To forewarn the periods of crests and troughs in the revenue cycle.

A leading retailer was keen on forecasting revenue of its departments based on historical data. The objective was to be forewarned about periods of crests and troughs in the revenue cycle.

The data quality was poor and this involved data engineering to make it available for analytics. The Advanced time series models including Arima and deep learning models (in few cases) were used to forecast the revenue.

The model helped in forewarning the team about the crests & troughs in the revenue cycle leading to efficient utilization of the resources and cut unnecessary costs during the troughs.