Optimizing Competitive Pricing with Predictive Analytics and TruAI

The client is a household name and a dominant force in India’s fast-moving consumer goods (FMCG) sector. As the market leader, the company operates across more than 20 diverse consumer categories—including soaps, tea, detergents, shampoos, and personal care products. With a robust product portfolio that touches nearly every Indian household, the company proudly serves over 700 million consumers across the country.

The Challenge: Fragmented Digital Landscape

In today’s hyper-competitive consumer market, pricing is more than a number—it’s a strategic lever. For the client, India’s leading consumer products powerhouse, determining the right price for its vast product portfolio was becoming increasingly complex. The leadership team, particularly one of the top business heads, faced mounting pressure to make real-time, data-driven pricing decisions that could hold ground across a fragmented digital marketplace.

With products spread across 87 brands and 506 SKUs, and competition intensifying across 10 digital marketplaces—including their own and those of nine major competitors—the task was anything but simple. The company had access to a massive data universe of 181,479 pricing records, but extracting timely and actionable insights from it was proving to be a formidable challenge.

The leadership wanted to go beyond static reports and backward-looking data; they needed dynamic, predictive insights that could guide future pricing strategies in sync with market behavior. Without a smart solution in place, they risked losing their pricing edge—a risk the top leadership was unwilling to take.

The Solution: Smart Competitive Benchmarking

To tackle the complexity of pricing in a volatile, multi-brand digital marketplace, Datamatics engineered a tailored, hybrid analytics solution rooted in the client’s own data. Leveraging three months of transactional and competitive pricing data, the team implemented a strategic blend of advanced statistical and AI-driven methodologies to deliver deep, predictive insights.

The solution wasn’t just about analyzing historical trends—it was about forecasting future pricing behavior with accuracy and confidence. Datamatics combined multiple analytical models including Descriptive Statistics, Time Series Analysis, Hypothesis Testing, Forecasting, and Inferential Statistics to uncover patterns, anomalies, and competitive movements. This multidimensional approach allowed the system to understand not only how prices were moving across 87 brands and 506 products, but why.

By synthesizing the data into actionable intelligence, the solution empowered leadership with real-time pricing benchmarks, competitor movement tracking, and early warnings on price volatility. Most importantly, it laid the foundation for a scalable, AI-powered pricing strategy that enabled smarter, faster decisions in an increasingly digital, consumer-driven landscape.