How AI-Led Intent and Resolution Modelling Enabled a UK Bank to Improve Customer Service Performance

A leading UK retail and commercial bank used AI-driven NLP to analyse chatbot and call-centre conversations and improve customer service performance. By applying intent modelling with BERTopic and call-resolution classification using semi-supervised machine learning, the bank identified clear reasons for customer calls and measured whether issues were resolved. Chat and telephonic transcripts were cleaned, embedded, and clustered through a single workflow that combined programmatic labelling, active learning, and SME validation. Built on over 200,000 call transcripts and 60,000 chat transcripts, the solution delivered over 80% accuracy for intent identification and over 70% accuracy for resolution classification, helping reduce repeat calls, improve agent benchmarking, and create a consistent view of customer conversations across channels.

How an Enterprise Data Foundation Enabled a Global CPG Company to Unlock the Value of Their Data

Our case study highlights how a global CPG company implemented a scalable enterprise data foundation using Azure, Databricks, and GenAI-assisted tools. This data modernization initiative unified disparate data sources, improved operational efficiency, and enabled actionable, data-driven insights across supply chain, procurement, and financial planning functions. The approach demonstrates best practices in cloud-based analytics, enterprise data governance, and GenAI integration for retail and consumer goods companies seeking a future-ready data platform.

How a Scenario-Based Pricing Tool Helped a Global Coffee Chain Strengthen Margin Decisions

Tiger Analytics helped a global coffeehouse chain optimize SKU-level pricing and improve revenue growth using a scenario-based pricing platform powered by machine learning and elasticity modeling. The platform replaced manual spreadsheet workflows with a unified web application that enables price simulation, scenario planning, and competitor benchmarking across 2,500+ stores and 200+ SKUs. Teams can evaluate multiple price sets, measure projected impact on revenue, margin, and sales, and make data-driven pricing decisions. By combining advanced analytics, transparent workflows, operational efficiency, and scalable price optimization, Tiger Analytics delivered a practical solution that ensures consistent, profitable pricing strategies across large retail networks.

Enabling a Large Payment Services Firm to Offer Meaningful Gen AI-Driven Financial Advisory Services

HR Data Mart Modernization Using Azure: Scalable Workforce Analytics for a Global F&B Leader

A leading global Food & Beverage retailer modernized its HR Data Marts by migrating from manual Power BI processes to a scalable Azure-based architecture using Databricks and Delta Lake. The initiative automated data transformations with PySpark and Azure Data Factory, embedded PII encryption for compliance, and integrated CI/CD pipelines for consistent deployment. This retail data modernization effort enabled faster workforce analytics in Power BI, improved data governance, and reduced manual processing. The result was a future-ready data platform that enhanced decision-making, scalability, and operational efficiency across HR analytics.

How Price Elasticity Modeling Helped a CPG Giant Navigate Regulation and Consumer Sensitivity

How GenAI Enabled a Major Retailer to Achieve Data-Driven Supply Chain Transformation

How Agentic AI Enabled a Global Food & Beverage Leader to Enhance Warehouse Operations

MLOps playbook transforms top US health insurer’s model migration

Tiger Analytics’ MLOps roadmap optimized a health insurer’s model migration, detailing resources and lifecycle PoC.

Fortune100 F&B boosts chip quality, cuts effort by 75% with deep learning

Tiger Analytics used deep learning CV for optimal potato peeling, improving chip quality and real-time plant efficiency.