Local market intelligence: Google Maps, reviews, and news with Apify

Local market intelligence: Google Maps, reviews, and news with Apify

Local competitors leave useful traces everywhere: map rankings, review velocity, star ratings, local press, opening hours, categories, photos, and website links.

The problem is that those signals rarely live in one clean report. When a client asks, "Which dental clinics are gaining ground in Austin?", the answer is scattered across Google Maps, review pages, news results, and local search results.

Local market intelligence is the practice of pulling those scattered public signals — Google Maps listings, customer reviews, and local press coverage — into one structured, weekly dataset an agency, franchise operator, or founder can use for decisions, instead of piecing it together from browser tabs and screenshots.

Quick answer

You can build a local market intelligence pipeline on Apify by combining a Google Maps scraper for business discovery, a reviews scraper for customer sentiment, and a Google News scraper for local press mentions. CrawlerBros Actors return structured datasets that can be exported to Google Sheets, BI tools, or an internal CRM.

The local intelligence stack

The stack combines three CrawlerBros Actors:

1. Google Maps Scraper for business discovery, ratings, locations, categories, phone numbers, websites, and coordinates.
2. Google Maps Reviews Scraper for review text, ratings, dates, reviewer metadata, and owner response tracking.
3. Google News Scraper for brand mentions, competitor mentions, article snippets, full-text extraction, and publication metadata.

The data collection runs in three passes:

\
Google Maps search
-> competitor and location dataset
Google Maps reviews
-> customer complaint and praise dataset
Google News search
-> local press and reputation dataset
\\

For a local SEO agency, this gives one repeatable view of a market. Instead of asking an analyst to search "plumber Dallas", open every competitor, copy ratings, and read reviews manually, the Actor run produces a structured export.

Key facts

• 3 Actors, 3 passes: Google Maps Scraper for discovery, Google Maps Reviews Scraper for sentiment, Google News Scraper for reputation.
• Maps search caps around 120 results per query: keep queries narrow (category + neighborhood) to avoid noisy, unusable output.
• Ratings tell you who's winning; reviews tell you why: a 4.8-rating business with 200 reviews and a 4.2-rating business with 4,000 reviews are different competitive stories.
• Narrow queries beat broad ones: "vegan restaurants in Williamsburg" produces a decision-ready dataset; "restaurants in New York" produces noise.

Actor configuration that matters

The Google Maps Scraper supports two modes: search for a text query and placeUrls for direct Google Maps place URLs. For broad market discovery, start with searchQuery, location, maxResults, and language. The public input schema notes that search mode typically returns up to 120 places for a query, so keep queries narrow enough to avoid noisy results.

A small first run looks like this:

\json
{
"mode": "search",
"searchQuery": "orthodontists",
"location": "Austin, TX",
"maxResults": 50,
"language": "en",
"proxyConfiguration": {
"useApifyProxy": true
}
}
\\

For Google News, the important fields are queries, maxResultsPerQuery, language, country, dateRange, optional siteFilter, optional excludeWords, and extractFullText. Keep extractFullText off during discovery and enable it only for the articles you actually want to analyze.

What the output looks like

The reason this workflow works is that every stage produces rows you can join later. For the Maps stage, the fields that matter are name, category, rating, reviewsCount, address, website, phone, location, and googleMapsUrl.

The review and news stages add the context the base listing cannot explain. Join by business name, website domain, and location, then keep the original URLs beside every insight. That way, when a client asks why you flagged a competitor, you can point back to the review or article instead of hand-waving from a summary.

In a small test run, Google Maps Scraper was asked for five Austin coffee shops. The Actor returned five rows with name, totalScore, reviewsCount, categoryName, address, phone, website, priceLevel, location, placeId, serviceOptions, hours, ratingHistogram, popularTimesSummary, photosSample, googleMapsUrl, and scrapedAt. That's enough for the first market snapshot before you even scrape reviews.

