If you do local SEO for clients, or you just run a shop with three rivals on the same street, you know the Monday chore. Open Google, search your category, copy everyone's star rating and review count into a spreadsheet by hand. I got tired of it, so I built the thing that does it.

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Schedule (Mondays) -> Set node with your business name, search term, city -> Apify Google Local API -> Code node ranks and compares -> Google Sheets append

One row per business per week. Columns: rank, business name, star rating, review count, place_id, Reviews vs you, Rating vs you. The last two only fill in when your own listing shows up in the results, which is how you get the gap without a second lookup.

Who this is for

Agency reporting. One search term per client, one copy of the workflow per client, weekly schedule. After a month the sheet is the local SEO section of your monthly report, with no screenshot-and-annotate step in front of it.

Running a local business. The number that matters is not your rating. It is whether the place across the street picked up 40 reviews last quarter while you picked up 3.

Why this needs a scraper at all

Google's own APIs will hand you your own listing and nothing else. Business Profile is ownership-gated, so a competitor's review history is not something you can request, which is why competitor reputation monitoring is normally a paid rank-tracker feature. This reads the public local pack instead. A rival's numbers cost exactly what your own do.

What it will not do

It records the star rating and the total count, not the text of individual reviews, and there is no sentiment analysis anywhere in it. If you need the wording, this is the wrong template. Ratings also drift a little depending on where Google thinks you are searching from, so treat the absolute number as a baseline and the week-over-week delta as the real signal. No AI model in the workflow, so no token bill either.

Template: Track competitor Google review ratings and counts

Every Monday I used to open five G2 pages and skim the recent reviews for the products we compete with. Twenty minutes, and I never wrote any of it down, so by Thursday I had forgotten what I read.

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This replaced that. Runs Monday at 08:00, appends one row per review to a Google Sheet.

Schedule → list of G2 product URLs → one Apify call per product → keep only real reviews → format and truncate → append to Sheets

Each row: week-of date, product, rating when present, pros, cons, reviewer role, company size, publish date, review link, review ID.

The column I actually read is Cons.

Those are the objections a prospect will bring to your next call, written by someone who already bought the competitor, in their own words instead of through a survey. Sort that column by product and you have a battlecard in about a minute.

Sales. Read the reviewer role and company size sitting next to each objection. A complaint from a 40-person company is not the same complaint from a 4,000-person one, and you will pitch them differently.

Competitive marketing. Cons tell you where to position. Pros tell you which of their features you are going to have to answer for whether you like it or not.

Where it falls short:

• G2 does not put a visible star rating on every review it publishes. About 40 percent of rows carried one in my testing, so a blank rating means unrated, not zero. That is why the template leads with the words and not the score.
• You will collect the same review twice if it is still recent when the next run fires. Every row has a Review ID. De-duplicate on that.
• The Actor sits at 90.9 percent success right now. It is new and I am still tightening it. I would rather say that than pretend it is 100.
• Public review pages only. No spend, no buyer intent, no private G2 data.

Cost: template is free, reviews are about four tenths of a cent each. Three products at 25 reviews a week runs roughly 30 cents.

Template: Track competitor G2 reviews with Apify and Google Sheets

I built a template that logs Google Shopping prices to a sheet every week, and the useful thing I got out of it was not the tracker. It was finding out which number on the page is worth recording.

My first version stored the lowest price per product, because that is the obvious one. It was useless. The lowest price on a Google Shopping result page swings all over the place week to week, and when I went and looked at the actual listings, the bottom of the range was almost always a different product: the wrong variant, a refurb, a case for the thing instead of the thing, or a seller with three reviews and a delivery date six weeks out. None of that moves because the market moved. It moves because Google matched a different listing.

So the row it writes now is lowest, median, highest, cheapest seller, and best discount, and it trims the bottom outliers before it computes any of them. Median is the one that behaves. It sits still when nothing has happened and moves when something has.

The second thing I did not expect to care about is the seller name. Watching which merchant keeps holding the cheapest slot on a query, week after week, turns out to be a better signal than the price. Prices bounce. The identity of whoever is willing to go lowest does not, and when it changes, something real has changed.

Actor: Google Shopping API. Pay per event, about two cents per page of roughly 40 listings plus a two cent setup fee per run, 100% success rate, 109 users so far. It takes min and max price, sort order, free-shipping and on-sale filters, and a country and language pair, which matter more than I expected: run the same query from a different country and you get different sellers at different prices.

Template if you want the whole weekly thing rather than the Actor: Track weekly Google Shopping prices

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Question for anyone else pulling price data on a schedule: what do you actually store per run?

I have gone back and forth between keeping every raw offer and keeping one summary row, and the summary row is much cheaper to read, but I have already wished twice that I had the raw ones.

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I check Google Shopping for a handful of products fairly often and never write anything down, so a month later I have no record of whether a price actually moved or whether I imagined it.

This writes it down. Every Monday it runs one Google Shopping search per product on your list and appends a single summary row per product: lowest price, median price, highest price, the cheapest seller by name, and the biggest discount showing that week.

