Customer Value Management

Our Customer Value Management (CVM) service uses all available data on customer circumstances and attitudes to understand their purchase motivations and enable you to leverage that knowledge to maximise the number and value of the customers who use your goods/services.

Our approach is generic and works across all industry sectors and for all types of customers (and consumers). There is no inherent difference in the approach between "customers" and "prospects, beyond any consequent differences in the detailed levels of data that we have about each group. For us, "Prospects" are simply "Customers with zero spend".

How it Works

Using all available data sources we construct econometric models of sales performance that explain individual purchasing behaviour as a consequence of each customer's unique set of circumstances and attitudes. These models enable you to predict, with a high degree of accuracy, each individual customers' (and/or prospects') potential future value to the company - in both the short and long term. Aggregating these conclusions across the customer base then tells you where and how to allocate your resources to produce the greatest return on investment (Diagram 1).

The models work because they embody the fundamental anthropological motivations that govern human purchasing behaviour. Things such as the size of your family; what represents (for you, as an individual) good value for money; where you live; your interests and hobbies and other types of measures gathered from all available data sources help build a picture that correlates with the pattern of purchasing that we see on the transactional database.

Data from Tweets, Facebook and other social media platforms then further enhance the predictive power of the models by providing additional information on your most current circumstances and attitudes. Circumstance data is anything that defines your current situation: your address; a picture of your house; a Tweet that says you have just booked a trip; a Facebook page that describes your recent holiday; all of these are factors that indicate your current situation.

Attitudinal data is then anything that defines your personal values. By combining data from surveys of your opinions on customers service, promotions, value for money and so on with observed metrics such as your past take-up of promotional offers or the types of places you like to go on vacation we are able to create "Values" scores for your likelihood to be interested in particular offers and propositions - including your likelihood to use particular companies, brands or outlets.

Effective Net Preference (ENP)

We then combine this mix of attitudinal and circumstance information to derive an overall "Effective Net Preference" score for each customer/prospect that tells us how likely you are to be interested in using the goods/services we are offering at this point in time, and in the future.

What's more, because the models are built using behavioural drivers, we can then tell you what would need to change, and by how much, in order to increase that likelihood. So you can see how the offer would need to change, and by how much, in order to be suitable to meet your current needs, circumstances and attitudes. It is a very powerful and effective technique.


By using this approach one major DIY store added over £134m to their annual sales performance whilst a major supermarket added £90m from their very first campaign. A major mobile phone retailer first used the modelling to target the roll-out of a new fashion-styled phone and produced their most successful product launch of all time. In general terms, the typical benefits are a doubling of the sales effectiveness of the CRM budget. Moreover, the benefits are replicable year-after-year and become bigger over time as greater knowledge is fed back into decision-making. If you know what the returns are from your current CRM programme, think what effect doubling those returns would have, from what is just a relatively very small incremental investment in our service.


Needless to say gaining the full benefits from the programme comes from a consistent and sustained application of the approach over time but to provide short term "quick wins" and build confidence, knowledge can be built incrementally using conventional "Test and Learn" methodology. As one of our former client sponsors stated at a public conference in London several years ago:

"What worked for us was to keep stopping so we could combine the new analysis with our existing ideas and segmentation approaches; setting up an empirical testing track early on, alongside the analytical one; having clear customer value management business-objectives that steer the research and analysis effort. We know attitudes are important but only by combining with knowledge of customer circumstances could we quantify their impact on the drivers of store choice behaviour. Factors such as "distance to store" and "frequency of need" dominate the first level of the behaviour but they are not things we can readily influence. It tells where to spend and how much to spend but not "what to say". So we are now leveraging Attitudes as well as Past Behaviours in our marketing strategy. Sub-segmenting customers and targeting by Circumstances & Attitudes, no longer just RFM. And the benefits have been very substantial."