Advanced Analytics for Retailers

Data Science & Retailers


Due to increased level of competition and customer expectations, the retailers have started leveraging analytics for competitive advantage.Advanced analytics in retail industry can help in improving operational efficiency (e.g. low level of inventory cost, effective supply chain management etc), higher marketing effectiveness (e.g. sales driver identification, optimal budget allocation etc), creating customer experience and increasing customer loyalty (e.g. personalized product targeting, effective pricing).

Some of the commonly used analytics themes in retail and CPG industry are as follow :

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Market Basket Analytics:

Generating insights around customer shopping behavior based on items in a basket. Based on the insights, various cross sell and product promotion campaigns can be rolled out.

Customer Segmentation/Clustering:

Grouping homogeneous customers into a segment to understand their needs and behaviors for appropriate marketing treatments, merchandising and other service strategies.

Store Segmentation:

Stores can be grouped together based on profiles of the customers who lives around the stores, store characteristics and merchandised sold at the stores. The store segmentation can help in testing & improving pricing, promotions and distribution discussions. Management can test strategies & tactical treatments on certain stores on a smaller scale and based on success, they can roll out strategies across similar stores at a larger scale.

Product Assortment Analysis:

Product assortment is one of the key decisions for a retailer. Assortment planning helps defining types of products from a category (it’s breadth) and count for each items from the category (it’s depth) at a store. Analytics plays an important role in identifying right product assortments.

Market Mix Modeling:

Retailers spend significant amount of money on branding and product promotions across media channels such as radio, TV, print, store and online. Marketing managers want to measure and understand is the reasons for driving sales at the stores or online. Media Mix or Market Mix Models helps in identifying sales drivers, measuring Marketing ROI and allocating marketing budget optimally.

Recommendation/Next Best Action- Collaborative Filtering:

Retailers in a scout to increase customer loyalty and share of wallet, find out ways to promote next product that a customer will be interested in. Amazon and Netflix are pioneers in using “collaborative filtering based algorithms” to identify the next best product for a customer.

Price & Promotion Analytics:

Product demand, customer price sensitivity, product price and promotions are very intricately linked. Retailers look for following aspects :

  • How product pricing affect sales volume?
  • What product promotions will drive overall sale?
  • Which products should carry discount to attract more customers and sales?

Analytics helps in answering about questions and also tracking the effect of promotion campaigns

Sales/Demand Forecasting:

Accurate forecasting is key component for retailer planning and it is done at various levels. Sale volume forecasting at a store level can help in planning for extension or additional stores set up, or building promotion strategy to improve the store sales. At a product or category level, forecast can play crucial role in managing stocks level in a store. Demand forecasting influences margin level, pricing & promotions, inventory cost, customer satisfaction, and sales volume.