Sales forecasting: how to improve it thanks to machine learning?

by Luna Marie--Taillefer

#Machine Learning #Sales forecasting

Discover how to improve your sales forecasting and reduce contextual risks with machine learning.

  1. How to improve sales forecasting with machine learning?

How to improve sales forecasting with machine learning?

Sales forecasting is an essential aspect of business management. While it may seem simple to forecast a trend using data you already have, it can be far removed from the market and sales reality . A solution is then offered to companies: machine learning . Thanks to mathematical models, machine learning algorithms can make predictions that are much more accurate than human analysis. However, the exceptional context of Covid19 and its lockdown has seen the French population’s consumption habits profoundly changed. This situation is an opportunity to study the new challenges of retail companies, which now have to deal with ever more complicated stock management. What are the sales forecasting challenges for companies? How can they react thanks to machine learning ? What are the conditions for using it?

How to forecast sales with machine learning?

Thanks to its data analysis capabilities, machine learning algorithms can identify trends in customers' consumption habits. Models take into account internal data such as product details, prices, distribution channels and their specificities or promotional operations. To this are added external data such as, for example, seasonality or competition, . All these factors directly influence the sales of a product, whether they are beneficial or not. It is, then, essential to know, understand and take them into account when forecasting sales.

With this data, machine learning algorithms will be able to model a prediction of what will happen. Let's take the weather as an example. To predict the weather of the coming days, meteorologists take into account impacting factors that they know and understand, such as previous temperatures or the movement of a heat wave. They also rely on historical data, with a long enough range to be relevant. Finally, a weather prediction cannot influence the weather itself.

We can see, through this example, the three principles of reliability of a prediction:

  • Understanding internal and external factors
  • Historical data
  • The non-influence of a prediction on its effectiveness

Sales forecasting in an uncertain context

In normal times, forecasts are therefore based on data that reveal a routine, a trend. This also applies to the consumption habits of customers in a store. However, sometimes the context under study changes, and changes in customer behavior can be observed. Let's take as an example the crisis linked to Covid19 and the French lockdown.

Changes in consumption habits

The Covid19 health crisis has seen a greatly increased demand on online points of sale, to the detriment of supermarkets. What's more, almost half of French households experienced a drop in income, leading to a reduction in their purchasing power, and therefore in household spending.

In this situation, many external factors (the reopening stages, the circulation of the virus, the possible loss of employment...) are added to the equation, making it more difficult to understand. Furthermore, the history of the data is drastically reduced due to the novelty of the situation.

New behaviors such as these are too recent to allow for analysis and predict their longevity. It is undertermined whether these behaviors will continue over time or whether new factors will emerge, further modifying consumption trends. For retail companies, such a sudden change of situation can be a real challenge. In fact, the unpredictability of consumer behavior complicates product management and encourages overstocking.

Identifying changes to better respond

While uncertainty linked to a shifting context is always present, companies that equip themselves with a machine learning solution are better able to anticipate the unexpected. The goal is to be attentive to consumer habits in order to react as quickly as possible, as soon as a change occurs. This is made possible by the speed of data processing in typical machine learning systems. The resources currently available make it possible to train models over a short period of time and reduce the reaction time to a few weeks or even days.

In addition, machine learning proves to be a real support during the decision-making process. By being able to process forecasts for several points of sale at the same time, machine learning gives a personalized guideline to each of the structures concerned.

Improve responsiveness

Contrary to popular belief, machine learning algorithms do not need an enormous amount of data to function properly. We have seen that in uncertain times, the data's history may seem insufficient. However, there are algorithms that work with a reduced data sample such as logistic regression or SVM. The same is true for the training time of models, the prediction is not necessarily generated in months but can be generated on the basis of weeks or even just a few days.

Machine learning is therefore a precise predictive tool, which can be adapted to all situations, provided that the right objectives are defined, which will give the right models. Adopting this technology can represent a competitive advantage. In fact, with a quick and exhaustive analysis of the company's internal and external data, machine learning helps decision-making in each of a company's piloting departments.

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