Uploaded byRichard Veryard
Customer Engagement Open Group Oct 2015
The document discusses the significance of consumer analytics and big data in creating personalized consumer experiences across multiple touchpoints. It highlights the challenges organizations face in harnessing this intelligence, with a focus on the role of enterprise architecture in enabling effective strategies. Key takeaways include the importance of omnichannel engagement, the evolution of personalization methods, and the need for a holistic understanding of consumer behavior and preferences.
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Customer Engagement Open Group Oct 2015
- 1. Boundaryless Customer Engagement RichardVeryard (presenter) Andrew Forsyth (coauthor) Open Group Conference Edinburgh October 2015
- 2. 2 Agenda • The businessvalue of consumer analytics and big data is not just about what you can discover or infer about the consumer, but how you can use this insight promptly and effectively across multiple touchpoints (including e-Commerce systems and CRM) to create a powerful and truly personalized consumer experience. • For most organizations, mobilizing this kind of intelligence raises organizational challenges as well as technical ones. • We plan to reveal how some leading companies are starting to address these challenges, and describe the vital role of enterprise architecture in supporting such initiatives.
- 3. 3 Key takeaways Reference model •Omnichannel consumer analytics and engagement. Architectural approach • Closed-loop integration across multiple consumer touchpoints and diverse data platforms. Template business case • Building and extending your business and technical capabilities for consumer engagement.
- 4. 4 Is Digital aNew Thing Yes it is • New mechanisms • Greater scalability • Potential visibility • Faster response No it isn’t • Good (small, local) retailers have always done personalization. • Innovative retailers (e.g. Gordon Selfridge) have always done engagement.
- 5. 5 Omnichannel Evolution forRetail 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.
- 6. 6 From Conversion toPersuasion 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
- 7. 7 Personalization involves fourcapabilities 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.
- 8. 8 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?
- 9. 9 Personalization Through Co-Creation Completethe Look Fashion Finder
- 10. 10 Learning & Development Knowledge & Memory Information Gathering Decision &Policy WIGO (what is going on) Organizational Intelligence Framework • Presented to Open Group in May 2011. • http://www.slideshare.net/RichardV eryard/orgintelligence- presentation-at-open-group- conference-may-10th • eBook available at http://leanpub.com/ orgintelligence Communication & Collaboration Sense-Making
- 11. 11 Engagement Framework Learning & Development DataScience Knowledge & Memory Consumer Genome Information Gathering Consumer Monitoring Decision & Policy Next Best Action Consumer Behaviour Communication & Collaboration Omnichannel Marketing Sense-Making Consumer Analytics
- 12. 12 Demographics and Life Events Socioeconomiccategory 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 Journey Zero Moment of Truth (Google, early consideration) First (Shop, pre-purchase evaluation) Second (Home, post-purchase evaluation) Third (Social media, sharing with network) Consumer Genome
- 13. 13 Inferences from IncompleteData 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”
- 14. 15 Knowledge to Inferenceto Decision – What We Know Recent activity Product Holding Profile What we know
- 15. 16 Knowledge to Inferenceto Decision – Potential Influences Recent activity Product Holding Profile What we know Product affinity Recent activity Product Holding Profile Potential inferences What we know Product A
- 16. 17 Knowledge to Inferenceto Decision – What We Infer 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
- 17. 18 Knowledge to Inferenceto Decision – What We Decide 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. 19 Capability Reference Model “Knowthe 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. 20 A simple model BusinessIntelligence. 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. 21 A slightly lesssimple 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. 22 Principles of ConsumerEngagement 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. 23 Consumer Characteristics • Suchthings 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. 24 Next Best Activity Thenext 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. 25 Plugging Personalization intothe 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. 26 Plugging Personalization intothe 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. 27 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. 30 Information Flow Customer Characteristics Propositions and Strategies DeriveCharacteristics Decision Support Candidate Activities Chosen Activity Reactive BehaviourHistorical Data Monitor Outcome Input to... Recorded as ...
- 28. 31 Feedback Flows Customer Characteristics Predictive Models Decision Support ChosenActivity Reactive Behaviour Historical Data Input to... Recorded as ... Validate Decisions Validate Models Adaptive Models
- 29. 32 What is theValue 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
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