Retailers and Analytics for Retailers

Author: Ajay Dadheech and Ram

There are several ways to improve business of retail stores or retailers. Analytics requirements across retailers are typically same, except very specific need of each type of retailers. A retailer works on to attract consumers to the retail store(s) cost effectively (Marketing & Advertisement), keep relevant products in the stores (Inventory & Vendor Management), sell more products in a visit (Merchandizing and Markdown) and ensure the consumer revisit the store (Loyalty Management).
Retail Analytics for improving customer experience, managing inventory cost, increasing basket size and customer loyalty and reducing operation cost of the retailers. Retail Analytics has following dimensions or categorizations

1. Customer Analytics

a. Customer acquisition – Attracting right customers and convert them to buyers using levers such as Advertisements and Customer segmentation.
b. Behavioral Segmentation –Understanding consumer needs across seasons, trends, fashion, month end etc. Also behavioral segmentation helps in targeting right products and offers in-line with consumer needs and preferences.
c. Customer Loyalty and Retention – Finding ways to understanding consumer needs and keep them engaged to a retailer. Tesco (UK based retailer) is one of the most successful examples of leveraging its loyalty card in increasing share of wallet and keeping consumer interested to its stores.
d. Web/Online Analytics – One of the important lever for eRetailer but not less important for other retailers of they have online presence. Online consumer interaction, visit, searches and purchases can provide key insights in identifying consumer needs and soliciting with right products.
e. Customer Calls and Services Analytics– Each consumer interaction is an opportunity for a retailer or any other service provider. Consumer calls and services interactions data can be used for creating consumer experience, increasing customer loyalty and satisfying consumer needs

2. Marketing Analytics

a. Market Basket Analysis – Understanding customer buying patterns and product combinations using association algorithms and market basket analysis.
b. Market Mix Modeling – Identifying drivers of sales and allocating marketing budget to right marketing channels
c. Campaign Analytics – Measure and Analyze campaign performance for improving future marketing campaigns.
d. Pricing and Promotion– Retailers leveraging pricing and promotions for attracting customers and increasing sales and also managing idle inventory levels. Effective Pricing and Promotions is critical in balancing between sales turnover and margin.
e. Competition Analysis – Understanding competitor strategies and developing counter strategies are important to remain competitive. Competition Analysis helps in being on top of competitor strategies and actions.

3. Merchandizing and Planning

a. Store Localization and Segmentation – Identifying location of next store and improving sales performance are strategic priorities of any retailer’s leadership team. Data Science and Insights can provide required inputs for effective decisions.
b. Product pricing and markdown optimization – Effective product pricing and bundling requires insights around consumer purchasing patterns and level of buys and a lot of additional factors.
c. Assortment and Shelf space optimization – A retailer has thousands of products and SKUs, but limited self-space. Data Science and Analytics can support in getting best and most out of all the products in lesser space.

4. Demand Creation and Supply Chain

a. Inventory Planning and Replenishment Analysis – Stock and Inventory planning are important to balance between stock outs and idle inventory at the stores.
b. Demand Forecasting – Category, SKUs and Store level Forecast for different period

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