Using AI Tools to Discover Duplicates at Scale

[cleverlyengaged]

I have a client with 700+ gas station locations and we are discovering a variety of problems on Google Maps.

1. Old gas stations that are still open in Google Maps at the same address as the client.
2. Convenience store partners that are incorrectly categorized as gas station, creating two businesses with the same category, same phone # and same address.

Here is an example of a duplicate:
https://maps.app.goo.gl/CitzGtr6ysHLB6MNA
https://maps.app.goo.gl/fbbcQ3pwhsPSVXcUA

I have attempted to get AI tools to help with this effort, but none appear to be able to crawl or access Google Maps. This is where most of the issues are coming from. Trying to scale and look at all locations, but curious if anyone has a recommendation

 

[ElizabethRule]

@noahlearner might be able to help with this one

 

[noahlearner]

If I had to solve this problem I would :

• build a list of all the gas stations in my client's portfolio into a google sheet
• get the zip codes for each one as a column in the google sheet
• work with a rank tracker api (I like dataforseo for adhoc problems like this)
• build a script that performs a search in each zip code for "gas station"
• store the results in Google BigQuery.
• build a looker studio report to help me look at each city to find problems
• compile a list of problem locations
• go through normal GBP processes to report problems.

I know this is a pain in the behind amount of engineering, but this is that kind of problem to solve.

 

[noahlearner]

I think that valueserp or scaleserp (both are Traject companies) might even allow you to use a csv to enter all your locations to perform the searches for: https://trajectdata.com/serp/value-serp-api/ and they can export the results straight to bigQuery for you. This convenience would solve nearly all the engineering problems for you.

Looking at their docs at https://docs.trajectdata.com/valueserp/search-api/results/google/places endpoint, I see that they return the maps data you could use to find problematic locations by comparing the values found in the gps_coordinates.longitude,gps_coordinates.latitude, address, or extensions.[3] fields.

You could also highlight duplicates by making a table in looker studio with the data that has any ONE of these fields in it, and then have another field called record count (ie just two fields in the table) and then make a filter on that table to only show rows where record count > 1. This would then show all the addresses that have more than one GBP at it.

I bet if you reached out to their team with your exact problem they'd help you set up the data for the data pull.

 

[WilliamGMBE]

@cleverlyengaged

Can think of a couple of ways you can do this. One of them.
Longer way:

Logic: You can just search on google maps for 'Gas Station' at this address, then export it into google sheet and filter it.

How:
Here are the steps.
Step1: You can use query parameter with Google Maps URL to find gas stations near your address. So it can be like url with this format: https://www.google.com/maps/search/?api=1&query=gas+station+near+<ADDRESS>.

Exact example with one of the url you provided will be like this: https://www.google.com/maps/search/...+S+Martin+St,+Warren,+AR+71671,+United+States

As you can see from the above URL, it gives gas station near the address. If I where you I will put all address into google sheet and then url encoding to create URL like above.

Step 2:
Once you got the URL, you can run this though any scrapper tool that works with Google Maps. If you plan to use APIFY.com you can use something like https://apify.com/compass/crawler-google-places. It can find the result the result from URL. In your case the sheet you created in step 1 will be input here. Once you get the flow right, you can run this as a batch. This should give you all Gas station near the address.

This should give you the list of business near that location which answers to the term gas station. Now from the sheet you can find the Gas Station near the address which should include duplicate businesses and convenience store which has Gas Station as a category.

If you want, you can also use Google Maps API for this.

Shorter way:
Just vibe code this solution. You can use Claude or ChatGPT for it. AI is really good for these kind of things. You can maybe create it end to end.


Hope this helps.