AI REVIEW RESPONDER
PRODUCTION FEATURE
IMPACT - MEDIUM
ADOPTION - HIGH
Problem Discovery
Enterprises receive hundreds of reviews across their stores every month.
01. Responding manually was slow. Resulted in repetitive responses.
02. Many reviews go unanswered.
03. Affected metrics like Response Rate & TAT (Turn around Time).
Source of Discovery
Client-side Analytics
Reviews Data
Client Interactions
User Personas
Account Managers, Store Managers
Why we Prioritized it
High weightage of affected metrics in building Reputation Score.
Immediate pain-point of Account Managers.​
AI enabled contextual, brand toned and empathetic responses at scale.
Templates or manual couldn't achieve it.
Why AI was the best solution.
MVP & Pilot
Piloted with 2 Enterprise clients.
Iterated Prompts.
Enabled Human-in-the-loop.
Output Evaluation
was based on
01. Output Quality - Tone, Relevance
​
02. Output Variety - Repetition Check
​
03. User Trust - Edit rate, Qualitative User Feedback
REPETITION EVALUATION
Sample Size
50​
Tuning Thresholds
​Cosine Similarity >= 0.5
for meaning similarity
​
Bigram overlap >= 65%
for word similarity
Result
​24% of responses generated were flagged as repetitive.
Cosine Similarity & Bigram overlap
Evaluation Scores
*Actual figures are masked to retain confidentiality
Outcomes
in Production
Client Outcomes
Response Rate increased from ~50% to ~80%.​
​
TAT reduced from days to hours.
Product Outcomes
Adoption of related Analytics features increased.
​
AI-Differentiation positioning strengthened.
Continuous Improvement
Prompt iterations for improved output.
Adding vernacular languages.
Collecting thumbs up/down user feedback.
