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Customer Reviews & Sentiment

Dotlas aggregates customer reviews from diverse online sources including food delivery platforms, review aggregators, social channels, and brand pages. Each review is processed through advanced reasoning and natural language understanding models to generate:

  • ABSA scores (Aspect-Based Sentiment Analysis) for food, service, ambiance, value, and more
  • NPS Sentiment Rating (1–5 scale) aligned with Net Promoter Score methodology
  • Categorized topics automatically extracted from the text (e.g. staff friendliness, food temperature, accessibility)
  • Time-series tracking of review quality per topic or outlet

This transforms qualitative customer voices into structured, quantifiable insights that can be compared across time, restaurants, brands, and topics.

Use Cases for Marketing, CX & Product Teams

  • Track dNPS movement across time to detect brand health deterioration early
  • Identify sentiment gaps — strong food but weak service? Value declining?
  • Pinpoint CX strengths or issues at the brand, outlet, or city level using topic-tag trends
  • Quantify qualitative feedback into structured, actionable dashboards
  • Benchmark seasonal shifts in brand experience
  • Use AI-tagged praise or complaints in marketing, operations and menu R&D
  • Evaluate campaigns — do new activations lead to better NPS scores in target cities?

Data Dictionary — Individual Review Level

Each row represents one customer review as interpreted by the Dotlas AI pipeline.

Column Name Data Type Description
review_id string Unique identifier for the individual review
restaurant_id string Dotlas-assigned ID of the restaurant being reviewed
brand_id string ID of the restaurant chain or brand
review_text string Raw text of the original customer review
review_source string Platform or website where the review was posted
reviewed_at timestamp Date and time the review was posted
nps_sentiment_score integer Customer sentiment on a 1–5 scale based on NPS reasoning
aspect_food float ABSA score for the food (1.0 to 5.0)
aspect_service float ABSA score for service (1.0 to 5.0)
aspect_ambiance float ABSA score for ambiance/environment
aspect_value float ABSA score for value for money
topic_tags array[string] List of detected topics (e.g. temperature, staff friendliness, portion size)
language string Language of the original review
neighbourhood_id string Neighborhood ID of the restaurant reviewed
city string City where the review was recorded

Each row aggregates customer review sentiment at the brand and time level for performance tracking.

Column Name Data Type Description
brand_id string Dotlas-assigned brand identifier
date date Daily or weekly aggregation date
city string City where reviews were aggregated
dNPS_score float Derived Net Promoter Score (-100 to +100), based on NPS bucket distributions
total_reviews integer Count of reviews contributing to this aggregation
avg_aspect_food float Average food rating score across reviews
avg_aspect_service float Average service rating score
avg_aspect_ambiance float Average ambiance score
avg_aspect_value float Average value-for-money score
top_topics array[string] Most frequently mentioned topics (e.g. slow service, good quantity)
negative_topic_count integer Number of reviews associated with at least one negative topic
positive_topic_count integer Number of reviews associated with only positive topics