BIRetail empowers retailers to make quicker and informed decisions, through inferences of their own data.


Introducing Personalized Recommendation System

Product Recommendation is a technique deployed by Retailers with a singular purpose, viz. to “cross-sell”. At the point of purchase, based on what the customer has put in her basket, “relevant” products are recommended to the customer, coaxing her to buy additional merchandise.

The effectiveness of recommendations has a higher yield if the suggested products resonate with the customer.
Personalized Product Recommendations enhance the relevance of the suggestions by customizing them specific to each customer.


BIRetail’s Personalized Recommendation System (BIR-MSS) runs an “ensemble model“, and has been fine-tuned over various iterations with a multitude of Retailers. The solution is a state-of-the-art Data Science algorithm deploying Machine Learning to result in the most effective suggestions.

The solution is architected through an iterative technique of model development which considers a combination of algorithms and picks up the best mix of them.

The result is a very high average Recall Ratio of the model of 50-75% based on the richness of data available.

Relevance Factors

The magic sauce of making Personalized Recommendations effective, is in making “relevant” suggestions. Since the recommendation is entirely based on historic data, the relevance is enhanced based on the depth of understanding of the data.

BIRetail exploits all possible permutations of the these Relevance Factors from historic data, categorizing each dimension to the a degree of depth as required to arrive at meaningful scores:

Current Trends

Highest Selling Brands / Categories / Sub-categories.
New Releases.
Top Promotions.

Current ‘Like-To-Like’ Scenario

Seasonal variances.
Festival and “Special Day” likeliness.

Demographic Information

Categorization based on Customer's Age, Gender, Income, Lifespan, etc.

Product Affinity

Overall, which items tend to sell well together.
For this customer, what is the Product Mix preference.

Personal Preferences

Which Brands / Categories does this customer buy.
How susceptible is the customer to Promotions.

Personal Shopping Statistics

What are the average repeat cycles of this customer, per product.
What is the typical ABV and ABS of this customer.

Browsing Data

Customer Search History.
Abandon Carts.
Page Views.

Use Cases served by BIR-PRS

People Who Bought This Also Bought ...
You May Also Like ...
Personalized Picks ...
New Releases & Top Promos ...

Business Benefits

Increased Revenue, ABV

Effective Recommendations induces the customer to add more items to her bill thus increasing Sales Revenue.
As a consequence, Store Metrics like Average Bill Value and Average Basket Size inch upwards.

Enhanced Customer Satisfaction

Recommendations, when relevantly personalized, make the customer feel special, recognized and cared for.
This leads to higher stickiness and customer loyalty.

Improving Promotion Visibility

Discounted merchandise is always a bait to cross-sell. Bringing up Promotion items in the Recommendation is a sure-shot method to achieve liquidation objectives of a campaign.

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