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Forecasting sales using mixed variable analysis

Sales forecasting has many interests for a company, such as an efficient supply chain management, an adapted pricing policy or a better profitability, and it is not without consequence on customer satisfaction and loyalty.

Choosing the most appropriate methodology among the existing sales forecasting methods cannot be done without a good knowledge of the sales process specific to each business. Moreover, each method relies on different variables that need to be mastered to make the right choice.

We propose to focus here on the most successful and accurate method in terms of sales forecasting: the multivariate analysis.
Méthode de prévision des ventes : l'analyse de plusieurs variables-1

The principle of the mixed variable sales forecasting method

The multi-variable sales forecasting method is partly based on the sales cycle predictive method, but it complements it by incorporating more values in the data analysis.

The sales process as the basis for a multi-variable forecast

A formalized sales process is the basis for sales forecasting. In addition, it allows sales and marketing teams to be more efficient thanks to a precise follow-up of the purchase path on the one hand, and the implementation of personalized actions according to the customer's needs on the other hand, in order to increase the chances of a sale.

The stages of the sales cycle and their duration (calculated from a global average of the time between the capture of the customer during prospecting and the purchase) vary from one company to another, but the general outline of a sales process includes 6 cycles:
  • prospection;
  • qualification;
  • demonstration (or meeting);
  • presentation;
  • conclusion of the purchase;
  • customer retention.
A sales forecasting model is used to allocate a probability rate to each cycle. Based on the analysis of historical data (such as the calculation of the average sales over a given period) and knowing that the probability rate increases as the sales funnel progresses, it is then possible to set a trend, for example, that 5% of the prospects contacted during prospecting will go on to purchase or that 50% of the qualified prospects who have had a demonstration of the product will purchase it.

Nevertheless, stopping at a linear analysis to elaborate a sales forecast is insufficient (which is also the weakness of this method) insofar as no other factor is taken into account, even though it may have an impact on sales. The multivariate sales forecast addresses this problem by integrating complementary values.

Some examples of factors determining the accuracy of the method

As we will see later, there are many factors that can impact sales, but here are some examples

The type of opportunity is a variable that must be integrated into the sales forecast since the length of the sales cycle and the probabilities previously defined can be subordinated to the origin of the lead. For example, a lead coming from a recommendation will be more likely to buy than a lead captured during prospecting, because he is already interested in the offer. Also, since this prospect is more aware (and therefore qualified), the purchase will be faster.

The experience, performance and personality of each salesperson on the team in forecasting sales is also an important factor, yet it is rarely taken into account in the calculation of probabilities. A salesperson's experience affects the sales process and the probability rate: a more persuasive salesperson will spend less time convincing a prospect, while another may need twice as much time for a similar result.

The most accurate sales forecasting method is based on the analysis of several variables, because it takes into account these and other factors.
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The data from the multi-variable sales forecasting method

As we have just seen, the more variables a sales forecast incorporates, the more accurate and reliable it is, as the analysis takes into account more values that can impact sales.

To this end, the sales forecasting method discussed here allows for this type of information to be taken into account. Based on historical data, forecasts are also made based on future trends identified during data processing. For this, the analysis uses two types of information.

The company's internal data directly linked to the flows to be predicted are integrated into the forecasting model's database. However, they must be the subject of a targeted extraction allowing to meet the objectives of the forecast. Indeed, companies are drowning under the mass of collected data and the common mistake is to want to include too much of it in the sales forecast, which harms the accuracy of the predictive models.

When it comes to sales forecasting, the most interesting internal data to use are the ones that go into the sales statistics, such as:
  • "product" values (price, availability, packaging, etc.);
  • data on the points of sale (sales space, average turnover, number of salespeople, etc.);
  • sales team data (number of employees, personality, experience, etc.);
  • sales channel data (delivery, point of sale, etc.);
  • etc.
Every company evolves in a constantly changing market and environment. If external data is not taken into account in the sales forecast, it is impossible to obtain a sufficiently accurate and, above all, reliable model.

The external data to be included must be relevant so as not to be a source of error and harm the accuracy objective that is being sought. In order to know which data to include, we need to ask ourselves a single question: does this information have an impact on sales?

The answer to this question depends of course on the specifics of the company's business sector (the weather factor, for example). A company that is dependent on seasonality must, among other things, take into account the variation of seasonal coefficients over a defined period (the last year or the smoothing with the average of several years). The competition must of course be taken into account (similar products, prices, geographical proximity of sales outlets, targeted customers, etc.).

Sophisticated and highly accurate, the method of forecasting sales based on several variables requires the use of an advanced analysis solution, in particular a software using artificial intelligence. Thanks to their overpowering algorithms and their ability to learn as they go along (machine learning), only this type of tool can cross-reference the numerous data that are essential to an accurate sales forecast.

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