You are what you read: Could readership data replace the third-party cookie?
With the “death" of the third-party cookie and a stronger focus on user privacy, the advertising world is rethinking its approach to targeting
Here we look at a better way to target people: by using readership data.
How does readership data work?
Readership data is aggregated across a network of users on a multitude of publisher sites, protecting individuals' privacy while informing advertisers about the interests of their audience. This is made possible by working with recommendation platforms that integrate into a vast number of publisher sites, like Taboola.
Because we are what we read, readership data is honest and current. For example, when you sign up for a social media profile, you might give your gender and date of birth. You might also like the pages of your favorite artists and sports teams. This gives the platform some data about you that advertisers can then use to target you.
However, when you browse the web, you read about the topics and issues you really care about and find relevant to you right now. Maybe you've just had a new baby, so you're reading articles about how to optimize their sleep schedule (or your own). Maybe you're getting married, so you're reading wedding-related content on your favorite lifestyle publishers. While these aren't necessarily things you'd indicate on your social media profiles, they're important in determining what content you respond to and how.
Changing data reaps new insights
As time passes, what you read about changes depending on what you're going through and what's happening in the world. This is what makes readership data reliably current for advertisers seeking to know what's on their audience's mind. It can lead to the discovery of seemingly “new" or unexpected topics their audience is reading about, allowing them to expand their ad placements to other websites they hadn't already planned on targeting.
Source: Taboola Newsroom’s UK ‘Topic Insights’ show readers in the UK are engaging with content about the royals and football.
This data can then be used to inform your creative strategy. With the insights you gain, you can create ads that speak to what your audience is currently interested in or experiencing. In turn, you'll likely see increases in engagement with your campaign, all while making your audience feel “seen" and understood.
Readership data + contextual targeting = better results
Contextual targeting is becoming a popular alternative to third-party cookies. In this strategy, the recommendation platform curates where to place an advertiser’s ads based on the context of relevant organic and editorial content, using unique content consumption data and world-class AI technology.
This might include the algorithm choosing to show an ad for a particular brand of car in the recommendation feed next to a relevant article in a car review website, or an ad for an investment platform on a financial advice blog.
Ads can then be targeted and relevant without relying on third-party cookies or stereotypes about particular demographics. In the end, this ultimately translates to better results for advertisers.
Contextual advertising doesn't only help advertisers — it also provides a better experience for users. Research by IAS reveals that UK consumers actually prefer contextual advertising. Here are a few of the key findings:
- 72% of respondents found contextual relevance to be important
- 81% said they prefer to see a match between the content and ads they view
- 70% agreed that their perception of an ad and the associated brand are impacted by the content that surrounds it
- 65% said their opinion of a brand will be more favorable if the brand serves contextually relevant ads
Theory into practice: turning data into a campaign
Let's look at an example of how an advertiser could use readership data to create an effective campaign. In this example, our advertiser will be a hypothetical bank called ABC Banking. According to Taboola Newsroom data, UK audiences are reading a lot about issues related to the rising cost of living. Here are a few search terms that have recently seen a boost in readership:
- Cost of living: up 111%
- Inflation: up 58%
- Recession: up 76%
- Savings accounts: up 57%
- Cash: up 18%
- Mortgage: up 53%
ABC Banking could use this information to create a targeted ad campaign that uses strategic content to help ease consumer anxiety while illustrating how the company can help them manage their finances. Here are a few example ad titles:
- 5 key tips to lower your cost of living
- Is another recession coming? Here's how to protect your savings account
- This mortgage refinance strategy gets cash in your pocket
ABC Banking could then rely on a recommendation platform to distribute these ads on financial websites like The Motley Fool UK, MarketWatch and Financial Times based on deep learning technology that uses unique data about people’s interests and information consumption to recommend the right content to the right person at the right time. The company can also use contextual targeting to display ads alongside financial content on national publishers like The Independent.
After testing, ABC Banking might discover that their ads are also getting good engagement on Marie Claire. With this unexpected data, they might decide to expand their targeting strategy to also distribute ads alongside fashion content on other websites.
Leverage readership data to capture your audience's attention
We've reached the end of third-party cookies, and with it comes the realization that outdated strategies like demographic targeting aren't cutting it anymore. More brands are turning to readership data and contextual targeting to fuel their campaigns. As your brand continues to find ways to connect with your audience, try incorporating readership data into your advertising strategy. You are what you read — and once you understand what your audience is reading, you'll be better equipped to consistently connect and engage with them.