Illustration of people positioned around a larger-than-life target and laptop computer, symbolizing digital advertising.

How to improve online ads with AI data

In the last 50 years, humans have gone from being exposed to 500 advertisements per day to somewhere between 5,000 and 10,000. Now more than ever, consumers expect to be served advertising content that is both useful and contextually relevant.

That's why brands are on the hunt for ways to make their ads stand out, convert at higher rates or otherwise engage their intended audiences.

Enter ad evaluation, also known as ad review, the inspection of online advertisements for relevance and efficiency. Regularly used as part of pay-per-click (PPC) advertising programs, paid ads can bring incrementally more visitors and revenue to consumer packaged goods (CPG), retail and other kinds of eCommerce sites. But only if they're executed properly at scale.

To ensure marketing teams are getting the most out of these paid listings, they need to be error-free and optimized for the target audience. Through data validation, brands can review and evaluate sponsored listings against specific guidelines and optimize ads with a thorough evaluation of relevance, intent and cultural context.

As more brands consider AI and machine learning technologies in their advertising, it's important for marketers to understand how and when this technology works best.

How exactly can brands use AI to evaluate ads?

AI-driven ad testing leverages the same combination of user data and machine learning that Facebook and Google use to deliver individually targeted ads. Rather than track or target individual users, however, ad evaluation technology typically blends broader demographic data with the clients' own ad performance results to guide the process.

Using AI to evaluate ads relies on a few key techniques. Some technologies such as computer vision, break human-crafted ads into key elements such as layout, imagery and language, serving up variations in phrasing, color scheme and other key elements.

Of course, wherever there is technology, there are also talented humans to implement and manage it, and ad review is no different. If selling globally, teams of talented multilingual and multicultural analysts can supplement the advanced technology to review and evaluate sponsored listings against specific guidelines.

The result? Higher engagement rates and ads that convert, creating a boon for performance marketers.

When should brands use AI for ad evaluation?

Brands should consider using AI for ad evaluation for a number of reasons, including:

  • Segmenting audiences
  • Testing ad creative
  • Optimizing budget and spend

Advertisements play a crucial role in building brand awareness and are often the first touchpoint in the customer journey. Setting targets around conversion and revenue tend to align best with ad evaluation, since they're easier to track compared to metrics like brand impression or views. With ad inspection, brands can ensure the right ads are being delivered to the right audience. It's also important for detecting grammar and spelling mistakes, especially when your ad is being localized to multiple countries and languages.

Key considerations for using AI in online marketing

The best ad evaluation partners maintain a rigorous quality assurance system with a global network of ad evaluators. In many instances, that means expert analysts spot-checking, evaluating and validating ad performance to ensure the highest quality of data production and flawless execution.

For example, as part of a multi-year project with one of the world's biggest social networking platforms, TELUS International recruits, educates and manages over 4,000 local evaluators in more than 10 geographic markets. Our AI Community reviews over one million ads per month, enabling our client to deliver more personalized ads.

For search marketing specifically, Google helps advertisers maximize their ad effectiveness through smart bidding, which leans on data and algorithms to deploy the best bid for ad space. Then, it builds in audience signals, which may include intent data and lookalike lists to determine which ads are more likely to convert with which customer groups.

There are clear benefits to using techniques drawn from different forms of AI, including machine learning, to increase ad effectiveness. But, there are also key considerations. Selecting an AI data solutions provider with strict quality controls is paramount to avoid cultural mistakes and other faux pas. And just because the technology says an ad is good doesn't mean it will land with audiences.

These concerns can easily be mitigated with an experienced, rigorous global partner. For brands looking to increase marginal revenues, ad evaluation using AI data could boost engagement with key customer groups, while unlocking significant value.

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