Back to last week… and back to our good -> better -> best model for setting up the sales team to garner three possible data points for PV assessment for every customer visited. “Good” (# of full-time-equivalent practitioners) will compromise accuracy as a PV proxy – but will be attainable by the sales team members for > 95% of customers. “Best” (# of appointments pa on the booking system) will maximise accuracy as PV proxy – but will harder to attain. “Better” (# of end users on the books) – in between on both parameters.
Lets assume that the sales team will gather 98% data for “good”; 80% data for “better”; 65% data for “best”.
Set up your three simple spreadsheet correlation charts – # full-time-equivalent vs # end users; #end-users vs # appointments; # full-time-equivalents vs # appointments. Check that there is a sufficient correlation strength in each to give you confidence to proceed. From the three lines-of-best fit, create the conversion factors to convert from good -> best; better -> best. Using either the “best” score or the converted score in the absence of a collected “best” score, give every customer a PV score in “best equivalent”. Best practice B2B sales management system.
Viola… now you have a “best” PV proxy score for every customer, having asked the sales team to try for gathering 3 specifically defined PV proxy assessment scores (good/better/best), knowing at the outset that multiple scores from each customer will be far from comprehensively attainable but that at least one will be.
Stay tuned for ‘Challenge first – then optimise!’