Shopping, or as the people on the other side of the counter call it, retail has become the number one breeding ground for predictive applications in the enterprise. What started as simple recommendation engines has evolved into a complex and powerful ecosystem of predictive applications that affect core processes such as pricing, replenishment and staff planning. In this talk, Ulrich Kerzel will share impact and experiences from building and operating predictive applications for large retailers, and explain why the future of retail is as much a science as an art. Dr. Ulrich Kerzel is a Senior data scientists at Blue Yonder and renowned scientist with research experience at the University of Cambridge and CERN. Ulrich Kerzel earned his PhD under Professor Dr Feindt at the US Fermi National Laboratory and at that time made a considerable contribution to core technology of NeuroBayes. After his PhD, he went to the University of Cambridge, were he was a Senior Research Fellow at Magdelene College. His research work focused on complex statistical analyses to understand the origin of matter and antimatter using data from the LHCb experiment at the Large Hadron Collider at CERN, the world’s biggest research institute for particle physics. He continued this work as a Research Fellow at CERN before he came to Blue Yonder as a senior data scientist.

1. Dr Ulrich Kerzel
How to predict the future of
shopping

2. 2008: Founded by
CERN Data Scientists
Since 2011: Award-
winning retail
solutions
2014: International
expansion, predictive
applications

3. LHC:
27km circumference
Photos: CERN, Blue Yonder
Blue Yonder History: Founded by CERN physicists

4. Our Journey
2014
Warburg Pincus commits $75m
Investment
first go-live: Customer Targeting
first customer project using Cyclic
Boosting algorithm
German Innovation Award
2008
company
founded in
Karlsruhe
NeuroBayes
introduced
2011
first go-live:
Replenishment
name change to
“Blue Yonder”
2013
first go-live: Online
Pricing
office in London, UK
opened
2015
first go-live: Brick&Mortar Pricing
Technology Review: 50 most
innovative companies
Gartner: Cool Vendor in Data
Science
milestone: 41B decisions/week
2012
Retail Technology
Award for best
enterprise
solution

5. Award Winning Retail Leader

6. The key to becoming a better
company are better decisions.
The key to better decisions is
using your own data.

7. What is „Data Science“ ?Big Data Landscape in 2012

8. 07/08/15
8
What is „Data Science“ ?
Source: M. Turck

9. What is Data Science ?
source: deathtothestockphoto.com
Business Development
Machine Learning
Statistics Causality
Data Exploration
Visualisation
Data Story Telling
Data Storage
DataQuality
Data Access
Programming
ETL

10. 07/08/15
Start with a vision….
Source: Flickr, by khegre, CC
What would the ideal use-case be?
How would it transform the
company into a predictive enterprise ?
Think big! Think outside existing
processes, constraints,…
Where do I want to be ?
Where am I now ?
What do I need to change in my organisation?
Business Development

11. 07/08/15
Source: Flickr, by vIZZual.com, CC
Start with the fundamental layout:
• Scenario: what and why
• Understand the processes leading to the decision
and what happens with the decision afterwards
• Data available? Can they be accessed?
New data needed ?
Data Quality ?
Define the“target”: What exactly needs
to be optimised ?
How to evaluate performance ?
Source: Flickr, by r2hox, CC
Business Development

12. Optimised approaches for:
► Discrete, semi-continuous and
continuous variables
► Ordered and
unordered classes
► Missing values
(contains information as well!)
► Statistical significance of
variables
► …
Influencingfactors
Influencing factors
discrete feature Continuous feature
Machine Learning
… requires a lot of data

13. Machine Learning
Machine
Learning
Prediction Decision &
Action

14. average event
individual event
For example: This customer, this product on this day
in this specific location, this machine with a
specific usage history, …
➢Non-Gaussian probability distributions
➢asymmetric error information (volatility)
➢Different localization and dispersion measures
What is special about the prediction of an
individual density distribution?
Machine Learning

15. Predictive Applications Enable
Ongoing Optimization
controller
Data delivery
Decisions & Actions
External
factors
Predictive Application
Predictive Applications

16. » Decisions beat
insights, any time. «
Prof. Dr. Michael Feindt

17. Supply Chain Pricing Marketing

18. Replenishment Optimization
predicts demand and creates
store orders that reduce out-of-
stock and write-off rate at the
same time.

