Track competitor reviews and SERPs — Use case

The problem

Most competitive analysis is annual ("we did a deep-dive at the offsite"). Markets don\'t change annually — they change weekly, in small ways that compound. A competitor adding their 8th location, a sudden spike of 1-star reviews about wait times, a new entrant climbing into the top-3 SERP for your category — these are leading indicators of your own next quarter.

The classic SaaS competitive-intel tool costs $1k/month and pulls from generic web sources. The data is broad but shallow. Livescraper gives you a depth-focused snapshot on the exact thing customers actually see — the local Google Maps experience — at a cost low enough to run weekly.

How it works in Livescraper

1
Build your competitor watchlist
A CSV of 5–50 competitors (their place_ids or Maps URLs). Mix in your own locations too so you can normalise — "did the whole category dip, or just us?"
2
Pull listings + the latest reviews weekly
Run Google Maps Data Scraper for the listings (catches new locations, rating shifts, hour changes, photo-count growth) and Google Maps Reviews Scraper for the recent reviews.
3
Tag and bucket the review text
Run reviews through a simple LLM tag pass ("complaint about price", "complaint about wait", "praise for staff"). Even a 5-min ChatGPT classification gives you usable categories. Aggregate by competitor and week.
4
Watch ranking via category queries
Once a week, run a Google Maps Data Scraper task with your top categories + city as the query. Google's Maps returns listings in local-pack order — the top 3 in your output rows are who Google currently ranks #1–3 for that query. Diff vs last week to spot climbers and fallers.
5
Pipe to a weekly dashboard
A Google Sheet, a Slack digest, a Notion page, or your BI tool. The data is small enough that a single Sheet handles 50+ competitors comfortably.

Worked example

A mid-size DTC mattress retailer with 24 showrooms tracked 12 regional competitors weekly. In month 3 they spotted that two competitors had been quietly opening new locations in their target metros — three new sites between them in 6 weeks — and one major competitor had a sustained spike in 1-star reviews with the words "delivery delay" appearing in 38% of them. They responded by accelerating their own metro openings and pushing a "same-week delivery" guarantee in their ads. Total Livescraper spend over the quarter: ~$58. The intelligence informed three real business decisions.

What you get back

Two CSVs per weekly run, both with stable schemas so you can stack them into a time series.

Listings (Google Maps Data Scraper): per competitor per week —

✓business_name, place_id, full_address, city
✓average_rating, total_reviews, reviews_per_score_1..5, price_range
✓working_hours, business_status, photos_count
✓Maps-rank position (the row order in your output — Google\'s local pack ranking for the query)

Reviews (Google Maps Reviews Scraper): per review —

✓review_id, review_text, review_rating, review_datetime_utc
✓author_title, author_link, review_likes
✓owner_answer, owner_answer_timestamp
✓place_id + business_name for join into the listings table

week	competitor	average_rating	total_reviews	maps_rank	photos_count
2026-05-05	Sleepfit Co.	4.6	1,842	2	142
2026-05-05	Dreamline Beds	4.4	923	4	88
2026-05-05	NorthRest Mattress	4.7	2,104	1	210

Sample rows · not a real query result · your data will be richer

Best for / Not for

Best for

Brands competing in fragmented local markets (dental, fitness, F&B, services)
Corp dev / strategy teams watching M&A targets pre-deal
Marketing teams tracking ROI of their reputation work vs the field
Investors monitoring portfolio company performance from the outside

Not for

Pure-online competitors with no Maps presence (use a web-traffic tool)
Highly regulated industries where review data is sparse
One-off "snapshot" reports — Livescraper shines on repeated weekly pulls

FAQ

How do I know which competitors to track?

A solid starting point: take your top 5 highest-intent local queries (e.g. "mattress store [city]"), run a one-time SERP pull, and watchlist whoever appears in the top 10 across multiple queries. That's typically 10–20 competitors per metro.

Does Livescraper do the sentiment analysis for me?

No — Livescraper gives you clean structured review data; you classify it. Most teams run a quick ChatGPT pass (the prompt fits in a tweet) at about $0.50–$2 per 1,000 reviews. Coverage is good enough that a custom ML model isn't worth the engineering.

How do I detect new competitor locations?

Run your competitor-search task weekly. The output now has 1 extra row → that's a new location. The first run is your baseline; subsequent diffs are tiny.

Is it OK to do this? Are there terms-of-service concerns?

You're reading publicly accessible Google Maps data — the same way a customer searching would see it. Livescraper handles the bot-mitigation layer so neither you nor your account hit any limits. This is standard practice across competitive intelligence.