October 2017Andrew Foley – Head of Retail Business Sytems

Putting Technology at the heart of what we do.

• Founded in 1984 by Doug and Dame Mary Perkins from a table-tennis table in their spare room

• First Specsavers-branded store was opened in Bristol followed quickly by Guernsey, Swansea, Bath and Plymouth and 1500 more!

• and don’t forget the great adverts!

l Pioneered by Specsavers

Store directors are shareholders in own store businesses

Specialist business services from support offices such as marketing, accounting, IT and supply chain

Directors freed up to focus on providing the best clinical care to their customers

Don’t talk about earwax..

Or this..

I nearly forgot about these….

More on this later…

We don’t do these….

Yet…

We are probably bigger than you think

Countries in which we operate

Massively successful business model

Great advertising

Number 1 or 2 in all our trading markets

Outgrowing the competition (5% L4L growth for UK in 2017)

??

Big Data from small spaces..

The Need for Change

Address changingcustomer expectations

Challenging and

evolving marketplace

Managing schemes is

complicatedInefficient back

office processes

“changing scheme's…can be very difficult at times…wish it could be simpler”

Do Nothing… is Not an Option!

• Systems have hit saturation

• Ability to do something different is restricted

• We need to grow to maintain market leader

status

• Changing system landscape is an enabler

Protect our Lensmail proposition whilst making enhancements to our offer

Phased approachReplacement of existing Contact Lens systems

Contact Lens Driving Growth

About MPP Global

Identify, Engage and Convert

customers in the digital age.

A global presence, working

with large multi-national

companies.

The wrap up bit.

• We are just opticians and we

didn’t have a choice.

• Embracing at all levels, its just

what we do.

• Be inquisitive and don’t be

afraid of the start up.

• Speed up – this takes time and

we are still learning.

More data beats clever algorithms, but better data beats more data.

Peter Norvig, Director of Research, Google Inc.

Over 4M

4M web sessions per week

72%80%

75%

>£880M

>£80M

N Brown Group

AB

DE

30-45 45-65 65+

Art and science

understand

motivations, desires and intent

relevant and timely

Capitalising on

Building better profiles with Celebrus data

CUSTOMER PROFILE

Name:

Brand:

Age:

Gender:

Region:

OPERATIONAL SCORING

Last ordered:

Last browsed:

Browser frequency:

Category last browsed:

Credit available:

Lifetime returns rate:

Propensity to buy score:

Segment:

Tenure:

CUSTOMER INSIGHT

Profit:

Modal size:

Modal colour:

Email open rate:

Device types:

Modal entry method:

Modal browse day:

Modal browse hour:

Lunchtime browser:

Price point:

Product area to push:

discount banner

subject line main body

Sales per email are 25% higher

Capitalising on

how likely a customer is to order from

Home department in the following 6-months

modelling techniques

Predictive modelling

Existing Model

Predicted RR Actual RR%Low

Resp

on

se R

ate

Low

Hig

h

Response RankHigh

Resp

on

se R

ate

Low

Hig

h

High Low

Predictive modelling

10%Removing the worst 10%

of contacts would improve return per

contact by 8%

VOLUME CUT DEMAND LOST

10% 3%

20% 7%

30% 12%

Respond

well to

marketing

Respond

Poorly to

marketing

Abandoned bag modelling

Abandoned

bag

Abandoned

bag model

Coming back No action

True

abandonment

In-session

treatment

Post session

treatment

Celebrus data

Price:

Reviews:

Sizes available:

Sizes not available:

Designed by:

Price:

Reviews:

Sizes available:

Sizes not available:

Designed by:

Celebrus on Microsoft Azure

Data Lake

HD Insight

Event Hub

Stream Analytics

AI Platform

Cognitive Services

SQL Server

Power BI

Microsoft Azure

on Microsoft Azure

PredictionPredictive analytics, Machine learning, diagnostic analytics & descriptive analytics

PersonalisationOffers, recommendations and content driven by intelligent real time decisions

ProtectionFraud detection, credit risk, compliance, and vulnerable customer detection

PerformanceSpeed of content delivery, broken links, latency and negative customer experiences

Gain Competitive Advantage in Retail with AI and Machine Learning

Matt HopkinsVP StrategyBlue Yonder GMBH

R E T A I L

What we offerWe are the leading provider of

cloud-based AI Retail solutions

focusing on merchandising and

supply chain.

