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Real-Time Personalization
Exploring the Customer Genome
Richard Veryard
Unicom Data Analytics Conference
London December 2...
2
Agenda
Retail and consumer
organizations have started
to develop more
personalized interaction
with customers, based on
...
3
Using Real-Time Analytics
Predictive
Maintenance
Fraud
Detection
…
4
Omnichannel Evolution for Retail and Consumer
Systems of Record
• Omnichannel eCommerce
• “Click and Collect”
• Some Per...
5
From Conversion to Persuasion
Conversion is not just about this week's revenue. We need
to develop the ability to detect...
6
Personalization involves four capabilities
Personalization
Targeting
• Starting with what we
want to promote.
• Selectin...
7
Targeting and Personalization
• Produces a list of consumers for a given
message
Targeting Algorithm
• Produces a list o...
8
Personalization Through Co-Creation
Complete the Look
Fashion Finder
9
Learning
& Development
Knowledge
& Memory
Information
Gathering
Decision
& Policy
WIGO
(what is going on)
Organizational...
10
Engagement Framework
Learning
& Development
Data Science
Knowledge & Memory
Consumer
Genome
Information Gathering
Consu...
11
Why Real-Time?
Next Action
Real-Time
Context
Product
Genome
Consumer
Genome
• “RTD and online
recommendation engines
ar...
12
Demographics
and Life Events
Socioeconomic category
Life events – work, marriage, children
Product
Experience and
Affin...
13
Product Genome
Product
Features
Strong features of product – browsing data suggests this is what attracted
customers to...
14
Real-Time ContextCustomer
Focus
Where is the customer right now?
What can we infer about the customer’s purpose? For ex...
15
Customer Journey
Zero
Moment of
Truth
Google Search
(early consideration)
First
Moment of
Truth
Shop / Website
(pre-pur...
16
Inferences from Incomplete Data
Visible Data
data we collect from our own
systems and processes
Processed Data
i.e. tra...
17
Knowledge to Inference to Decision
Recent activity Product Holding Profile
What we know
Product affinity
Recent activit...
22
Capability Reference Model
“Know the Customer
Base” is a plural
capability, understanding
the mass of consumers to
dete...
23
A simple model
Business Intelligence.
Transformations.
Descriptive models.
Predictive models.
Marketing
Propositions
In...
24
A slightly less simple model
Interaction
Channel
Decision Support
Candidate
Activities
Best Activity
Monitor
Outcome
De...
25
Principles of Consumer Engagement
Holistic Understanding how multiple factors interact to produce particular
behaviours...
26
Consumer Characteristics
• Such things as name, age, length of tenure etc., subject to simple transformations.
Simple a...
27
Next Best Activity
The next best activity for a given consumer
is selected based on consumer data …
• Current consumer ...
28
Plugging Personalization into the TouchPoint Process (Email)
Plan Email
Campaign
Create
Consumer
List
Compose
Email
Del...
29
Plugging Personalization into the TouchPoint Process (Online Interaction)
Identify
Consumer
Customize
Display
Customize...
30
Key Questions - Summary
Why?
• Cross sell? Upsell?
• Retention?
• Acquisition? Cost
• Savings?
• Drive margin?
Who?
• B...
33
Information Flow
Customer
Characteristics
Propositions and
Strategies
Derive Characteristics
Decision Support
Candidate...
34
Feedback Flows
Customer
Characteristics
Predictive
Models
Decision Support
Chosen Activity
Reactive Behaviour
Historica...
35
What is the Value of Personalization?
• Message across all channels are more relevant to consumers increasing
their aff...
Contacts
www.replyltd.co.uk
r.veryard@reply.com
http://twitter.com/richardveryard
https://twitter.com/gluereply
Acknowledg...
