+12%Revenue Per Available Room (RevPAR) vs. non-responsive hotels
+12%Monthly revenue growth from acting on guest feedback
+10%Increase in guest loyalty
+40%Previously unactionable negative reviews now surfaced and resolved
About The Client
Our client is a 5-star luxury hotel chain with 11 award-winning properties across key destinations in India. Guest feedback arrived scattered across online reviews, social media, and in-house feedback forms, with no way to centralize it, filter out noise, or tie it back to the performance of specific properties or business units. BigOhTech designed and built an AI-driven sentiment analysis platform to close that gap, from architecture through launch.Business Challenges
Guest feedback came from review sites, social platforms, and paper/digital forms, with no centralized pipeline to bring everything together.
C-level executives lacked a reliable, consolidated view of how individual properties and business verticals were performing.
A significant portion of incoming reviews was low-quality or gibberish, making it difficult to separate meaningful insights from irrelevant feedback.
Without a dedicated dashboard for vertical-wise performance, teams struggled to identify trends and make faster decisions on expansion and investment.
More than 40% of negative reviews remained unactionable, as the organization lacked a practical system to turn feedback into clear next steps.
Our Approach
Unified data ingestion We built APIs to pull reviews from every major source — online review platforms, social media, and feedback forms — and map them into a single database, replacing the fragmented, manual process the client relied on before.
Preprocessing at scale Pandas handled cleaning and preprocessing across large volumes of unstructured text, stripping out noise and irrelevant content before it reached the analysis layer.
NLP-based sentiment classification Google BERT was used to extract relevant keywords and classify each review as positive, negative, or neutral, giving the client a consistent, automated read on sentiment instead of manual tagging.
Deep learning for nuance A CNN was trained to learn patterns across different parts of each sentence, and deep learning models handled the non-linear relationships in the data — the kind of subtlety that keyword-matching alone misses (e.g., sarcasm, mixed sentiment within one review, or context-dependent complaints).
From insight to action The client used the classified data to identify concrete, fixable issues — slow service, outdated facilities, cleanliness gaps — and acted on them directly: staff training, facility renovations, and increased room-cleaning frequency. Positive sentiment was fed back into marketing to highlight genuine strengths and unique selling points.
One dashboard, two clicks All of this rolled up into a single unified dashboard giving business owners a full sentiment view of any property or vertical in just two clicks — replacing what had been a fragmented, multi-source manual review process.
The Impact
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CASE STUDY - AI/ML
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