Scientific fact is indifferent to hypothesis.  No matter how intuitive an idea may seem, a good scientist must be prepared to change his or her thinking if the evidence suggests it’s necessary.  Take Johannes Kepler for example:  when he first tried to calculate the orbit of Mars in the early 17th century, he (wrongly) assumed that the orbit was circular.  As a result, he could never reconcile his calculations with the observations.   It was not until years later that Kepler revised his thinking about planetary motion and realized the shape of Mars’ orbit to be an ellipse.  As soon as he made that revelation, Kepler’s Laws of Planetary Motion came into being.

While sales analytics is certainly easier than astrophysics, intuitive hypotheses can still be cast into doubt by the objectivity of good data.  When AGI set out to map the connection between “sales time” and revenue, we naturally assumed that more time spent in front of customers meant more sales, and more time spent doing other things, e.g., customer service calls or administrative tasks, meant less revenue for the company.  How could it not?

However, AGI’s time study data told us something counterintuitive: that the value of a selling hour could be negative!  We worried our data was dirty so scrubbed it for outliers; same result.  We worried the model was over-simplified, so added more variables, e.g., tenure, account size, etc.; same result.  We began to worry that we’d have to go back to our clients empty handed, saying something like, “the value of sales time depends…,” until one day we decided to scatter-plot sales revenue against sales time.  We had done this before (at the rep level) for individual clients, but combining data from many sales forces resulted in a slight but clear curve!  As soon as we saw that curve, we immediately realized we hadn’t been successful because we had ignored the law of diminishing rates of return.

The productivity of sales time is non-linear.  A company can increase revenue at first by increasing the amount of time its reps spend selling, but once it manages to reach an optimal level, any further investment in sales time will actually result in a decline of average “per-selling-hour” value.  The chart on the right is an example from one of our Medical Device clients.  Of course reps in different industries will have different optimal selling times, and different values per selling hour, but the basic shape of the sales time curve remains the same.

Our patience, determination, and ability to take a cue from nature to shape our hypothesis led to our success.  But also crucial was the sheer amount of data we had on hand for the task.  Kepler could calculate Mars’ orbit partially because his teacher Tycho Brahe had accumulated data from more than ten years of celestial observation.  For us, access to AGI’s massive proprietary sales time database, which contains data on over 100 Fortune 1000 companies, provided an analogous advantage for getting the job done right.

Learn more about the Alexander Group’s sales analytics and sales benchmarking practices.

Originally published by: Ian Zhao


Insight type: Article

Industry: Cross-Industry

Role: C-Suite, Sales and Marketing Leadership

Topic: Benchmarking, Sales Analytiсs

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