Predictive Customer Engagement A Work in Progress

The Opportunity? How can we provide information that we have … that customers would value… but don’t know to ask for? © 2015 Consortium for Service Innovation

The Goal of the Support Organization? Definition of Service Excellence Maximize customer realized value/success through the use of our products and services Easy and seamless service integrated into the context of use Continuous improvement of the whole customer experience © 2015 Consortium for Service Innovation

- Customer Value + Customer Value Expectations Exceeded “Added Value” Customer Value Expectations Not Met “Maintenance” - Time © 2015 Consortium for Service Innovation

* * - Value Erosion + Customer Value Value Erosion Exception Open Incident Exception Self-service and Forums Automation * Customer Expectations Customer Value * Chat Value Erosion - Time © 2015 Consortium for Service Innovation

Customer Effort Model * High Case Customer Effort Self-service and Forums * Integrated Resolution Customer Effort Low Time © 2015 Consortium for Service Innovation

Customer Value/Effort Model and Knowledge Activation High High Case Community Automate Prevent Eliminate Chat Customer Value Customer Expectations Customer Effort Knowledge Activation Low Low Time © 2015 Consortium for Service Innovation

“Added Value” Reduce Customer Effort High Customer Expectations Customer Effort Automation Ease of use Context sensitive help Low Time © 2015 Consortium for Service Innovation

“Added Value” Increase Capability Sense and respond to customer intent Learning On-demand Functionality High Customer Expectations Customer Value Low Time © 2015 Consortium for Service Innovation

What if we could do both! Increase capability and reduce effort Automation Ease of use Sense and respond to customer intent Learning Context sensitive help On-demand Functionality High High Customer Value Customer Effort Low Low Time © 2015 Consortium for Service Innovation

Service Excellence Customer support is not about solving incidents or customer sat with incident resolution It is about our ability to contribute to customer success and productivity It means we have to be good at: Minimizing value erosion Maximizing value realization by reducing customer effort and increasing their capability © 2015 Consortium for Service Innovation

The Value Stack Value Customer’s Customer Success Customer Success and Productivity Predictive and Preemptive Fix © 2015 Consortium for Service Innovation

The Value Stack Value Customer’s Customer Success Customer Success High High Customer’s Customer Success Customer Success and Productivity Business Acumen Know-me Factor Level of Trust Predictive and Preemptive Co-creation Factor Fix Low Low © 2015 Consortium for Service Innovation

Added Value Enabling pre-defined customer capability and service levels is necessary. Responding to un-anticipated needs and opportunities is compelling! How do we: Dynamically create the capability and capacity to respond to unanticipated customer needs? Predict opportunities to co-create value with customers (without them asking)? © 2015 Consortium for Service Innovation

A Generic Customer Touch Point Model Purchase or Upgrade Renew GET Cycle USE Cycle Consideration Support Awareness Usage © 2015 Consortium for Service Innovation

CX Framework Customer Touch Points “the journey” Awareness Consideration Purchase/Upg rade Use Support Renew Customer Touch Points “the journey” © 2015 Consortium for Service Innovation

CX Framework Customer Modes of Interaction Awareness Consideration Purchase/Upg rade Use Support Renew Customer Modes of Interaction Apps Print Media Web/Social Group Chat People (WoM) Analog Media Agent Assisted Search Engine Digital Content Social Network User Community Comm of Interest © 2015 Consortium for Service Innovation

CX Framework Customer Modes of Interaction Traditional Digital Mobile Awareness Consideration Purchase/Upg rade Use Support Renew Customer Modes of Interaction Apps Print Media Web/Social Group Chat People (WoM) Analog Media Agent Assisted Search Engine Digital Content Social Network User Community Comm of Interest © 2015 Consortium for Service Innovation

Customer Scenario Traditional Digital Mobile Awareness Consideration Purchase/Upg rade Use Support Renew Apps Print Media Web/Social Group Chat People (WoM) Analog Media Agent Assisted Search Engine Digital Content Social Network User Community Comm of Interest © 2015 Consortium for Service Innovation

Customer Scenario Traditional Digital Mobile Awareness Consideration Purchase/Upg rade Use Support Renew Apps Print Media Web/Social Group Chat People (WoM) Analog Media Agent Assisted Search Engine Digital Content Social Network User Community Comm of Interest © 2015 Consortium for Service Innovation

Where Should You Focus Your CX Efforts? Identify your map of space and time Map the customer journey and modes of interaction Look at frequently visited touch points and modes of interaction Gather data on the high traffic touch points (surveys) Where does CX not meet brand promise expectations Don’t need to look at all touch points © 2015 Consortium for Service Innovation

Where to Focus Predictive Customer Engagement? Uni-directional 1-to-many Uni-directional 1-to-1 Bi-directional many to many Bi-directional 1-to-1 or 1-to-few © 2015 Consortium for Service Innovation

Customer Scenario Traditional Digital Mobile Awareness Consideration Purchase/Upg rade Use Support Renew Apps Print Media Web/Social Group Chat People (WoM) Analog Media Agent Assisted Search Engine Digital content Social Network User Community Comm of Interest © 2015 Consortium for Service Innovation

Company/organization The Event Loop (A Loop) Events Listening Posts Communication Mechanisms Analysis People Product/ services Knowledge articles Company/organization Work Action Rules Discovery © 2015 Consortium for Service Innovation

Company/organization The Black Box Analysis Context Channel Stage of journey Asset characteristics Inferred intent Universal data Relevance defined by context Trends Events over time Universal trends Results of success of action Meta data Usage/consumption Algorithms and data science Data analysis can be input or output Analysis People Product/ services Knowledge articles Company/organization Work Rules © 2015 Consortium for Service Innovation

Company/organization The Black Box Rules Define: what, who how, when Profiles and Assets People Work Company/organization Knowledge Articles Products/services Analysis People Product/ services Knowledge articles Company/organization Work Rules © 2015 Consortium for Service Innovation

Knowledge Assets People Profiles Vendor Offerings Knowledge Articles Work or Tasks Customer Entity Original names People, knowledge, customer entity, work/tasks, offerings © 2015 Consortium for Service Innovation

The Improve Loop (B Loop) Impact Assessment Engagement Assessment Asset Quality Rules Effectiveness Analysis Effectiveness People Product/ services Knowledge articles Company/organization Work Analysis Rules Event Action © 2015 Consortium for Service Innovation

The Role of the Data Scientist in Service Innovation Objective: predict or anticipate value co-creation opportunities Cross functional perspective Sources of data Data collection Analysis techniques Rules development Assessment of relevance and accuracy of rules outcomes © 2015 Consortium for Service Innovation