Customer LifeTime Value

Fintechs have enhanced the traditional metric of customer lifetime value away from the single point Net Promoter Scores to include behavioural characteristics and alternative sources of data klike social media

CLV has been traditionally measured only from the point of view of the individual’s value to the firm. These calculations are well documented although there is a strong assumption regarding loyalty and longevity that underpins this. (SAS has a comprehensive existing solution). However, it is recognised that the propensity of an individual to influence sales in his network also has inherent value for the firm.

The best known of these is the Net Promoter Score (NPS) developed by Bain in 2003. The advantage lies in its simplicity although its shortcomings are also well documented.

Another version is the Customer Referral Value (from an HBR article) where customers were segmented according to their likelihood to refer others. Other companies have also created similar referral metrics. Indeed, there seems to be no universally accepted metric for this. It seems that a framework has to be developed peculiar to the company and a method required to extract relevant data and combine them into a single metric.

However, all of these historically used internally generated survey data. Fortunately the advent of publicly available large data sets has changed this as has the ability to analyse such large and disparate sets.

Some solutions

Access to other databases and ability to combine them to give richer data sets

• Geocoding by using external data sets like location, distance to bank etc.
• Augmentation by cleaning and adding other relevant external data
• Obtains data from voice conversations (e.g. can search for words within recorded calls)
• Data enrichment for financial transactions that enables better data analytics (e.g. can use statement to determine travel and buying patterns)

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