Case Study: Agentic eCommerce - Competitor Price Intelligence & Dynamic Pricing Suggestions

Client Overview

A leading consumer brand in the home appliances sector sought to bolster its e-commerce dominance amid fierce market competition. Operating in India's burgeoning home appliances industry, which is projected to generate US$64 billion in revenue in 2025, growing at a compound annual growth rate (CAGR) of 7.33% from 2025 to 2030; the brand aimed to leverage advanced AI solutions to maintain a competitive edge.

The Challenge

In the highly competitive e-commerce landscape for home appliances, the client faced significant hurdles in maintaining market share. Competitors frequently undercut their stock-keeping units (SKUs) on major platforms such as Amazon, Flipkart, and other regional marketplaces, leading to lost sales and eroded buy-box positions.

The brand lacked comprehensive, real-time visibility into competitor pricing dynamics, including not just base prices but also layered elements like bank offers, instant discounts, coupons, and promotional bundles that collectively determined the "effective price" visible to customers. This opacity was exacerbated by the sector's rapid evolution.

According to recent e-commerce trends for 2025, pricing strategies are increasingly influenced by AI-driven personalization and real-time market fluctuations, with over 70% of retailers adopting dynamic models to respond to demand shifts.

The client's pricing team relied on manual checks, which were time-consuming and prone to errors, often missing short-lived promotions or regional variations (e.g., pincode-specific offers). Moreover, without an intelligent system for recommendations, decisions were reactive rather than proactive, resulting in missed opportunities during peak seasons like festive periods.

Industry data highlights that brands without robust competitor intelligence can see up to a 15-20% drop in market share due to unaddressed price gaps. The team not only needed monitoring but also actionable, self-improving suggestions that aligned with internal margins, channel strategies, and risk tolerances.

The Solution

Agentics deployed a cutting-edge AI-driven competitor monitoring and pricing recommendation engine tailored to the client's needs. This solution transformed raw data into strategic insights, enabling the brand to anticipate and counter competitor moves effectively.

Leveraging machine learning algorithms, the system processed vast datasets in real-time, incorporating factors like historical pricing patterns, demand elasticity, and external market signals.

Key to the implementation was a focus on scalability and adaptability, aligning with 2025 e-commerce trends emphasizing AI for dynamic pricing. The engine was built with resilient data collection methods to ensure accuracy amid anti-scraping measures, and it integrated seamlessly with the client's existing e-commerce infrastructure.

Deliverables

Agentics provided a comprehensive suite of tools and features to empower the pricing team:

1. Competitor SKU Mapping: A detailed mapping of the client's 20 flagship products to 5–10 comparable competitor SKUs across multiple marketplaces. This created a dynamic benchmark database, updated in real-time to reflect product variations and substitutions.

2. High-Frequency Data Monitoring: Automated scraping with multi-attempt protocols and proxy rotation to capture prices, buy-box winners, stock availability, and promotional offers several times daily. This ensured coverage of volatile elements like flash sales, which can change hourly in competitive sectors.

3. Effective Price Computation Engine: An AI module that aggregated and calculated the true customer-facing price by factoring in discounts, coupons, bank-specific offers, and EMI options. This went beyond surface-level pricing to reveal hidden competitive advantages.

4. Dynamic Pricing Recommendation AI Agents: Intelligent agents that analyzed competitive data against the client's pricing rules. Recommendations were contextual and actionable, such as:
- "Reduce price by ₹300 in Delhi to regain buy-box dominance, projected to increase sales volume by 12% based on elasticity models."
- "Maintain current pricing; competitor's promotional discount is set to expire in 48 hours, avoiding unnecessary margin erosion."

5. Learning Feedback Loop: A closed-loop system where each recommendation was logged as "accepted" or "rejected" by the team. This data retrained the AI models, refining thresholds for tolerance, margins, and strategies over time. Integration with business intelligence tools allowed for customizable dashboards visualizing trends, alerts, and simulations.

6. Reporting and Analytics Suite: Customizable reports, real-time dashboards, and API endpoints for integration with ERP systems. This included predictive analytics on upcoming promotional cycles and competitor behavior patterns, drawing from broader market data.

These deliverables were rolled out in phases, starting with a pilot on select SKUs, ensuring minimal disruption while maximizing ROI.

The Impact and Results

The deployment yielded transformative outcomes, backed by quantifiable metrics and aligned with industry benchmarks on AI dynamic pricing benefits, such as improved profitability and faster market adaptation.

Performance Metrics

- Strategic Clarity: The pricing team gained pincode-level insights into competitive positioning, identifying high-impact adjustment opportunities. This reduced blind spots in a market where regional pricing variations can account for up to 25% of sales differences.

- Time Savings: Manual reviews were eliminated, saving the team several hours weekly—equivalent to freeing up resources for strategic planning, as noted in retail studies where AI automation boosts efficiency by 30-40%.

- Recommendation Acceptance Rate: Rose to 70% within three months, as the AI learned from feedback to produce more aligned suggestions, fostering trust and adoption.

- Competitiveness Gains: The brand recaptured buy-box share in key marketplaces, especially during festive seasons, leading to a 15% uplift in e-commerce sales volume. This mirrors broader trends where AI-powered pricing can enhance revenue by 8-15% through optimized adjustments.

- Self-Improvement: Over six months, the system's accuracy improved by 25%, incorporating nuances like historical elasticity and cycle predictions, resulting in higher margins and reduced over-discounting.

Overall, the solution contributed to a more agile pricing strategy, helping the brand navigate a market where dynamic pricing is becoming essential, with 2025 forecasts indicating widespread adoption for real-time updates and customer engagement.

Why It Matters

This project exemplifies how Agentics' AI solutions turn competitive pressures into opportunities. In an era where e-commerce pricing trends lean toward sustainability and personalization, the system not only monitors at scale during high-discount periods but also delivers recommendations that balance competitiveness with business constraints. By learning from interactions, it evolves to mirror the brand's operational realities, creating a compounding advantage.

Evolution of the System

- Initial State: A basic manual monitoring setup, limited to sporadic checks.

- Current State: A sophisticated decision-support engine with predictive capabilities.

- Result: Pricing evolved from reactive tactics to a continuous, adaptive process, empowering leadership to respond proactively to market shifts.

Key Takeaway

Agentics' self-optimizing AI ensures that every pricing decision refines future recommendations, building a sustainable competitive edge. In India's dynamic home appliances market, where competitor intelligence is now a necessity rather than an advantage, this approach drives long-term growth and resilience.