Last week I continued the journey of our case study manufacturer selling its products into nationally deployed healthcare practitioners.  For competitively superior customer classification and in-field targeting purposes, they have PV graded each centre on the number of practitioners working within.  This is (only just!) “good”.  More than 90% of centres will feature 1-4 practitioners.  This is not granular enough  for classification differentiation.  Enter a proxy for number of end user/customers who are regularly “enrolled” at the centre.  “Better”.  Then a proxy for number of appointments between practitioners and end user customers.  “Best”.

Now we need a mechanism to accommodate all possible scenarios whereby one form of data is collected for a given centre……two forms…….or all three.  Depending on how successful the Sales Exec is in collaborating with each customer in this way.  Assuming a total national customer base of at least 1000, and working on the notion that there will be a reasonable proportional split between those customers where the Sales Exec obtains either the “good” form of profiling data  vs the “better” vs best vs any multi combination, and assuming that there is likely to be some level of correlation between number of practitioners; number of end user consumers enrolled; number of appointments for service delivery, then we have a “ball game”.

Set the Sales Execs on the task of data collection.  Give them the timeframe of a single cycle around their whole territory / customer base visit.  Give them simple software tools to key and load up all the data scores collected.  Brief them to go for broke and ask each customer for all three, but as a minimum at least walk away with one such profile data point score.    Load all that collected data in such a ways as to run the correlations between the three data sets.  Using the emerging correlation conversion factors then convert all customers to number of appointments for service delivery equivalent .  After all that is your deemed “best” profiling score type.  All key to competitively superior B2B sales management system.

Viola!  Now you have a competitively superior set of profiling data that is scientific, objective, defensible (in a world where there is no such thing as perfect profiling data) upon which you can undertake your customer classification for sales team targeting.  Come back again next week to find out how.

Stay tuned for ‘How to develop your B2B sales management system with PV (customer potential value) proxy models- and why you MUST! (Part 4)

Want to learn more? Check out ‘How to develop your B2B sales process with PV (customer potential value) proxy models- and why you MUST! (Part 2)’