Schedule → split the product list → Apify Google Shopping → drop products with no offers → aggregate → Google Sheets

Median is the column I actually use. The lowest price on a Google Shopping page is frequently a listing for the wrong variant, a refurb, or a seller nobody would buy from, so a chart of lowest price is mostly noise. The median moves when the market moves. The workflow trims the bottom outliers before it aggregates for exactly that reason.

A few ways people use this shape of data.

• If you sell the product, you get a weekly record of where your price sits against everyone else ranking on the same query.
• If you are buying something expensive and not in a hurry, you get the price history Google Shopping does not keep for you.
• If you source or resell, the cheapest-seller column is the interesting one. Who keeps winning on price week after week tells you more than the price does.

No AI model anywhere in it, so there is nothing to pay OpenAI for and no number that can be hallucinated. Cost is the Apify side only, roughly four cents per product per week at one page of about 40 listings each.

Template: Track weekly Google Shopping prices and log summaries to Google Sheets

It calls a Google Shopping API I wrote.

Limits worth knowing before you set it up:

• Google Shopping results are localized, so the same query from a different country returns different sellers at different prices; the template pins country and language so your history stays comparable week to week.
• It reads the search page and not individual merchant pages, so there is no stock level and no per-seller shipping cost.
• And it is a weekly snapshot, so a two-day flash sale that starts on a Wednesday is invisible to it.

Two things I keep going back and forth on adding: a week-over-week change column so the sheet flags movement instead of you scanning for it, and an alert when a named competitor undercuts your own price.

Would either of those be useful, or is there a field you would want in the row that is not in there?

I've been converting Actors into free n8n templates to see whether the template pages pull their own traffic. This week's was a hiring watcher: a Monday schedule, a role list, one LinkedIn jobs search per role, filtered to the last N days, appended to a sheet.

Two things I got wrong on the first pass.

I assumed posted date was the signal. It isn't, or at least it isn't alone. A three-day-old posting with 400 applicants and a three-day-old posting with 9 applicants are not the same event, and only the second one is worth a message. Once I put applicant count in the sheet, the sort order changed completely and the whole thing got more useful.

The second one was cost shape. My first version pulled everything and filtered inside n8n, which meant paying for jobs I threw away. Pushing the lookback into the Actor's timeRange parameter instead of filtering downstream cut the bill by most of it. Obvious in hindsight. Pay-per-event pricing punishes you for over-fetching in a way that a flat monthly plan never does, and I keep having to relearn that.

If you track Walmart prices or chase unauthorised resellers, the seller list is the part that usually breaks. Most tools either skip it or hand back half the offers. This one returns the full offer list for a listing and keeps the registered legal entity name next to the storefront name, so you can see who is actually behind "BestDealz2024".

Four modes in one Actor:

• search: keyword search, returns title, price, was-price, rating, review count, seller, stock and shipping flags, plus both product ids and the storeId
• product: UPC, manufacturer number, category path, variant count, full rating breakdown
• sellers: every company offering the item with price, availability, delivery date, delivery cost, return policy
• reviews: title, full text, rating, date, author, verified-purchaser status, helpfulness votes

Two things that cost me time to get right. Walmart prices and stock are store specific, so sellers mode takes a storeId you copy off any search row. And the modes want different ids: product and sellers need the alphanumeric productId, reviews needs the numeric usItemId. Pass the wrong one and it tells you which you needed instead of quietly returning nothing, which is how I lost an afternoon early on.

Billing is per result and there is no monthly rental: 75 cents per 1,000 products in search, 75 cents per 1,000 reviews, 1.5 cents for a product detail lookup, 1.5 cents per seller offer. It is new, so the sample is small, but every run so far has finished clean. You can call it over MCP from Claude, Cursor, or ChatGPT.

I kept hitting the same gap building agent workflows for reviews: I could get a place's reviews easily, but not a single reviewer's history, which is what you actually need to judge if a review is trustworthy.

So I wired one up as an MCP tool. The agent passes a contributor ID and gets back that reviewer's full history as JSON: every review with rating, text, date, and photos, the place and its coordinates, and the reviewer's Local Guide level and points. From there the agent can summarize patterns and flag things like an account that only ever reviews one chain, or a burst of reviews across unrelated places in a short window.

It is callable from Claude, Cursor, or ChatGPT over MCP. Link in the first comment per sub rules.

A single Google Maps review tells you almost nothing. The reviewer's whole history tells you a lot: whether the account is real, whether it only ever props up one business or trashes a competitor, and roughly where the person is based from the GPS on the places they review.

The manual way is slow: open a contributor profile, scroll, and eyeball it. I wanted it structured, so I built a small Actor that pulls a contributor's full history as JSON, one record per review, with the rating and text, the place and its coordinates, and the reviewer's Local Guide level and points. Batch a list of suspect IDs and you can compare behavior across accounts to spot a network.

Two caveats for this sub: it reads public profile data only, no private info or facial recognition, and it is a paid tool, so I am putting the link at the bottom.

If you vet reviews for trust and safety, research, or fraud work, this one reads the opposite direction from a normal Maps scraper: it pulls one Google Maps contributor's full review history, not one place's reviews.

