...In Which, Dave Has a Bright Idea

A friend recently described her customer experience with a months-late furniture order. The store was sincerely apologetic, but explained that manufacturers accept an order only to find, when they go to build it weeks later, that the fabric is no longer available. The mills themselves don’t give the manufacturers current information, so there is little the manufacturer can do. The store itself is even more helpless but is left to face the customer’s wrath.

I believe I looked suitably sympathetic as she spoke, but in fact my inner consultant was off and running. This was a classic description of an out-of-control process. The first step to gaining control is measurement: in this case, build a scorecard tracking performance of each vendor. This would let the store determine which vendors had the most problems, and either pressure them to improve their performance or steer business elsewhere. Scorecard results over time would also allow the store to measure its own progress in dealing with the situation.

In itself, this a good example of using back-office analytics to improve the customer experience—something I’ve been thinking about a lot recently. But why stop there? Scorecard information could also be exposed directly to customers as they make their purchase decisions, informing them which options are likely to be delivered on time and which have a higher risk of delay. This would give the customers more control over the outcome, help to set more reasonable expectations, and, not least, deflect blame from the store should a problem arise. Here’s another excellent application of the customer experience management approach.

Then I had a really bright idea. If the problem is fabric availability, why not let the customers supply the fabric themselves? The store could actually order it for them; the goal is to get the manufacturer out of the loop.

Presumably the furniture manufacturers are reluctant to participate in such an approach because it adds complexity to their own processes. But, also presumably, everything has its price and they would be willing to accept customer-supplied fabric if they were paid a suitable premium. Some customers would be willing to pay this in return for a more certain delivery date; others might prefer to pay less and take their chances. Combine this with scorecard information that lets customers identify high-risk choices, and you now are giving them a range of options, from picking low-risk products in the first place to picking high-risk products and paying to get them on time.

We at Client X Client refer to this as “transparency”, but that’s just a buzz word. The point is that you can substantially improve the experience for different kinds of customers by letting them understand the implications of their decisions and make whichever choice best fits their needs. All kinds of marketing applications are possible: imagine an “on time or it’s free” offer from a furniture store. This would be economically feasible if the store carefully limited it to product combinations where experience has shown the suppliers are reliable. You could revolutionize the industry.

One final note: the standard Customer Experience Matrix approach would start a project like this by quantifying the cost of delayed orders. This includes the immediate financial impact and long-term customer value. It would be fairly easy to do and the number would certainly be impressive. But in this case, the importance of the problem is already so obvious to management that formal analysis would strike them as an unnecessary academic exercise. That might change if the solution were very expensive, in which case a formal financial analysis might be needed to justify the investment. But given the critical harm done by bad word-of-mouth from unhappy customers, it’s hard to imagine store management rejecting any approach that promised significant improvements.