Before we rest for Christmas, there is just time for some added value from this blog with advice on Customer Value Analysis.
Given the interest in our recent series of posts on execution and delivery, I was delighted to be recontacted by guest blogger Paul Weston.
You may recall that Paul shared with us before on the topic of Data Quality Reporting. That series of 3 posts have proved a popular perennial for those seeking practical advice to improve their data quality & their measurement.
So, it’s encouraging to see that Paul has turned his attention to the topic of Customer Value Analysis. Like Paul, I have seen too many attempts to deliver this hampered by lack of pragmatism or clarity, so I know his advice is needed.
In this post, Paul will share the first 3 barriers to avoid, the next 3 will follow soon. So, over your mulled wine & mince pies, enjoy Paul’s practical advice…
The first 3 barriers and how to get over them
Reflecting the value of different customers in everything from channel planning to marketing campaign selections and customer segmentation is a major business performance enhancer. And yet, really good value analysis is still, in my experience, rare.
Having developed value analysis and segmentation frameworks for many organisations in a wide range of industries there are 6 “reasons-not-to” that I hear more than others.
These can result in no analysis at all or in results that are treated with a low level of confidence by the colleagues who need to be using them. So, I thought I would share these and some of the techniques I have used to address them.
(1) We can’t agree whether ‘Value’ means Revenue or Margin
There’s always a part of me that starts to answer this question by asking “Does it really matter?”
I have often put huge amounts of effort into creating solutions that enable both options to be delivered, or changing from one approach to the other.
The reality is that when the requirement is to deliver value groupings or segments we are interested in ‘relative value’ and not absolute value. So, whether customer A is 20 and customer B is 12, or customer A is 15 and customer B is 9, is not critically important.
In reality, and especially where there is a wide mix of profit margins across the portfolio, the difference between revenue-based results and margin-based results can be substantial.
The answer as to which to use is very much dependent on individual circumstances. My experience suggests that the key factor is whether reliable and consistent margin-per-transaction can be extracted from transaction systems.
If it can then use it. If it can’t then work with the Finance Team to agree on a single margin percentage per product and apply this to the revenue figure. If this is also impossible then stick with revenue-based analysis.
(2) Relative value of customers changes with time span considered
This usually manifests itself as the ‘current value’ versus ‘lifetime value’ dilemma.
I have found that meaningful ‘Lifetime Value’ allocation is rare and even where it is analytically robust it is not universally understood or accepted within an organisation.
The other challenge with ‘Lifetime’ value is that it changes slowly, making even dramatic changes in customer transaction levels difficult to represent.
So, it is far more common to work with some form of ‘current’ value. Even this can have different interpretations: Total Purchases Value over last 12 months; Current Subscription Value; Average Monthly Value etc.
Many industries have commonly accepted definitions that help select the right version of Current Value. Where there is no obvious start point and where purchase cycles are normally measured in days, weeks or months I would always use Total Purchases Value over the last 12 months as a start point.
(3) Long purchase-cycles for products make comparisons between customers difficult
Where purchase cycles are long (e.g. Vehicle Purchase, Property Purchase, Machine Tools) the concept of ‘Current’ value can be misleading.
If an individual customer buys a new car one year then we would not expect them to buy another one the following year, but should they then really be considered a zero-value customer.
My approach to this has been to use a technique that I call ‘Value Annualisation’.
It essentially means the distribution of purchase value across a known, predicted or typical purchase cycle. Simplistically, if a customer buys a car for £20,000 in one year and we know (or expect) them to keep it for three years then they are allocated an Annualised Value of £6,666.
Needless to say, the calculations are a little more sophisticated than this, but not much.
How is your Customer Value Analysis progressing?
Thanks to Paul for identifying those barriers & his practical tips to overcome them.
Have you successfully delivered Customer Value Analysis for your organisation? Did you have to overcome those pitfalls or others?
If you haven’t yet heard the answer to your issue, don’t worry. There are 3 more barriers to be identified & overcome in part two…