Connexion Contact Us

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.
By using predefined predictive models based on one or more forecasting techniques that meet their objectives, companies can ensure the accuracy of their forecasts in order to adapt to market demand and thus generate good revenues. By modeling the sales forecast of products or services, teams are freed from a rather time-consuming task and can focus on managing and steering the company's performance.

Sales forecasting is then part of a Business Intelligence approach since a set of IT solutions allows the company to make the right decisions. The results of predictive analysis can be presented through decision-making tools such as a dashboard or reporting.

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

Artificial intelligence is an important tool for business performance, regardless of size. Artificial intelligence techniques and technologies, especially machine learning, are becoming more and more accessible and are the basis of any effective predictive model.

Yet, many companies choose to forecast using basic tools such as an Excel spreadsheet. This type of tool has many limitations when faced with the volume and complexity of data.

Machine learning relies on algorithms to give computers the ability to learn from the data they collect. These algorithms are becoming more and more complete thanks to the emergence of Big Data: predictive models are then more precise, relevant and efficient, but the tools available to companies must be able to execute these algorithms. It is therefore essential to choose the right Business Intelligence software, knowing that not all of them include the same functionalities.

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.).

It is therefore essential for companies to model their forecasts from an evolving database. The algorithms will be able to take into account recent data, evolving according to trends or particular events (covid-19, organic trend, launch of a new product, etc.).

When choosing the predictive model, the company must consider different elements such as the current turnover, the possible introduction of a new product or service, the nature, size and quality of past data, the company's short, medium or long term objectives.

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

The main goal for any company is to reach a certain level of profitability by generating value to ensure the sustainability of its business.

The exploitation of the data integrated in the predictive model must allow the company to identify the sources of return on investment (ROI). Indeed, companies have to carry out actions related to the sales process, each requiring an investment. For example, it can be promotions, destocking, reinforcing or reducing the sales force according to the sales forecasted by the model. Each action generates value, whether negative or positive.

The return on investment, or ROI, is a financial indicator that measures the return on an investment by comparing the money invested in an action and the money gained or lost as a result of that action. It is therefore a question of evaluating the degree of profitability of the action in relation to the expected sales: the sales must cover all the costs incurred.

Before investing, companies should evaluate the return on investment linked to the analytical modeling solution and to the actions taken to anticipate sales.

Discover Verteego's predictive platform and its ROI-driven approach.