## Week 51 – Leadership – Chapter 13

*“We have to reduce complexity” is the battle-cry of the unsuspecting. I’ll explain why in this article. Above all, I’d like to help you focus on what is essential for your business, namely the optimal distribution of sales projects. After all, what is at stake is nothing less than your future!*

First off, a question: What does complexity even mean? Complexity can be determined by establishing the number of states that a system can be in. In the case of a light bulb, for instance, these states would be, at the very least, “on” and “off.” And perhaps “malfunction.” There is not much more that you have to know about a light bulb.

Complexity cannot be reduced, but complicatedness can be. By that I mean there is a lot of information whose meaning cannot be directly inferred. For example, when you’re in your car and in addition to the information about vehicle illumination, you get the headlight temperature, the current electricity consumption, and the color temperature in Kelvin. The color temperature might be relevant to automobile engineers conducting a test drive on a prototype, but certainly not for you as a driver.

For the efficient management of your sales team this means: Eliminate unnecessary complicatedness, but accept complexity.

So get rid of unnecessary indexes and reports that no one reads anyway (perhaps because they’re unintelligible?). Focus on indexes and data that allow direct measures to be taken.

Let’s consider two central points and the lessons we can draw from them about how to approach things differently.

## 1. Sales Forecast

Companies have to know how business will develop in order to make decisions based on it. You can exploit sales forecasts much better if you employ the methods of probability assessment that I discussed earlier. In addition, the following aspects are important to consider:

*A) Time Frame*

Sale comes at a certain point in time. Only when a sales opportunity has reached a certain level of maturity, is it worth determining that point in time.

*B) Deviation Probability*

If at the beginning of a pregnancy a gynecologist sets a due date, it is the most likely date. However, the actual probability that a child will be born on that day is under ten percent. Is this a contradiction? Absolutely not. Because there are 20 days before and after the expected date, on which the child can come into the world just as naturally.

For the forecast this means: In addition to the revenue predicted by means of its probability, you should also take the standard deviation into account. This deviation value will be low if the individual sales opportunities have a similar revenue potential. Where there is a large discrepancy in the revenue potential, the probability of deviation will be higher.

*C) Steps in Development*

If management works with forecast instruments, they normally do this in predetermined time intervals. Oftentimes on Friday a new version of the forecast will be called up and subsequently “discussed” at a meeting at the beginning of the week.

These are meetings that you can simplify considerably and therefore conduct more efficiently. The most interesting point of the forecast is, after all: What exactly has changed since the last report? What activities are planned for the coming week? And how were the propositions of the last week implemented?

If sales management and its staff confine themselves to these questions, the forecast discussion will be quickly concluded. All parties concerned will also know how much priority to assign to each given issue.

## 2. Pipeline Overview

I’ve spoken at length about setting reasonable goals. Here’s another example: “What we aim to achieve is that every sales professional should have a portfolio of sales opportunities that are optimally distributed over the different milestones.”

What do we mean by optimal? One way to approach it would be this: Start by drawing up a plan for an optimal distribution of your sales projects across the milestones. For example, you could establish how many sales projects in total a salesperson should be working on at the same time. Let’s say it’s 50. Then it would be ideal if at any given time this salesperson had an opportunity situated in the last phase A. It would be equally ideal if she had one in B. Then perhaps five in C, ten more in D, and the remaining 34 in E, as a reserve, so to speak.

Then you can use this plan to see how well this optimal distribution holds up. For different industries there will be different values. You might have to select different distributions for rookie salespeople than you would for veterans.

Once you’ve found an optimal distribution, you can make it a target value. From here on it’s easy to compare the target value with the given results. By reading off the deviations, you can immediately tell what needs to be done—both in discussion with the individual employee and in the company as a whole. And since graphs sometimes speak louder than words, here is a concrete example.

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