Understanding the “Why” Behind the “Buy"

With Mapp’s recent acquisition of Dressipi, we’re taking AI-driven personalization to new heights, empowering fashion brands to create hyper-personalized, intent-driven experiences that enhance engagement, optimize inventory, and maximize profitability.

By combining Dressipi’s fashion-specific AI expertise with Mapp’s insight-driven marketing cloud, retailers can now understand the ‘why behind the buy’—giving them the intelligence to make smarter, data-driven decisions that improve both customer experience and business performance.

To explore Dressipi’s transformative impact on fashion retail, CXM Media spoke with Sarah McVittie, co-founder of Dressipi, about how their AI-powered solutions are tackling some of the industry’s biggest challenges. From hyper-personalized product recommendations to reducing overstock and increasing full-price sell-through, the discussion uncovers Dressipi’s core innovations—and how they now align with Mapp’s mission to help brands deliver next-generation, data-driven customer experiences.

1. Tell us about Dressipi and the challenge it solves for the industry

Dressipi’s AI platform solves this problem by breaking products down into customer-centric attributes, enabling truly personalized experiences and smarter operational decisions that reverse these downward trends.

2. How does Dressipi’s AI optimize product assortment and inventory management?

We tackle this challenge with a dual approach:

1. Personalization Algorithms: By creating customer propensity models at both size and feature levels, retailers can sell fragmented stock without resorting to markdowns, increasing full-price sell-through.
2. Demand Forecasting: Our AI-driven forecasting solution integrates with existing retail systems, delivering precise size and volume predictions across products, categories, and stores.

This deep attribute-level understanding of both products and customer intent enables brands to optimize inventory allocation across all channels, leading to an 8% improvement in full-price sell-through rates.

3. How does Dressipi help reduce stockouts and overstock situations?

By aligning customer behavior insights with inventory planning, Dressipi ensures better stock distribution and replenishment decisions.

• Stock Optimization: AI-powered personalization models match fragmented stock with high-propensity buyers, increasing full-price sales.
• Predictive Demand Planning: Our AI delivers precise size and volume predictions, allowing retailers to adjust inventory dynamically and avoid both understocking and overstocking.

4. What makes Dressipi’s AI unique for fashion and apparel?

Fashion is an inherently complex industry, with several unique challenges that generic recommendation engines fail to address:

1. Rapid product turnover (33% of inventory is new each month), creating cold-start problems
2. Sparse customer data (70% of shoppers make just one annual purchase)
3. High return rates and size availability challenges

Dressipi’s AI is built specifically for fashion, using advanced product attribution models and deep learning algorithms that increase incremental revenue by 8%.

5. What is the role of AI in demand forecasting?

Traditional demand forecasting relies on historical data, but fashion requires a more dynamic approach. Our AI models combine:

• Customer propensities
• Garment attributes
• Historical sales patterns
• Store profiles

This multi-layered approach creates highly accurate forecasts, unlocking revenue opportunities that historical data alone would miss.