Machine Learning for Retailers

retail_iconRetail industry has been in the forefront in adopting Data Science and Machine Learning. Some of the common applications of Data Science and Machine Learning are list below. Definitely, these are not exhaustive and smart professionals are bringing new applications everyday.

Some of the commonly used Machine Learning and Data Science Techniques are

Other application of Data Science for a retailers are

Scenarios of Data Science in Action for Retailers


Industry Vertical Scenario Scenario Description
Retailer – Marketing Retailer Response Model Retailers send marketing mailers or campaigns to attract customers to its store or online portal. Aim of a response model is to predict those customers who are more likely to respond to a specific campaign from a retailer.
Retailer – Marketing Retailer Cross Sell Model Identifying next best product to be recommended to a customers. Identifying a new product category which a customer has not bought in last few weeks but has higher likelihood of buying.
Retailer – Marketing Uplift Model Finding out customers who increase chances of response given it’s contacted.
Retailer- Operations Return Model Product or Return returns are key cost concerns for any retailer. An Order Return Model can help in identifying orders which have higher chances of getting returned.  Similar to order return model, product returns gives probability of a product getting returned.
Retailer - Marketing Loyalty Subscription Model Identifying customers or prospects who have higher chances of subscribing to a loyalty schemes.
Retailer - Marketing Spend Reduction Leverage customer spending patterns to finding customers have higher chances of reducing spend at a retailer and this will help in building customer spend management programs.
Retailer - Loyalty Loyalty Renewal Model A predictive model to estimate likelihood of a loyalty member’s likelihood to not renew its membership based transactional behaviour, membership history and other demographic data
Retailer –Merchandising Contact Mix Model Product Return is key concerns for a lot retailers, so developing an optimal contact strategy (mix and frequency of contacts) for different product sub category to maximise the revenue from returns products.
Retailer – Merchandising Dynamic Pricing Model Building predictive model to set product prices based on customer behaviour and loyalty to a retailer and linking back to optimal pricing. Some of the constraints will be price differentiation, minimum margin % and inventory level.

Predictive Modelling Scenarios for Financial Services

Retail Analytics Overview

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