Using multi-model AI models to work around Google limitations.
Monthly reviews analyzed for Google Local Guide content.
Reviews auto-tag with Local Guide rankings.
Google Local Guides are participants in a program that encourages users to contribute reviews, photos, and other information about local businesses and places on Google Maps. As Local Guides contribute more, they earn points and level up within the program, which comes with certain benefits.
Higher-level guides often receive higher priority when submitting reviews and suggesting listing edits due to their increased trust and credibility, which comes from consistently providing accurate and valuable contributions. Their input is often processed faster, with less need for verification, making it more likely to be approved and published quickly. Additionally, their contributions can have a greater impact on business listings, with their reviews more prominently displayed, influencing the information seen by other users.
Given the outsized impact of the reviews and images supplied by Google Local Guides, a national retailer wanted to track the contributions of Local Guides across hundreds of locations. They wanted to better understand the ratings, comments, topics and sentiments broadcast to their current and potential customers. Unfortunately, this type of analysis is not directly supported by Google as they do not distinguish or otherwise identify Local Guides submissions versus all other content.
The one element that identifies a Local Guide contribution to the public is a small icon layered over the associated user image in the form of a multi-pointed orange star. Beginning with a 4-pointed star for a level 4 Local Guide, system goes up to a 10-pointed star to reflect guides who have accumulated of 100,000 contribution points.
The LocalClarity team saw an opportunity to leverage the recently expanded multi-modal capabilities of LLMs presented LocalClarity to “read” the reviewer image. Within a couple of weeks, a new workflow was establish to analyze each user image associated with each Google review to look for the icon and count the number of star points. This data is automatically tagged in the LocalClarity platform to the review content, allowing for review KPIs and analysis for Local Guide submissions.
The new workflow automatically tagged 16% of this client's reviews with associated Local Guide rankings, helping them to better understand how high-priority reviews were influencing customer perceptions across their locations. Critically, they discovered that Local Guide reviews were, on average, more lengthy and slightly less positive. The automated process delivered greater visibility into review trends and sentiments, driving several store operational changes.
This streamlined, automated process saves time and allows clients to scale their analysis, uncovering valuable insights to help improve their business. It’s all about simplifying complex challenges, so clients can focus on growth and making smarter decisions. This analysis will be rolled out to all clients in the coming months as part of a standard update.