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Integrating an AI-powered solution into sales forecasting

Sales forecasting is a major challenge for companies. Today, there are sales forecasting solutions that provide a new way to optimise the launch of a new product. These tools can also improve the management of existing products through the use of sales tools that integrate artificial intelligence into their internal functioning. Artificial intelligence has become a future solution for many companies: data management, results analysis and sales anticipation. On this page, let's find out how to improve business growth by integrating a solution powered by artificial intelligence.

Definition of artificial intelligence

Artificial intelligence belongs to the new technologies for managing company data:

  • improvement of KPIs;
  • anticipating customer behaviour;
  • improved stock management and procurement;
  • decision support;
  • development of commercial value.

Artificial intelligence can be defined as a high-flying computer programme, developing advanced techniques and technologies, capable of simulating human behaviour in a given situation. Artificial intelligence is therefore composed of complex algorithms deployed in a computer environment where action and dynamics are put at the service of the company.

Artificial intelligence and human behaviour

Artificial intelligence therefore tries to get as close as possible to human attitudes and behaviour. In terms of sales forecasting, this can be translated into the integration of a predictive computer model which will thus make it possible to anticipate:

  • the number of products sold;
  • the quantity to be included in the stock;
  • the integration of the stock into the supply chain;
  • the cost of logistics management;
  • the investment (sometimes in millions of euros) to be made;
  • customer behaviour;
  • the marketing plan for launching a product.

In addition to economies of scale on stock entries and forecasting the number of sales, artificial intelligence is used to anticipate a possible stock shortage. It helps to control storage costs, understands and analyses the search methods of users of the online sales site, analyses the bounce rate in great detail and provides a reliable response on customer behaviour.

Based on these data (KPIs), the company can adapt its marketing and sales policy in order to satisfy its customers. It will also make major structural savings, not limited to a few euros.

Sales and marketing in the light of artificial intelligence

Machine learning is the intelligent solution to improve turnover forecasting. The computer learns and develops its abilities to act as a human would. This artificial intelligence puts all its power at the service of the company, through a computer reflection on the behaviour of customers in front of a new product. By deploying an artificial intelligence interface within the company's supply chain and also reaching the sales and marketing departments, the various employees will work together on the behaviour of future customers. The aim is to optimise the various actions to be taken for the successful launch or promotion of a product:

  • the need for promotional or non-promotional offers;
  • choice of the best period for launching a product;
  • creation of print marketing materials adapted to the recommendations of the intelligent solution;
  • financial investment required;
  • deployment of an e-commerce site or a landing page;
  • presence on a marketplace or social networks.

The presence of artificial intelligence is therefore the key to an improved user experience (UX) in the context of online sales. It is also important for the customer experience when marketing products in shops.

Predictive models and sales forecasting

A well-run predictive model is the integration of artificial intelligence into sales forecasts. Let's look at the supply chain of logistics. Artificial intelligence is thus present at all levels of this chain:

  • management of goods flows: stock entry and exit;
  • optimisation of strategic flows: delivery of goods and organisation of rounds;
  • returns management: analysis of the reason for the return of goods;
  • Optimised triggering of a supplier order;
  • real-time analysis of stock, taking into account future sales.

The Supply Chain and artificial intelligence thus make it possible to know at any time the purchasing intentions of customers for a given product, by matching sales forecasts with the quantities to be held in stock.

Machine learning and sales forecasting strategy

The integration of machine learning takes place in several stages. The computer will first "understand" the various data collected, then analyse them using a specific algorithm, before returning them and making a forecast.

Collection of company data

The prediction interface must first of all collect all the data necessary for its training. This includes internal company data: turnover, products in stock, margins, turnover per customer and per department, accounting and financial data, products under development, seasonal data and any element necessary for the commercial forecast. External data is also collected: amount of external purchases by type of product and supplier, discounts obtained, regularity of supplies, manufacturing time, financing of these purchases, etc.

This collection will enable the machine to learn from these elements and to draw the first predictive elements. It is followed by a second complementary stage, the definition of objectives.

Definition of objectives

The integration and learning of the elements will be linked to the company's defined objectives: better regulation of stocks, search for savings on a larger or smaller scale, launch of a new product, implementation of promotional periods, internal organisation of the workforce, etc. It is therefore a purely business objective that is integrated into the machine learning solution.

Mathematical models and the training phase

Once the company's data collection and objectives have been integrated, the machine will then engage in methodical learning using mathematical models. This training phase forms the intelligent basis of the business forecasting solution.

Test phase for predictive models

Once the training phase is over, the project managers will start a training phase which consists of making the machine work in complete autonomy in order to verify:

  • its learning after the first three phases;
  • the possibilities for initial predictions;
  • possible points to be taken up again or to be perfected.

These predictions are analysed by the company's employees. They check that the machine is learning correctly, such as the reality and quality of the forecast submitted, and that particular elements (e.g. seasonality) are taken into account.

Generation of sales predictions by artificial intelligence

Once the test phase has been validated, the new operating model using artificial intelligence will be officially deployed and used by the company's employees: sales, marketing, logistics, accounting and finance departments, etc.

Why anticipate to better adapt your behaviour?

Lack of foresight can cost a company millions of euros. What is known today as UX or user experience provides valuable information to the company. Knowing in advance the behaviour of its customers, knowing what service to provide them and understanding why one product works better than another are often the subject of long and complicated studies. Artificial intelligence can be used to boost the company's technical and commercial analyses. In this regard, it is possible to anticipate:

  • seasonality in a very precise way;
  • consumption patterns;
  • changes in customer behaviour;
  • stock and its optimisation;
  • the company's reactivity in the event of a crisis or urgent need.
The use of artificial intelligence in business prediction ensures a much faster response from the company in the event of a sudden change in behaviour. The algorithm detects almost immediately any deviation, slowdown or, on the contrary, any increase in sales of certain products. The reaction time is considerably shorter, provided that the objectives have been well defined from the start.

Every detail analysed by the algorithm is taken into account in the final result. This means that calculation errors are almost non-existent and the search for results leads to a low, fast and very useful report in the sales forecast.

Process automation and business objectives

The business objective may be defined by the regular launch of new products on the market. It can also be the integration of a new management mode within the company's services or it can be financial, with a defined turnover to be reached for a CAC40 listed company. This solution of scalable services with artificial intelligence can be shaped to the needs of the company and benefit from the automation of a personalised process.

The use of artificial intelligence in the service of the company is therefore an excellent way of saving precious time in the analysis of the company's management. As soon as one is interested in technological innovation or the creation of a project optimised by artificial intelligence, the company benefits from an essential, reliable and useful technological contribution to its development.

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