Sales forecasting software: use and selection
Sales forecasting, when done right, can help avoid unnecessary inventory costs, capital lock-in, and better anticipate your company's staffing needs, among other things. There are now SaaS sales forecasting software solutions available to assist companies of all sizes in developing their sales plans.
These software solutions are today faithful allies for the company. They allow not only to anticipate sales, but also to plan cash flow, to optimize stocks and to help the company in its development. These predictive tools help launch new projects. Their technological features, which combine artificial intelligence and software power, are becoming indispensable to today's and tomorrow's companies. How to choose a sales forecasting software and what is its value for companies?
Sales forecasting: definition
Sales forecasting applies to all levels of the company. In the past, it was more particularly reserved for the sales department, but today it is integrated in the different levels of the company. On this page, we will see that sales forecasting combines known company data, market trends, competitor positioning and other external factors such as the statistical curve of potential customers, seasonality or sales price.
We can therefore summarize sales forecasting as a very advanced analysis of a set of internal and external elements to the company, leading to a refined prediction of future sales management. A dedicated software solution allows to make these forecasts in a reliable way, which brings a personalized and adapted solution in the management of the launch of a new product or in the pursuit of the company's business.
Sales forecasting also includes a set of mathematical and statistical methods or machine learning methods that allow to anticipate orders over a given time interval (a year, a quarter, etc.), logistic costs, human resources to be allocated to ensure the growth of the companies. Among the methods used, we can mention: forecasting, linear regression, Winters method, etc.
The role of a sales software
A sales forecasting software takes in a set of data that allows it to draw a picture of your company's performance. The purpose of this analysis is to model opportunities and trends to follow in the future. The input data can include:
- macroscopic data related to the company (sales figures, customers, points of sale, etc.);
- structured information linked to the points of sale, which allows us to consider their specificities and to gain in precision;
- data from the entire supply chain (suppliers, carriers, logistics costs, changes in raw materials, etc.);
- exogenous data linked to competition, market evolution or the macro-economic context;
- additional information to take into account the effects of seasonality, climate and weather.
The forecasts generated downstream of the analysis established by the sales forecasting software lead to a better understanding of the orders to be put in place, the minimum, safety or alert stocks to be planned and the human resources to be allocated to achieve the business objectives of your structure.
Criteria for choosing a sales forecasting software
Cloud and data governance
In the case of SaaS (Software as a Service) sales forecasting solutions, it is important to question the data hosting methods. In order to respect your company's data governance policy, a dialogue on this issue must be established between your organization and the host of the sales forecasting cloud solution.
Analysis method implemented in the tool
Choosing a sales forecasting tool requires an understanding of the forecasting method on which it is based. Are the predictions based on statistical analysis or artificial intelligence methods? Does the software take into account different data sources or does it only use the company's historical data? Is the solution able to generate predictions by item and by point of sale (physical or electronic)?
Trial period, support and ease of use
Before making the choice to acquire a sales forecasting software, it is necessary to consider the ergonomics of the tool. Is it easy to use? Does it offer an intuitive and user-friendly interface? Are the reports generated by the application easy to interpret? Before buying a sales forecasting software, consider a test phase to understand if it meets your needs or not and to determine its strengths and limitations. Finally, check the availability of the support teams of the company offering the software solution in question.
Interoperability and scalability
SaaS sales forecasting applications can connect to your internal tools to facilitate data extraction. Not all sales forecasting tools can provide this interoperability. Furthermore, depending on the size of your company and the size of the data to be fed to the software, scalability can become an important criterion. Is it possible to add new data easily? Can you integrate new data sources, especially exogenous ones, into the tool? Is it easy to link the solution to each of your points of sale? All these questions must be considered before choosing the solution that best suits your organization's needs.
Pricing, scalability and software upgrades
SaaS sales forecasting software offers different payment options. Pricing can be based on annual or semi-annual subscriptions. Moreover, once adopted, the solution provider must ensure the follow-up of bugs and the evolutivity and update of the tool through time.
Machine learning and personalization
Sales forecasting solutions based on machine learning have emerged in the last few years and have reached an interesting level of maturity. They allow, thanks to machine learning, to push the analysis of your data in a personalized way. The deployment projects of these solutions take into account different data sources in order to better understand the historical functioning of your sales, your teams, your customers and the ecosystem in which your points of sale evolve.
These interoperable, scalable and scalable solutions allow the generation of dynamic and real-time prediction lines. They also offer the possibility to customize forecasts by SKU for each point of sale (web, mobile, store, etc.). These solutions can be used in two phases: a first test phase (POC) and a second phase of industrialization and deployment on all sales units.
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