How to backfill your incomplete customer base “current value” and “potential value” data…
The cornerstone of customer targeting for any B2B on-the-road sales team is good data. From over 150 sales team optimisation assignments spanning c25 years, we have never yet seen a sales organisation with a full set of accurate and up to date data for all clients – top to bottom. Fair to say, that ideal is impractical in a turbulent, ever-changing world. That said, too many sales organisation’s data sets are very poor indeed. Sadly, too many of such organisations don’t challenge themselves enough to improve their data. Subsequently poor targeting ensues and ultimately sales rep subjectivity over who is important and who is less so reigns.
Potential value data of each customer across the base is typically way harder to scope and accumulate than current value. Especially so if the sales organisation does not trade directly with customers and trades via a channel intermediary that is very parsimonious in sharing downstream sales revenue (current value) to each end customer. But for both current and potential value for all customers across the base, incomplete data sets should not stop us in our tracks there.
The key is in developing proxy data sets to backfill absent real data. This will require setting up the sales team members to professionally profile all their customers on a set of profiling characteristics that can be used as proxy scores for either potential and/or current value. I would argue that topflight sales teams should be continuously profiling their customers as part of best practice account management anyway. So this endeavour can double up to serve both purposes.
With a FULL set of proxy scores for all customers top to bottom and an incomplete set of real data for the same customer base, current and/or potential value, you can run some simple statistical correlation analyses to backfill missing real data from the high correlation proxy scores.
How? We will dive into that one in our next instalment. Stand by….