Analytics, by itself, holds little value without the ability to drive meaningful action. True transformation lies in the ability to leverage all available data, including organizational and ecosystem intelligence, to answer complex ‘why’ questions, evaluate options, and enable real-time execution at the moment of customer interaction.
Yet, most organizations today have built capabilities in fragmented silos; Data Engineering, Analytics Development, Insights Operationalization, Application Development, and Business Execution often operate independently. This creates significant latency, ultimately leading to a poor customer experience.
Moreover, customer experience isn’t just about customer insights or analytics. It demands that organizational processes, spanning Product Design, Sourcing, Supply Chain, Inventory Management, and Merchandising, operate in complete synchronization. The real challenge is not just developing better analytics solutions, but reimagining business processes with an AI-first mindset to deliver seamless, real-time experiences.
At Tiger Analytics, we are leveraging Agentic AI to help organizations address this challenge. Our goal is to empower businesses with true self-service capabilities, enabling them to deliver exceptional customer experiences with agility and precision.
How we use Agentic AI to predict, personalize, and perfect customer experiences
Case Study 1: Faster insights with Agentic workflows for a large apparel retailer
We partnered with a leading retailer to transform how different personas across the organization create reports and insights. Using our Agentic AI platform, users can customize their personas, select areas of focus, and generate actionable reports aligned with their execution needs. The platform allows users to easily adjust views with natural language prompts and to create workflows that drive real-time action.
Case Study 2: Streamlining experiences with AI agents for retail execution
As a provider of handheld devices and retail execution software, Zebra Technologies collaborated with us to develop frontline worker solutions that enhance operational efficiency, reduce costs, and improve customer experience. Together, we built Agentic AI-powered knowledge assistants that provide timely, actionable inputs directly to retail associates through their handheld devices, helping them execute tasks more effectively.
Case Study 3: Optimizing RGM for a major food company
Revenue Growth Management (RGM) is undergoing a shift from siloed, reactive planning to more connected and agile decision-making. In collaboration with a leading food company, we developed a solution that enables users to create dynamic workflows that connect multiple existing tools, answer complex business questions, run simulations, and decide on campaign strategies — all with minimal IT dependency. Users maintain control over execution decisions, dramatically reducing time-to-action and increasing campaign effectiveness.
Case Study 4: Creating campaigns that connect for a leading QSR brand
We explored hyper-personalized marketing driven by Agentic AI with a major QSR brand. Together, we developed a system that generates insights on personalized campaigns, facilitates rapid testing, and enables real-time implementation of offers, all of which are designed to enhance the customer experience at speed and scale.
Rethinking existing strategies to bridge the gap between customer demands and experiences
While we are still in the early days of this transformation, the pace of productization is accelerating. Agentic AI is proving crucial in scaling solutions across markets by handling disparate data sources, fine-tuning analytical models, and integrating seamlessly with existing systems and processes. Most importantly, it puts the power back into the hands of business users, eliminating the long wait times traditionally associated with IT-driven enhancements.
The case studies demonstrate a fundamental truth: Organizations that continue to treat analytics as an end in itself are missing the real opportunity.
Hence, we believe that it is time for a complete rethink, where analytics is not the destination, but the enabler of intelligent, real-time, and customer-centric action.
