3 more barriers to achieving useful Customer Value Analysis (CVA)
Continuing our two-part series on CVA. How can you achieve an analysis that is acted upon?
Guest blogger Paul Weston returns to share the final 3 barriers to identify & overcome. This series follows on from the 3 posts he shared with us on how to improve your Data Quality Reporting.
Now back to the theme of Customer Value Analysis or CVA. Beyond what Paul shared in his first post, what are the next 3 barriers to breakthrough?
Here is the next barrier that Paul has identified…
(4) High customer churn, so many customers not active for a full year
Even organisations with a static customer base size can have high levels of churn.
I have worked with companies that have had the same total number of customers for many years and yet are seeing 15% annual customer losses and 15% annual customer gains. This means that 30% of their customers could be allocated an inaccurate value for the current year, resulting in under-estimates of overall value lost and gained.
For these reasons I always use some form of calendarization, in allocating full-year values to customers that have been won or lost during the current year. This goes further than, for instance, doubling the value of a new customer that was won 6 months into the year.
It takes into account the natural timing variation that occurs in the company’s business. For lost customers, this can be based on their individual pattern if they have been a customer long enough. For other lost customers and for new customers, a typical calendarization can be calculated from the transaction data provided.
This may vary depending on customer type or may have to be a single calendarization for all part-year customers.
(5) Customers are allocated to a lower/higher value group than they deserve based on the current value
I have lost many arguments when I take the line of “We have to put the thresholds somewhere”. I rarely take this simplistic view now. In all value analysis that I carry out, I always propose the use of other ‘dimensions’ of value are considered in addition to the current value.
The most common additional dimension I propose is ‘Value Trajectory‘. This is a simple representation of recent value increase or decrease and how steep this is.
It is normally based on some simple form of regression calculation. This prevents customers from being placed just below a value group threshold even though they will cross the threshold very soon.
A typical third dimension is value consistency, which is particularly important to organisations that need predictability for production management or cashflow. Other dimensions have included the breadth of product mix, the average size of order and others.
(6) It is almost impossible to extract complex customer transaction data from our systems
This is one of the most common objections to moving forward with CVA.
The reality is that insightful value analysis can be built on very thin data. The example to the below shows the simplicity of data that can deliver good results.
The most important thing is a customer code that enables all the transactions for a single customer to be joined together.
It is not even always necessary to have data at the transaction-by-transaction level. Data at various levels of aggregation can be used very effectively. For instance, value-per-customer-per-month.
For even more insightful analysis it is useful to add in a small number of additional fields and a little more granularity so a great data set would like the one below.
Also, remember that it is not completely necessary to have any customer-related data at all. Even the customer reference number can be shielded to prevent any trace-back from analysis to individual customers.
Final words of advice for your CVA initiative
In summary, I would say that, despite having heard many reasons why it is very difficult for ‘my organisation’ to carry out a useful CVA, it has never proven impossible.
Perhaps using some of the approaches that I have identified here, but always balancing precision with pragmatism. The reaction to the outputs invariably ranges from ‘interesting and insightful’ to ‘game-changing’.
Thanks, Paul for those encouraging words & useful tips. I hope those were useful for you our valued readers & Merry Christmas!