Every day, we deliver decisions

to our customers that boost

revenues, increase profits and

enable rapid response to

changing market dynamics.

We have been adding value to

our customers since 2008.

“If you don’t have an AI strategy, you’re

going to die in the world that’s coming”

Devin Wenig, CEO eBay

Fulfilment ActivitiesO

nlin

eO

fflin

eOffline Online

Traditional offline

Retail 0.0

Offline Experience

Ship to Customer

Research Online,

Pick up in store

New Online Retail

1.0

Data

/In

form

atio

n A

ctiv

itie

s

Retail 2.0Location, Location, Location, Activities

Weekly change of importance of demand influencing factors over time

Current SC systems are not agile enough to

reflect new dynamics/economics of Retail

Trading Performance heavily impacted by

growing complexity & cost

Siloed decision making and conflicting KPIs

increase execution gap

Retailer AI & Machine Learning

Customer Driven

Supply Chain

New Economics

Of Retail

Execution gap

Market Driven, Customer

level daily decision making

Demand complexity and evolving Retail models are outstripping capabilities and operational efficiency

Market Driven, activity

based cost decisions

Automation and KPI

alignment

Blue Yonder Retail Solutions

Competitive Advantage

Complexity

Increasing cost-to-serve

Processes

Retailer

Technology

ERP WMS POS

Plan Buy Move Sell

Blue Yonder

Allocation

Store Replenishment

DC Replenishment

Supply Chain

Per product Per dayPer store

Markdown Pricing

Dynamic Pricing

Base Pricing

Customer

Volatile

DemandMerchandising

0 4 8 12 16 20 24 28 32 360 4 8 12 16 20 24 28 32 36

Precision at Scale

Pork Loin Chops

Store A Store B

Probability x of sales within a day

Mean sales 15 in both stores

30% reduction of shelf

Enhance Morrisons in-store customer experience by: • Improving availability of store levels assortments – focusing initially on Ambient &

Fresh

• Increase profitability by reducing missed sales and eliminating waste

• Streamlining complex IT infrastructure and Retail supply chain systems

Morrisons adopts AI technology

Challenge

Solution

Value

Susan McGeorge, Supply Chain Director, UK & Western Europe at Kimberly-Clark, said: “Morrisons partnered with

Blue Yonder to implement an impressive supply chain solution, using AI and cloud technology, that has

improved a key business metric – on-shelf availability.

“The solution was developed with the customer firmly at the heart and the judges were

particularly impressed by the boldness of the timelines. Having identified the business

need, Morrisons’ innovative approach has helped it move from design to implementation

very quickly, delivering impressive results for the business.”

5

1

AI-Supply Chain in Retail

99% • Automation

• Write-offs / waste

• Freshness

• Capital

• Out-of-stock

• Turnover

• Efficiency

Automation

99%

0

Summary

• AI already on superhuman level in many core Retail

Processes today.

• Creating huge value and competitive advantage today.

• DSaaS, hide mathematical complexity. Provide as an

easily consumable end-to-end-service in the cloud.

• Intelligence Layer on top of existing ERP systems.

• Crawl, Walk, Run

Blue Yonder for RetailINDUSTRY CHALLENGES

Changing Customer demand & Shopping habits

Increasing Cost-to-serve

Manual Processes & Intervention Rates

SOLUTIONS

AI Based Forecast & Replenishment Price Optimisation

PROOF POINTS

Selected Retail Customers:

Automated order decision based on

internal/external data KPIs and business

constrains

Shelf-gaps reduced 30%

80% Reduction in Out-of-stocks

Days in Inventory reduce by 2 days

Automation rates for promotions > 90%

13M Daily Automated Decisions

Improved Revenues up to 15%

✓

Blue Yonder GmbHOhiostraße 876149 KarlsruheGermany+49 721 383117 0

Blue Yonder Software Limited 19 Eastbourne TerraceLondon, W2 6LG United Kingdom +44 20 3626 0360

Blue YonderRetail AI

Blue Yonder Analytics, Inc. 5048 Tennyson Parkway Suite 250 Plano, Texas 75024 USA

5

5

https://www.blue-yonder.com