is tomorrow’s customer loyalty found in today’s data?...to stay relevant, you must understand...
TRANSCRIPT
Retail DnA
Is tomorrow’s customer loyalty found in today’s data?
Creating value from customer insight
Only 7% of retailers can innovate to meet the changing
needs and wants of their customers
Just 9% can keep up with the fast-paced changes in tech
on offer
100110010011100010 001011001001101110 100110110010100100
Most retailers have data available but cannot transform it into value.
Only 11% of retailers are confident they can drive actionable insights from data
Drive valuefrom data/bridge
the gap
Source: EY Retailer research, 2016 (ey.com/balance).
Old methods of creating customer value are no longer viable. Today, changes in technology and challenges impeding timely innovation are disrupting the retail industry and increasing pressure to remain competitive.
To stay relevant, you must understand your customers so intimately that you can predict their behavior. Analyzing your customers will help you unpack these insights and equip you to make data-driven decisions.
Retail DnA is built on the MS Azure platform and combines human expertise with machine learning effectiveness.
Retail DnA can accurately predict future customer actions, thus enabling marketing teams to develop and provide the right personalized offers.
Allowing marketing to profide the right …
Humans have the knowledge and creative brain power.
Marketers develop the right offers for achieving the leading outcomes.
Machines have scale, speed and the ability to continually learn.Machines handle the data crunching “grunt work” to generate insights.
Combining the best from humans and machines
Engaging customers and encouraging purchasing activity
Engaging customers
and encouraging purchasing
activity
ProvidesA single view of customer based on behavior
Predictions from behavioral data
Best offer or intervention, most likely to encourage action
Offer Time Channel
With results acrossNext best offer
Price improvement
Offer improvement
Supply chain efficiency
Source: EY Retailer research, 2016 (ey.com/balance).
* Source: EY Retailer research, 2016 (ey.com/balance).
Turning insight into execution
Using real customer behavioral data to form natural clusters, Retail DnA goes beyond traditional demographics to create an unbiased way of understanding customers on what they do, not who they are. By combining a machine’s ability to scale, speed and learn human functionality, Retail DnA can help close the customer insights gap.
With a single view of their customers, retailers can understand their buyers on an intimate level and build Retail DnA into their plans to engage, and re- engage, with customers.
With the ability to accurately predict future customer actions, the Retail DnA recommendation engine gives retailers the competitive advantage to model and design tailored campaigns that can help provide accurate personalized offer recommendations.
To get close to their customers and stay relevant, retailers need to understand their customers‘ motivations. Their shoppers’ needs and behaviors will change over time, and the retailers need to be ahead of those changes to ensure they don’t become a forgotten brand.
Retail DnA continuously works to improve prediction so that retailers can maintain relevance and deliver value to their customers.
Retail DnA combines information from various data sources to understand the digital footprint, transactional data, physical interaction and verbal dialogue of a customer. This allows Retail DnA to generate insights and develop natural clusters based on actual customer activity, not their profile.
A single enterprise-wide view of customer’s purchasing patterns and shopping behavior allows retailers to plan for the right offer, communicate it at the right time and send it through the right channel.
Only
28% of retailers say they know how to stay relevant*
1Integrate all information on customer behavior
2Increase engagement with tailored communication
3Improve loyalty by understanding brand affinity
Retail DnA overview
Retail DnA is transforming the retail industry. Closing the customer insights gap starts with analyzing your customer data, buying behavior and purchase patterns.
Automatedmarketingapplication
Optimizedcampaign
list
Master data(customer, product etc.)
Pos transactions
Digital
Marketing
Automated
Configured
MANUAL
Microsoft azure (cloud)
Retail DnA
Campaigndecision hub
Customer or product
affinity matrix
Featureengineering andmachine learning
module
Datagovernance (QA)
Extracttransform
load Central datastore
Customer profilingKnow Your Customers:
DemographicSegmentationSpend attributesRecent Behavior
Client marketing strategyand campaign innovationMarketing overlays:
►
Product Margin
►
Supply chain
►
►
►
►
►
Product Seasonality
1. Input all dataWeb traffic, product sales, POS are all inputted to create a single customer view
2. Generate insightsE.g., female shoppers who buy t-shirts are 80% more likely to purchase accessories
5. Personalized offerssent to consumersvia automated marketing solution
6. Learn and optimizeA feedback loop measures intervention success and continuously improves modelling
3. Modelling and designCustomer, brand, product category,product affinity model
4. Buildpersonalizedoffers
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Ondrej ZakAssociate Partner,
Ernst & Young, s.r.o.
+420 730 191 792
Michal MajewskiSenior Manager,
Ernst & Young spółka z ograniczoną odpowiedzialnością
Business Advisory sp.k.
+48 660 440 [email protected]
Panos PapadopoulosManager,
Ernst & Young Business Advisory Solutions S.a.
+30 694 026 [email protected]
Fusun PatogluPartner,
Ernst & Young Kurumsal Finansman
Danısmanlık A.S.
+90 533 309 60 [email protected]