Customer Satisfaction Survey Intelligence Agent

Summary

• A multinational manufacturing company conducted biannual customer satisfaction surveys across thousands of clients in Latin America, North America, and international markets, but lacked the tools to act on the data in real time.
• Survey results were locked in flat files, requiring manual filtering, pivot tables, and analyst effort before any insights could reach commercial teams.
• High-value signals buried in open-ended comments, dissatisfied clients, competitor mentions, and churn risk were going unnoticed between survey cycles.
• We built an AI-powered survey analytics agent giving commercial and operations teams a natural language interface to 35,000+ responses across multiple cycles, geographies, and service dimensions, no SQL or spreadsheet work required.
• The agent supports both typed queries and voice interaction, and uses a multi-model architecture optimised for both speed and analytical depth.

Teh Challenge

• Survey data is locked in static files. Results accumulated in flat files segmented by cycle, region, account manager, and service aspect, with no live query layer on top.
• Manual extraction bottleneck. Extracting meaningful insights required analysts to run pivot tables and cross-reference dimensions manually, creating delays between data collection and decision-making.
• Business users are locked out. Commercial directors, account managers, and regional leads could not independently query their slice of results; every data request had to go through the analytics team.
• Free-text responses were noise, not signal. Thousands of open-ended comments required manual parsing to surface themes, complaints, and competitive mentions. Cycle-over-cycle, this was impractical at scale.
• Churn risks going undetected. Signals of dissatisfaction and competitor consideration buried in comment text were rarely caught before accounts were at risk.
• Underdelivering a strategic program. A structured, high-investment biannual survey program was not generating proportional commercial or operational value due to these access and analysis barriers.

Solution

We developed an AI-powered survey analytics agent that gives commercial and operations teams a natural language interface to the company's full customer satisfaction dataset, covering 35,000+ responses across multiple cycles, geographies, and service dimensions.

• KPI Dashboard on demand. Instantly surface overall satisfaction rate, average score, % satisfied (4–5), % dissatisfied (1–2), and response volume for any segment, without opening a spreadsheet.
• Aspect-level scoring. Compare how each service dimension is rated: Sales, Delivery, Product Quality, Technical Support, Credit & Collections, and Product Development.
• Segment breakdowns. Drill into results by country, business unit, market sector, or account manager in a single conversational query.
• Top and bottom client rankings. Identify the highest- and lowest-rated clients with minimum response thresholds applied for statistical reliability.
• Low-score analysis. Isolate poor ratings, identify which aspects and clients are driving dissatisfaction, and surface the specific questions that scored lowest.
• Free-text comment analysis. The agent reads, groups, and synthesises open-ended responses, identifying recurring themes, urgent complaints, and positive highlights without manual review.
• Competitive intelligence. Automatically detects comments mentioning competitors or migration intent, flagging churn risk signals for commercial review in real time.
• Full report generation. Produces complete multi-section satisfaction reports combining all of the above into a single structured narrative, filterable by account manager, service aspect, or client.

The agent supports both typed queries and voice interaction. Under the hood, it uses a multi-model architecture: a lightweight model handles filtering and scope resolution, while a full-size model drives analysis and report generation, balancing response speed with analytical depth.

Impact

• Survey results become a live resource. Instead of a one-time report delivered post-cycle, insights are available on demand throughout the year, enabling continuous commercial decision-making.
• Commercial teams act faster on risk signals. Dissatisfied clients and competitor mentions surface immediately, before accounts are at risk of being lost.
• Account managers own their data. Each AM can independently query their segment without relying on the analytics team, freeing up analyst capacity for higher-value work.
• Executives get structured reporting instantly. Satisfaction reports are generated on demand rather than compiled manually, compressing the time from survey close to strategic review.
• Open comments stop being noise. Thousands of free-text responses are synthesised into actionable themes automatically, turning qualitative feedback into a structured intelligence layer.
• Cycle-over-cycle tracking. Comparative analysis across survey editions reveals whether service improvements are landing with customers, closing the feedback loop between operations and client experience.