User Data and Paid Search: A match made in heaven

2017 trends: increasing opportunities to incorporate user data in PPC strategy


The sheer amount of user data available to us can be, at first glance, daunting. Brands understand that within the complex web of data points, there are golden nuggets of relevant data. But identifying the useful bits, and interpreting this useful data into insight to inform strategic marketing decisions, can be tricky. However it is worth it, as users demand more relevant digital experiences and brands aim to deliver more effective, targeted communications. Here, I'll look at why user data matters to Paid Search strategy specifically, and how we can harness the opportunities it presents.

Why does user data matter?

Because digital marketing is increasingly more competitive, not less. Every year, more businesses compete for a finite digital audience, which puts ad inventory at a premium, not to mention the steady creep of ad fatigue this creates in users.

In 2016, CPC in AdWords rose by 6% for desktop devices compared to the previous year, according to Merkle Digital Marketing Report Q4 2016.

Were it not for a big increase in mobile ad inventory in 2016 to eat up advertiser budgets, desktop CPCs would probably have risen by a larger degree.

As marketing professionals, our only hope is to use every new lever available to us to reach the right audience, at the right time, with the right message - clichéd but true!

This means moving beyond the traditional ways of forming ideas and hypotheses about who customers are and where to reach them, such as ONLY relying on keyword searches or ad inventory from specific publishers. Now we can reach target audiences with data captured from real user interactions like website visits, email sign ups, video views & subscriptions, social ad or organic post interactions.

This data can help us get closer to our target audience with less guesswork and wasted spend.

Data, data, everywhere…

Ad server platforms like Facebook & Google (not to mention programmatic platforms like AdRoll or RocketFuel) are now gathering more data points about their users than a single human could ever keep track of when trying to identify their target audience.

Each new item of information that can be gathered about users of a platform presents an opportunity to target an audience more effectively, if advertisers can tie this information to a business objective. For example, a gym looking to reach middle-income users in their 30s-40s might find a subset of this audience who’s browsing history signaled intent to buy a new pair of running shoes has a higher chance of joining than the rest of that demographic.

As ad inventory platforms like Facebook, Google & Twitter compete for ad cash on the effectiveness of their ability to generate connections between businesses and their target audience, 2017 will see more opportunities for utilising these data points than ever before. This includes both the data businesses capture on their own and upload to platforms, and the user data that ad platforms like supply for use.

These data points have been turned into several targeting options, or "audiences", over the past couple of years, such as…

Facebook audiences

  • Custom audiences - a segment of Facebook users based on a particular set of first party data, e.g. website visitors, converting customers, emails etc
  • Lookalike audiences - a new audience of Facebook users based on common themes present in a custom audience e.g. age & interest

AdWords audiences

  • Affinity & In-market audiences - user classifications based on browsing history as expressing an interest or being "in the market" to buy a certain thing, these users are accessible anywhere they may be browsing on the GDN
  • Demographics for search - layering demographic targeting options like age, gender and parental status on top of keyword targeting e.g. targeting users who search for "gyms in Glasgow" but ONLY if they are female and under 60
  • Remarketing audiences lists - Google's version of Facebook custom audiences
  • Customer match – an audience list creator specifically for finding users based on their email
  • RLSA - remarketing audience lists for search ads, targeting people searching for a certain keyword, but only if they happen to also be part of a certain "audience"
  • Similar audiences - Google's version of the Facebook lookalike audience feature

What’s next?

2017 has already seen Facebook roll out the option to creating audiences of people who interact with a Facebook ad or organic post, as well as options to segment users based on the percentile of time they spend on your website i.e. top 25% of users, top 10% and top 5%.

These techniques (and the stuff which Facebook and Google have released on beta that I can’t mention here yet!) can get you closer to your exact target audience than ever before, enabling you to drive better results in your PPC campaigns, even as costs continue to rise.