On with the 6th in a series (#1) (#2) (#3) (#4) (#5) discussing Customer Experience Indexing (CEITM) as a way to measure, plan and act on customer feedback. Many thanks to those already asking questions or offering comments.
As B2B marketers we know that our businesses are fundamentally made up of three types of targets. These are the customers you have, those you’ve lost, and potential accounts who – so far – have decided to do business elsewhere. CEI is a metrics-based planning tool for driving revenue growth from all three of these targets.
Over the past few weeks we’ve been working our way down a list of 6 areas that frame up a basic CEI initiative set up:
- Planning
- Optimizing the flow of both loyalty and satisfaction feedback
- Analysis of feedback and calculation of actionable CEI metrics
- (We are here)Using the data for short, mid and long term account plans for retention and growth
- Using the data to locate new prospects using rule based company profiling and role-based targeting
- Using the data to plan and deliver action plans aimed at reshaping customer attitudes and opinions
Last post we discussed the Key Weight – or how long and how often does a customer “experience” your company, and how account-by-account Key Weight scores should influence how the entire body of survey response data gets interpreted. Not taking newness or lower frequency of use metrics into account leaves open a broad chance that important dangers or opportunities get overlooked as you update Account Management plans (which we’ve demonstrated using example analysis from a recent ReachForce Customer Experience Survey results i.e. Key Weight + Data Accuracy + Project Manager Expertise). See (#5). To build on last week’s discussion, let’s take a look at some other lens-building using Key Weight as the prime factor:
Customer Categorization – by putting customers with common Key Weights into separate buckets you’ll get true apples-to-apples comparisons in terms of stack-ranking other response scores. Examples of the kinds of questions/scores that can apply were given in drop (#4) but here they are again:
Quantitative question examples:
- Repeat purchase
- # Data quality issues
- Data value (ROI)
- Frequency of use
- Length of use
- Have you recommended
- 3 most important purchase criteria
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Qualitative question examples:
- Purchase experience
- Usage experience
- Repeat purchase experience
- Expertise
- Compare with other vendors
- Overall satisfaction
- Would you recommend
- Will you renew
- Would you seek our brand for related services
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It is advisable to bucket in a way that creates a “big” middle (i.e. Top 15%, Upper-middle 35%, Lower middle 35%, Bottom 15%). This is because the main gist of the plan is to create a process of continuous improvement that pulls customers (see below) from Bucket D into C, C into B and B into A.
If you think back to what a Key Weight is comprised of (combined scores of “how long” and “how often”) pulling customers towards the top bucket means two really good and important things need to happen … i.e. keeping the account active/open and increasing the amount of meaningful contact (defined as “use”) you have with them. Again, don’t think of Bucket D being all bad and Bucket A being all good. It really just gives you an instrument to ascertain the degree relationship maturity and of the certainty you should have for response scores to other questions.
To build another example lens, let’s say you’ve come to the stage of your 2009 Account Management plan where you need to sort a list of customers ranking how likely they are to renew their contract with your company and what you need to do to maximize your probability of success for > 80% of them.
Again, we’ll use some data taken from ReachForce’s Q4 2008 Customer Experience Survey and see what we come up with – starting first with rating each customer Key Weight category and cross-tabbing scores for “Will you renew?” “Repeat Purchase Experience,” “Data Value ROI,” “Compare with other Vendors” and “Usage Experience.”
The questions/responses I’ve used as cross-tabs to track down answers to the questions at hand (how likely they are to renew their contract and how do we maximize probability of success for > 80% of them) have to do with stated intent to renew (cross tab 1), how good our renewal + up/cross sale experience is (cross tab 2), is the customer recognizing ROI (cross tab 3), how do we stack up versus alternatives (cross tab 4) and usability (cross tab 5). I think this mix gives us a clear view of the renewal picture.
Because increasing probability for successful renewals is in large part about eliminating barriers, what I initially look for in a chart formation such as this [above] is ascending point values as they are an indication of trouble. The logic is that scores in all columns need to get better (and show up as descending) as length of engagement and frequency of use increase.
For example:
- The ascending values in the Cross Tab 2 column tell me that we are not up-selling or cross-selling as well or effectively as need be … i.e. newer customers coming fresh out of the sales pipe (Bucket D) seem happy and impressed (9.6 avg.) erosion starts to occur (9.3, 9.1, 8.9 C-A respectively). It’s clear we need to write a remedy for this into our 2009 Account Management plan.
- Although scores are pretty high, the lack of consistency in the Cross Tab 5 column may indicate that the high-touch nature of our on-boarding process – where planning, kickoffs and software activations are the norm – may lose a bit of its shine as time goes by. This begs the question, are we being complacent with older customers, or are they distracted – and by what? To make sure this does not become a barrier to future renewal campaigns we need to take a close look at how to mitigate this trend.
In all other cases the cross-tab columns and the Analysis Score (last column) on the chart above are pretty high and have a nice descending order of value. But as an Account Management planner I can use CEI to continue drilling into things that I normally would not see that need tactical consideration:
For example:
- Bucket B is nice and full (45%) and we likely need a specific program to increase conversions to Bucket A before it becomes a log jam. Again, placement in any of the buckets is based on length and frequency of engagement and should not be seen as an index of good or bad – rather, as mature relationships versus less mature relationships. So in 2009 we need plans that focus on increasing mindshare and quality time with Bucket B accounts.
But notice a couple of additional things about Bucket B:
- The lowest two cross-tab average scores for this group are:
- Data Value ROI (8.9)
- Compare w/ other vendors (8.9)
While 8.9 for both questions are pretty solid averages, to a careful Account Management planner this is still a notable indication that work needs to be focused/done in these two areas to reduce the chance of them becoming obstacles to our 2009 customer renewal plan.
More next week. As always, thanks in advance for your questions and comments.
