Location-aware devices and geotargeted ads are combining with artificial intelligence to deliver a new generation of microtargeted marketing. The rise of mobile apps has further refined the approach – users can now be tracked to specific landmarks and “check ins” resulting in contextual ads with hyperlocal click-throughs and conversion.
69% of consumers expect business search results within 5 miles of their location
Here are four ways that AI is reshaping local advertising.
1. Location-aware recommendations
Among consumers, recommended products tend to be more “trustworthy” by virtue of their crowdsourced nature — more people think it’s good, so it must be good. Location-based recommendations take it to the next level by integrating physical location and machine learning to provide suggestions specifically tailored to the user’s interests.
This has led to “geo-social” apps like Yelp and Foursquare, which combine geo-tracking and crowd recommendations to generate Top 10 lists. A Harvard Business study found every 1-star increase in a Yelp rating brings in 5-9% increase in revenue.
Even Facebook and Google are jumping on the trend, by displaying nearby products, services or events of interest. For instance, Siri might recommend nearby Thai restaurants or art museums, and Facebook can notify music fans of an upcoming concert in their neighborhood.
As AI improves, location-aware recommendations, which take into account other data like purchase history, friends, and even the weather, will provide ever more accurate suggestions. Google’s “promoted pins” is an example of this evolution: it shows not just promoted businesses in the Map app, but store promotions that may be relevant to the user.
2. Location-based search
Thanks to mobile data and voice assistants, an increasing number of shoppers are using location-based searches. Instead of looking at maps and directories for nearby Asian restaurants, today’s shoppers can just fire up their map app, go to Yelp, or even have the searching done for them by Siri or Alexa.
What most people don’t realize is that the nearly instantaneous result doesn’t happen by magic. It involves a complex combination of geo-tracking, aggregation and AI. First, the system pinpoints where the user is, then checks the aggregated list of restaurants nearby. Finally, AI enables the system (or mobile device) to “learn” the habits of the user, resulting in improved search results by weeding out contextually irrelevant ones. If the user has a particular taste for Thai cuisine, then Thai restaurants are likely to show up at the top before other Asian fare.
3. Geotargeted programmatic ad buying
It used to be that media buying meant talking to a sales rep to purchase air time or ad space. Today, thanks to AI algorithms, it’s as easy as logging online, setting the target audience and campaign spend, and sitting back as automation does the rest.
Programmatic ad buying, the automated purchase of ad space and placement in real time, is becoming increasingly local. All the brand owner needs to do is set the audience’s location parameters, and AI manages the bidding against competitors that target the same audience. This makes it faster, more accurate and more efficient than traditional media buying. Programmatic advertising is widely used on Facebook and Google, so that even the smallest mom-and-pop store can push out relevant ads to their surrounding neighborhood, all with just a few clicks.
According to Borrell Associates, 61% of local ad spending will go to programmatic buying this year to the tune of $47 billion, a dramatic rise from just 10% in 2015.
In addition, Facebook’s extensive local targeting options have enabled businesses to reduce new customer acquisition expenses by almost 8%, while costing less than $8 per thousand impressions on average, versus over $35 for local TV commercials. Easy-to-use interfaces that automate the process of placing geotargeted ads have also contributed to the growth in local programmatic by making location a tactic accessible to small and medium business owners.
4. Reputation management
Online reputation can make or break a business, and nowhere is this more critical than for local. Brightlocal’s 2018 survey of local consumers found that 86% of respondents read online reviews before making a purchase, 89% of those read the business’ response (or lack thereof), and that 57% will only patronize the business if it has 4 or more stars.
Businesses with 4 or more negative search results on Google lose up to 70% of potential customers
This has led to firms that offer reputation management services that gauge consumer sentiment. In the past, this was done by human experts who relied on SEO and SEM techniques to measure and improve a brand’s online impact. Now, advances in neural networking and machine learning mean that AI can do the same task much faster, more efficiently and for less cost.
A London-based startup called Signal Media recently raised $26 million for its AI-powered reputation management platform. The system crawls through nearly 3 million information sources, from traditional to online media, and takes advantage of machine learning to streamline the process. To digest traditional media like print, TV and radio, it uses a combination of audio/video transcription and optical character recognition to transform those sources into machine-readable text. Reputation is a critical component of local marketing where consumers are less familiar with brands and more open to being persuaded by a positive or negative review.
Local is a strong growth area for digital advertising, and tools powered by AI are improving the impact of hyperlocal marketing campaigns.