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Measuring Customer Affinity - Part 2

By Peter Lavers

Design and Development Principles for Customer Affinity Measures

 

In my first Blog on this subject I proposed a model for measuring ‘Customer Affinity’, as follows:
  • Attitudinal Affinitymeasuringaffinity
    • Level of positive sentiment or bonding to the brand/ organisation; usually assessed via external customer research
  • Behavioural Affinity
    • Level of positive interactions/ transactions demonstrated by the customer; achieved from internal data analysis/mining; dependent on the quality of the information and how well joined-up it is
  • Affinity Value
    • A measure of how valuable the customer is, however the organisation defines ‘value’ (not just monetary)
I suggested that modelling this would be a very useful tool for businesses seeking to optimise Customer Experience.
 
Here are some Design and Development principles to create such a measurement tool.
 

Design Principles:

  • Each dimension will probably consist of several factors, which will need to be either computed, attributed or inferred
  • The model works for B2C, B2B and indeed for the Public and Voluntary sectors – the factors that constitute the dimensions will vary significantly
  • Empirical values can be used in each factor, but rules-based mechanisms to derive ‘scores’ are preferable (and simpler to populate)
  • The model also lends itself to be included in segmentation (high score in all three = ‘best’ customers) – or indeed to drive it!
  • Behavioural and Value scoring is dependent on good internal data
  • Value factors could include revenue, profitability, life-time value, share of available spend/giving, but need not necessarily be purely monetary value (e.g. volunteering)
  • Behavioural factors could include frequency and recency of interaction/transaction, plus propensity if that is modelled
  • Recently researched Attitudinal data is unlikely to be available for every customer
  • Attitudinal factors can be sourced/supplemented from internal data (e.g. sentiment recording, permissioning, responsiveness) and from social media if monitored
  • Avoid unstable Attitudinal factors e.g. satisfaction, which can change quickly whereas net promoter (NPS) is more stable
  • Beware inferring Attitudinal data from Behavioural data – continuity does not equal loyalty!
  • Geodem (market) segments can be overlaid to help targeting

Development Principles;

  • Create a practical “working definition” of each dimension and factor 
  • Identify what source data is immediately available; what needs consolidating & cleaning; what can be sourced; and what is currently unavailable
  • Populate for a statistically valid sample group to test & tune the scoring algorithms; drop insignificant/unreliable factors 
  • Commission further research or data mining if required 
  • Finalise and fully populate the model
  • Enable scores for each dimension to be recorded on your database
  • Map scoring into geographic and geodemographic databases to identify trends/gaps
  • Operationalise the scoring – incorporate in segmentation and develop propositions and treatments to grow each dimension
  • Sensibly develop and improve factors and dimensions over time
 


Peter Lavers, 03/04/2013


Blogs from the Archive


 

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