Why Journey Analytics Is Not a Straight Line

Part 1 of a 3 part series on the Journey Analytics life-cycle by Lauren Smith and Tim Friebel

To best understand the life-cycle of journey analytics, the best place to start is by looking at the end: the business outcome one hopes to achieve: Real Measurable Value.

Companies need to be able to repeatedly evaluate the stories their data has to tell, operationalize solutions that monitor and improve journeys in a way that continuously moves the needle on the metrics that define their business; whether that’s improving efficiency, productivity, revenue, or customer satisfaction. REPEATABILITY is the key, as so much time and money is spent on finding and cleansing the right data, pushing the truly valuable work, Journey Orchestration and Value Delivery, much further into the future.  Acquiring and preparing data should be a non-event, leaving time focused on actually solving problems.

We’re happy to report that we’ve found the winning formula that leads to repeatedly harvesting improvement opportunities from data and would like to share it in this series.

This; however, is at odds with the growing consensus around what are the core components of the journey analytics process. To paraphrase Gartner, the journey analytics process entails:

  1.           Gathering data
  2.           Connecting data
  3.           Visualizing journeys
  4.           Acting on discoveries

In a broad sense, theirs is a handy framework, one that’s comfortably familiar to most in analytics: you identify a business problem, gather a set of data, execute analysis, generate a report, and make a change. That’s a winning formula for getting a one-time 2 percent increase in CSAT score, for example.

But, with all due respect to our friends at Gartner, we disagree.  That’s not what companies should be aiming for. That straight-as-an-arrow construct is missing a crucial, final step. That last step—where journey analytics becomes an inherent, ever-present part of business operations—is where the ultimate realization of value lies. The lifecycle of journey analytics is meant to be a recursive process: to make data-driven change possible, to see and measure the results of change, to adjust the factors that drive any journey that matters to a business, and then to measure again and again and again. Complex business problems are rarely solved by one report or a single action, but rather by recursive manipulation of the myriad switches and dials available to fine-tune journeys.

So, why do the majority of companies struggle to really execute this recursive process?

A complete journey analytics lifecycle must account for the question, what happens when you want to repeat your journey analysis once, ten times, a thousand times, or even see its results every day? This can’t be done in an ongoing, timely, cost-effective way if every journey analysis has to start at square one with the fundamentals of selecting and connecting data. Sure, one can do journey mapping projects in that way and produce periodic reports. But active, operational journey analytics that make your business better every day can’t be driven by static data and analysis results.

There is a Fortune 500 company about to spend millions, and they know that journey analytics is a proven method of understanding their promoters and detractors and they will build an analytic model to understand what drives the promoters and detractors that impact net promoter score (NPS). They’ll implement a change, perhaps see a shift in NPS and … that’s it. Without the critical last step in the journey analytics lifecycle, the one that feeds the result into the operation, the NPS number may very well change, but the company has no capability to explore whether the change worked. They’ve broken the journey analytics lifecycle.

It doesn’t have to be that way.

The fundamental difference between a recursive journey science approach and a dead-end report approach is that the fruits of journey analytics efforts have a continuous impact on ongoing efforts.  The same data used to understand what drives a promoter can then be used to measure whether the changes made actually moved the needle and to identify opportunities for further refinement. Then when the question you want to answer changes, all you have to do is change the outcome of interest since the data that supports the journey to NPS…or churn…or cost reduction…or fraud…is the same data that answers any business question.

In a future blog post, we will explore how a robust journey dataset, one that is broad, deep, and cuts across organizational silos, is the connected data foundation that makes it possible to repeatedly evaluate journeys in a timely way. We’ll look at how building the right base makes it possible to propagate journey data everywhere in the organization where it can make a difference. We’ll also look at how making journey mapping and management available to people from across the organization through an accessible, shared, single descriptive language, makes it possible for all those eyes to see and act on what was hidden before.

The implementation of journey analytics in your business should begin with the end in mind, with a stated goal about a problem to solve. Make journey analytics a broad based approach, not a one-off project, by organizing in a way that enables findings to flow endlessly back into the organization in the form of system and process changes. You can apply this Journey Science methodology with the people you likely already have.  That broad base of connected data, combined with a recursive process, represents the difference between a dead end and a journey analytics lifecycle that fuels more and more victories over time.

Continue reading Part 2:

Blog - Journey Analytics Lifecycle Part 2

Written by Tim Friebel

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