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In the Age of AI, Local Listings Matter More Than Ever
Blog Feature

By: Duane Forrester

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March 9th, 2018

In the Age of AI, Local Listings Matter More Than Ever

If you own a local business, you’re no stranger to the important role that listing data plays in your success with customers. That data—foundational to local businesses for well over a decade now—is relied on by search engines and consumers alike. And as artificial intelligence becomes more commonplace (and more powerful), the role that local listing data plays in your success will only continue to increase.

Managing Your Local Listing Data Is Important

Unlike a decade ago, local businesses now have dozens of data points to manage:

  • Store hours
  • Holiday schedules
  • Products
  • People profiles
  • Contact information

The process is often complicated by the fact that you need to maintain that dataset across multiple locations. This eats up time, and inaccuracies can hurt listings, rankings, and overall discoverability. On top of all of that, we also have elements, such as reviews, that play a large role in consumer influence.

Any business with a local footprint has their work cut out for them managing everything. But let’s not forget the answer providers—the search engines.

Enter: Artificial Intelligence

With the sheer volume of data available to consumers today, the systems responsible for parsing that data and giving consumable answers need a way to process everything. And that’s where machine learning, an AI technology, shines.

AI can easily spot patterns in data that would take a human weeks to tease out. AI does this in seconds. As customers leave reviews, AI can spot emerging trends—good and bad—quickly. This allows the search engine to shuffle all possible results into a subset of useful, reliable results that ensure the searcher is likely to have a positive experience with the businesses shown in a search.

Think of a time when you searched for “best pizza near me.”

To you, it was a simple request. To the search engine, its reputation was on the line—as it is with every query. The search engine needed to:

  • Determine your location
  • Check your account history for related information
  • Find businesses local to your physical location
  • Sort these businesses by category (you wanted pizza, not sushi)
  • Look at distances
  • Review hours of operations
  • Look up reviews and ratings

When it had all of that data in hand, it needed to start forming an answer for you. Drawing concentric rings around you based on distance, it started placing pizza shops around you on a map ordered by distance. It filtered any that were closed (unless all near you were closed, then it showed you them), and started looking at review data.

If the closest pizza shop was a two-star joint, the search engine wouldn’t have ranked it well if several other shops existed with higher ratings. But if only low-rated shops existed within a defined circle of your location, then it showed them to ensure you got an answer.

Typically, we get decently rated businesses in results today, so we know we can rely on this approach to finding what we want. That’s because AI learns patterns well and can begin to predict our satisfaction based on our behavior. As we ask for pizza, they can check your history to see what they gave you before, look for instances where you seemed to be pleased (you clicked, you called, and repeated that pattern), and attempt to bring you a result that closely matches the pattern.

All of that work the engine does to come up with an answer takes place in a few seconds. Some of the data is easier to manage for them because we tend to establish patterns—work, home, friend’s homes, etc.—meaning that we’ll typically find a local pizza shop we like and stick with it. That makes getting you an answer super easy.

Where AI comes back into the picture is when the world around you changes. Some businesses will close and new ones will open. This happens every day, and as this new data enters the system, it can impact the results customers see. If we look at our pizza example again, a well-rated shop in one community can benefit from that reputation as it expands into new communities. With no local footprint to rely on as they open the new shop, an engine will make the leap that if that shop is good in one community, they may be good in their new location. So the new shop gets tested in local results and consumers ultimately decide their fate. If all signals are positive, the new location does well in the rankings and flourishes.

If they get it wrong, however, reviews pointing to flaws will quickly shift their rankings.

Machine Learning and AI Are Facts of Life Today

Businesses need to be aware that while they may close for the evening, all the data being generated about them (reviews from the day’s customers, traffic to their website, people engaging with their menus, etc.) continues to build their digital footprint. And the systems at the search engines are constantly finding, reviewing, and sorting that fresh information to help the next time someone asks for pizza.

Your business might close for the night, but machine learning runs 24 hours a day, every day, to help serve consumer needs.

Want to learn more? Read How Voice Search Changes Everything and discover how you can build consumer trust, brand reach, and connectivity in the voice-enabled future.

Get free access to the Ultimate Beginner's Guide to AI in Marketing: https://www.marketingaiinstitute.com/beginners-guide-access

About Duane Forrester

Duane Forrester is VP of Industry Insights at Yext, a company pioneering a new category called Digital Knowledge Management, which gives businesses control of all of the public facts that they want consumers to know across the intelligent ecosystem.

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