A fundamental assumption…
All professional sales reps, managers and executives are committed to the relentless pursuit of excellence.
Unfortunately, this statement does not apply universally. Even a less ambitious assumption – that all are committed to sustaining continuous improvement – might well be too aggressive. Be that as it may… Those who genuinely embrace either form of this outlook will find the greatest value in this book and its underlying Sales Process Engineering principles.
There is another key factor inherent in this assumption. That is the need for relentless, never-ending, methodical discipline. Discipline in critically and formally analyzing the weaknesses in your sales process and its execution is essential. The more discipline one can muster and maintain, the greater the value of these results and principles.
Please take the paragraphs above seriously. There are no silver bullets or quick fixes presented here. Be prepared to move outside your intellectual comfort zone. Those who do will be both humbled and energized by the potential for sales performance enhancement that’s out there.
The Need for Metrics
Most sales managers measure the effectiveness of their reps based on a single number – revenue production. Clearly, that is the key metric. By itself, however, it is woefully inadequate as a comprehensive management tool. Many sales managers, use 4 or 5 metrics, including things like calls made, proposals submitted, profitability and growth rate. A few use as many as a dozen or more. So, how many metrics of sales performance is optimal? Fundamentally, the answer is, “more than you have now.” Consider the following scenario…
You have just “volunteered” to manage a little league baseball team. Maybe you played the game yourself at age 10 or so, but assume that for the most part, you really have no idea what to do. (By the way, even if you really do know little or nothing about the game, the analogy will still be quite clear.)
One of your first tasks will be to decide on the batting order, the sequence in which the kids will step up to the plate and attempt to hit the ball. Having no metrics at all, a random decision is the only choice. That is, the success of your first management decision will be based purely on luck.
Change the scenario. Add a metric. Assume that you find a listing of last year’s batting average for each kid on the team. Now you know the percentage of time that each batter is likely to get a hit. You can now make a better batting order decision. One sensible approach would be to put the kid with the highest average first, the second highest, next, etc. That way, the best hitters have a greater chance at getting more turns at bat. Other approaches – based on your data – could also make sense. The point is, your decision is no longer random. Success in no longer based purely on luck.
Change the scenario again. Add a second metric. Assume you also find the percentage of time each kid actually got on base last yea