Most Maps tools answer "what do people say about this business." This answers "who is this reviewer, and what else have they posted." Give it a contributor ID, or a batch of them, and you get one JSON record per review:

• rating, review text, date, likes, photos, and any owner response
• the place reviewed: title, address, category, GPS coordinates, thumbnail
• the reviewer's profile: Local Guide level, points, and contribution counts

That is enough to check a reviewer is a real person before you trust them, catch an account that only ever reviews one chain or its competitor, or profile a set of suspect accounts to find a coordinated campaign. The GPS on each review even lets you estimate a reviewer's home region from their footprint.

I built it because every Maps scraper I could find was place-first, and the reviewer side is where fake reviews hide. It runs pay-per-review at about $1.50 per 1,000 (roughly a tenth of a cent each) plus a small start fee, success rate is around 96%, and you can call it from Claude, Cursor, or ChatGPT over MCP.

Any suggestions on the best app to create explainer videos? I'm ok with paying, but I'd really like to do it automatically through an exposed API if it's possible?

Google publishes every ad an advertiser is currently running, in the Ads Transparency Center. It is genuinely useful and almost nobody checks it, because checking it means opening a browser tab you forget about.

So this checks it on a schedule. Give it a competitor's advertiser ID, and every Monday it appends one row per live ad to a sheet: format, when the creative first ran, how many days it has been running, and a link to the ad itself.

The days-running column is the one I actually use. Anyone can see what a competitor is advertising this week. Seeing that one text ad has been live for 827 days tells you which message they keep paying for, and that is the one worth studying.

6 nodes, no AI in the loop, no ad spy subscription.

Template: https://n8n.io/workflows/17698-track-competitor-google-ads-transparency-center-creatives-with-apify-and-google-sheets/

Fair warning on scope: Google does not publish spend, impressions, or targeting for commercial advertisers, so this is creatives and dates, not a spend report. Anyone selling you competitor ad spend is guessing.

For SEO and market-research folks who need to see how something ranks on Yandex, this pulls the live Yandex results page back as clean JSON.

What each page gives you:

• Organic results with position, title, URL, snippet, date, sitelinks, and rich snippets
• Paid ads with position and creatives
• Knowledge graph and entity cards: title, description, type, image, source, attributes
• Inline image strips and video carousels, plus the full Yandex Images and Videos verticals with full-size image URLs, hosting pages, durations, view counts, and channel data
• Region and language targeting across 6+ Yandex domains, 19 languages, and a 123,000+ location ID system
• Pagination, sort by date or relevance, time-window filters, and image/video filters

I built it because I kept needing Russian-market keyword checks and every option was either a monthly subscription or nothing. This one you pay for by the page, no monthly rental.

If you need the other search engine most Western APIs skip, I also have a Baidu scraper.

For anyone building local lead lists or tracking where businesses rank on Yelp: this reads the ranked Yelp search page and gives it back as clean JSON, one page at a time.

Each business row has:

• name, rating, and review count
• price tier and categories
• neighborhood and open/closed state
• service options like delivery, takeout, outdoor dining
• sponsored placements flagged separately from organic results
• the Yelp place_id, so you can pull the full profile and review history later
• the page's refinement filters (distance, price, categories, features)

Why I built it: Yelp's own API makes you apply for a key and caps how much you can pull, and it doesn't hand back the ranked page with ads and filters the way the site actually shows it. I wanted to point at a query and a city and get the ranked results as data, no monthly rental.

For anyone building an event-discovery app, a calendar copilot, or an AI agent that needs live events, this pulls Google Events search results back as structured JSON.

What each run returns:

• Event name, description, and date/time range
• Venue name, full address, and a map link
• Ticket links pulled together across providers (Ticketmaster, Eventbrite, SeatGeek, AXS, Spotify)
• Venue ratings and review counts when Google has them
• Date-range and event-type filters, with pagination

It covers concerts, conferences, festivals, sports, theater, and online-only events, across 240+ countries and 200+ languages.

I built it because Google never shipped a public Events API, and the listings sitting inside Google Search were a pain to get out in a clean shape. It's callable from any MCP client, so Claude, ChatGPT, or Cursor can use it as a tool.

If you're wiring up a travel or local app, it works alongside my Google Hotels and Google Flights scrapers.

Context for the Apple devs here: App Store Connect only returns reviews for apps you own, which is useless when you want to benchmark a competitor or watch sentiment across country stores. I built a small service to pull reviews for any app and return clean JSON.

What it returns per review: star rating, title, full body, author name plus a stable author ID, the app version at review time, the date (locale-formatted and ISO), helpfulness counts, country store code, and platform (iOS or macOS).

The parts that were fiddly:

• App-name to product-id resolution. Agents often only know an app by name, so if you pass a name instead of a numeric ID it runs an App Store search first and reviews the top match.
• Helpfulness counts arrive as prose ("X out of Y customers found this helpful"), so I parse them into integer helpful_count and total fields.
• macOS behaves differently: it ignores the sort order and always returns most-recent, and pages hold ~10 reviews vs ~25 on iOS. Deep crawls cap around 1,250 iOS reviews per app.
• 50+ country stores, each driving both the storefront and the locale of the text and dates.