This is the third and final blog in our series that explores the life-cycle of journey analytics. We started with a look at the “end,” where the creation of a recursive process makes it possible to continuously feed results back into the organization for the benefit of incremental, ongoing change. This enables the punchline: Real Measurable Value. We then looked at the importance of the non-sexy, but critical, Data Prep step that provides a broad, deep and well curated foundation of connected data to facilitate ongoing questioning of journeys.
That leaves the important central phase of the life-cycle; the bit in the middle—journey mapping and journey management —and likely the topics that first comes to mind for many when they contemplate journey analytics. There too, however, we find the classic, pervasive perspective to be at odds with our own understanding of the requirements of today’s business.
Journey mapping isn’t new; consultants were offering it back in the ‘90s. While an interesting visualization exercise for decision-makers, it didn’t catch on for a long time because there was no efficient means of linking real data to the journey map designs being produced and no method of testing if the design actually drove the outcomes businesses were after.
Today journey mapping has the potential to be far more fruitful, especially when we embolden the process with modern capabilities. Defining the journey, as journey mapping is typically imagined, is an important element, but it should not be viewed as an end in itself. Journey mapping is not the whole story; it is a step.
Where journey mapping still comes up short is when it relies on assumptions and sample data for analytics. We continue to see big data-driven environments rely on sample data. Even if using a large sample, aggregate data still only provides a narrow and frankly dated view of how people are interacting with your company. In practical terms what this means is that journey you’ve designed, the one you want your customers to take, may not reflect what they are doing. But how will you know and more importantly, how will you analyze, change and re-evaluate the journey? Continuously. Against all available data.
That’s the key difference in this life-cycle to to be aware of. Defining the journey is of limited value if it is only considered through the lens of sample data. ClickFox advocates supporting journey mapping with richer, big data-style data capabilities. While many companies have already undertaken journey mapping exercises, they are underutilizing what they have created. The most effective use of those maps is to test them against granular data. That is the only way to evaluate what is actually happening in the data and on a regular basis monitor, track, analyze and build real KPIs around what those journeys were designed to achieve.
To make this possible, journey mapping needs to include the creation and management of extensive definitions so that the entire organization can have a shared language that allows them all to talk, with the same understanding, about the same journey. A company needs one definition of what it means to make a payment, even if the IVR, web, mobile devices, and contact center each have their own sub-process that relate to payments from their perspective. That common journey language is what enables a company to get past the usual internal battles that occur when one channel’s data doesn’t jibe with another’s. A uniform definition lets one look at the entire landscape of payments from any angle. You need every part of the organization to have the same definition of a journey in order for the company to measure journeys at scale.
This leads to the other key feature of this middle phase of the journey analytics lifecycle: journey management. As we have said in previous posts, the journey analytics lifecycle is a dynamic, ever-changing, recurring process, not a straight line to an end point. To support that recursive process, one needs a tool that lets you redefine or modify journeys when a new step or process is added to the payment journey, for example. These detailed definitions, that started as journey maps, become powerful metadata that can be automatically monitored at scale with a tool like ClickFox. This enables that connected data and the underlying context of what a customer was experiencing to be made available for ongoing monitoring, or to feed downstream predictive models or proactive treatment processes.
The steps in the journey analytics life-cycle that we’ve covered in this blog series, each equally important toward building a foundation for recursive wins, shapes up like this:
- Data Prep
- Journey Mapping
- Journey Management
- Journey Analytics
- Measurable Value Capture
Continuous management and measurement of all of the journeys a customer can take with a brand, and their underlying definitions, is what makes journey analytics part of the life blood of a company. It establishes a common language across teams not previously seen. Our experience shows that when companies put the skills, tools and a journey-centric corporate culture around this recursive life-cycle it facilitates the continued harvesting of tangible insights driving real return on investments that have been historically non-existent in the big data space.