Dr Ulrich Kerzel

How to predict the future of shopping

2008: Founded by CERN Data Scientists

Since 2011: Award-winning retail solutions

2014: International expansion, predictive applications

LHC: 27km circumference

Photos: CERN, Blue YonderBlue Yonder History: Founded by CERN physicists

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

Award Winning Retail Leader

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

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

07/08/15

8

What is „Data Science“ ?

Source: M. Turck

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

07/08/15

Start with a vision….

Source: Flickr, by khegre, CC

What would the ideal use-case be? How would it transform thecompany into a predictive enterprise ? Think big! Think outside existingprocesses, constraints,…

Where do I want to be ? Where am I now ? What do I need to change in my organisation?

Business Development

07/08/15Source: 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

Optimised approaches for:

► Discrete, semi-continuous and continuous variables

► Ordered and unordered classes

► Missing values(contains information as well!)

► Statistical significance ofvariables

► …

Influ

enci

ng fa

ctor

s

Influencing factorsdiscrete feature Continuous feature

Machine Learning… requires a lot of data

Machine Learning

MachineLearning

Prediction Decision &Action

average event

individual event

For example: This customer, this product on this dayin 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

Predictive Applications Enable Ongoing Optimization

controller

Data delivery

Decisions & Actions

External factors

Predictive Application

Predictive Applications

» Decisions beat insights, any time. «

Prof. Dr. Michael Feindt

Supply Chain Pricing Marketing

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

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

► High risk of OOS ► low stock

► Low risk of OOS ► large stock

99% Quantile

90%

80%

60%

50%

Out of Stock [%]

Writ

e –

off [

%]

1-3%

From prediction to business decision

Prob

abili

tyQuantity

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

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

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

Price and Demand

0

20

40

60

80

0 20 40 60 80

Demand

What is the ideal price?

Price and Demand

0

20

40

60

80

0 20 40 60 80

Demand Revenue

Price and Demand

0

20

40

60

80

0 20 40 60 80

Demand Revenue Cost

Price and Demand

0

20

40

60

80

0 20 40 60 80

Demand Revenue Cost Profit

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

What do mixed strategies look like?

Mixed Strategies Applied

0

12.5

25

37.5

50

0 20 40 60 80

Revenue Only Mostly Revenue Both Mostly Profit Profit Only

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

» 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

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

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…

Rule based targetingHalf of the customers (identified by the experts) have received a catalog.

30% of these bought something evaluation period.

The other half of the customers didn‘t get a catalog.

Rule based targeting

Only 6% of these customers bought something

+ budget

+ target ?

Large effect found! +400% increase in turnover by sending a catalogue Action: Increase the marketing budget, send more catalogues

Rule based targeting

Implementing the action you find this ….

Rule based targeting

+ budget

+ target

only about 30% improvement…. what happened …? Where is the big improvement …?

Rule based targeting

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 correlatedX and Y are correlated X can causally influence Y Y cannot causally influence X

Source: http://www.tylervigen.com/spurious-correlations

Correlation isn’t everything - Causality matters

Customer targeting: Causality

Historic data on

customer

Target

Action

who bought what, when, where, ….

decide to send outa catalogue (or not)

e.g. increased sales turnover, …

Customer targeting: Causality

Historic data on

customer

Target

Action

high correlation

but were are interested in this: who bought because wesent a catalogue ?

Customer targeting: Causality

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

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

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

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