Developing Predictive Selection Criteria

At ECI, we have been devising selection criteria for many years to identify people who will be top performing sales people.  The trick to identifying predictive, reliable criteria is to make sure you have a good linear relationship between the results that top performers deliver and the results that bottom performers deliver.   Top performers deliver better results and bottom performers deliver poorer results, so we pay the good guys lots more than the guys who are less successful.  Sounds logical?  Well, for many companies, it isn’t so simple any more.

In an effort to devise a compensation or rating plan that keeps everyone happy and generates a proper reward for good work, companies often create quite complex formulae to use in tracking the sales rep’s performance to determine final pay outs in compensation.  The most complicated of these will allow for several different factors to figure into the final payout, such as the product mix or territory potential multipliers, along with some complicated additives to offset how the overall corporation performed.  In the better of these, at least there is an equalizing factor that can be reviewed and used that really is linear in demonstrating the comparison of top and bottom performers.

So why not just use the top performing group?  This is a question people ask us all the time.  The reason you can’t just look at the top is that you won’t be able to tell which of the common factors are true of the total population and which are predictive of success for only the top group. Without a better comparative, you are in effect guessing.

One would think that if you work for Company A and are the best sales rep , you will probably take home the biggest check.  But these days, if you work for Company A and are the best performer and live in Podunkia, South Dakota, you might earn 80% of the highest pay rate, because it costs a lot less to live there than it does in San Diego, California.   All of this is part of the complexity that plays into doing a statistical analysis of data in order to identify predictive selection criteria.

So simple is always better.  Sales rank works, providing it is not complicated by combining the results for multiple products, which it often does.  In these cases, we have to take a look at results for each product, then divide the groups by quartiles, then by product to identify commonalities of the highest group v. the lower groups.   What we are really concerned about is not the difference between performer #1 and performer #6, but rather identifying the factors that differentiate performers #1-150 from performers #1207-2509.

By getting the criteria straight first, the resulting models have a much better opportunity to be predictive.   At ECI it is about putting science behind behavioral factors.  Sometimes that endeavor is a bit more complicated than it should be these days.


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