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CUSTOMER SENTIMENT ANALYSIS

Customer Sentiment Analysis: AI Clinical
Trials Customer Feedback Mapping

The client, an emerging AI-based clinical trial recruitment platform provider, partnered with Roots Analysis to execute a comprehensive customer sentiment analysis of client reviews and feedbacks

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The Challenge

Decoding User Feedback to Strengthen
the AI-based Platform

Incomplete View of Sponsor
and Site Sentiments

The client had an advanced AI-enabled platform to monitor clinical trials, however, struggled with fragmented visibility into how sponsors, CROs and sites actually perceived its product experience across study phases

Unclear Drivers Behind
Churn and Low Adoption

Although the platform showed strong technical performance, the problem with limited adoption across the client base persisted. Internal teams could track usage metrics but lacked a structured sentiment lens to explain why decision‑makers hesitated to scale

Undefined USP Narrative
in a Crowded Market

Operating in a fast‑evolving AI landscape, the client struggled to articulate a differentiated USP based on real customer sentiments rather than internal assumptions

The Roots Way

How Roots Helped?

Roots Analysis deployed a structured customer sentiment analysis approach integrating feedback from product reviews, surveys, support logs and public channels.

Built a Data Collation Framework: The team designed a comprehensive framework that first mapped all relevant channels featuring reviews / product feedbacks. Once identified, we collated real-time feedbacks from respective sources post which, data sanitization was done to remove irrelevant information.

Mapped Customer Sentiments to Features: Utilizing advanced NLP models on the collated sentiments and feedback data, we generated granular, user-specific insights covering ease of usability of the platform, pricing transparency and support responsiveness. These models highlighted the direct link on how the customers perceived the platform and the likeliness of scale-up to multiple studies.

Delivered Quick-win Goals: The insights generated through NLPs were processed to identify gaps in the client’s platform, subsequently delivering highly specific improvement areas which could be taken care of with relatively lesser effort.

Change We Created

Results You Can Quantify & Impact
Our Clients Can Count On

32%

Reduction in Negative Reviews

Utilizing the sentiment framework, negative feedbacks related to integration effort and disruption to existing trial workflows dropped by roughly 32% across sponsors and sites. This improvement correlated with a significant increase in multi‑study deployments.

24%

Improvement Over Peers

Post‑engagement surveys indicated a 24% rise in respondents rating the client as “clearly differentiated” or “best in class” on key dimensions, such as ease of integration, patient‑matching precision and transparency of performance metrics.

18%

Better Customer Retention

The combination of targeted feature enhancements, proactive sentiment monitoring led to an increase of 18–22% retention in existing customers. Users were satisfied and showed eagerness to deploy the platform across multiple studies.