Retail & E-commerce

Retailers — whether operating through physical stores, online channels, or both — have long optimized operations using experience and intuition. The next frontier is turning the data generated across every customer touchpoint, warehouse node, and transaction into a competitive edge. Data can be put to work by harnessing information from ERP, CRM, and proprietary systems — browsing behaviour, purchase history, inventory positions, and logistics networks — through machine learning and agentic AI to make merchandising, marketing, and fulfilment decisions faster and smarter.

Product Recommendations & Agentic AI for Marketing

In a venture driving customer engagement through data and AI, Business Brio partnered with an omnichannel fashion retailer to build a recommendation engine paired with an agentic AI solution for the marketing team. Business Brio integrated data across ERP, CRM, and proprietary systems to build a unified view of the customer and developed models that could deliver personalized product recommendations. The client was further enabled with an agentic AI solution for the marketing team for reducing manual campaign-building effort and improving the relevance of offers reaching shoppers.

Shipping Cost Optimization for Multi-Warehouse Fulfilment

In an engagement focused on logistics efficiency, Business Brio worked with a multi-warehouse e-commerce company looking to bring down its fulfilment costs. Business Brio analyzed order patterns, warehouse inventory distribution, and carrier rate structures across the network, and built models to determine the most cost-efficient warehouse-to-customer fulfilment path for every order. The client was equipped with a solution that lowered logistics costs while maintaining delivery service levels.

Business Brio helps retail and e-commerce brands turn customer data into smarter merchandising, marketing, and fulfilment decisions.

Forecasting

At Business Brio, we have used Single Exponential Smooth, Double Exponential Smooth, ARIMA and back-propagation neural network. We did find Neural network to have better forecasting performance than the classical forecasting algorithms in case of wind energy forecasting for a particular project and won the NASSCOM Analytics Innovation award in 2015 for effectively using the same for business.

Optimization

Methods like Goal Node, Integer Linear Program, Simplex Method and Interior Point Method are used depending on the context and relevance. At Business Brio we use Lindo as a tool for optimization.

Machine Learning

Unsupervised and supervised learning methods like regression, support vector machines (SVM),KNN, K-means, PCA are used to recognize patterns and make data-driven predictions or decision outputs. We extensively use Python and R for applying the algorithms.

Decision Trees

CART, CHAID, Random Forests, mathematical and computational techniques are used to aid the categorization and classification of a given data information. Apart from programming tools, we also use WEKA for decision trees.

Sentiment Analysis

Opinion mining or emotion AI refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study affective states and subjective information. In past projects, we have heavily used LSE, HSA, text mining for semantic algorithms.

Forecasting

At Business Brio, we have used Single Exponential Smooth, Double Exponential Smooth, ARIMA and back-propagation neural network. We did find neural network to have better forecasting performance than the classical forecasting algorithms in case of wind energy forecasting for a particular project and won the NASSCOM Analytics Innovation award in 2015 for effectively using the same for business.

IoT/Sensor analytics/AI

Methods like Goal Node, Integer Linear Program, Simplex Method and Interior Point Method are used depending on the context and relevance. At Business Brio we use Lindo as a tool for optimization.

Machine Learning

Unsupervised and supervised learning methods like regression, support vector machines (SVM), KNN, K-means, PCA etc are used to recognize patterns and make data-driven predictions or decision outputs. We extensively use Python and R for applying the algorithms.

Recommendation Engine

GenAI/LLM

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Leverage your data to comprehend customer dynamics and increase revenues

Social Media Analytics

Know sentiments of your consumers through social media and capture brand perception, issues in pricing and distribution through a web and mobile based application. It uses semantic algorithm and machine learning techniques for data cleaning, mining and classification.

Forecasting Demand

We use forecasting and simulation techniques to understand demand patterns and help you build inventory plans for perishable and non-perishable products considering promotions and seasonality.

Customer value Analytics

We leverage data visualization and analytic models to analyze customer interactions, behavior and feedback to add value to increase customer base, grow share of wallet and address attrition.

Campaign Performance

Identify the most profitable campaigns, segment and know your customers better, cross-sell and up-sell to improve your campaign performance.

Marketing Channel Effectiveness

Understand the channels that are working better and improve the performance by designing the right metrics and real-time tracking through interactive dashboards.

Applications

Advantages

View how our technology works

Scalable solutions for future for seamless upgrades of features and integration with other IT system/data sources for upstream and downstream linkages

In-depth process study to understand the nuances of specific project scope for your product/service

As most of the analytics/data science projects have research based probabilistic outcome and is not as deterministic as other IT projects, understanding and setting a process/benchmark for such projects are very important