Predicting what the company’s sales will be should always be taken seriously. At the end of the day, sales is by far the most important source of revenue at a direct selling company and profit is highly impacted by revenue. Forecasting is not easy, on the other hand. Some say it is a science, some say it is an art. I believe it is a blend of science and “informed intuition”, if not art.
A forecast is a projection based on past performances and an analysis of the future. This process is even more difficult when there is no past data. This is always the case for instance, when a company enters a new market. However, no matter how difficult it can be and no matter how wrong the initial predictions are, forecasts have to be made and then, improved in time.
A method that helps achieving realistic forecasts is making it on a rolling basis. In a direct selling environment, a suggestion could be forecasting every month for the next twelve months, and on a yearly basis for the next three to five years. Not only a forecast should say what will be achieved, it should also say how these will be achieved. However overwhelming this whole process may seem, once the related parties in the organization get used to it, it becomes a part of life within the company.
How should we approach the forecasting process, then?
A company’s total sales figure is like a finished construction, made up of various building blocks. So, the best way is to start with identifying those building blocks. And as a principle, sales forecasting and field force projections (i.e. predicting new recruits, drop-outs, re-activates etc.) should go hand in hand in direct selling. These two are intertwined closely.
Segmenting the Field Force
Field members with different profiles behave differently, too. A newcomer’s first month with the company does not look like another distributor’s who has been in the sales force for a longer time. Likewise, characteristics such as being a “consumer”, “seller” or a “recruiter”, age, gender… all can have significant impacts on an individual’s contribution to company sales. My suggestion is to break down the sales force into segments, instead of treating it as a homogeneous group.
Defining the Variables
Then, there are those variables that will need to be be predicted for each of these segments. Order sizes, orders per distributor, recruits per “recruiting” member, number of those who do not recruit, number of auto-ships and their average sizes are some of the examples to these variables.
Methodology
Once the field force has been segmented and the variables have been defined, the next step is the process of predicting what these “building blocks” will look like. On the way, some of the predictions will seem unreasonable (in both directions) and will need to be revised until all those involved in decision making are comfortable with the whole picture.
This inductive approach helps in two ways:
1) Reduces the risk of making wrong overall forecasts since an unrealistic assumption easily shows itself.
2) Helps identify the areas that contributed to the end result, while reviewing the performance once the forecasted period is over.
One last point to remember when structuring this whole thing: Only those variables that can be influenced should be included in the forecasting process. Variables that cannot be impacted do not help at all. For instance, we are all interested in seeing the geographical distribution of the sales force. It is important, for sure. However, unless you have the tools to make a change in that distribution, this information has no use in the forecasting process (it may well have important uses in some other areas, though).
We all know that we are dealing with an ever-increasing level of uncertainty in business life. Some create an excuse out of this for not making any forecasts, but this is not an acceptable excuse. On the contrary, volatile market conditions force us to place more emphasis on forecasting and to do it more professionally than ever. Increased uncertainty only increases the need for planning in business.
