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The Data-driven Future of Customer Journey Mapping

Customer journeys aren’t new

Customer journey mapping has been a staple of successful CS and CX programs for many years.

Well-documented and reiterated journeys will help your company find and expand on differentiators, identify risk points and moments of high touch, and provide customer resources at the moments they are most needed. Additionally, documented journeys ensure cross-functional alignment from leadership to the front line about what the organization delivers and when. And for customers, seeing a journey laid out shows them what to expect from your partnership.

Recent developments in the world of journey mapping: automation.

While mapping, and regular review of maps, has long been a CX best practice, technology has caught up to a point where we can review our journeys with an eye for areas that can be automated. Implementing automation at strategic moments lets us free up human touch for more strategic, high-value activities.

A common example I see is the use of automated Success Plans throughout later stages of the journey. With the right setup, a good system of action can recognize and alert CSMs when a customer gets off track, helping them take action early. Automating playbooks is another way to pull CSMs into the journey at moments that matter, such as when new product functionality is released.

The future of journey mapping: prediction.

Even more exciting, we are moving into a new stage of technological capability: from automation to prediction. The new field of Customer Intelligence — centralizing data and letting AI perform the analytical lift to assign health scores and identify gaps — brings companies closer than ever to true customer intimacy. CS and CX teams can see areas of opportunity and concern even before their customers express them.

My team’s customers can (and do!) apply that predictive power to their own unique customer journeys. Unified data and AI have allowed them to identify moments that matter specifically for them. For example, one company discovered resource-intensive trainings that were not impactful to customer satisfaction and retention. Another identified that their time to value was too fast (!) by assessing data from that segment of the journey (note: internal measure of time to value is often different than the customer measure of time to value!).

By analyzing data from specific moments in the customer journey, its impact on outcomes, and predictions for future outcomes, CS and CX teams can regularly re-assess and determine high touch, low touch, tech touch, missing, or obsolete moments in their customers’ journeys.

Tell us

Does your company use a data-driven approach to journey mapping? If so, what does that look like? What are the barriers? How have you overcome them? We can all benefit from your experience and knowledge, so please don’t hesitate to share!


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