Sales Forecasting Guide
One of the most important tasks for business leaders is to predict the future of their company, especially in terms of sales. The concept of sales forecasting is very common in marketing because of the impact it has on the development of a company, regardless of its size. Indeed, forecasting its sales will allow it to anticipate cash flow difficulties and to better manage its resources in terms of personnel, production and financing. It is therefore an essential component of any effective business strategy. Combined with artificial intelligence (AI), sales forecasting is a sustainable growth vector for organizations. It is important to master the principle in order to get the most out of it.
Some preliminary concepts on sales forecasting
For some specialists, sales forecasting represents the future of marketing. It would be hard to argue otherwise, as the benefits generated are so important. Indeed, sales forecasting:
- assists management decision-making;
- promotes a better allocation of resources;
- improves the quality of services;
- contributes to the growth of the company's activities.
Other data such as seasonality, change of territory or price are taken into account when making a sales prediction. Situation-specific variables (product launch, new store opening, new sales, etc.) must also be taken into account for a complete analysis. The more variables that are considered, the more reliable the prediction will be.
What are the different methods of sales forecasting?
A good sales forecast will allow a company to develop winning pricing, promotion and assortment strategies. But, depending on the approach, the results will be more or less nuanced. A company can use several methods to develop a sales forecast. The most common are: historical data-based forecasting, customer intent survey-based forecasting, pipeline-based forecasting and opportunity-based forecasting.
Forecasts based on historical dataA sales forecast based on data corresponding to past sales can allow a company to predict its performance over a period of time. This data can be refined if the company combines it with historical growth data (e.g., 7% sales increase per month).
Using an Excel spreadsheet, CRM software or self-learning technology, such as machine learning, to generate interpretable data saves companies a considerable amount of time when dealing with an overwhelming amount of information (internal and external), for example. But that's not the only benefit. Predictive models guarantee more reliable results.
However, this method has often shown its limitations. It does not sufficiently take into account seasonality or changes that can be made to a product. Thus, there is a risk that predictions are not accurate.
Forecasts based on customer intent surveyBy basing its sales forecast on the survey of customer intentions, a company can have reliable statistics for its predictions. This method will first consist of interviewing a sample of customers to survey the general and specific needs of each player. The results of the sample will then be extended to all the company's customers.
When the survey of customer intentions is combined with machine learning technology for a better understanding of the answers provided and the audience's plans, the results are more than guaranteed. Indeed, based on the data it possesses, the machine will be able to predict buying behaviors and model a prediction to allow the company to estimate future sales with some accuracy.
Forecasts based on the pipelineThe business pipeline is the most reliable way for a company to forecast its sales. This is because it includes all leads (old or new) handled by the sales team, regardless of their stage. Thus, the pipeline-based forecasting method consists of evaluating the probability of closing for each of the opportunities in the pipeline.
The effectiveness of this model will depend on the quality of the data entered in the CRM or sales management software. For companies that have a large volume of data, the use of machine learning will simplify the processing and allow them to have reliable data to make accurate predictions.
Opportunity-based forecastsThis method is based on the analysis of prospects' behavior and on the probability of conversion. How does it work in practice? A sales management software or CRM will analyze the behavior of a prospect through his browsing habits to model his predictions.
In this sales prediction model, machine learning can be a real asset. With its ability to learn, it will be able to more quickly highlight trends in customer consumption habits.
All of these methods are used to enable business leaders to make accurate predictions about inventory sourcing, assortment and pricing. However, some may be more appropriate than others depending on the industry and business objectives of the company.
What are the best sales forecasting software?In a company, sales forecasting software can analyze the flow of data from internal and external sources to best adapt sales strategies to the buying behavior of prospects. There are many sales forecasting software on the market. However, not all of them are efficient.
Here are the top 5 sales forecasting software:
- Forecast Pro
- Adaptive Planning
Forecast ProThis stand-alone business forecast analysis tool has an intuitive interface. It is easy to use and quick to implement, and combines statistical methods that integrate easily with planning systems.
The software develops a wide range of forecast calculations taking into account many variables such as product-specific sales offers, seasonal variations and product characteristics. It provides customized models and offers a system for combining forecasts. However, Forecast Pro does not provide results beyond the forecast and is not available in French.
