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Building a Strategic CX Roadmap, Part 2: The Right Stuff

In Part 1 of this multi-part blog post, I reviewed what CX professionals face when evaluating CX assessments in the marketplace.  While there’s no silver bullet to CX roadmapping and action planning, assessing your needs and prioritizing your opportunities is not an impossible task. At least, it’s not impossible when you have good data, a good model, and good consulting to guide the way.

In this blog post I’ll discuss the importance and implications of employing a CX solution that is grounded in empirical data. A large, validated dataset, rather than one that relies on subjective, “squishy” judgments, is key to developing a good CX framework (aka model) and strong consulting.

Good data

When evaluating the competing customer experience assessments out there, I recommend you question whether it’s based on “good” data.  Below is a checklist I use to evaluate CX offerings:

  • It’s grounded in a dataset that’s large enough – with enough statistical power – to reliably predict real-world outcomes, like revenue and retention
  • It uses metrics – both high-level and granular, so you can make the right comparisons – that are proven to predict those outcomes
  • It provides benchmarks that compare your capacity to deliver great CX to your industry’s norms and to CX leaders’ best practices
  • It produces actionable, specific next steps based on what is proven to work according to those benchmarks and tied to ROI
  • It combines quantitative and qualitative feedback for a robust view of where you fit into the landscape and where you need to go next, tailored to your unique nuances

These checklist items will help you make sure you’re selecting a CX assessment that actually delivers the goods.  With these criteria in mind, I’ll turn to why good data matters for CX improvement planning.

Use data to find the right recipe

In today’s world, there’s no excuse for not using a data-driven approach to seeking explanations or building plans.  As Jason Goldberg from our partner, Protiviti, remarked at CXFusion 2017’s closing keynote, “Let data drive your need.”

With this in mind, to get results you’ll need, to use a metaphor, a tried-and-true (or, put differently, data-tested) recipe. You’ll want this recipe to capture the ingredients that drive CX change overall, with, as the back of the brownie mix instructs, adjustments for altitude.  Then, depending on how evenly your oven distributes heat and your humidity level, you may need further refinements.  Want a cakier brownie?  Use less flour and add baking powder.  Want something denser?  Add flour, maybe eggs.  The combinations can vary a lot, but they’re fairly predictable because this recipe has been tested time and again. All the major combinations needed to create an excellent product have been substantiated and refined with data.

Good model

Our CXEvolution maturity framework is grounded in a large dataset of more than 5,000 (and growing) respondents.  Using our own experience as the premier CX services and technology provider and research from organizational development (OD), we created a model that we tested and refined with data. Our benchmark dataset includes respondents from the business world at large (side note: our own clients constitute only a portion of our benchmark dataset; our dataset not skewed from including only our clients) and it captures the gamut of inputs and outputs that constitute a successful CX program.  And our dataset drove insights of its own: it gave us the ability to predict what makes a CX program successful with more precision, more granularity, and a stronger correlation to ROI than what we could have created using experience or instincts alone.

How we created the model

When we created our model, we expected that our approach would be different. Other assessments aren’t based on a large, objective dataset. We’ve also observed that our competitors’ offerings often don’t employ a clear point of view of how the various components they measure fit together.  But because we use a combination of a well-established POV (from OD theory and from our decades of experience) and an empirical dataset, we are able to pinpoint some unique patterns that other assessments miss or that may even feel surprising to companies relying on subjective evaluations alone.

If you aren’t familiar with OD theory, our framework might feel surprising. It takes the POV that the way an organization is structured (not just what data it collects or who uses it) is central to CX success.  Who would consciously consider that your own organization might be your biggest barrier to CX improvement? Using the brownie recipe metaphor, how often do we consider that the oven we’re using for baking, the pan we’ve poured the batter into, and the accuracy of our kitchen timer might also be critical?  The concept of organizational structure understandably feels almost too all-encompassing, vague, and out-of-reach for any single CX professional to feel a sense of control over, especially if it turns out the organization is structured in a way that impedes CX success.

Our data shows that by implementing small, incremental steps, in the right order and with the right group of stakeholders, you can get to the next stage in CX maturity and improve ROI .  Structuring your organization so it’s set up to effortlessly deliver superb CX turns out to be very do-able with the right framework.  You can spur the organizational change needed for great CX when you have a clear, step-by-step action plan that’s grounded in granular, comprehensive data.

Good consulting (e.g., consulting based on data, not feelings)

Another surprise that came directly out of our data is that moving up the maturity curve does not necessarily happen in an intuitive, orderly, 1:1 stepwise fashion.  Rather, each stage is a constellation.

picture of some constellations

Just like constellations are made up of groups of different stars, your strengths and weaknesses interact with each other in a unique fashion, in a shape that’s reflective of you. That means that what it takes for you to move up the maturity curve might be different from someone else in the same stage.  Given our large, granular dataset, we know what those constellations are, or how your own recipe might need to change as you start to make improvements.

Now, if our consulting were based on our subjective experience or an untested theory, our advice to our clients might be little better than a stab in the dark.  We certainly wouldn’t know how those improvements translate into tangible, quantified ROI.  Using a CX Assessment that is grounded in a real-world, empirical dataset is essential to helping you make tangible, measurable CX change – and that is exactly how we have designed our CXEvolution framework.

In this blog post I’ve tried to outline the importance and implications of using a CX improvement tool that’s grounded in empirical data.  In my next post, I’ll talk more about the contours of what an ideal CX improvement solution looks like, including its ability to deliver a clear, data- and ROI-derived roadmap and action plan.