[Title Image Abbreviations: CRM – Customer Relationship Management, OPM – Offer Portfolio Management, PMML – Predictive Model Markup Language]
The perceived status of the consumer experience enhancement industry from an innovation practitioner’s vantage point is that it is mature. Operational perspectives and best practices are continuing to emerge and seem to be linked to corporate engagements in one or more of Fix/Enhance/Create/Optimize type pursuits.
Across these various engagement possibilities, customer data availability with embedded triggers fueling machine learning and Predictive Analytic techniques are used to enhance the experience. Currently the consumer experience management industry is on its fourth generation of innovation. It was led by user interface experts who passed the baton to infrastructure service experts and more recently to the corporate brand experts.
The learning from each generation fortifies the need for data to drive enhancements and for machine driven personalization versus human executed engineering.
First generation experience measurement were based on customer satisfaction “measure” and usually closely affiliated with the Net Promoter Score (NPS.) In the field the Predictive Analytics driven operational techniques replaced “NPS Driver Analysis” – helping determine which controllable factors had an effect on NPS and how much impact they had. Second generation measurement based on customer frustration “measure” and usually closely affiliated with delta from channel hopping behavior. Field operational technique focused on monitoring “actual behavior against anticipated behaviors” – helping to prioritize which experiences to refine or eliminate. The current generation is based on customer affinity “measure” and usually closely affiliated with determining the customer’s question that actually needs answering. It is an operational technique focused on anticipating intent– helping to prioritize speedy response.
A single measure for tracking the consumer experience has been proposed and commercialized by a number of leading authorities. You could choose to adopt one of these or roll your own. Where everyone is headed seems to be to measure contextual experience rather than most other proxies for customer experience and the associated emotional behavior. Information Gain within a dataset often can be a strong interchangeable indicator of contexts as well as interestingness.
A typical contextual experience index (cei) score could be designed to range from a value of 1 to 10 based on customer triggering events of interest (those who searched on a set of terms) and a subsequent understanding of how different from a baseline behavior (e.g. non-purchasers of searched product within a four week period) are the recent set of differences. From an operational perspective, Trigger Event Habituality (i.e. how frequently affine trigger events are being set off by the customer) and the relative interestingness (proxied by the information gain score) could be used to score a channel specific cei (see image below.)
Looking forward to your comments below in this forum or privately!
Madhusudan Raman (Madhu) heads Verizon’s Ideation Services based out of their Massachusetts Innovation Center. An alum of the MIT Sloan Executive Strategy & Innovation Program and an Electrical Engineer, Madhu is a practitioner who incubates beachhead market ideas. His innovations include numerous granted or in-process patents leveraging Big Data contextual insight harnessing predictive models, the cloud, consumer social media, and mobility. Data Science Analytics driven "Contextual Learning" capabilities created by Madhu center around the use of machine learning to derive context. Contextual Learning has been central to multiple solutions for Automotive, Fintech, Martech, Transportation, Utility, Wearables, and Wellness industries. His solutions integrate native, open source, and third party Intellectual Property.
Madhu’s three decades of experience combines deep innovation with general management, building and leading best-in-class data driven ideation in the U.S and around the world. Apart from running startup teams and guiding senior resources across the business and technology divides, his background includes repeated success in transforming risk averse product teams to entrepreneurial, data-driven product-organizations.
Note that what is expressed by Madhu here is of his own interest and is in no way reflective of his employer.