Page Comparison

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• There are two different metrics that can be forecasted: Quantity or Revenue. The Quantity forecast is pretty similar to /wiki/spaces/ACCDEV/pages/4425580574 Accelerate Multifactor Elasticity Optimization. Depending on your choice, some options will be different. They are detailed along the documentation.

• There are checkboxes at the bottom of the tab for automatic filtering of negative quantities and prices; it is recommended that you keep these checked.

• If the Revenue at List Price is set, values will be used to calculate the discount percentage from the Revenue and store it in the processed_data table, as part of the model training features. The discount percentage values will be automatically updated over the price range of the elasticity calculation, leading to more accurate predictions.

• There is a checkbox to perform a log transformation of the metric (quantity or revenue). This may help to produce a better forecast in some cases, for example, in cases where sales are heavily skewed towards low values – i.e., there are many long-tail products present in the data.

• The Time Period field defines the level of aggregation for the forecast – daily, weekly, or monthly.

• The forecast generated in the final step may be extended up to 15 future time periods (e.g. 15 weeks for a weekly forecast).

• Additional categorical and numerical features may optionally be added to the model from the source to improve the forecast.

  • Examples to include in the categorical features are product hierarchies and product Pareto information. Categories would be unique for a given product and time period – fields such as channel and store should not be included here. If a category is not unique for a given product, the mode will be kept (i.e. the value most often associated to the given product).

  • Numerical features may include any numerical attributes that can be averaged over the selected time period, such as discounts, stock levels, or seasonal events. Values do not need to be unique – they will be averaged over time.

  • While numerical features can be included in the categorical features (for example, categories that use numerical codes), the same features should not be included in both categorical and numerical features at the same time. A warning will be displayed in this case.

  • In the case of a Revenue metric forecast, you can add a Customer field and some customer categorical features. The aggregation would be by default at the Product x Customer level. There is no Customer level in the Quantity-metric forecast.

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You define the aggregations to do before training the model. The product aggregation levels will define the granularity on the product side. By default, the value is set to the mapped product field, but you can remove it. Using a lower granularity will increase the training velocity, but decrease the precision of the results. You can define any of the product categorical features (defined in https://pricefx.atlassian.net/wiki/spaces/ACCDEVACC/pages/57681182995849055504/Business+User+Reference+Optimization+-+Forecast#Definition-Tab) as an aggregation level.

If the model is revenue-based, and you have defined a customer field, there is also a user input to define the customer aggregation levels. The default value is the mapped customer field. You can remove it and replace it with as many as customer categorical features (defined in https://pricefx.atlassian.net/wiki/spaces/ACCDEVACC/pages/57681182995849055504/Business+User+Reference+Optimization+-+Forecast#Definition-Tab) as you want.

You define also a replacement value for the null values. The default value is __null__. This string is used to replace the categorical feature null values. This way, the aggregations take all the aggregation level values into account, including the null one. If you export the forecast data (see https://pricefx.atlassian.net/wiki/spaces/ACCDEVACC/pages/57681182995849055504/Business+User+Reference+Optimization+-+Forecast#Export-Forecast-Step), the corresponding category values will be set back to null.

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This tab displays the forecast at a detailed level, set by the user. The granularity level corresponds to the aggregation levels, plus the export key levels, both defined in the step Additional Configuration, tab Aggregation Levels (https://pricefx.atlassian.net/wiki/spaces/ACCDEVACC/pages/57681182995849055504/Business+User+Reference+Optimization+-+Forecast#Aggregation-Level-Tab). As for the Overview tab, the green shaded area represents the future forecasted values.

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If you are happy with the training results you can click Continue. This will begin the calculation for the Export Forecast step. You have defined the parameters for this calculation in the step Additional Configuration, tab Aggregation Levels (see https://pricefx.atlassian.net/wiki/spaces/ACCDEVACC/pages/57681182995849055504/Business+User+Reference+Optimization+-+Forecast#Export-parameters). The calculation saves the forecast data in a DataSource, accessible in the Data Manager. This Data Source contains the forecast data, at a product, customer (if defined) and date level. For each raw, the forecast metric is saved, the unit price and the second metric are calculated.

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