Retail Mart Sales Forcast

About Retail Mart

“Retail Mart” is an online store super giant having worldwide operations. It takes orders and delivers across the globe and deals with all the major product categories - consumer, corporate & home office.

The Mart caters to 7 different market segments and in 3 major categories.

The Project Goal

• To help the Sales Department to build a 6 Month plan by giving a forcase of the sales and the demand for the next 6 months, that would help them manage the revenue and inventory accordingly.
• To forecast at 7 market segements in 3 categories at granular level
• To find the top 5 most profitable and consistent segment from these 21 and forecast the sales and demand for these segments.

Project Execution

Data Understanding:

The data has the transaction level data, where each row represents a particular order made on the online store. There are 24 attributes related to each such transaction. The “Market” attribute has 7-factor levels representing the geographical market sector that the customer belongs to. The “Segment” attribute represents the 3 segment that the customer belongs to.

Data preparation:

• Segmented the whole dataset into the 21 subsets based on the market and the customer segment level.
• Converted the transaction-level data into a time series.
• Create an aggregate the 3 attributes - Sales, Quantity & Profit, over the Order Date to arrive at monthly values for these attributes.
• To find the 5 most profitable and consistently profitable segments,using the coefficient of variation of the Profit for all 21 market segments.

Model building:

With the 2 most profitable segments, the next challenge is to forecast the sales and quantity for the next 6 months. Data Smoothening is done before classical decomposition and auto ARIMA is performed for forecasting.

Model evaluation:

The final best fit model is used to forecast the sales/demand for next 6 months using this model. The model is tested to predict the last initially separate out data of the last 6 months values from the dataset, after aggregating the transaction level data into the monthly data. The results is then checked against the seperated 6 months forecast using the out-of-sample figures.

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