Transforming retail through advanced analytics &AI April 11, 2018

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Introduction

Javier Anta Callersten Partner at BCG in London

Leading BCG Gamma for Western Europe

Core member of global retail leadership team,

leading Advanced Analytics in Retail

Has led various advanced analytics programs in retailers across 14 different countries

Markus Hepp Partner at BCG in Cologne

Leading BCG's Consumer Practice in Germany

Core member of global retail leadership team,

leading the Retail Sector in Europe

Specialized in retail transformations across major retail sectors

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Contents

Advanced Analytics & AI as an opportunity for retailers Main challenges that need to be overcome Our beliefs on how to successfully transform through AA & AI and typical journeys

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The world is changing rapidly, driven by digital and data…

Unprecedented visibility on customers, business activities and market trends

Omnichannel, sensors, always connected

Processing power, storage and robotics ready for AI and automation

Ready to engage with brands anytime, anywhere

Disintermediation, sharing economy, crowdsourcing, etc.

Data Explosion

Pervasive Digitalization

Enabling Technologies

New Consumer

New Market Forces

Ability to build and monetize data assets drives competitive advantage

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… leading to high impact opportunities

Segment-of-1 content creation and recommendations

Real time fraud detection with lower risk of false positives

Predictive asset maintenance across industries

Personalized health services

$1B Annual Value

-50% Fraud False

Alerts

$1B Cost

Savings

-50% Antibiotic

use

Hyper-personalized offers and continuous test and learn

Optimize resource utilization through multiple neural nets

Telematics to optimize routing, personalized service

Predict next customer contact channel and product for servicing request

3x Net

Incremental Revenue

-40% IT Cooling

Costs

100M Miles

Reduction

88% Accuracy

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Large part driven by a huge step-up in analytics: self-learning AI systems can now be leveraged by businesses

• Financial reports • Geo analysis • Heat maps • ....

Prescriptive Predictive Descriptive

Business Intelligence

Traditional Analytics

Deep Learning

• Campaign Management • Sentiment analysis • ....

• Efficient personalization • Context-aware (e.g. mobile)

recommendations • ....

• Structured • Low Volume • Batch Load

Type of Insights

• Unstructured • High Volume • Real Time

Type of Data

Artificial Intelligence

Machine Learning • Next best action • Recommendation engine • Churn prediction • ....

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80%

60%

20%

0% 40% 20%

80%

0%

40%

60%

Large effect of AI on Offerings

Manufact. Transp. / Travel

Public Sector

Insurance

Utilities

Prof. Serv.

Logistics

IT & Tech.

Logistics

Energy

Manufacturing

Constr.

Energy Automotive

Consumer

Public Sector Insurance

IT & Tech.

Ent. / Media

Cap. Markets

HC Equip. / Serv.

Chemicals

Transp. / Travel HC Equip. /

Serv.

Consumer

Construction

Telco

Banking

Automotive

Pharma / Biotech

Agriculture

Cap. Markets

Telco

Retail

Utilities

Prof. Serv.

Chemicals

Pharma / Biotech

Retail

Ent. / Media

Agriculture

Banking

Larg

e ef

fect

of

AI o

n Pr

oces

ses

Across industries, AI is expected to have a strong impact in coming years

In 5 years Today

% of respondents

% of respondents

Average

Source: Joint BCG-SMR research, AI@BCG

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Retail: Use cases across the entire value chain Non-exhaustive

Production & logistics CRM /loyalty / marketing Selling Store layout

& build-up Category

management

• Inventory optimization across the logistic network • Utilization optimization of transport capacity • Routing optimization • Origin transparency through blockchain • Optimized joint planning through shared data with suppliers • Supplier risk management through shared data & analytics • Real-time inventory tracking through RFID • Accelerating picking through augmented reality

• Trend detection + real time customer feedback • Optimized & localized assortment and pricing structure • Identification of savings potential with price elasticity analysis of parts • Predictive modeling of new product launch uptake based on non-traditional inputs (e.g. social buzz) • Assortment optimization (e.g. modeling impact of assortment change)

• Customer centric store lay-out (online & offline) • Optimize store locations • Seamless Omnichannel • Check-out free shopping • Perfect Store 2.0 and on-premise customer activation • Dynamic assortment based on real-time conditions • Store workforce optimization

• Personalized 1:1 promotions and targeting • Optimized mass-market promotions • Automated targeted buying process for online ads • Loyalty program optimization through user behavior and incentive response analytics • Recommendation engines for app and website optimization • Promotions optimization through automated post-event analytics • Customer churn reduction