Why Google Maps is the starting point

Google Maps is usually the best first source for local market research because it captures the businesses that customers actually discover. A local competitor might have a weak website and still dominate discovery because its Maps profile has 2,000 reviews and strong proximity coverage.

The Google Maps Scraper gives you the base table:

• business name
• category
• address
• phone number
• website
• rating
• review count
• Google Maps URL
• latitude and longitude

From there, sort competitors by review volume, distance, category, or rating. For agencies, this is useful before a sales call. For franchise teams, it helps compare location density across neighborhoods.

Reviews turn rankings into explanations

Ratings tell you who is winning. Reviews help explain why.

A restaurant with a 4.8 rating and 200 reviews is different from a restaurant with a 4.2 rating and 4,000 reviews. The second business may still dominate demand because of location, brand awareness, or delivery coverage. The review text shows the operational pattern behind the score.

Use the Google Maps Reviews Scraper to pull recent reviews from each competitor, then group comments into recurring themes:

• slow service
• pricing complaints
• staff mentions
• cleanliness
• appointment availability
• product quality
• parking or location issues

This is where the dataset becomes useful. A local dentist can see that competitors get repeated complaints about long wait times. A gym can see that customers praise flexible hours. A home services company can spot which competitors are praised for same-day availability.

News adds reputation context

The Google News Scraper completes the picture. Local businesses often show up in news for openings, lawsuits, awards, acquisitions, health inspections, funding, or community events.

The Actor supports keyword searches, date filters, country and language settings, optional full-text extraction, and deduplication across queries. That means you can search for:

• the client brand
• competitor names
• category keywords
• neighborhood + service terms

The output gives article title, source, URL, publication date, snippet, and optionally full article text. News volume isn't a perfect popularity metric, but it's a useful reputation signal.

Production notes

Keep the market definition tight. "Restaurants in New York" produces noise. "Vegan restaurants in Williamsburg" produces a dataset someone can act on.

Store historical snapshots. A single scrape is useful. Weekly snapshots are better. Review count growth, rating changes, and new competitor appearances matter more over time.

Separate business discovery from review scraping. Run Maps discovery first, inspect the dataset, then pass selected URLs into the reviews stage. That keeps review scraping focused on relevant competitors.

Use review text carefully. Reviews can include personal data. Collect only the fields needed for analysis and avoid unnecessary storage of sensitive reviewer details.

Broad queries return datasets, not decisions. An early test query of "restaurants in Chicago" returned a dataset, but not a decision. Narrowing the search to a category, neighborhood, and use case made the output useful — a smaller, sharper input usually beats a giant scrape.

Cost comparison

Approach	Monthly cost	Freshness	Custom workflow	Raw data ownership
Manual research	Analyst time	Inconsistent	Yes	Spreadsheet only
Local SEO SaaS	$50-$300+	Varies	Limited	Limited export
CrawlerBros on Apify	Pay per run	On demand	Full	Yes

For the Apify row, check the current Pricing tab on each Actor page before running at scale. These Actors may be priced by compute usage, result/event count, or a platform pricing model that changes over time — the point is you only pay for the runs you need and keep the raw dataset.

FAQ

Can Apify scrape local business data from Google Maps?

Yes. CrawlerBros provides Google Maps Actors that extract structured business and review data from public Google Maps pages.

What can I do with local market intelligence data?

You can monitor competitors, find weak review themes, prioritize sales outreach, compare locations, and feed local SEO dashboards.

Do I need to code this workflow?

No. You can run each Actor from Apify Console and export the datasets. Developers can also automate the workflow with the Apify API.

What's the fastest way to try this?

Start with a small query such as "orthodontists in Austin," run Google Maps Scraper, pick 10 competitors, then run the reviews and news stages.

Try it yourself

Start with a small query such as "orthodontists in Austin" or "coffee shops in Denver." Run Google Maps Scraper, choose 10 competitors, then run Google Maps Reviews Scraper and Google News Scraper. You'll know very quickly whether the market is crowded, who owns attention, and what customers are saying.