Case Overview

The hyperlocal retailer started their business with milk and egg delivery. Once they had onboarded significant number of customers, they expanded the items being sold to ~ 10,000 SKUs. Their biggest issue was that about 70% of their revenue was still being generated through milk and breakfast product offerings. They wanted to implement a solution that would help them in SKU visibility - that would eventually lead to a more diversified revenue stream.

Not just in-app recommendations

We suggested taking a leaf out of Amazon's practice of not only in-app recommendations, but also through personalized communication with the customers - through emails and SMS

Improved unique SKU visibility per user by 14%

Repeat sale of non-milk SKUs increased from 3.2% to 5.8%

Average basket size improved from 1.53 to 1.61

Historical purchase data and few other data points associated with purchase history were used to develop a collaborative filtering based recommendation engine.

Multiple attributes. Lack of clarity

The hyperlocal retailer was inundated with purchase and browsing data. They were use association rules and market basket analysis to analyse the purchase basket and using it to reach out to the customers. This solution was useful in the shorter term when they were offerings limited number of SKUs. With addition of more than 10,000 SKUs, the biggest challenge for them was to make customer aware that they were selling more than milk and egg products.

Another unique element of their business model was the repeatability of purchase. They observed a clear trend of customers buying a few products at regular intervals e.g. a customer would buy 1 litre pack of a specific brand of olive oil every 3 weeks. They wanted the recommendation solution to incorporate product purchase cycle into the algorithm. We developed a constraint based recommendation engine solution for our client.

A wise business turns chance into good fortune!

Any business spends a lot on customer acquisition. And the cost of acquiring new customers is about 8x more than retaining an existing customer. Cross sell and Upsell are proven retention strategies that are used by offline businesses.

The issue with implementing a cross sell solution (similar to offline businesses) is the volume of products on display and the limited real estate for app based purchase platforms. Recommendation engine solves these problems for digital businesses by:

a. Improving SKU visibility

b. Suggesting products based on propensity of purchase [Cross Sell & Upsell]

c. Personalized SMS and email campaigns

These benefits make recommendation engine an indispensable solution for digital stores