What predictive models are sales forecasting software based on?
Sales forecasting allows companies to better plan the flow of goods, capital and human resources in the future. Sales forecasting uses a combination of statistical and machine learning tools to anticipate inventory management of products sold in physical or virtual stores.
As a decision support tool, sales forecasting software uses data from the past to anticipate the future. They generally combine different predictive methods in order to gain in accuracy. What are these methods? Why are they called predictive? Here we detail the concepts to understand about predictive sales models.
Predictive methods: definition
Predictive analytics are the set of algorithms used to process data selected by a company to forecast its sales and marketing actions. Sales forecasting is based on these computer techniques in order to anticipate future customer demand. The time frame for forecasting can be one week, one month, three months, six months or one year for example.
Predictive methods, derived from data science, use different sources of data to train the algorithms. These predictive analysis methods require different stages of data processing before reaching the prediction generation stage. Indeed, after having established the choice of data necessary to model the business problem, a company must ensure the cleaning and homogenisation of the data. Once this first stage has been completed, a descriptive analysis phase based on data mining techniques follows. The aim of this second stage is to extract patterns and trends that are difficult to see on a human scale.
Thirdly, the predictive model takes a set of (past) data as input to generate predictions over a time interval chosen before the sales forecasting project for example. The predictive method is therefore part of a project that meets a business objective. The business use case can be sales forecasting, reduction of logistic costs, proposal of promotional scenarios, etc.
Factors taken into account when forecasting sales
Sales forecasting requires the consideration of different factors related to both customers (acquisition, retention, attrition), sales channels (promotions, sales channels, communication) and the supply chain. Therefore, effective sales forecasting combines different predictive models.
Sales forecasting models are first used to forecast the evolution of stocks of a given product. This step takes into account the promotional scenarios to be allocated to it as well as cross-selling and up-selling techniques to optimise sales of other related products. The preliminary analysis phase will have enabled the items linked to the product studied to be highlighted first. Finally, the predictive models must also propose an anticipation of stock management. This being linked to customer demand, it must forecast the quantities required for each product in the various dedicated stocks (minimum stock, safety stock or even the alert stock).
In addition, models linked to customer behaviour must characterise the production needs linked to the generation of prospects and customers. These models also take into account the company's retention and loyalty capacities. Finally, they are also used to forecast customer losses or attrition.
Predictive models and machine learning
Machine learning, a branch of artificial intelligence, offers methods for using data to generate more accurate predictions. Combined, they provide so-called automatic learning based on historical data, both internal and external to the company.
Without good data, a predictive model cannot generate reliable predictions. Therefore, it is important to be as comprehensive as possible when selecting the data to be exploited by collecting:
- information characterising the products studied (reference, packaging, merchandising, etc.);
- the history of promotions dedicated to the product whose sale is to be forecast;
- information about the points of sale where the product is sold;
- customer baskets, represented by receipts, where the product in question appears;
- the sales force linked to the points of sale;
- data related to the supply chain and related to the product under study;
- data about the competition and the sector in general.
Other external elements must also appear in order to allow the algorithms to identify correlations that cannot be highlighted on a human scale. These include local and regional characteristics of a sales outlet, consumption seasonality, weather data, etc. Thus, any factor that may have an impact on the product must be considered in the form of data.
Some quantitative predictive models for sales forecasting
Machine learning software for sales forecasting can combine different predictive models. We describe some of them below.
Linear regression
This predictive model generates the values of a variable from several variables. The variable to be predicted is called the explained variable while the others are called the explanatory variables. The relationship that this model seeks to highlight between the explained variable and the explanatory variables is, as its name indicates, linear.
Logistic regression
This is a special case of linear regression where the explained variable (value to be predicted) is explained by a vector of predictive values. The explained variable has only two values (binary variable). This model makes it possible, for example, to predict which part of the clientele will be sensitive to a given product.
Time series
Time series are used to predict the values of a variable over time. Forecasting techniques project, by extrapolation, into the future from historical data internal or external to the company.
In conclusion, sales forecasting models are a set of algorithms aimed at predicting and anticipating the evolution of stocks of a given product. The predicted information is generated through the analysis of historical data, either internal to the company or exogenous. This past data is chosen according to the impact it may have on the product being analysed. Sales forecasting software combines several predictive models.
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