Retail Analytics

For businesses to stay abreast of the latest shopping trends, it is important to unearth, interpret and take action on meaningful data insights. Customer analytics plays a key role here. With the rise in omnichannel engagement through data points like in-store, online, social media, mobile and more, customer analytics is more complex than ever before! Huge volumes of POS data generated across stores every second, dramatically increases the complexity and variety of data types that are to be aggregated and analyzed. To make informed business decisions, Retailers, both online and offline, need to use a Single Source of Data, about the customer and the product that is consistent across the enterprise.

Sigmoid’s Data Science and Data Engineering practices help to develop intelligent machine learning models for Retail enterprises, as well as set up, improve and manage Big Data infrastructures. Thus enabling retailers to accurately identify what product the customers are most likely to buy and what to sell them next.

Our Data Engineering enables retailers to:

Integrate multiple and disparate sources of data
Derive insights from real-time customer analytics data
Productionize demand forecasting and other ML models
Manage data quality and consistency across sources

Providing End-to-End Data Solutions at each stage of the Retail Value Chain

Demand and Sales Forecasting Real Estate Planning Competitor Analysis Product Trends
Price Elasticity and Optimization Markdown Optimization Market Basket Analysis Cross Sell and Upsell Analysis Cannibalization Analysis
Supply Chain Optimization Supplier Analysis Inventory Planning and Optimization Route Optimization Shipment and Delivery Optimization
Category and Shelf Management Assortment Planning Store Profitability Analysis Workforce Optimization Theft Identification and Reduction
Pricing Analytics Customer Segmentation Market Mix Optimization Marketing Effectiveness
Channel Attribution Campaign Optimization
Loyalty Program Management Customer Insights Customer Lifetime Value Analysis (CLTV) Personalized Recommendations
Customer Retention and Reactivation Delivery Optimization

Our Expertise

Customer Analytics

Tackle frequent retail customer challenges like improving conversion rates and lowering acquisition costs through robust analytics

Demand Forecasting

Make accurate decisions on your data with our forecasting and planning expertise for efficient business planning across teams

Personalized Recommendation

Leverage our ML capabilities to get the best out of your data and offer unique personalized service for your customers

Data Lake Creation

Create global storage repositories for all data types from multiple sources to effectively store, manage and analyze retail data

Productionize & Scale ML Models

Build & develop production grade scaling of ML models to generate faster, larger, more accurate actionable insights and business results

Cloud Migration

Improve your business agility and save on costs by architecting and seamlessly migrating to the right cloud infrastructure

Marketing Effectiveness

Engage more effectively with your customers through personalized marketing and in turn, optimize profits and improve long term sales

Customer Life-Time Value

Explore ways to increase the value of your existing customers and drive growth for better overall customer experience

Customer Segmentation

Identify and understand the different behaviors and interests of the user and target them with relevant marketing content

Success Stories

Customer Analytics and Data Warehousing

Processed 250+ TB of customer and POS data, generating insights within seconds through effective data management.
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Personalized Recommendation System

Used Reinforcement Learning techniques to create personalized strategies that boosted the firm’s profitability
by 8%.
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