Sales forecasting: generalities and challenges.
Sales forecasting or "Demand Planning" is a way to efficiently meet customer demand for products and/or services. Business leaders therefore engage in this exercise to ensure the availability of stocks. In addition to improving inventory management, other challenges are linked to this practice of sales forecasting: development of marketing plans, expense planning, team sizing, optimization of sales levers, etc. A deeper analysis allows us to distinguish 3 main groups: strategic issues, financial and commercial issues and tactical issues. That said, before presenting these issues, it is important to define sales forecasting and briefly explain the principle.
Sales forecasting: definition and principleThere are many definitions of sales forecasting. However, the approaches are the same. Sales forecasting is a business practice in which future sales are estimated, with varying degrees of accuracy, using predictive models or an adapted sales forecasting method. In this exercise, the consideration of variables (both qualitative and quantitative) is necessary.
The principle is quite simple: model customer behaviors with an adapted CRM software or by using a self-learning technology like machine learning. This technology will allow a company to highlight patterns on which it can base its predictions. However, in order for the predictions made, for example on inventory levels or product mix, to be closer to reality, reliable statistics are absolutely necessary.
In the end, sales forecasting is a process that guarantees positive results for companies in retail and mass distribution, regardless of their size. However, it requires the involvement of the managers who are responsible for defining the sales objectives and the departments, in this case the sales team, who are responsible for implementing them.
Sales forecasting: what's at stake?
In this section, we will talk about the challenges of sales forecasting. They are generally situated at 3 levels: strategic, financial and commercial, and tactical.
The strategic challenges of sales forecastingSales forecasting meets the needs of organizations in terms of performance and profitability. It therefore plays a central role in a company's sales strategy, regardless of the field or sector of activity. When we talk about strategic issues in sales forecasting, we are actually referring to the problems that, if solved, would allow a company to implement its sales strategy more serenely.
Thus, the strategic issues of sales forecasting are:
- intelligent financial planning: predictions from both internal and external data allow companies to plan well for unforeseen situations. Risk management is therefore a key function of financial planning. Forecasts also describe the periods when cash flow is most important. This way, cash availability can meet demand and avoid cash advances, stock-outs and write-downs of unsold inventory;
- respecting production deadlines: the company's resources are sometimes insufficient to meet production objectives within the allotted time. The company is therefore unable to meet delivery deadlines. The accuracy of forecasts will allow to anticipate cash shortages and to optimize the use of resources for tasks that are limited in time;
- developping new products and/or services: thanks to machine learning technology that analyzes the evolution of user needs, a company is able to anticipate the needs of its customers and to offer them (qualitatively and quantitatively) products and/or services that they had not even thought of before, based on reliable statistics. This improves the user experience and increases the number of visitors in the points of sale.
What are the financial and commercial stakes of sales forecasting?
The financial and commercial stake of sales forecasting is to improve the profitability of a company, in the medium and long term. Profitability is assessed in relation to:
- the increase of market share: sales forecasting is generally used by companies to maximize their profits. Some companies also use it to increase their market share by lowering their prices through predictive models. Indeed, sales forecasting software and other tools, such as Forecast pro and Anaplan, are able to identify products (with high future conversion potential) on which to position themselves to win new customers;
- the increase in turnover: isn't this ultimately the goal of sales forecasting? By controlling its resources, improving its inventory management, optimizing its assortment and offering persuasive prices, a company can be sure to increase its turnover and accelerate its growth;
- the adoption of a good pricing strategy: developing a pricing strategy is not always easy for companies. They therefore charge prices that are not always in line with the commercial objectives they set at the outset. However, a good pricing strategy should allow them to maximize their profits. Sales forecasting will allow to model consumer behaviors and especially to proceed to an audience segmentation thanks to artificial intelligence to determine the best price for each target group.
What about tactical issues?
The tactical issues of sales forecasting relate specifically to the applications of a business strategy, including product assortment, inventory management and promotions. The issues are as follows:
- Product assortment optimization: sales forecasting allows an organization to anticipate the needs of its customers based on data collected on their profile, but also on the competitive environment in which it evolves and on its DNA to produce reliable statistics. The accuracy of forecasts is guaranteed with the use of predictive models developed with artificial intelligence. It has never been easier to build a store's assortment in terms of width, depth and breadth;
- Inventory and supply chain management automation: Using forecasting software will allow a company to optimize its sales by ordering the right stock and avoiding over- or under-stocking. This is possible as long as a good sales forecasting software is able to estimate future purchases based on the company's historical data, for example. The software will also automatically synchronize the information contained on the supplier's and the reseller's machine to highlight any discrepancies that may need to be adjusted;
- The implementation of the right promotional mix: customers are not sensitive to the same type of promotion. The processing of information about a buyer's profile will enable the company to offer him the right promotion and thus maximize its sales on this product. Indeed, with the machine learning algorithm, it is possible to identify precisely the needs of consumers and even anticipate their future desires to offer them either a discount, a flash sale or an additional free service. All these formulas can be mixed in the same promotional campaign, but directed towards different audiences.
Ultimately, in terms of marketing management, sales forecasting is an unavoidable exercise. The stakes for the company can be observed at different levels. This proves the importance of this practice in the life of a company, especially in the development of an effective supply chain strategy.
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