19. Welt.de
Wien.gv.at
3sat.de
Replenishment Optimization
Up to 12% of perishable goods
wasted in supermarkets. That’s
about 20M tons of food

20. ► High risk of OOS
► low stock
► Low risk of OOS
► large stock
99% Quantile
90%
80%
60%
50%
Out of Stock [%]
Write–off[%]
1-3%
From prediction to business decision
Probability
Quantity

21. Automation doubles impact
0%
2%
4%
5%
7%
Prescriptive Analytics, human decisions Automated Decisions, same stock levels
5% average out-of-stock rate
1% average out-of-stock rate

22. » The introduction of
forecasting methods in
replenishment is an important
investment in the future of our
stores and their processes. «
Dr. Hendrik Haenecke, KT

23. Price Optimization measures
price elasticity of demand and sets
prices to increase sales, revenue
and margin by 5-15%

24. Price and Demand
0
20
40
60
80
0 20 40 60 80
Demand

25. What is the ideal price?

26. Price and Demand
0
20
40
60
80
0 20 40 60 80
Demand Revenue

27. Price and Demand
0
20
40
60
80
0 20 40 60 80
Demand Revenue Cost

28. Price and Demand
0
20
40
60
80
0 20 40 60 80
Demand Revenue Cost Profit

29. The Ideal Price is Strategic
Strategy Ideal Price Worst Price
Maximize Demand 0 EUR 66 EUR
Maximize Revenue 16 EUR 66 EUR
Minimize Cost 66 EUR 0 EUR
Maximize Profit 28 EUR 0 EUR

30. What do mixed strategies look
like?

31. Mixed Strategies Applied
0
12.5
25
37.5
50
0 20 40 60 80
Revenue Only Mostly Revenue Both Mostly Profit Profit Only

32. Mixed Strategies Applied
Strategy Ideal Price
Revenue Only 16 EUR
Mostly Revenue 19 EUR
Both 21 EUR
Mostly Profit 24 EUR
Profit Only 28 EUR

33. » A machine learning system
such as Blue Yonder suits our
dynamic business model. The
solution helps us adjust early
on to future developments. «
Michael Sinn, Otto

34. Customer Targeting models
uplift to distribute personalized
advertising (direct mail, coupons)
to maximize marketing ROI with
small circulation.

35. Rule-based
targeting
Based on your knowledge of the
customer, define rules that
describe a customer worthy of
being targeted.
Data-driven
targeting
Based on your data about
customer behavior, identify
customers that consistently drive
revenue.
Traditionally, you do this….
… which leads to…

36. Rule based targeting
Half of the customers (identified by the experts) have received a catalog.
30% of these bought
something evaluation period.

37. The other half of the customers didn‘t get a catalog.
Rule based targeting
Only 6% of these customers
bought something

38. + budget
+ target ?
Large effect found! +400% increase in turnover by sending a catalogue
Action: Increase the marketing budget, send more catalogues
Rule based targeting

39. Implementing the action you find this ….
Rule based targeting

40. + budget
+ target
only about 30% improvement….
what happened …? Where is the big improvement …?
Rule based targeting

41. Customer targeting: Causality
Correlation isn’t everything - Causality matters
Sending out a catalogue is a (conscious) business decision.
The real question is: What difference does the catalogue make for the individual ?
X
Y
X
Y
X and Y are correlated
X and Y are correlated
X can causally influence Y
Y cannot causally influence X

42. Source: http://www.tylervigen.com/spurious-correlations
Correlation isn’t everything - Causality matters
Customer targeting: Causality

43. Historic
data on
customer
Target
Action
who bought what,
when, where, ….
decide to send out
a catalogue (or not)
e.g. increased sales
turnover, …
Customer targeting: Causality

44. Historic
data on
customer
Target
Action
high
correlation
but were are interested in this:
who bought because we
sent a catalogue ?
Customer targeting: Causality

45. Rule-based
targeting
Based on your knowledge of the
customer, define rules that
describe a customer worthy of
being targeted.
Data-driven
targeting
Based on your data about
customer behavior, identify
customers that consistently drive
revenue.
Causality-based
targeting
Based on your data about
customer behavior and
advertising effectiveness, target
customers that drive marketing
ROI.
Customer targeting: Causality

46. Uplift Modeling
Sure Things buy
in any case,
targeting or not
Sleeping Dogs
stop buying when
you target them
Persuadables
only buy if they
get targeted
Lost Causes don’t
buy anyway,
targeting or not
negative neutral positive

47. Direct Mail
Solution
• Customer Selection for Advertisement and Targeted Campaigns
• Selection of the most uplifted customers
• Individual selections for any campaign
Results
• ROI within one month
• Nearly zero reduction of revenue, due to better customer selection

48. Predictive applications enable the
retailer of the future, giving more
profitable growth, through
automated decisions that turn
strategy into concrete action.