Sales forecasting software for optimized management
A sales forecasting solution must be well thought out to be successful. In this guide to sales forecasting, not only the methods, but also the tools for sales forecasting are presented, including forecast management software. The latter are used because of the time savings they offer. They also guarantee more reliable predictions to improve the profitability of companies. There are several types of sales forecasting software. Thus, the choice is not easy for a company. It must necessarily take into account certain criteria. How to choose a sales forecasting software? What is their interest for the company? What are the predictive models used by these software? These are some of the questions to which answers are provided.
How to choose your sales forecasting software?
To choose its sales forecasting software, a company must take into account several criteria: the business sector, the sales horizon, the number of SKUs (Stock Keeping Units), the algorithms, the scalability of the software and the maturity of the company.
Knowing your industry
This is an essential criterion that must be taken into account when choosing a sales forecasting software. Indeed, it would not be wise to opt for a solution dedicated to mass distribution if your company is mainly involved in heavy industry.
There are, for example, sales forecasting software specialized in e-commerce. These often establish estimates over a half-day period. In reality, the purpose of choosing a forecasting solution according to the sector of activity is to accelerate the processing of data in order to obtain results more quickly. It also allows you to anticipate the problems specific to each sector.
Taking into account the forecast horizon
The sales forecasting tools available on the market propose mainly weekly or monthly estimates. However, there are some sales forecasting software programs that offer a 24-hour estimate. If this is the company's need, it will have to opt for computer programs with a high computing power.
On the other hand, the choice of a software adapted to a need for consolidation and which is at the same time able to ensure the management of files coming from different sources could be interesting. Some software simply cannot perform multidimensional forecasts.
Studying the number of SKUs and the need for automation
Contrary to popular belief, not all sales forecasting software can natively manage more than 100,000 SKUs. This is even more unlikely if the calculations are done repeatedly. It is best to consult with companies that have already tested a similar solution before purchasing software.
In addition, the number of SKUs must be taken into account if the company intends to have its teams manually check the calculations or if the distributors wish to be informed of the modifications or transformations that have occurred on certain models.
Analyzing the complexity of the algorithms and the model
Today, there are many sales prediction tools offering fairly standard algorithms. However, the implementation of certain models to be processed is not systematic on all predictive solutions. For example, there are software that can handle the impact of promotional campaigns or that can include certain exogenous data (weather, global health crisis, etc.).
Depending on the company's needs in terms of sales forecasting, it will be necessary to draw up detailed specifications. This will allow company managers to choose an editor that can meet the specificities and the particular functioning of their companies.
Verifying the integration of the software in an information system
Another criterion to consider when choosing a sales forecasting tool is its scalability. Can it automatically update with specific enhancements? Will other modules (planning or inventory replenishment) be added over time? The technical aspect as well as the complexity of integration are parameters that must be taken into account.
If the company has a limited budget, it would be more advantageous to opt for a forecasting tool operating in SAAS mode.
Choosing a tool adapted to the company's maturity
Choosing a complete forecasting software is not necessarily a good idea. In implementation, it could have more disadvantages than advantages. Before adopting CRM software, a company should study the features and options of each program and then opt for the one that precisely meets its needs.
For example, it would be pointless for a new business to choose forecasting software that automatically makes corrections to historical data. To make the right choice, consult the software publisher's website. The latter provides all the information on the solution.
Why use sales forecasting software?
For business owners wondering about the benefits of using sales forecasting software, here are the main advantages of this solution.
Improve the accuracy of sales forecasts
The first advantage of sales forecasting software is the high accuracy of the results obtained. Indeed, these tools make it possible to perform a large number of calculations in a relatively short time with a reduced margin of error. They also take into account a multitude of variables in order to provide the best possible estimate.
Also, these software programs combine several forecasting methods and take into account several scenarios to provide good results. However, the company will need to define the cycle length to obtain even more accurate forecasts.
Facilitate the simulation and planning of new products or services
The best sales forecasting software is based on artificial intelligence (AI). This allows to simulate different promotional scenarios before the launch of a new product. These simulations help design the best assortments and choose the most effective pricing strategy.
Thanks to these tools, the company can determine which products are best suited to its different channels. Forecasting software also takes into account the business constraints of organizations.
React quickly to market changes
It is important for a company to have a tool that can make adjustments based on changes in the market. Sales forecasting software identifies changes in consumer habits. This allows them to make more accurate predictions and take the necessary steps to improve existing processes.
Reduce procurement costs
By optimizing sales forecasts, predictive tools help reduce procurement costs. With this software, the company can devote maximum resources to the most efficient services and invest in the acquisition of new equipment.
Improve safety stock planning
The advantage of using sales forecasting tools is that they can determine the rate at which inventory is moving and identify oversupply. This will give forecasting teams more time to investigate the cause of unsold inventory. In addition, better safety stock planning would significantly reduce the risk of stock outs.
Integrate all the players and data of the company to carry out the forecasts
Sales forecasting software involves all management and sales personnel within the company. It also takes into account every link in the supply chain to perform the calculations. This allows the company to effectively meet the needs of its customers.
What are the predictive models used by sales forecasting software?
The predictive models used by sales forecasting software are able to perform several functions.
Predictive models to create personalized offers
The predictive models used by forecasting tools enable offers to be tailored to consumers' needs. To do this, they leverage customer and transaction data. Customer feedback is also taken into account to determine the potential customer loss score. Based on this score, offers will be made to encourage loyal customers.
Predictive models to help strengthen sales forecasts
Various decisions are made during the existence of a company. It can be either to integrate new members or to open new branches. All of these changes are only possible if the sales made are equal to or greater than the forecasts.
Predictive models allow for better sales forecasts. The company's managers will then use these forecasts to make their decisions.
Predictive analytics models capable of setting fair prices
High prices can discourage potential customers and significantly reduce sales volume. Optimizing product pricing becomes a less daunting task for companies that employ predictive tools.
These sales forecasting software programs use AI (artificial intelligence) based analysis models to suggest better prices. This improves inventory management and increases company revenue.
In conclusion, sales forecasting software is very beneficial to companies in the context of a sales promotion or an optimization of their product assortment. However, it is necessary to make a judicious choice to obtain good results.
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