• Automation / robotization • Regional segment detection and assortment optimization • Real-time in-store personalized promotions • Smart markdowns • Dynamic (online) pricing • Monitor and improve performance with real-time end-to-end dashboards • Predictive demand forecasting based on non-traditional inputs (e.g. social buzz) • Cross-format selling

Digitization-based Analytics-based

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Hyper- personalization

Selected AA & AI use case examples in Retail

1 2 4 5

• Massive re-allocation of investment to most effective

• +250m$ margin • Value >35x

programme cost

• 150% increased marketing engagement

• 300% increase NIR • 100m$ additional

revenue year 1

Supply chain optimization

• Leaner footprint • Better utilization • Better fulfillment • 10% reduction in

warehousing/ transport costs

Locali- zation

• More relevant to customer, more profitable E2E

• 350-450m$ unlocked sales

Promotion optimization

Smart markdowns

• 44% savings in markdown costs

• ~€20M in single market

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AI algos can deliver unprecedented levels of accuracy for promo analysis and forecasting

Base sales + uplift

Halo: Foot Fall

Halo: Complementarity

Pull Forward Cannibalization Discount Vendor funding

Machine Learning Elastic Net algo accounting for 20+ dimensions Category-SKU regression Pre & post customer composition Conditional probability

Machine Learning Time series with frequency modulation

Entirely bespoke solution

Battle of the algorithms

1

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Hyper personalization at Starbucks: Each customer's experience personalised "just for the individual"

• Personalised offers and experience

• Anticipate customer behaviour

• Drive transaction and ticket

~3x improvement in campaign results run rate

3x+ incremental revenue per redeemer per year

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Example: Email offer optimization

12 million 30 400,000

Segmentation Individualisation

12 million 380,000 32

No. People : No. Variants : People/variant:

Multiple machine learning models individualise the message for content as well as the point on the economic efficiency curve. The sole reason for a cluster of >1 is that some people simply like the same thing Dynamic construction of the message Real-time tracking of the progress for the experience

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The analytics engine was built based on three key dimensions

Customer DNA • Pathways • Habits/Preferences • Social graphs • Headroom • Propensities

Offer DNA • Type • Product (s) • Sequence, Timing • Reward level

Context & location • Location • Time/day • Proximity • Weather

What is the right Offer for this customer ?

What is the right curriculum?

What behavior(s) do we want for this customer?

When is the right context (location, time and format)to place the offer

this customer ?

2

13

An illustrative view on the analytics engine

ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Location 0000001-ABCDEFG-00001 Locn Type A

0000001-ABCDEFG-00002 Locn Type B

0000001-ABCDEFG-00003 Locn Type C

0000001-ABCDEFG-00004 Locn Type D

0000001-ABCDEFG-00005 Locn Type E

0000001-ABCDEFG-00006 Locn Type F

0000001-ABCDEFG-00007 Locn Type G

0000001-ABCDEFG-00008 Locn Type H

0000001-ABCDEFG-00009 Locn Type I

0000001-ABCDEFG-00010 Locn Type J

0000001-ABCDEFG-00011 Locn Type K

0000001-ABCDEFG-00012 Locn Type L

0000001-ABCDEFG-00013 Locn Type M

0000001-ABCDEFG-00014 Locn Type N

0000001-ABCDEFG-00015 Locn Type O

0000001-ABCDEFG-00016 Locn Type P

0000001-ABCDEFG-00017 Locn Type Q

0000001-ABCDEFG-00018 Locn Type R

0000001-ABCDEFG-00019 Locn Type S

0000001-ABCDEFG-00020 Locn Type T

0000001-ABCDEFG-00021 Locn Type U

0000001-ABCDEFG-00022 Locn Type V

0000001-ABCDEFG-00023 Locn Type W

0000001-ABCDEFG-00024 Locn Type X

0000001-ABCDEFG-00025 Locn Type Y

0000001-ABCDEFG-00026 Locn Type Z

0000001-ABCDEFG-00027 Locn Type AA

0000001-ABCDEFG-00028 Locn Type AB

0000001-ABCDEFG-00029 Locn Type AC

Day time preference -1.000 1.000

Avg. headroom

0.0000 0.2500

Risk variations 0 60

Avg. game score 0.0 100.0 |1.000

Engagement score Risk score Headroom

Engagement score

Risk of attrition and fading

Headroom to grow share of wallet

Each row is one customer

Product preferences captured Location preference

Time of day preference

43 54

2 59

11 90

27 36

0 27

100 4

77 69

86 16

63 2

28 53

33 46 42 44

100 18

1 23 25

1 4

25 73

89

40

10 94

60 53

5 11

38 46

12

3

58 38

5%

3%

6%

8%

1%

12% 2%

12%

5%

15%

Product Customer-Product propensity score

2

Illustrative

14

Significant impact was achieved

150% increase in marketing

engagement

We are building a true, real-time, personalization capability which will begin to power personalized experiences and communications within our app... Our digital flywheel momentum accelerated ... with the launch of true one-to-one personalization ... Starbucks hyper-personalized e-mail reward offerings – with more than 400,000 variations – have more than doubled customer response rates over previous segmented email campaigns, translating into increased customer engagement and, importantly, accelerated spend.

