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ReputationRestaurantsSalons

AI Review Intelligence

Analyze Google and Yelp reviews to surface recurring themes, sentiment trends, and competitor benchmarks.

reviewssentimentlocal-seoanalytics

What you need to build this

Connect these tools, give the AI this data, expect these outputs.

1

Connect

APIs & tools to set up

OAuth, API keys, or webhooks depending on the tool.

2

Gather

Data sources to pull from

3

Give to AI

Inputs per run

  • Your Google Business Profile reviews (last 90 days)
  • Your Yelp reviews (last 90 days)
  • Competitor review samples from Yelp Fusion API
  • Business context: services offered, price range, location
4

Expect

AI outputs

  • Theme clusters for praise and complaints with example quotes
  • Month-over-month sentiment trend
  • Competitor rating comparison table
  • Top 3 operational recommendations based on review patterns
IntermediateFully automatedHigh value3 no-code · 2 low-code · 3 custom options

Business problem

Business owners read reviews reactively but never systematically analyze patterns. Recurring complaints about wait times or staff go unnoticed until they show up in multiple 1-star reviews.

Expected outcome

Weekly report showing: top praise themes, top complaint themes, sentiment trend vs. last month, and how your ratings compare to 3 nearby competitors.

Who it's for: Local businesses with 20+ reviews across Google and Yelp who want operational insights from customer feedback, not just star ratings.

Required data sources

What data goes into the AI

  • Your Google Business Profile reviews (last 90 days)
  • Your Yelp reviews (last 90 days)
  • Competitor review samples from Yelp Fusion API
  • Business context: services offered, price range, location

What the AI should output

  • Theme clusters for praise and complaints with example quotes
  • Month-over-month sentiment trend
  • Competitor rating comparison table
  • Top 3 operational recommendations based on review patterns

APIs needed to automate this

System behavior

  • Runs weekly on a schedule
  • Read-only — does not post responses (see Review Response Assistant)
  • Aggregates across platforms into unified theme view
  • Includes verbatim quotes as evidence for each theme
  • Flags new complaint themes not seen in prior reports

No-code vs low-code vs custom

No-code

Lowest effort

Non-technical owners, quick validation, under 50 runs/day

3 options listed

Low-code

Medium effort

Operators comfortable with Zapier/n8n, need more control

2 options listed

Custom build

Highest effort

High volume, custom logic, or strict data privacy requirements

3 options listed

No-code optionsLow-code optionsCustom build stack
ChatGPT with pasted review exports (manual weekly)n8n weekly pull from Google + Yelp → OpenAI analysis → email reportScheduled job pulling reviews via APIs
Birdeye or Podium built-in sentiment dashboardsRelevance AI review analysis agentOpenAI for theme clustering and summarization
Google Sheets + manual review paste + GPT formulaReport delivered via email or Notion page

Start with no-code to validate the workflow. Move to low-code when you hit rate limits or need branching logic. Custom build when volume, privacy, or integration depth requires it.

Step-by-step implementation

  1. 1Connect Google Business Profile and Yelp Fusion APIs
  2. 2Identify 3 local competitors for benchmarking
  3. 3Pull 90 days of reviews for your business and competitors
  4. 4Run OpenAI analysis with structured output template
  5. 5Deliver weekly report every Monday morning
  6. 6Share complaint themes with operations team for action items

Risks and limitations

  • Small review volumes produce unreliable theme clusters
  • Competitor benchmarking may use businesses that aren't true competitors
  • Yelp API terms restrict how review text can be stored and displayed
  • AI may over-index on recent negative reviews vs. long-term trends

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