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Executives today consider some form of forecasting in virtually every decision they make. Using Deep Learning to predict sales provides deeper insights and higher accuracy with more complicated input data.
Poor visibility into future revenues can lead to bad resource allocation and cash flow issues resulting from poor budgeting and goal setting.
The opportunity for deep learning
Often, sales data is very complicated and highly fluctuating, which is difficult to predict using traditional forecasting techniques. Deep learning models are able to identify dynamic and complicated relationships in the data in a way that is not possible with traditional modeling techniques.
How is the AI model implemented ?
That model uses past data on external factors and how they have affected sales in the past. This data is then used by the management teams to predict future sales.
What can be archieved
What kind of data should be included in sales forecast models depends largely on the industry in question. Among the typical examples are economic developments, regulatory changes, details about the respective products or services, marketing measures, and much more.
Where to learn more?
To learn how to use zerocode.ai to build a regression model and combine a variety of input data types try out here our tutorial.
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