Google Reviews Analysis MVP: From Ingestion to Insights

This MVP helps businesses automate the ingestion and analysis of Google Reviews using Snowflake and Python, with the added intelligence of sentiment scoring powered by Snowflake Cortex LLM.

Whether you're a retail chain, hospitality brand, or service provider — this pipeline enables a scalable, automated way to monitor customer satisfaction across multiple locations.

💡 Why Automate Google Reviews?

Without automation, most teams rely on:

• Manually checking reviews in Google Business

• Copying data into spreadsheets

• No historical trends

• No centralized visibility across all locations

This MVP eliminates manual work and turns reviews into structured data + sentiment insights — ready for BI or CX dashboards.

✅ MVP Outcomes

• 🔐  OAuth-secured API integration => Fully automated, secure connection to Google Reviews

• 🔄  Multi-location support => Ingest reviews across all branches dynamically

• 🧼  JSON flattening into Snowflake => Structured tables with all review metadata

• 🧠  Cortex sentiment scoring => Qualitative analysis of customer tone and emotions

• 📊  BI-ready outputs => Power dashboards, alerts, and location comparisons

🔁 Google APIs Used

🔹 [Business Information API (v1)]

Used to get:

• Account ID: /v1/accounts

• Locations: /v1/accounts/{accountId}/locations

🔹 [My Business API (v4)]

Used to get:

• Reviews: /v4/accounts/{accountId}/locations/{locationId}/reviews

🔐 [OAuth 2.0 Authorization Flow]

• Access/refresh tokens handled securely

• Refresh logic built into ingestion step

🔐 Secure by Design

All credentials are stored in Snowflake Secrets, and tokens are refreshed automatically using OAuth2 — ensuring the pipeline is secure, scalable, and production-ready.

No keys are stored in plain text or notebooks.

🛠️ Pipeline Overview

Step 1: Authenticate and get account_id

Step 2: Retrieve all Google business locations

Step 3: Pull reviews for each location (supports pagination)

Step 4: Flatten and insert into Snowflake (upsert by review_id)

Step 5: Run sentiment analysis using Snowflake Cortex

Step 6: Explore insights in BI dashboards

🧠 What Sentiment Scoring Adds

Reviews aren’t just about stars — the text of comments tells you far more.

By using Snowflake Cortex's built-in LLM sentiment function, we can classify each review into:

• Positive

• Negative

• Neutral

Example:

This adds another dimension to your customer intelligence.

🧠 How It Works

• Works directly in SQL

• No need to use Python or external services

• Fast and cost-effective for daily or weekly review scoring

📊 Sample Dashboards

• ⭐ Review trends by rating => How average ratings change over time

• 💬 Comment cloud by sentiment => Common words in positive vs negative reviews

• 🏆 Top vs bottom locations => Based on sentiment, not just rating

• ⏱️ Volume of reviews => Weekly/monthly insights for CX campaigns

• 🧯 Alerting logic => Spike in negative reviews this week? Get notified

🧩 Artefacts

• Google Review API interaction flow (account → location → review)

• Review table schema and column mapping

• Cortex sentiment enrichment logic (via SQL)

• Dashboard mockups for experience teams

✅ Summary

This MVP delivers a complete, modern solution to turn raw Google Reviews into structured customer insights — powered by:

✅ Secure API integration with Google Business
✅ Flattening & merging logic using Snowflake
✅ Sentiment classification using Cortex LLM
✅ Zero manual intervention
✅ Plug-and-play for BI & CX teams

It’s simple, fast, secure — and most importantly, business-ready.