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Artificial intelligence for sales forecasting: predictive software

Sales forecasting is essential for any company offering products or services. How can you adapt your activity (supply chain, R&D projects, pricing policy, etc.) and adapt to your market without taking into account its realities?

Furthermore, sales forecasting is essential to anticipate supply chain processes and even more so for efficient inventory management. Inventory management is not without impact on a company's activity (customer dissatisfaction, decrease in turnover, losses and unsold goods, immobilization of cash flow, additional storage costs, etc.).

Today, sales forecasting is more and more complex, Big data and changing consumer habits are two perfect examples of the problems encountered. Fortunately, technological advances, especially in artificial intelligence, allow companies to develop new solutions. Let's zoom in on forecasting tools.

Why use a forecasting software?

Even before tackling the technical aspect of a forecasting tool, it seems more judicious to us to first grasp the stakes and advantages of its use, which will facilitate even more the understanding of its specificities for the sales forecast.

To meet the many challenges of corporate sales forecasting

As we mentioned earlier, sales forecasting is a crucial strategy for a company, as it conditions the entire supply chain (procurement from suppliers, transport and warehousing logistics, production, order dispatch for online businesses, assortment of points of sale, etc.). To do this, sales forecasting must meet various challenges.

  1. Forecasting permanent sales on the one hand, while including slow-moving products (often neglected by companies, as they represent a very small part of the turnover) for which a stock shortage has a greater impact on customer satisfaction.
  2. Forecasting promotional sales is much more complicated, as you never know what impact the promotion will have or if the selected products or services will be of interest to customers, especially when no promotion on these products has taken place in the past. This is because sales forecasting is based primarily on the analysis of historical data.
  3. Forecasting sales of new products or services, a new business or a rollout in a new region is an additional challenge due to the lack of internal data. This is why the forecast also relies on data from outside the company.
  4. The volatility of the market depending on several criteria or contexts, e.g. the health crisis with the increase in online sales (already growing strongly in the last decade) and the tendency of households to stock up during containment. The instability of the sanitary situation and its future consequences are not without consequence on the stability of the market, the habits change and new needs appear.

Sales forecasting is therefore a challenge for companies that, in order to be sustainable, must guarantee a positive customer experience to build loyalty while working on increasing their revenue over time.

To enhance customer satisfaction and business profitability

If there are two essential dimensions to take into consideration for a company, they are customer satisfaction (few customers = few sales) and company profitability (too many expenses = less margin), dimensions not without a strong correlation.

Indeed, customers are the ambassadors (or detractors if they are not satisfied) of the company: a satisfied customer is a loyal customer and a source of new customers (impact on the quantity of stock available or the availability of the team for service companies). But, to satisfy this customer, the supply chain must be functional and profitable (speed of delivery for online businesses or services, quality and price of products, assortment of points of sale, etc.). And there is no need to make you aware of the fact that the majority of a company's expenses are attributed to the supply chain.

The anticipation of sales volumes has many benefits, whatever the context of the company, but even more so in the specific sectors of catering, mass distribution or retail with the management of several points of sale and/or perishable goods. And the more accurate the forecast, the more the company can work towards customer satisfaction while focusing on its profitability.

That's what forecasting software is all about: accurate demand forecasting. According to the Gartner analysis, a 1 point gain in accuracy corresponds to a 0.5% increase in revenue. But because examples speak louder than words, here are the results you can expect from forecasting software:
  • anticipation of trends (luxury sector for example) and changes in consumer habits, allowing the company to adapt its stock management and its resources (material, human, financial, etc.), but also to increase its reactivity;
  • a decrease in stock-outs and therefore an increase in the availability of products, and consequently in sales (particularly profitable during promotional sales) as well as in customer satisfaction;
  • better management of foodstuffs (e.g. large-scale distribution or food retailing), guaranteeing the availability of fresh quality products (customer loyalty) and reducing losses or unsold products, thus contributing to the fight against waste (environmental responsibility of the company and loss of money);
  • control of storage and logistics costs increasing profitability and supply chain performance while allowing better cash flow management (cash flow strategy) and financial flows;
  • Increased margin levels for the company through increased sales and reduced losses.

A forecasting software is therefore a decision-making tool at the service of the company's strategy since it allows a more precise analysis of the internal and external realities essential to its overall performance as well as saving time for the forecasting team.

What is sales forecasting software?

The above clearly demonstrates the importance of accurate sales forecasting, but even more the difficulties encountered by companies in designing reliable and accurate predictive models. We could also add the human factor, because despite all the good will of neutrality in the forecasting process, human cognitive biases (increased pessimism or optimism, distortion of reality, influence of a third party) cannot be entirely removed from the already complex equation.

In addition, there are still far too many companies that are ill-equipped for efficient sales forecasting. In the best case, they use a CRM software (but lacking in accuracy), but how many of them are still working with the good old Excel spreadsheet, very useful in the past, but insufficient in the era of Big Data.

Forecasting tools are today a new solution developed to help companies in their forecasts. Designed thanks to the progress in artificial intelligence, they use powerful algorithms and other complex mathematical calculations to increase the reliability and accuracy of their predictive models while integrating a wide range of internal and external data: sales, products, sales channel, marketing, inventory, production, but also environmental information, such as weather, vacation periods, major events, legislation or competition. In short, far too much information to integrate for the human brain, which is already incredibly functional.

Based on the concept of machine learning (ML), or even autoML, designed specifically for forecasting and integrated into certain software, these tools have been developed to predict more reliably while reducing analysis time in order to increase the reactivity of companies in terms of strategic decisions. Thanks to an intuitive dashboard for monitoring KPIs and ROI, it is possible to follow the evolution of sales in real time, refine the predictive model and automate all forecasting and planning tasks.

But forecasting software is not limited to predicting trends or demand. The beauty of artificial intelligence is that it can simulate multiple scenarios (promotional sales, pricing, assortment, etc.) in a time frame that is confusing to the human mind in order to choose the most effective strategy for different channels, regions or products.

Because not all software, no matter how powerful, is equal, there are certain criteria to consider when choosing your forecast provider:
  • the availability of a modular and adaptable platform for integration in any company, whatever its sector, products, catchment area, business constraints, etc.;
  • the power of the algorithms and learning methods;
  • the availability of data in the cloud and its security;
  • Ease of deployment (technical support, IT adaptability, internal constraints, etc.);
  • Transparency of pricing (subscription, updates, correction, etc.);
  • Compliance with internal IT policy.
Finally, we recommend that you consult customer reviews of the provider and look at examples of project implementations or case studies.

Discover the Verteego predictive platform.