Tag Archives: key drivers

From drivers to design thinking

networkDriver analysis is great, isn’t it? It reduces the long list of items on your questionnaire to a few key drivers of satisfaction or NPS. A nice simple conclusion—”these are the things we need to invest in if we want to improve”.

But what if it’s not clear how to improve?

Often the key drivers turn out to be big picture, broad-brush, items. Things like “value” or “being treated as a valued customer” which are more or less proxies for overall satisfaction. Difficult to action.

Looking beyond key drivers, there’s a lot of insight to be gained by looking at how all your items relate to each other, as well as to overall satisfaction and NPS. Those correlations, best studied as either a correlogram (one option below) or network diagram (top right) can tell you a lot, without requiring much in the way of assumptions about the data.
In particular, examining the links between specific items can support a design thinking approach to improving the customer experience based on a more detailed understanding of how your customers see the experiences you create.

Your experiences have a lot of moving parts—don’t you think you ought to know how they mesh together?

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Are you measuring importance right?

One of the universal assumptions about customer experience research is that the topics on your questionnaire are not equally important.

It’s pretty obvious, really.

That means that when we’re planning what to improve, we should prioritise areas which are more important to customers.

Again, pretty obvious.

But how do we know what’s important? That’s where it starts to get tricky, and where we can get derailed into holy wars about which method is best. Stated importance? Key Driver Analysis (or “derived importance”)? Relative importance analysis? MaxDiff?

An interesting article in IJMR pointed out that these decisions are often made, not on the evidence, but according to the preferences of whoever the main decision maker is for a particular project.

Different methods will suggest different priorities, so personal preference doesn’t seem like a good way to choose.

The way out of this dilemma is to stop treating “importance” as a single idea that can be measured in different ways. It isn’t. Stated importance, derived importance and MaxDiff are all measuring subtly different things.

The best decisions come from looking at both stated and derived importance, using the combination to understand how customers see the world, and addressing the customer experience in the appropriate way:


  • High stated, low derived – a given. Minimise dissatisfaction, but don’t try to compete here.
  • Low stated, high derived – a potential differentiator. If your performance is par on the givens, you may get credit for being better than your competitors here.
  • High stated, high derived – a driver. This is where the bulk of your priorities will sit. Vital, but often “big picture” items that are difficult to action.

That’s a much more rounded view than choosing a single “best” measure to prioritise, and more accurately reflects how customers think about their experience.

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