october 2017 andrew foley - .net framework
TRANSCRIPT
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
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)
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+
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:
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
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
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%
✓