Leverage forecasting models to make Sales Budgeting and Financial Planning more accurate

Today, no company can afford to make bad choices, business risk is always increasing, and the market increasingly complex. The sailboat is the perfect metaphor for the company:

  • Before we leave, we ask ourselves where we are going, how long, by what means, and what route to take
  • During the journey we ask ourselves: where are we? Are we staying on course? Are we making it to where we planned? Do we need to change our destination?

Therefore, it becomes a priority and urgent need to know on time the business results achieved and especially the forecasts that outline the future direction. For this reason, making the sales budget as accurate as possible is one of the main factors in staying on track. The more traditional forecast budget tools, however, are no longer adequate for a market in which the number of variables in play and their speed of change are ever greater and more hectic.

Why build a Machine Learning model to make budget forecasts

The sales budget forecasting process, where the starting point is the sales forecast for the coming year, often occupies Area Managers or Sales Managers to produce assumptions about the future with respect to their target market, leveraging past history.

This is an indispensable step in financial planning, but this onerous task takes all the more time the larger the size of clients and products to be considered in producing a forecast of the future.

Sales forecasting models, obtainable through Machine Learning (or machine learning) techniques, are powerful tools for relieving the work burden on humans, freeing them for less repetitive tasks and increasing the accuracy of the results obtained.

How to Build Sales Forecast Templates

Through the study of past history and the selection of appropriate contextual variables with which to enrich the information pool, these AI models are able to produce forecast curves and create a sales forecast model that is all the more reliable the better the data provided.

From the totality of a company’s customer-products, subgroups are identified, which could be, for example, Italy/Foreign Market combined with Product Families: the Machine Learning models are then trained separately for each of the groups.

Within the budget process, the forecast curves of the different groups can thus be summed to produce an initial sales budget input.

Another choice to be made concerns time granularity: as always, the best trade-off between good results and as detailed a projection as possible must be sought. Considering sales time series for each individual day of the year could bring great variance in the forecasts produced, because of the year-to-year differences in the day on which the same date may fall.

Certainly many businesses can make do with less precise detail, such as week or month, while also getting easier results to compare over different years.

Combining Machine Learning and Human Experience

Any AI algorithm knows no perfection: within the company’s customer and product base, not all sales situations are always reconstructed optimally: it is only through human supervision and experience that the best results are achieved.

In any Machine Learning project, it is important to reserve some of the data to test the goodness of the models obtained: if we are producing the sales budget for 2022, we could use the months of 2021 that have already passed to put the model to the test.

At this point, within the customer/product groups, one can assess where the model on the test year achieves the best results and use the forecast on the next period as the captured result.

Where, conversely, satisfactory results are not obtained in the test, an intervention is required from Area Managers or Sales Managers who, possessing experience and information that could not be passed on to the models, adjust the values to compose the final sales budget.

Integrated tools for producing sales forecasts

The implementation of Machine Learning models for demand prediction can be approached in two ways:

  1. Some commercial systems provide out-of-the-box tools for generating forecasts from loaded sales history, with some small configurations available. This is the case with SAP Analytics Cloud, with its tool  Smart Predict.
  2. In other cases, where it is necessary to study the phenomenon in greater detail and depth, out-of-the-box tools may not guarantee satisfactory results.

The team of Data Scientist Regesta LAB has studied and implemented at client companies a number of forecasting algorithms, which can also take into account socio-economic factors, capable of achieving very accurate performance. The results of these algorithms can thus be integrated with the platforms used for the planning and budgeting process.

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