How to optimize sales forecasting with AI?

For a company, knowing how to manage its sales necessarily involves inventory management. Indeed, the flow of products has a significant impact on customer satisfaction and business development, even if only in relation to competitors. In this respect, whether it is transport, storage or even in-store supply, several points must be skilfully anticipated in order to respond quickly and effectively to the various possible scenarios.

However, it must be kept in mind that inventory management is directly dependent on customer behavior. This creates some uncertainties and uncomfortable situations, as it is not always easy to take into account sudden changes in habits. Fortunately, thanks to artificial intelligence, it is now possible for a company to precisely optimize its inventory management.

But then, how to use machine learning for sales forecasting? That's what we'll see.

What is sales forecasting?

As the terms themselves indicate, sales forecasting consists of anticipating the activity of a business, and more specifically the sale of its products. This analysis is most often done over a specific period, which can be weekly, monthly, quarterly, or annually. Of course, such a forecast implies to have internal and external data on the sales that have already taken place, as well as on the customers' habits and the frequentation of the points of sale.

In any case, implementing an efficient sales forecasting process allows the company to optimize its supply chain, from the production of products and goods, to procurement and distribution. This impacts the preparation of stocks, the choice of sales prices, the strategy of promotional actions, or the reduction of unsold goods. For all these reasons, it is essential that forecasts are as close as possible to reality, at the risk of profoundly altering the company's performance.

Why use AI for sales forecasting?

Having the right product at the right price in the right place is what sales forecasting and everything that goes with it is all about. Logically, it is therefore necessary to put in place an efficient process and not to entrust this task to people who are not competent. The same goes for the tools, since many companies are content to use a spreadsheet, a software that is far from being precise enough to do a high-precision data analysis. For all these reasons, working with a sales forecasting specialist seems to be the best solution, especially because he has the right platform for this type of mission.

As such, it is possible to use a machine learning expert, a sales forecasting model that works thanks to artificial intelligence. Through a relatively advanced data analysis system, such an algorithm is able to anticipate customer consumption trends, allowing the company to avoid problematic situations and to give an adequate response before the problem occurs. To do this, the AI is based on internal data directly related to products, prices, or means of distribution, as well as external data, such as those related to the competitors of the said company or even the seasonality (Christmas, Valentine's Day, etc.).

It is therefore directly the artificial intelligence that makes the forecasts, provided that it can access a sufficiently relevant data history.

How to implement sales forecasting with AI?

As you might expect, artificial intelligence has had a huge impact on sales forecasting models. Indeed, these models are now very accurate, an evolution that would not have been possible without machine learning. However, AI algorithms can't work miracles without the right data. In order to implement an effective sales forecast, it is indeed essential that the information provided is of good quality. Not all data is suitable for this operation, whether it is consumption habits, customer behavior, customer profiles or sales based on seasonality.

Generally speaking, sales forecasting methods can take different forms and rely on various strategies. However, AI has a clear advantage over all other solutions, as it respects a few essential principles to obtain optimal results. In concrete terms, it allows the company to:

to be able to identify sudden changes in consumption habits, as was the case at the beginning of the health crisis;
to be able to anticipate solutions linked to these changes;
to be very reactive to the competition.

In order to be reliable, the data transmitted to the artificial intelligence must automatically be based on measurable objectives, similar processing methods, or stable sales processes. It is also important to keep in mind that a number of factors can influence the predictions, such as:

recruitments;
changes in compensation;
the acquisition of new geographical areas;
changes in the competitive environment;
the national economic environment;
regulatory changes;
etc.

In all cases, the sales forecast is based on controlled data selected for their relevance to the desired model.

In what areas does AI-based sales forecasting apply?

It is difficult to list all the fields that can implement sales forecasting with artificial intelligence. Indeed, this type of solution is aimed at a multitude of companies, whatever their activity. However, as an example, there are 2 sectors that have every interest in choosing this strategy: mass distribution and retail.

Sales forecasting with AI in mass distribution.

On the mass distribution side, it is clear that forecasting and anticipating sales is one of the very pillars of these companies, both to be successful and to ensure a successful customer experience. During the health crisis, stores were put to the test, particularly with regard to their inventories, and sometimes to a very different degree. While food stores fared well after a few stock-outs during the initial containment, this was not necessarily the case for all businesses, which often found themselves with many products on their hands.

In general, it should be kept in mind that for the same period, the mass distribution sector can experience very significant fluctuations in purchasing behavior. Whether they are influenced by the weather, by trends, or even by seasonality, particularly during periods such as Christmas or Valentine's Day, a multitude of parameters can disrupt the state of the retailer's inventory. However, it is difficult for a large-scale distributor to supply its shelves in real time, since it is itself dependent on other supply chain players. For all these reasons, the use of artificial intelligence has quickly become a saving grace, as it allows to take into account several factors that would go unnoticed with a less precise technology.

Thanks to the use of AI in mass distribution, the availability of products in stores is optimized to avoid stock-outs and unpleasant experiences for the customer.

Sales forecasting with AI in the retail sector

Machine learning is also very useful in the retail sector, since companies in this sector must be able to predict their customers' demand. During the Covid 19 crisis, this market has been particularly hard hit, as successive confinements have had a major impact on it. In order to optimize the supply chain, and to be able to face the uncertainties, the retail sector has also seized on artificial intelligence to fine-tune its sales forecasts. Whether it's a question of dealing with a temporary peak in activity, or on the contrary, a terrible crisis like the last few months, responding quickly and efficiently, by providing concrete solutions, is one of the major assets of artificial intelligence.

This means that retail companies can take short-term trends into account almost immediately, in order to review their forecasts and act on their strategy. Constantly updated, thanks to the data needed for this process, the information transmitted in return allows the company to be much more efficient, avoiding stock-outs and overstocking. Thanks to AI, sales forecasting in retail impacts many aspects and has de facto many consequences:

a better organization;
better control of storage costs;
greater production efficiency;
a continuous adjustment of stocks;
a reduction in the risk of unsold products;
better optimization of logistics costs;
improved customer satisfaction;
etc.

To counter the sometimes disastrous effects of traditional forecasting methods and tools, machine learning is a process to be favored, especially since it gives a significant competitive advantage to companies that use it.