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In general, you should remember the following rules while choosing the predictors for your models:

1. Avoid including correlated predictors into the model, e.g. don't include 'Discount' and 'Discount %' as they have a strong correlation

2. Avoid including the predictors with non-linear (exponential, logarithmic, power) impact to your sales for a specific SKU or product group, instead include their regressed values, e.g. if the price has non-linear, but the exponential impact to your sales, include 'Regr price' instead of 'Price' predictor.

3. Exclude the predictors that do not impact your sales or their impact is too low, this will improve the model accuracy

Adding predictors

You can add more predictors, but this requires more work than just adding more models based on the combinations of already existing predictors. In order to add a predictor, first, you should prepare the data in the source system for download, add it into your 'sql.js' file and set it up in 'options.js' file. Often it also requires some coding to convert the predictor into the appropriate form of data.

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is also taken into account in price optimization module in cross-elasticity. In the price optimization module, can see the dependencies between specific products. Cross-elasticity is described in detail in the Cross-elasticity section.

Using a Key Element in a Forecast

The system can take into account the effect of cannibalization of interchangeable and similarly priced positions in a direct way.

To do this, it is possible to combine several items that are interchangeable and close in price into one key element (KE). This join is done by adding the KEY_ID field to the PRODUCTS table where the list of SKUs is loaded. Also, it is possible to set up maintaining a directory of key elements in the MySales DB Editor. If several positions have the same key element (usually, this is the SKU_ID of the earliest position), then the forecast is built as follows:

• Initially, the system consolidates sales and inventory data for all key element (KE) positions
  Next, a forecast is built for the KE, i.e. forecast based on consolidated data

• For those weeks when each item of the KE was included in the assortment matrix (AM), or if the item was withdrawn from the AM, before the forecast week of the end of the balance, the system builds an independent forecast

• Further, the forecast of KE positions is adjusted so that the sum of the forecasts of all KE positions is equal to the total forecast for the KE. Thus, the KE forecast is redistributed to the forecast of each CI position.

• Those weeks where the “Daily adjustment” predictor worked are not adjusted
  Thus, if, for example, there were 3 KE positions in AM and sales were distributed between them, and one of the positions is withdrawn from AM, then the forecast of the remaining two will be increased. And vice versa, if there were 2 KE positions in AM, and a third one is entered, then the forecast of the previous two will be reduced.

Note 1: combining KE positions does not cancel the recommendation to put down an analogue. If there is no analogue for the new KE position, then the forecast for it will be built as an average for the group, adjusted for the price.

Note 2: It is recommended to test the forecast for positions combined by one KE, since such a combination may not always make a better forecast. For example, even if positions are close in price and consumer purpose, but differ in consumer preferences and each of them has its own target audience, i.e. in fact, one position cannibalizes the other only partially.

Price elasticity

Price elasticity of demand - this is a measure of the change in the product demand, which is caused by a change in the price of the product.

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