. Five decisive moments:
2012-2014: 8% steady growth
2015: setback I
2016-2017: focus on data and BI team
End 2017: setback II
2018: start with Data Science
Some years we outsmarted the market, some years we needed to take measures
2012 2013 2014 2015 2016 2017 2018 2019
Initial plan vs realization
Initial target Realized New measures

17. What is the difference?
Business Intelligence
(and DWH):
Descriptive – what happened?
Diagnostive – why did it happen?
Data science
(or ML, AI, PA, BD, …):
Predictive – what will happen?
Prescriptive – what is my best next action?

18. Our current data landscape
BI tooling: DS tooling:
2012 2013 2014 2015 2016 2017 2018 2019
Initial plan vs realization
Initial target Realized New measures

19. Agenda
 Introduction OTTO
 BI and Data Science landscape at OTTO
 Two use cases
 The future: An offer you can’t refuse

20. We started with Competitive Pricing and Personal Recommendations
Market potential
Recommendation
(cross-sell opportunities)
Consumer Lifetime ValueCompetitive Pricing
Recommendation
(product website)
Credit scoring
Targeting Timing
Personalized content:
When to show what to who?
Price and personal discount:
How much?

21. Use case: Competitive Pricing (from business perspective)
Personalized content:
When to show what to who?
Price and personal discount:
How much?
Why?
 Pricing is very important marketing instrument
 Started with one product group: Electronics
 Comparable products with competitors
 Competitor prices are daily available at kieskeurig.nl
Commercial target:
To increase revenue while keeping EBT fixed

22. Use case: Competitive Pricing (solution design)
INPUT
For all Electronics
products:
 Expected sales amount
with new price
 Expected profit with new
price
 Delta new price with
current price
 Expected sales amount
with current price
 Expected profit with
current price
 New optimal price
suggestion
OUTPUT
Predict
Output:
 Expected sales
amount
 Expected profit
Input:
 Otto price strategy
 Competitor’s price
strategies of
tomorrow
Optimize
Two strategies available
per product:
1. Maximize sales (€)
under constraint of
fixed EBT
2. Maximize profit (€)
PRESCRIPTIVEPREDICTIVE

23. Use case: Competitive Pricing (results)
+18%
more traffic
+38%
more visits
+23%
more order value/revenue
+28%
more order value/revenue from
kieskeurig.nl
+4%
more gross profit
EBT remained the same
 Reinvestment cycle

24. Personalized content:
When to show what to who?
Price and personal discount:
How much?
Use case: Personal Recommendations (business perspective)
Why?
 180K different products at OTTO:
Customer wants relevant content
 Started with one product group: Fashion
 Biggest impact on commercial strategy
 Many behavioral touchpoints (clicks and transactions)
Commercial target:
To increase revenue while keeping EBT fixed

25. Use case: Personal Recommendations (solution design)
A.
User specific
recommendation
B.
Item specific
recommendation
C.
Bestsellers within
product group
Enough data for
recommendation
algorithm?
Yes
Yes
No
No
Customer
known?
Suppose a visitor
enters the website…

26. Use case: Personal Recommendations (results)
+95%
more clicked on
recommendations
in Variant group
+191%
more transactions
in Variant group

27. Data Science + Business knowledge = Success
Always keep the algorithm on the leash!
? ??

28. Data
preparation
Modelling
(train model)
Deployment
(exploit model)
OperationData collection
Teradata
BigQuery
Otto.nl
Business
Understanding
Data
Understanding
Evaluation
Data science
life cycle
A proven process that works:
Cross Industry Standard Process for Data Mining (CRISP-DM)

29. Data
preparation
Modelling
(train model)
Deployment
(exploit model)
OperationData collection
Teradata
BigQuery
Otto.nl
Business
Understanding
Data
Understanding
Evaluation
Data science
life cycle
Don’t stop improving when solutions is live
This is where you get the best feedback from your customers!

30. Agenda
 Introduction OTTO
 BI and Data Science landscape at OTTO
 Two use cases
 The future: An offer you can’t refuse

31. But what happens if we combine these solutions?
Recommendation
(cross-sell opportunities)
Competitive Pricing
Recommendation
(product website)
Personalized content:
When to show what to who?
Price and personal discount:
How much?

32. How most recommendation algorithms work…
ProfileID
12345
12345
12345
12345
Recomme-
ndatio rank
1
2
3
4
ProductID
77123
9824
43941
3102
Score
12.3
11.8
1.2
0.9
Algorithms calculate artificial
(not really interpretable)
values to decide what you like most

33. But what if we could estimate the conversion chance,
given that we recommended the product?
ProfileID
12345
12345
12345
12345
Recomme-
ndatio rank
1
2
3
4
ProductID
77123
9824
43941
3102
Score
12.3
11.8
1.2
0.9
Conversion
chance
0.32%
0.31%
0.25%
0.17%
Chance to convert if recommended at
rank 1 (or 2, 3, …)

34. Maybe we should reconsider the ranking..?
ProfileID
12345
12345
12345
12345
Recommen-
ation rank
1
2
3
4
ProductID
77123
9824
43941
3102
Score
12.3
11.8
1.2
0.9
Conversion
chance
0.32%
0.31%
0.25%
0.17%
Price
€ 60
€ 189
€ 89
€ 49
E[Revenue|
recommended at rank 1
to ProfileID = 12345]
E[Revenue]
€ 0.19
€ 0.59
€ 0.22
€ 0.08
A simple maximizing heuristic
can solve this problem easily

35. Conclusion:
Not easy to understand your customer in the digital era

36. But step by step, we should to close the gap…
Website rec.

37. Website rec.
…between customer needs and data science possibilities
But step by step, we should to close the gap…

38. Only then we can realize a customer-centric vision…
…in a digital era

39. Dirk Verzijden
dirk.verzijden@ottobv.nl
Duy Ha Ly
duy.haly@ottobv.nl
Thank you for your time