Data management and AI are essential for dynamic customer profiles

Behind the AI hype is a realization of the critical importance of data….

….but it must be protected and used wisely.

While the last couple of years has seen an enormous amount of discussion, promise, and hype around AI, both enterprises and vendors realize that without good-quality data, machine learning (ML) and natural language processing (NLP) will produce spurious and potentially dangerous results.

In the old days of CRM, vendors relied very heavily on systems integrators to figure out how to arm wrestle data.  Back then, everything was transactional, revealing nothing about the experience of being a customer. Since then, the more advanced CRM applications have steadily morphed into customer engagement platforms (CEPs). Leading companies have adopted platform thinking, driven by the need to think more holistically about the customer and the quality and consistency of their experiences in real-time. Interaction data and customer feedback have become essential elements to deliver a responsive environment. This has placed the emphasis for data management and real-time synthesis onto the CEP vendors that promise to trigger the “next best action” throughout every customer journey. And do that at scale across any interaction channel the customer chooses to use.

A dynamic customer profile is now an essential element of leading CEPs.

To have any chance of triggering the next best action through ML, the customer must be identified and understood within the context of their specific journey. This assumes that the customer has given explicit permission to use their data for this purpose. Any business will also want to know the value of that customer and if they have earned special privileges.

Historical data must be married with real-time interaction data. Pre-existing relationships and the individual’s roles in a buyer group are especially important in business-to-business (B2B) environments. It helps forge closer bonds.  Location data may have relevance in providing support for timely and location-based offers. Emotion detection (still in its infancy) can help build empathy for the customer. Behavioral data allied to transactional data helps tease out the customer’s intent within the context of their specific journey. These elements contribute to more profound insight into the customer’s context and provide critical fuel for ML algorithms and automation to trigger the most relevant response while respecting the customer’s preferences and permissions.


Automation may lead to faster annoyance if the customer experience is primarily just a marketing method to generate leads.  It may appear to the customer predatory, akin to stalking, if there is little thought to the outcomes customers seek. In that case, automation triggered by a synthesis of real-time interaction and historical transaction data will ultimately alienate the customer – only faster.

It’s vital that customer experience is viewed as an enterprise-wide concern and is treated holistically, not in isolation or for a narrow, self-serving departmental reason.  If the CMO is responsible for the initiative, the sponsorship and backing must come from the CEO.

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