Starbucks has delivered personalized offers to customers directly on the front screen of the mobile app. By early 2017, the company expects to complete the rollout of suggested selling and recommendations (suggesting items for pairing or additions to a customer’s order) during Mobile Order and Pay checkout, which the company believes will further fuel engagement and growth.

Kevin Johnson, President & COO

Matt Ryan, Chief Strategy Officer

Howard Schultz, Chairman and CEO

Our new one-to-one personalized marketing capability ... will prove to be a retail industry game changer.

300% increase in net incremental revenue

=

+$100M in year 1

2

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When delisting this chair how many should we send to each store and how should we price it?

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Goal is to improve management of discontinued goods

Decrease manual / ad hoc efforts in management of

discontinued goods

Improve predictability of demand, assist in stock management

Decrease markdown costs

Increase overall profitability

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We built a robust optimization engine...

Base demand model

Uplift model

Substitution & Complementation

model

Optimization engine

Predicted sales volumes by store over time without

discounts

Predicted incremental demand by store over time due to

discounts

Predicted impact on demand by store due to presence of

other products

Allocation & markdown by store over time to

maximize net profit

Time-series forecasting (Prophet – additive regression models), Bayesian models

Hierarchical model selection, exponential regression, Decay effects

Association rules, basket analysis

(Mixed integer) linear programming, stochastic optimization

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...to be integrated into business processes

Allow for read and react during

markdown period

Integrate into ways of working

Incorporate business rules

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Model found 44% savings in markdown costs, ~€20M in single market

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However, most players struggle to capture value

Only 15% of companies with big data investments have put solutions into production

• Art of the possible not well understood • Talent supply limited

• Pressure on profitability impedes deep

investment

• New ways of working required

• Legacy technology and trapped data

• Processes and operating model designed for weekly vs. real time

• Innovation culture difficult to institute – stifling of new ideas

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´To take a use case from idea to production is 10% algorithms, 20% technology,

70% about changing how people work'

BCG Gamma

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Three observed approaches companies take

Analytics led

"Lets hire a bunch of data scientists and find

problems to solve"

Data/tech led

"Lets collect and clean all the data and then

find problems to solve"

Business led

"I have a problem, how can analytics help me

solve it"

Recommended

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Recommended approach is to think big, start small, grow fast

Start with the business opportunity

Build, test, iterate

Scale to solution

Transform organization

• Business first

• Value focus

• Lean technology

• Right design

• Practical application of AI and Big data

• Well defined use cases

• Iterative technology scale up

• Purpose fit tools from existing technologies

• New ways of working

• Analytics and business strategy in lock-step

• Right organization and processes

• Advanced analytics as BAU

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An integrated approach is required to actually change

Analytics Transformation

Technology & Deployment

Strategic design

Data & Analytics

Extensive use of real-time data; deep learning and AI analytics

Analytics and digital as an integrated part of the overall strategy, approach and governance

Scalable technology, real-time access, secure platform

People & Capabilities

Ways of working; agile approach; rapid test & learn; developing capabilities, acquiring and developing talent

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Use cases: rapid testing and scaling is essential

Pressure test one use case

• Set ambition • Define &

evaluate specifications

• Assess data quality & accessibility

Make go / no go decision

Launch MVP in market and improve through test and learn

• Run agile sprints to test solution “in-market” and learn how to improve

• Design and test new ways of working

• Run technology in controlled environment

Commit to scale-up

Build customized Proof of Concept to validate business case and feasibility

• Backtest on historical data

• Confirm value • Put first brick

of technology in place

Agree on plan to incubate

Value creation

Scale up solution, transform organization, increase value impact

• Run technology and business process at scale

• Analytics resources/ governance in place

• Teams trained • Client capability to

own full solution in place

P&L neutral in first 12 months, with exponential growth beyond

Articulated case for value capture Tangible prototype with business case and plan to execute MVP with impact assessment and scaling plan Full scale solution integrated into environment New ways of working instilled in your team

Outcomes

6–12+ weeks 2–4 weeks 3–6 months 6+ months

Prototype Proof of Concept

Incubate Scale

bcg.com