Sales & Promo Method

This method is useful for retailers wanting to account for outliers, out-of-stock, and promotion events. This is a causal approach and goes through multiple stages to generate the final forecast. Typically, the data requirement for this method is at least two years of sales history. This is among the best methods in AIF that can produce reliable forecasts at the lowest levels of merchandise and location hierarchies with only the minimum requirement of sales data. The stages for Sales & Promo method:

  1. Preprocessing corrects bad data points that represent unusual sales behavior, so that the demand forecast does not replicate undesirable patterns. Possible causes of history adjustments include out of stocks, outliers, or events. During preprocessing, the quality of data is analyzed, and faulty data points are corrected before further calculations. This provides pre-processed data to produce reliable demand parameters for the next process steps.

  2. Promo Estimation consists of heavy data mining of the relationships between historical sales (sales lift) and promotions (different promotion-relevant features). The two available models to quantify the impact of promotions on demand are:
    • Decision Trees:

      • Tree-based Machine Learning classifier model that predicts an outcome based on several input features.

    • Generalized Linear Model:

      • Regression-based Statistical model that predicts an outcome based on the values of another set of input variables.

  3. Seasonality Estimation utilizes various machine learning techniques to understand the time series in which data experiences regular and predictable changes that recur every calendar year. This reflects a sales pattern that repeats over a one-year period based on the time of year (season). The estimation of seasonality occurs at different Escalation levels which are aggregated sales at different merchandise and location hierarchy levels. The four options for estimating seasonality are:
    • Additive Winters Exponential Smoothing (Additive Winters ES):

      • Additive Winters ES extracts seasonal indexes assumed to have additive effects on the de-seasonalized series. Effects are applied additively to levels and trends.

      • Additive Winters ES requires sufficient data of at least two years of history.

      • Additive Winters ES is effective when there are zeros in the history.

    • Multiplicative Winters Exponential Smoothing (Multiplicative Winters ES):

      • Multiplicative Winters ES extracts seasonal indexes assumed to have multiplicative effects on the de-seasonalized series. Effects are applied multiplicatively to levels and trends.

      • Multiplicative Winters ES requires sufficient data of at least two years of history.

      • Multiplicative Winters ES does not work when there are zeros in the history.

    • Auto Seasonal:

      • Selects the best method based on the inverse of Root Mean Square Error of Additive Winters ES or Multiplicative Winters ES.

    • Blend Seasonal:

      • Utilizes the inverse of Root Mean Square Error as the weights for the two methods of Additive Winters ES and Multiplicative Winters ES and combines the weighted seasonal indexes.

  4. Base Demand Estimation calculates the average demand signal after the cleansing of data and removal of promotional and seasonality effects. This is generated at the same merchandise and location hierarchy level as the required forecast. The six options for estimating base demand are:
    • Simple Exponential Smoothing (Simple ES):

      • Simple ES ignores seasonality and trend features in the demand data.

      • Simple ES produces a flat line (Level only) estimate.

    • Holt Exponential Smoothing (Holt ES):

      • Holt ES captures trends but is non-seasonal.

      • Holt ES treats data as a one-dimensional trend (up or down).

      • Holt ES provides estimates by combining an estimated trend for the forecast horizon and the smoothed level at the end of the series.

      • Holt ES provides a damping effect for long horizons.

    • Moving Average:

      • Moving Average is calculated as a simple moving average estimate from past time frames.

      • Moving Average gets priority over Simple ES and Holt ES when the data is too noisy.

    • Poisson:

      • This helps in estimating for low sellers assuming a Poisson distribution on the sales history.

    • Pick Best:

      • System determines which method (Simple ES, Holt ES, Moving Average, or Poisson Base Demand) provides the best fit based on the inverse of Root Mean Square Error.

    • Blend:

      • System generates estimates from various methods (Simple ES, Holt ES, Moving Average, or Poisson Base Demand) and uses the inverse of Root Mean Square Error as weights to combine them.

  5. Forecast Generation calculates the demand forecast combining the output of all previous steps. This forecast multiplies base demand, seasonality, and promotional lifts. In this manner, changes in seasonality and promotional effects can be evaluated and combined with the de-seasonalized base demand.