On with the 5th drop in a series (#1) (#2) (#3) (#4) discussing Customer Experience Indexing (CEI™) as a way to measure, plan and act on customer feedback. All questions and comments are extremely welcome and I do appreciate those who have already jumped in.

Working our way down a list of six areas:

1. Planning
2. Optimizing the flow of both loyalty and satisfaction feedback
3. Analysis of feedback and calculation of actionable CEI metrics
4. (We are here) Using the data for short, mid and long term account plans for retention and growth
5. Using the data to locate new prospects using rule based company profiling and role-based targeting
6. Using the data to plan and deliver action plans aimed at reshaping customer attitudes and opinions

In the last installment (#4), we rolled out some quick analysis of data from a recent ReachForce Customer Experience survey ― zeroing in on the different angle we took by measuring prompted versus non-prompted advocacy  — and what differences exist between companies that are a reference account (92% spending x amount) versus a full blown advocate (73% spending y amount).

It is, glory be, nice to have such high numbers for both reference accounts and advocates at ReachForce. And it’s even nicer to know that as satisfied customers (x) evolve into proactive advocates (y) they also tend, as explained last drop, to buy software and services more often, and in greater amounts (#4). So as a Customer Success team planner it becomes imperative to first figure out why it happens – and set out a continuous plan of improvement to make it more predictable.

To get there, we first establish the Key Weight - or how long and how often does a customer “experience” your company? I have been scolded for this approach in the past by people who say it’s not fair or smart to weigh qualitative feedback from new or infrequent customers more lightly than older ones, and I understand the concern. But I don’t think of it as lower weight = less important (all feedback is important) ― rather, lower weight = less sure.

To work a very simple example, if planning 2009 MBOs for our project managers requires a comparison of two key accounts assigned to the same Project Manager, the following analysis might be used to help step us in the right direction:

For key account planning these numbers tell me the project manager (PM) assigned to these two customers is delivering high marks on both quantitative (data accuracy?) and qualitative (expertise?) fronts ― and with two very different scenarios (new customer/once per month and old customer/once per week). This is good. But because the lower of the cross tab scores are from the (quantitative) ‘PM expertise?’ question, I can foresee the MBOs assigned to the PM in the case of both customers will be warm, fuzzy and relationship directed in order to bolster the customer’s perception of the PM’s expertise. Or maybe the PM gets more training. And closing the planning loop, I’d probably use “Moving Customer 2 up to weekly engagement” as another measurable objective. A higher level of meaningful contact would help.

And as you can see by looking at the % Analysis Scores above, without factoring the Key Weight in the above example, you’d only be fooling yourself about Customer 2 data accuracy and PM Expertise ratings, because you would not be taking newness, or lower frequency metrics into account and an important danger or opportunity might be overlooked. To some, planning account by account MBO strategy this way may seem overly analytical, but I have found no better way to customize and create MBOs to and pinpoint action plans right where the rubber hits the road.

To get some ideas about which cross tab questions to use as lenses for various situations, think of it in terms of Value Delivery (quantitative) versus Obstacles for Value Delivery (qualitative) ― as in our example of Data Accuracy versus Project Manager Expertise ― wherein bad Project Management would be an obvious obstacle to delivering high Data Accuracy.

I’d be happy to provide further example scenarios here, but I think you get the drift. Remember, I think it’s less of a service to create some sort of template, than it is to just spark some thought and let folks craft CEI indexing tools that mean the most to your specific world.

Next week we’ll look at a few more of these CEI ‘planning lenses.’