Artificial intelligence (AI) for sales forecasting

Aiming to anticipate sales over one or more given periods, the establishment of reliable sales forecasts plays an essential role in the development of a company.

However, in order to make predictions that are as close to reality as possible, the sales forecasting solution adopted must be efficient. However, it is clear that the performance of CRM (Customer Relationship Management) software, sales management software, or even an Excel spreadsheet to manage sales, is far from satisfactory, in terms of accuracy, regarding the sales forecasts generated.

So, how can you optimize and make your sales forecasts more reliable? The answer to this question lies in the use of machine learning. Artificial intelligence, via machine learning, is made to process the astronomical quantities of Big Data and to automate, make reliable and optimize sales forecasts.

Before going further in the presentation of the use of predictive analysis and machine learning in sales forecasting, let's first see what are the benefits of this for the company.

Sales forecasting: what is at stake for the company?

Sales forecasting: what is it?

Sales forecasting, also known as demand planning, is a method whose objective is to predict a company's future sales as accurately as possible.

Linked to the marketing, sales and production departments, it influences many decisions that are crucial for the growth of the company. These decisions concern, among other things, the commercial objectives to be reached, the products or services to be pushed in order to increase the turnover more quickly, as well as the inventory and supply management.

Sales forecasting is therefore an essential link in a company's supply chain. It allows the latter to apprehend the directions to take for the future years. But not only! Because the benefits of sales forecasting are indeed numerous, especially in the supply chain field.

Supply chain: the benefits of an optimal sales forecast

A sales forecast influences the entire supply chain. It must therefore be as accurate and reliable as possible.

As part of an approach to improve the company's performance, a realistic forecast of future sales offers multiple advantages:

• better anticipation and optimized production planning;
• improved regulation of order and production flows;
• optimized inventory management;
• an optimized distribution strategy;
• supply chain optimization;
• better management of the product portfolio;
• smarter financial planning;
• significant improvement in cash flow (allowing the company to prepare for unexpected expenses);
• the ability to better negotiate supplier prices when sourcing goods;
• a more accurate calculation of the right amount of raw materials to purchase (neither too little nor too much).

Forecasting future sales also allows you to optimize the sales prices of the products and services offered, and to consider promotional actions with more serenity. It facilitates planning and supports the company's growth.

Sales forecasting, a vector of motivation

Having access to an estimate of future sales that is as close to reality as possible also gives the company the ability to publish periodic sales forecasts (e.g. daily or quarterly) in order to:

• encourage the sales team to reach their sales objectives;
• encourage sales people to improve their performance;
• motivate the teams.

In addition to being a potential motivator for sales teams, sales forecasting also helps calibrate the size and composition of such teams.

Anticipating possible problems

Accurate forecasting is impossible today. But by forecasting sales as close to reality as possible, a company can anticipate potential problems and put in place the appropriate measures as soon as possible to resolve them quickly.

By anticipating future sales, a company can also make informed decisions regarding, among other things, resource management, recruitment, setting sales targets and budgets, and investments. The definition of sales prices and the programming of promotional actions are also facilitated.

In view of these numerous advantages, the automation of sales forecasts demonstrates its value in boosting company performance. And this is all the more true since such an approach simultaneously impacts many supply chain professions.

Sales forecasting: types of data analyzed

In order to make a relevant sales forecast that is as close to reality as possible, it is necessary to analyze and process a very large amount of data, some of which can be derived from Big Data. Concerning these data, they are mainly:

• internal company data, such as customers, products sold, prices, promotions, logistics, different distribution channels and their specificities, management of distribution points, and promotional operations;
• data external to the company, concerning competition, seasonality, commercial events, legislation, but also the impact of the COVID-19 health crisis.

Using a software to automate your sales forecast

The automation and reliability of sales forecasting requires the digital transition of the company. It is therefore essential to equip yourself with sales forecasting software solutions.

Being able to rely on sales forecasting software allows you to rely on reliable real-time information feedback from the field (customers, suppliers, sales representatives, inventory) and from business software (ERP, CRM, etc.) already used by the company.

Sales forecasting then becomes a reliable tool and a solid ally, among others, for:

• ensure good inventory management;
• make the supply chain more fluid;
• help the company to improve its performance;
• improve customer satisfaction.

Above all, it allows the company to respond nimbly to market fluctuations.

Sales forecasting: using predictive analytics and machine learning

Machine Learning is an application of artificial intelligence (AI) that gives computer programs the ability to learn and improve automatically, without being explicitly programmed. This is achieved through the data analysis - in particular of data extracted from software (ERP, CRM, etc.) used by the company - and from experience.

Machine learning and data analysis

Machine learning algorithms have the ability to automatically identify often subtle correlations between many variables in order to build predictive models. Each model is built against a pre-determined business objective.

In order to generate models that accurately predict future sales, machine learning relies on multiple variables and varying amounts of qualitative data. This data is mostly historical data from the company, such as point-of-sale traffic, product prices, sales receipts, or promotional operations.

Data analysis combined with machine learning can reveal trends in customer consumption habits.

The models it generates take into account the internal and external data mentioned above. Thanks to the analysis of these data, machine learning will be able to model a prediction of the future. This is called predictive analysis.

How does predictive analytics work?

Predictive analytics is the use of data, statistical algorithms and machine learning techniques to anticipate probable outcomes. It aims at analyzing and understanding the reasons and causes that led to the occurrence of certain events. It also aims at recommending actions to benefit the forecast.

In other words, predictive analysis explores the past and is based on what exists in order to anticipate and forecast the future. For example, it allows the company to know the level of satisfaction of a customer, to evaluate their probability of purchase and to anticipate with great precision his buying behaviors, in other words the actions he takes (online and offline) before buying a product or a service.

Taking all this information into account allows, for example, a physical store with an online presence to optimize the availability of its products in store and to maintain an ideal customer experience.

Predictive analysis also offers the retailer the possibility to have a short or medium term vision of its turnover.

Note that, contrary to popular belief, machine learning algorithms can work very well with a limited set of data (Small Data).