Sales prediction models to generate value

Sales forecasting is one of the fundamental tools of a company's strategic planning. These forecasts are usually made using a predictive analysis model built from internal or external data. This is the concept of predictive marketing, data-based marketing.

Companies have access to more and more efficient forecasting methods, thanks to the development of machine learning algorithms, a discipline of artificial intelligence. Predictive models deliver information that is as close to reality as possible so that company members can make informed decisions about future sales management.

Predicting sales with a predictive model

What is a predictive analytics model?

In a business or marketing context, a predictive model is a data processing model based on algorithms and designed to anticipate and forecast future actions. In the case of sales forecasting, the model allows the company to anticipate future demand in order to make decisions to adapt to this future demand. It will then be able to anticipate its stocks, prepare its possible promotional policies, set the price of its products or services, plan its assortment or manage its investments in equipment or energy.

In practice, a predictive analysis model is first based on a descriptive analysis. Based on data mining techniques, i.e. the aggregation and exploration of data, descriptive analysis makes it possible to transform the data collected into information. In this sense, descriptive analysis is a way for the company to assess what has happened, based on historical data. It can thus model a behavior, a trend at a precise moment.

Predictive analysis is therefore based on the analysis of past data to predict and visualize what could happen in the short, medium or long term. Depending on the company's choice, a predictive model can forecast the sale of each product or service line by week, month, quarter or year.

Whatever the context, most of our decisions are based on past experience. In the case of predictive modeling, it includes forecasting techniques that are based on historical data. These data, or factors impacting sales, can be internal or external to the company. Internal factors include:

• sales receipts;
• prices of products or services;
• policy changes (promotions, commissions);
• Point-of-sale traffic;
• prospect behavior;
• the size of the sales force;
• the location of the company.

External factors include demographic changes in the area where the company is located, economic changes in the sector, changes in demand, the level of competition, seasonality and weather. In recent times, the global pandemic related to covid-19 is an external factor that has a strong impact on the sales forecast of companies.

Which companies are concerned by predictive modeling?

Sales forecasting with predictive models is interesting for any business sector, including:

• mass distribution;
• luxury and fashion;
• the industry;
• pharmaceutical industry;
• catering;
• tourism and events;
• consumer goods;
• insurance;
• specialized retail.

What are the different types of sales forecasting models?

To forecast its sales, the company can rely on one or more types of models:

• Acquisition models, to forecast the production flow of leads that are later converted into customers (this is the concept of the sales pipeline);
• Attrition models, which predict the reasons why a customer stopped buying a product or service provided by a company;
• Cross-selling or up-selling models that predict the sale of additional products related or unrelated to the product sold in the first place;
• Value models that predict, for example, the quality of the customer relationship or the value generated as a result of the sale of a product or service;
• Tone models linked to the company's communication strategy;
• Risk models used to identify and avoid potential risks (fraudulent activity, fund problems, etc.).

Artificial intelligence for sales forecasting

Predictive models based on machine learning

How to build a model with machine learning?

To integrate machine learning into its sales forecasting, a company must follow several steps, including:

• reflection on its needs and goals;
• collecting and processing internal and external data gathered in a quality database;
• the integration of the data with the algorithms to create the predictive model;
• the learning stage during which the model is trained;
• the prediction stage during which the algorithm is executed to make predictions;
• the decision-making stage: the exploitation of the information obtained allows the strategy to be adapted.

A predictive model will only be relevant if the data preparation is properly done. Whether they are internal or external, known or unknown, controlled or experienced, these data are often complex. The information derived from a predictive model can be erroneous due to a lack of data, its difficulty of extraction or its variability over time. This is particularly the case for data from time series that represent a quantitative evolution over time (number of sales, number of customers over a given period, etc.).

Some of the most common predictive analysis methods

The construction of a predictive model is carried out according to specific mathematical and statistical methods. Among the best known and most effective quantitative methods are:

• linear regression, which models a proportional linear relationship between the measure to be predicted and time;
• Logistic regression which works like linear regression, only the measure to be predicted is qualitative;
• polynomial regression, which is used when the data set and their relationships are more complex;
• exponential smoothing, a technique that takes into account the depreciation of information over time.

Predictive modeling and generated value: ROI calculation