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

AI/ML Prescriptions

AI/ML Prescriptions

While BIRetail’s “Inferences” can provide answers to every question you ask, it is extreme grunt work to manually scavenge for every single issue that could go wrong for a Retailer in a single day.

Prescriptive Analytics is the technique to get actionable recommendations from BIRetail, without asking!

With our rich domain expertise, BIRetail has identified common operational areas in Retail that tend to grow slack and require constant tweaking to optimize their business efficiencies. Each of these Business Use Cases use a combination of extensive data and a massive weave of ‘if-then-else’ type of decision trees that look at a plethora of permutations of rules to arrive at a recommendation for each granular actionable.

The approach built within such recommendation engines is to simulate the step-by-step problem-solving thought process that a human undertakes to arrive at the best solution for a single problem. And that is why techniques are named ‘artificial intelligence’. The way such systems are coded, they allow the engine to constantly learn ways to handle new scenario based on past patterns, and hence these engines also deploy ‘machine learning’ techniques.
BIRetail uses Data Science algorithms to create each such Prescriptive Analytics Use Case. Once the business rules are configured for each individual Retailer, the benefit is to have a cheat sheet of prescribed recommendations to be carried out, without diving into a plethora of data and analysis.
BIRetail has a ready library of AI/ML based Prescriptive Recommendations, some of which are listed below:

Merchandise Support System (BIR-MSS)

This is BIRetail's Auto Replenishment System (ARS) which provides prescriptions on a daily basis, for movement of merchandise between warehouses and stores, based on demand and supply.

Personalized Recommendation System (BIR-PRS)

BIR-PRS provides the top 5 product recommendations to be presented to the customer at the Point of Purchase (in-store or online), based on the customer's product and price point preferences, current items in the cart, current top selling and top promotion items and the like.

Demand Forecasting

This is a classic regression technique to predict the demand of each and every item based on the Retailer's buying, stocking and selling history, planned promotion days and events calendar, season and seasonality, etc.

Churn Prediction

From a deep study of Customer engagement across all touch points of Retail, including buying trends over time, sentiment analysis through contact centre interactions, social media posts and engagements, BIRetail helps in early identification of potential Customer Loyalty erosion.

Promotion Predictions

Analysing past Promotion Efficacy data in combination with statement of intended objective of a planned promotion, this Use Case recommends the best Promotion Campaigns by Category and Demographics.

Many More

Contact Us to know of more such Prescriptive Analytics Use Cases in the BIRetail Library.

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