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  2. Trade promotion forecasting - Wikipedia

    en.wikipedia.org/wiki/Trade_Promotion_Forecasting

    A bottom-up sales forecast at the SKU-account/POS level requires taking into account product attributes, historical sales levels and store specifics. The large number of different variables which describe the product, the store and the promotion attributes, both quantitative and qualitative, could potentially have many different values.

  3. Tracking signal - Wikipedia

    en.wikipedia.org/wiki/Tracking_signal

    Forecasts can relate to sales, inventory, or anything pertaining to an organization's future demand. The tracking signal is a simple indicator that forecast bias is present in the forecast model. It is most often used when the validity of the forecasting model might be in doubt.

  4. Demand forecasting - Wikipedia

    en.wikipedia.org/wiki/Demand_forecasting

    An example of a model for forecasting demand is M. Roodman's (1986) demand forecasting regression model for measuring the seasonality affects on a data point being measured. [11] The model was based on a linear regression model , and is used to measure linear trends based on seasonal cycles and their affects on demand i.e. the seasonal demand ...

  5. Forecasting - Wikipedia

    en.wikipedia.org/wiki/Forecasting

    Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis. Prediction is a similar but more general term.

  6. Product forecasting - Wikipedia

    en.wikipedia.org/wiki/Product_forecasting

    Product forecasting is the science of predicting the degree of success a new product will enjoy in the marketplace. To do this, the forecasting model must take into account such things as product awareness , distribution , price , fulfilling unmet needs and competitive alternatives.

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  9. Customer lifetime value - Wikipedia

    en.wikipedia.org/wiki/Customer_lifetime_value

    A retention example. CLV (customer lifetime value) calculation process consists of four steps: forecasting of remaining customer lifetime (most often in years) forecasting of future revenues (most often year-by-year), based on estimation about future products purchased and price paid; estimation of costs for delivering those products