Healthcare Retailer

Company Profile

Fortune 50 Retailer

Industry

Healthcare Retail

Region

Executive Summary

A Fortune 50 Retailer’s product management and brand teams aimed to improve products using direct customer feedback, but were drowning in noise. With over 500,000 customer reviews across multiple channels, extracting relevant insights to improve in-house branded product quality and brand perception specifically, was nearly impossible. Off-the-shelf tools provided irrelevant insights as they could not distinguish reviews containing product-related insights from those centered around customer complaints or in-store experiences. This resulted in wasted product team’s time given the lack of actionable intelligence.

Aible helped the Retailer shift from “review-noise overload” to insight-led product innovation. By leveraging AI to understand product-specific context and intent, Aible empowered product managers to uncover hundreds of critical product issues, identify brand risks early, and align messaging with customer sentiment.

This led to faster decision-making, a significant reduction in manual analysis time, and the identification of high-impact product changes. As a result, the U.S. retailer increased customer satisfaction, mitigated brand risks early, and unlocked millions in potential topline revenue improvements.

Challenges

The retailer’s product teams were attempting to mine actionable insights from a massive trove of reviews using conventional AI and analytics platforms. The problems they faced included:

Irrelevant Insights: Product Managers and analysts spent countless hours manually sifting through data, often with little usable insights. Existing tools pulled in general store feedback (eg. poor in-store experience) and operational complaints (e.g., lost packages, shipping delays), which had no bearing on product quality. There was no ability to focus exclusively on store-branded products or distinguish between marketing vs. product vs. store concerns.

Limited Sentiment Clarity: The teams lacked a scalable way to understand overall customer sentiment trends by product line or compare sentiment between their products and competitors.

Solution

Aible deployed a tailored AI Agent that allowed the Retailer to extract insights relevant to each product line and team. The solution included:

Team-Specific Insights

For Product Managers: Aible identified product-specific issues like “bandage containers are hard to open” or “adhesive on bandages sticks too strongly,” helping them prioritize redesign.

For Brand Teams: Generated comparative sentiment analysis across in-store brands (eg. packaging frustration with bandage containers) vs competitors' products in categories like granola bars and vitamins.

For Marketing: Created on-brand web and packaging copy that emphasized features customers loved most (e.g., “chewable,” “best value for quality”).

Rapid Deployment: First prototype in 24 hours; 6 iterations in 48 hours; 74 iterations completed within 30 days.

Technical Innovations
Aible’s AI solution helped transform a noisy review dataset into precise, actionable insights, empowering multiple teams and supporting faster, more informed decisions across product development, branding, and marketing.

Scalable LLM Sampling: Smart sampling of 2–5K reviews from datasets up to 30K reviews per product to fit within LLM context windows while preserving relevance.

Persona-Specific Views: Translated AI outputs into actionable summaries based on the persona, depending on whether the user was a marketer, brand manager, or product leader.

Cross-Product and Competitive Benchmarking: Enabled insightful revelations across systemic versus brand-specific issues. For example, the application would provide apt responses for queries like “Show me all bandage complaints across brands”.

Voice of Customer

Fortune 50 Retailer Improves Product Roadmap Development and Go-To-Market Using GenAI by Surfacing 360-Degree Customer Sentiment in Days

Impact

Aible's Agentic AI application transformed overwhelming and disparate review data into targeted, actionable insights, driving measurable improvements in product quality, brand health, and business performance. It laid the foundation for future plans to generate tailored insights, in contrast to generic AI tools, across more product lines and business units.

Revealed 100s of Actionable Product Issues: Ranging from design flaws to formulation concerns, enabling product teams to proactively address quality and usability.

Enabled Strategic Product Decisions: Helped prioritize which product lines to revamp based on aggregated customer feedback.

Boosted Customer Satisfaction: More responsive product fixes improved public perception and potentially increased repeat purchase behavior.

Brand Risk Mitigation: Identified brewing sentiment issues before they went viral or caused wider brand damage.

Collateral Generation: Automatically created product marketing content aligned with customer excitement and real language, speeding up time to market for packaging and digital updates.