I’m going to wander from my normal product agnostic blog. I was inspired to share my (admittedly) nascent thoughts about a tried and tested innovation framework (outcome-driven innovation, based on Jobs Theory), and an emerging analytics platform that I’m becoming familiar with (Thunderhead ONE Engagement Hub). I haven’t tested any of the embedded hypotheses (yet).
A few years ago I developed a “so-so” customer-centric targeting operating model which attempted to unify the qualitative and quantitative market analysis around Jobs Theory with the realities that exist in the enterprise IT arena. The tools available for performing proper Jobs-to-be-Done market analysis don’t easily fit into traditional IT any more than they fit into the marketing and agency world. I’ve been on the lookout for a way around that.
The “so-so” part was that there were no good tools available to understand the omni-channel, intent-based journey customers actually take when interacting with a brand. All we really have are the static journey maps that we’ve created that are absolutely worthless from a day-to-day (or minute-by-minute) understanding of what is actually happening. Journey maps are also focused on made-up personas that aren’t a true representation of customer segments. We can argue about what made-up means, but qualitative assessments are not a good foundation for market segments, in my opinion.
Outcome-driven Jobs-to-be-Done Summary
I’ll link to more detailed content around this but I’d like to begin by laying out some assumptions about what a Job is (taken from The Customer Centered Innovation Map):
- A Customer Job is a process — a job is mapped out as a series of steps from the customer’s perspective
- Customer Jobs have a universal structure — every job has a set of common steps that address things that must be accomplished before the job is executed, through the steps that must be accomplished after the job is executed
- Customer Jobs are separate from solutions — we shouldn’t focus on the current solution; except when we are mapping consumption jobs; which is what we are exploring here…in the customer experience realm
To complete this picture, customers will use metrics (called desired outcomes) at each step to evaluate how well they perceive the step getting done with a current solution. Keep in mind that competing solutions may not be in the same product or service category. These outcomes are essentially the customer needs — and are solution agnostic as well
A completed job map, with it’s associated outcome metrics is basically a customer value model, which qualitatively describes perfect execution of the job…the ideal. We identify a job executor, but we do not create personas, profiles or segments yet. This is a static model which is updated quantitatively over time to understand market segmentation and opportunity — based on needs instead of the more traditional ideas. It will help us to understand the What and the Why.
Journey Analysis & Orchestration Summary
Surfacing the real customer journey(s) — the real path they take when interacting with a brand — will help us to understand the When and the How from the paths offered, or possibly paths that weren’t offered.
Obviously, the company would like a customer to complete an action, or a top task, by taking the least expensive path. For example, a bank might be alarmed to find that customers signing up for a loss-leading checking account are dropping off a self-service channel — such as the web — and dialing into the far more expensive call center.
Thunderhead ONE Engagement Hub does a very good job of capturing interactions that take place on various touchpoints, across all channels. This data tells us where (channel) and when (journey stage) our customers are making their critical decisions; e.g. drop out, skip stage, change channel, or even change journeys.
This gives us the real-time data necessary to understand where and when moments of truth occur. These are the critical decision points where customers make a choice; and many times, the data is telling us a different story than all of the pretty (static) journey maps we have hanging in our team rooms.
The other extremely interesting part of this platform is its ability to orchestrate. Data is most valuable when you can do something with it. In this case, Thunderhead ONE allows you to do this in real-time, at a personal level…and across all channels and touchpoints using a light-touch abstraction layer. Perhaps one of the most disturbing capabilities for agencies and content designers is it’s ability to dynamically overlay content on static websites; eliminating the expense and poor customer experience of walled-garden landing pages we, as consumers, have had to deal with for so many years.
But that’s only one channel, and we’ll have to leave all of the many use cases for another time. They are impressive!
How they could work together
Both Outcome-driven Innovation, and Journey Analysis, strive to develop a common language. For ODI, that is the common language of innovation. For Journey Analysis and Orchestration, it’s a common language of engagement. Joining them together should give us a common language for customer experience. No more guessing.
One of the challenges with journey analysis, even when you have data from a platform like Thunderhead, is that while it targets the when and the how, it leaves us to guess at the why; and this is where Jobs-to-be-Done comes in. Job maps typically represent steps that occur outside the purview of the brand. They also integrate into the known journey, but use a different language. So, we’ll need to frame our journeys differently. We need to integrate the language.
We will also need to make our journeys more granular to more closely match the potential granularity of job steps, and the collection of a customer’s brand-interacting life-stage jobs that we need to track across various channels. There appears to be a simple way to do this (I’ll let you know how it goes).
With the internet of things, it could even be possible to monitor the execution of the core job (using the product / service) to some extent.
The outcomes we develop qualitatively, and the scores that we capture through quantitative analysis will allow us to look at the where and when, and drill into the why; which in this case would be the latest valuation of desired outcomes for a step (a more granular version of a journey stage).
Imagine drilling into a journey node and having access to the latest unmet needs broken down by customer segment.
Using this information should provide further value to marketers or customer experience professionals that want more precision with regard to how they design and measure customer experience in the future
More later. I’m still thinking about this. I’m told my blog posts are often too long, anyway 🙂
