Home Furnishings Retailer Maximizes Revenue and Sales Win Rates with C3 AI Pricing Optimization

Challenges

Before partnering with C3 AI, a leading U.S. home furnishings retailer faced significant hurdles in its pricing strategy across its 40 operating markets, severely impacting its margin and total revenue capture on $1B annual sales. Discounts were largely driven by individual sales representatives, resulting in inconsistent pricing across the organization. Without a centralized, data-driven, or AI-enabled pricing framework, the company struggled to maintain healthy margins, achieve consistent win rates, and deliver strong customer satisfaction.

Most of the company’s pricing strategy and the sales expertise were fragmented and undocumented, making it virtually impossible to establish a scientific, fact-based approach to pricing and sales performance. This lack of structure made it difficult to align pricing strategies with actual sales outcomes, leading to missed opportunities for margin optimization.

The company also lacked automated analytical tools for real-time pricing insights. Decision-making was slow, and the long response cycles prevented the business from adjusting the strategies quickly. Additionally, there was limited visibility into key factors such as customer willingness to pay and market segmentation, making it difficult to tailor offers to specific customer needs.

Approach

Over the course of a 12-week collaboration, the joint team successfully integrated over 7 million rows of pricing data from multiple customer sources. This created a comprehensive, data-driven foundation to support pricing optimization at scale. Next, the team developed a new market segmentation framework based on the regions served—each with distinct goals, constraints, and levels of customer willingness to pay.

With a clear understanding of both products and market segments, the team constructed price elasticity curves for every SKU and market combination. These curves enabled the identification of optimal price points that would maximize total return by balancing margin contribution with win rate.