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Real-Time Personalization

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Presentation at Unicom Data Analytics conference, London December 2015

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Real-Time Personalization

  1. 1. Real-Time Personalization Exploring the Customer Genome Richard Veryard Unicom Data Analytics Conference London December 2015
  2. 2. 2 Agenda Retail and consumer organizations have started to develop more personalized interaction with customers, based on rapid analysis of a broad range of customer attributes and propensities, known metaphorically as “genes”. These may be used to target campaigns more accurately, or to generate the next best action in real- time for a specific customer. Business Opportunities Technology Challenges
  3. 3. 3 Using Real-Time Analytics Predictive Maintenance Fraud Detection …
  4. 4. 4 Omnichannel Evolution for Retail and Consumer Systems of Record • Omnichannel eCommerce • “Click and Collect” • Some Personalization Systems of Engagement • Omnichannel Marketing • “Click and Connect” • Full Personalization Glue Reply has helped a number of leading retailers to implement Omnichannel eCommerce Our retail and consumer clients are now looking seriously at Omnichannel Marketing Other possible applications of Omnichannel Engagement include education (pastoral care for students) and citizen-led journalism.
  5. 5. 5 From Conversion to Persuasion Conversion is not just about this week's revenue. We need to develop the ability to detect slow-acting and cumulative effects as well as instant one-off effects. Obviously this is more difficult, but it is not impossible. The future for Internet marketing lies in developing non- linear systems that deliver exactly what prospects need, when they need it, so they can accomplish their goals in the manner most comfortable to them. Conversion is a linear process. Persuasion is a non-linear process. Source: Digital Intelligence Today
  6. 6. 6 Personalization involves four capabilities Personalization Targeting • Starting with what we want to promote. • Selecting consumers for a given campaign Customization • Starts with what the consumer asks for. • Take consumer demands at face value Contextualization • Engaging with the consumer’s world. • Infers consumer desires from context. Co-Creation • Providing a platform for active consumer engagement.
  7. 7. 7 Targeting and Personalization • Produces a list of consumers for a given message Targeting Algorithm • Produces a list of messages and other actions for a given consumer Personalization Algorithm • Both personalization and targeting require some kind of matching algorithm. • The desired “match” is the same in both contexts. So the two algorithms should probably have a common core. Similar or Different? Targeting • From Content to Individual • Here’s a campaign message – who are the best people to receive it? Personalization • From Individual to Content • Here’s a consumer – what message do we want to give them?
  8. 8. 8 Personalization Through Co-Creation Complete the Look Fashion Finder
  9. 9. 9 Learning & Development Knowledge & Memory Information Gathering Decision & Policy WIGO (what is going on) Organizational Intelligence Framework • eBook available at http://leanpub.com/ orgintelligence Communication & Collaboration Sense-Making
  10. 10. 10 Engagement Framework Learning & Development Data Science Knowledge & Memory Consumer Genome Information Gathering Consumer Monitoring Decision & Policy Next Best Action Consumer Behaviour Communication & Collaboration Omnichannel Marketing Sense-Making Consumer Analytics
  11. 11. 11 Why Real-Time? Next Action Real-Time Context Product Genome Consumer Genome • “RTD and online recommendation engines are great for helping you understand that customers who bought X also bought Y, but that doesn't capture the intent that the customer is expressing in that current session. It won't tell you that somebody is shopping for a gift, not buying what they normally buy. And it won't tell you that the customer just purchased a TV, so stop showing them other TVs and start showing them HDMI cables and speaker systems.” Christophe Bisciglia, CEO Wibidata
  12. 12. 12 Demographics and Life Events Socioeconomic category Life events – work, marriage, children Product Experience and Affinity Which products do they already have? Which products are they likely to be interested in? Responsiveness Price-Sensitivity Feature Sensitivity Response to Merchandising and Marketing Response to Direct Offers Preferences Communication Style Channel Privacy and Consent Consumer Genome
  13. 13. 13 Product Genome Product Features Strong features of product – browsing data suggests this is what attracted customers to buy Neutral features of product – little link to purchasing decision Weak features of product – this is what persuaded customers to buy something else Pricing and Promotion History of deals for this product Current / planned deals for this product Product Connections People who viewed X bought Y instead People who bought X also bought Y at the same time People who bought X also bought Y at a later time.
