The Challenge
The UK's No. 1 kitchen retailer records thousands of showroom conversations between customers and kitchen designers every year. The business wanted to understand what actually separates conversations that end in a sale from those that do not. Rather than relying on assumptions or anecdotal feedback, the company wanted objective, data-driven insight that could help improve sales performance and enhance training programmes.
With almost 3,000 showroom recordings available for analysis, the initial challenge was not a lack of data, but how to extract meaningful insights from thousands of hours of customer interactions at scale.
The commercial stakes made this worth solving properly: even a 1% improvement in showroom conversion is worth an estimated £100k in additional revenue per showroom, per year.
The Solution
Advancing Analytics worked with the kitchen retailer to apply AI and machine learning techniques, delivered on Databricks, to showroom conversation transcripts. The team analysed approximately 2,948 recordings from multiple showroom locations, transforming audio conversations into structured insights.
The approach included:
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Using AI to identify behavioural patterns and recurring themes.
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Mapping conversations against their existing customer journey framework.
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Comparing successful and unsuccessful sales conversations to discover statistically significant differences.
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Analysing key sales behaviours, pricing discussions, design decision-making, competitor conversations, and closing techniques.
What Was Analysed
The project examined thousands of customer conversations, with an average recording lasting around two hours. The dataset represented a broad cross-section of their showrooms, providing a solid initial sample for behavioural analysis.
The AI models were used to identify:
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Sales behaviours associated with winning outcomes.
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Common traits of lost opportunities.
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Customer sentiment around pricing.
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The impact of competitor discussions.
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The effectiveness of Wren's sales process and customer journey.
Business Impact
The engagement delivered more than interesting observations. It provided the organisation with evidence-based recommendations which could be incorporated directly into training, coaching and sales processes. Several findings confirmed assumptions the business already held, while others highlighted opportunities for immediate improvement.
Because even a 1% improvement in showroom conversion is worth an estimated £100k in additional revenue per showroom, per year, these findings represent a substantial and repeatable commercial lever rather than a set of marginal gains.
As a result, they began exploring enhancements to customer-facing pricing visibility and gained data-backed validation for key elements of its sales methodology. The insights also created a foundation for future initiatives, including a coaching framework built around the identified behaviours, real-time conversation scoring, and a playbook capturing the language patterns of their highest-converting designers.
Outcomes at a Glance
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Processed 2,948 showroom conversations across multiple locations.
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Identified 5 coachable behaviours that separate sold from lost opportunities.
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Validated and enhanced their existing customer journey framework.
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Generated actionable recommendations for training, pricing transparency and sales effectiveness.
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Established a roadmap for future AI-led conversation scoring and performance coaching.
Conclusion
By combining AI, machine learning and business expertise, Advancing Analytics helped the kitchen retailer transform thousands of customer conversations into actionable business intelligence. The project provided clear evidence of the behaviours that influence customer decisions and created a data-driven foundation for improving sales performance across the organisation.
