challenge

For large enterprises, sales forecasts are as critical as they are tedious and slow. However, current tools and processes, more often than not, fail to deliver the confidence in the forecast that is required to support critical decisions on everything from budgets to profit. In most of the organizations:

• Sales forecasting has been supported by primitive techniques, systems and tools like spreadsheets and CRM.

• The sales team continues to carry out this process using a series of individual spreadsheets that are biased by personal instincts and emotion.

• Even the most valued companies in the Fortune 100 sometimes get their forecasts dead wrong.

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Historically, when trying to predict sales based on different factors, managers have applied business logic based on experience—the quality of a brand, the shelf placement, the promotion, and so on.  They typically use a series of linear regressions, plotting known sales volume against the variables, to get a decent forecast for the next promotion. This approach essentially relies on the human brain to select and analyze data.

But machine learning is much more powerful. A machine can look at history to determine which factors are most important, and to find the best way to predict what will occur based on a much larger set of variables. With machine learning models, and today’s super-charged computational power, you can expand the number of variables from, say, five or so to 20 or more.

In the old forecasting world led by the brain, you used one model for just about every category or type of business.  In a changing environment, using customized models for each category or type of business increases the accuracy of predictions, because even if two categories are similar, they have underlying intrinsic differences that require customized machine learning methods to capture.

In the new forecasting world of machine learning, you can build a customized model for every category or sub-category or type of business. Instead of a few decision trees, machine-learning algorithms randomly create thousands of decision trees based on sub-groups of explanatory variables; typically, if there are 20 explanatory variables, the random trees will only use four or five variables at a time (which could easily be handled by any computer). The algorithm then combines the thousands of trees to make a single predictive model that incorporates all the variables. Once “trained,” the algorithm is able to automatically predict sales at the product level during any promotion. And it continues to learn as it takes in more data and results.