  14. 14. 14 Real-Time ContextCustomer Focus Where is the customer right now? What can we infer about the customer’s purpose? For example, buying Christmas presents, or something to wear for the Christmas party? Which product is the customer looking at now? Which products has the customer viewed recently? Customer Network Alone, shopping with friends, social media? Product Inventory What is the stock / supply situation? Product Transactions What has the customer previously bought? (Including earlier today) What is the customer buying now? (Already in basket) What was the customer buying earlier today? (Left in basket)
  15. 15. 15 Customer Journey Zero Moment of Truth Google Search (early consideration) First Moment of Truth Shop / Website (pre-purchase evaluation) Second Moment of Truth Getting the product home (post-purchase evaluation) Third Moment of Truth Social media (sharing with network) Traditional Extended
  16. 16. 16 Inferences from Incomplete Data Visible Data data we collect from our own systems and processes Processed Data i.e. transformed by processes under our control Dark Data e.g. interactions with competitors Transformed Data i.e. transformed by processes outside our control (e.g. Social Media) Conventional BI converts operational data into useful analytics Welcome to the world of “Big Data”
  17. 17. 17 Knowledge to Inference to Decision Recent activity Product Holding Profile What we know Product affinity Recent activity Product Holding Profile Potential inferences What we know Product A Profile Product affinity What we infer What we know Left in basket Product A Churn Propensity C Credit ScoreA Demographic segment D Profile Product affinity What we decide What we infer What we know Left in basket Product A Churn Propensity C Credit ScoreA Demographic segment D
  18. 18. 22 Capability Reference Model “Know the Customer Base” is a plural capability, understanding the mass of consumers to detect common patterns and trends. “Know the Customer” is a singular capability, applying (common) patterns and trends to an individual consumer.
  19. 19. 23 A simple model Business Intelligence. Transformations. Descriptive models. Predictive models. Marketing Propositions Interaction strategies Decisioning Choose Personalised interaction Customer Available propositions Matching logic Strategy Channel Context Customer Profile Historic Data Behaviour BI Repository
  20. 20. 24 A slightly less simple model Interaction Channel Decision Support Candidate Activities Best Activity Monitor Outcome Derive Characteristics Transform, Aggregate, Descriptive Models Predictive Models Current Customer Characteristics Historical Data Propositions and Strategies Strategy Management Master Data Management OperationalSystems Propositions and Decisioning Rules Propositions Customer LevelData Raw DataOperational History Zero Latency Characteristics Chosen Activity Subsequent Behaviour Master Data Marketing Analytics Customer Context Trigger Response
  21. 21. 25 Principles of Consumer Engagement Holistic Understanding how multiple factors interact to produce particular behaviours and preferences at a given point in time. Consumer Context Understand consumer pathways – including changes and repeating patterns over time. Understand the consumer’s network – friends and influences. Consumer Perspective Don’t just see things from the company’s perspective. Understand what these events mean to the consumers themselves. Closed Loop Feedback The outcome of each action helps to calibrate the next action. Rapid feedback supports broader experimentation and promotes effective learning. Ethical Respecting consumer preferences and values.