AnaplanAnaplan's sales management platform allows companies to structure their organizational methods around the tools and software they already use and to synchronize them.
In concrete terms, Anaplan allows companies to:
- unify data collection and analysis to provide global and detailed information to share with all stakeholders using ETL connector APIs and nested integrations;
- manage all changes in real time;
- transform complex inputs into actionable recommendations with optimization, predictive algorithms and built-in AI capabilities;
- model the most complex scenarios and anticipate change with multidimensional modeling.
SedAptaSedApta is a complete software suite for companies, whatever their sector of activity, dedicated to sales planning and optimization, but also to production scheduling, computer-aided maintenance management and real-time production monitoring.
The sales forecasting component at SedApta level is managed by the sales forecasting tool: "Demand Management". This tool offers to the managers:
- sales forecast curves based on historical statistics;
- a sales forecast calculation, involving all employees (consensus forecast), generated by the interface and by the "Web Sales Collaboration" module.
Adaptive PlanningThe Adaptive Planning platform provides a simple planning solution for businesses. It uses real-time simulation models that optimize sales force capacity to improve sales predictability. This sales management tool provides executives with reporting, dashboarding and collaborative forecasting tools to gain a clear view of past and future situations.
Adaptive Planning was developed to replace cumbersome and inflexible IT systems. Thus, the sales forecasting and management process becomes simpler and more efficient.
This business performance management (SPM) software from XactlyCorp helps companies accurately forecast future performance, maintain productivity, and anticipate market contingencies. In practical terms, the platform takes care of the management of a company's marketing strategy from its initial development to its execution:
- territories and quotas;
- pipelines and forecasting.
What are the issues related to inventory and supply management?
Inventory management is often a problem without a consistent and structured storage solution to limit the inherent difficulties of supply. Implementing such a solution should allow companies to efficiently meet demand. However, what really matters is that the fundamentals of inventory forecasting are met, including:
- adopting an inventory management approach that aligns with company values and business objectives;
- conducting regular inventories to avoid frequent inventory errors that can lead to delays in delivery;
- maintaining an optimal inventory quantity by avoiding both over- and under-stocking (supplier);
- automatically updating inventory or automating updates to product sheets and inventory status for real-time synchronization of availability (distributor).
How to improve promotion forecasting?
Promotions are an important part of a company's business strategy. A targeted promotional offer can increase sales. However, there are a number of factors that contribute to the failure of promotion management strategies. To improve promotion forecasting and maximize sales, a company must:
- use a self-learning tool for data processing: automated information processing allows the company to make reliable predictions;
- optimize prices with AI: thanks to machine learning technology, it is possible to set up a pricing strategy adapted to each point of sale;
- personalize its offers: machine learning allows to prevent risks related to cannibalization, deterioration of the company's image and decrease of consumers' interest.
How to make assortment forecasts?Sales forecasting solutions will allow a company to optimize inventory at the local level, but also to fine-tune prices and assortments on the shelf in order to increase sales. However, optimizing certain applications of a marketing strategy, such as assortment, can also help maximize sales.
One technique for implementing an effective assortment optimization strategy is to obtain statistical data on customer behavior from traditional segments. However, ideally, artificial intelligence (AI) based technology will analyze internal and external company data to provide reliable statistics about a customer. This results in highly accurate predictions about the ideal assortment for a store.
How to develop a good pricing strategy?
Developing a good pricing strategy requires a company to have sufficient knowledge of the competitive environment in which it operates. Of course, business objectives also play a role in setting prices. Therefore, different pricing scenarios can be used to develop a good pricing strategy.Some pricing methods, such as cost-based pricing and value-based pricing, advocate a fixed price strategy. However, the disadvantage of these techniques is that they do not take into account the evolution of the market. A company that adopts this strategy can quickly find itself out of the market without constant monitoring of the competition and customer behavior.
In the end, it may be best to opt for an evolutionary pricing strategy. The principle is simple: offer prices according to demand. The company can then choose between:
- dynamic pricing: prices increase when demand is high and decrease when demand is low;
- personalized pricing: prices are set according to the customer profile;
- yield management: prices are increased to reduce demand and vice versa.
Ultimately, sales forecasting is a valuable exercise. Companies must commit to it if they want to see their profits increase over the years.