  22. 22. 26 Consumer Characteristics • Such things as name, age, length of tenure etc., subject to simple transformations. Simple attributes • What products or what types of product does the consumer hold or have they held. • Subject to transformations informed by master data management. Product holdings • Simple mathematical derivations such as “total average monthly spend over last 6 months”, “spend this month to date”, “average number of calls to call centre per month”. Aggregated values • A descriptive model classifies consumers, but without reference to predicting any specific future behaviour. • Examples would be segmentations, which classify consumers into various groupings based upon their demographics and behaviour. Descriptive model outputs • A predictive model uses consumers’ past behaviour and demographics to predict future behaviour. • An example would be a churn propensity model, which predicts the likelihood of a consumer to leave the organisation for a competitor. These may be derived by data mining techniques to determine predictive attributes. • A particular subset of predictive model is the scorecard, which assigns a score to various attributes, giving a total score that is used as a predictor. This derivation is particularly open and may be used in cases where transparency is required for regulatory reasons, for example credit scoring. • Predictive models tend to be informed by proposition information, whereas the preceding types tend to be simply descriptive. Predictive Model outputs
  23. 23. 27 Next Best Activity The next best activity for a given consumer is selected based on consumer data … • Current consumer characteristics. – May include real-time data from operational systems, and pre- calculated data based on history. • Interaction channel context. – Provides the consumer identity, the channel identity and any other information available about the triggering interaction. • Propositions and strategies. – Provide the logic by which a decision is made, and define the possible next actions. … as well as relevant decision rules and strategies relating to … • The aims of the organisation • The needs of the consumer • The channel by which the consumer is interacting • The eligibility of the consumer for the various available propositions • The suitability of the consumer for the various available propositions • The costs to the organisation of the available propositions, and the potential margin to be made • Preferences expressed by the consumer (including privacy and consent)
  24. 24. 28 Plugging Personalization into the TouchPoint Process (Email) Plan Email Campaign Create Consumer List Compose Email Deliver Email Consumer Data Personalization Control Customer Selection Control Email Content Control Delivery Timing Inhibit Unwanted Emails In this model, we take an existing marketing process (eCRM) and plug in some intelligent personalization based on the consumer characteristics. The model shows four different points in the eCRM process where intelligence could be plugged in. These do not necessarily have to be implemented at the same time.
  25. 25. 29 Plugging Personalization into the TouchPoint Process (Online Interaction) Identify Consumer Customize Display Customize Navigation Customize Offer Consumer Data Personalization Select Banners and Images Control Search Sequence Select Relevant Offers In this model, we take an existing online interaction and plug in some intelligent personalization based on the consumer characteristics. The model shows four different points in the online interaction where intelligence could be plugged in. These do not necessarily have to be implemented at the same time. Build “Just For You” Panels
  26. 26. 30 Key Questions - Summary Why? • Cross sell? Upsell? • Retention? • Acquisition? Cost • Savings? • Drive margin? Who? • Business units: • Marketing? • Analytics? • Product planning? When? • Product lifecycle? • Latency constraints? • Strategy reaction time? Where? • Which channels? • Centralised or distributed decision making? What? • Level of decision making (person, account, device, organisation)? • What products? • What facts? How? • System landscape • Means of customer • identification? • Means of strategy • control?
  27. 27. 33 Information Flow Customer Characteristics Propositions and Strategies Derive Characteristics Decision Support Candidate Activities Chosen Activity Reactive BehaviourHistorical Data Monitor Outcome Input to... Recorded as ...
  28. 28. 34 Feedback Flows Customer Characteristics Predictive Models Decision Support Chosen Activity Reactive Behaviour Historical Data Input to... Recorded as ... Validate Decisions Validate Models Adaptive Models
  29. 29. 35 What is the Value of Personalization? • Message across all channels are more relevant to consumers increasing their affinity with the channels and brand • Consumer-led – consumers should feel that we are directly responding to their actions and preferences. Engagement • Improved conversion rate on campaigns. • Reduced churn. • Reduced price sensitivity – offers can be based on consumer desire rather than discounts • Lifetime value of consumer. Align consumer incentive to consumer value. Economics • More effective use of digital campaigns as more targetted, more coordinated , more timely. • Growing accuracy of consumer profile, thanks to continuous feedback. • Support for innovation (e.g. trial offers or campaigns), because faster and more comprehensive feedback takes away some of the risk Efficiency
  30. 30. Contacts www.replyltd.co.uk r.veryard@reply.com http://twitter.com/richardveryard https://twitter.com/gluereply Acknowledgements With help from Andrew Forsyth Retail Reply Thank You

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