A few years later, Google unveiled Customer Match audiences, allowing advertisers to load email lists into AdWords in order to call on those audiences in paid search targeting in much the same way RLSA audiences are used.
Over the years, the reach of these audiences has grown with Google updates and increased advertiser adoption. In turn, the importance of deploying these targets effectively has also grown, though doing so can be more complicated than many portray it to be.
RLSA and Customer Match audience growth
Taking a look at a set of Merkle (my employer) advertisers that have deployed at least one RLSA or Customer Match audience since Q4 2016, we’ve seen a rapid rise in the amount of traffic attributed to both.
Note: Customer Match is plotted against a secondary axis on the right side of the chart.
As you can see, the share of traffic coming from RLSA has tripled over the course of the last 21 months. This can be attributed to a couple of different factors:
- Increased adoption. Even for those advertisers that have used audiences in some form since late 2016 (such as those included in this sample), many have expanded such targeting to more campaigns and have targeted more users in this way through additional and/or larger audiences over time.
- Extended membership duration. In October 2016, Google tripled the maximum number of days that a user can be included in an RLSA audience from 180 to 540. This opened up a host of new targeting capabilities, as advertisers could finally use RLSA to target users on annual or otherwise extended buying cycles. It also meant RLSA audiences could include a larger number of website visitors in general.
However, when creating an RLSA audience, it will only be backfilled with recent visitors for up to 30 days prior to the creation of the audience. This means that if an advertiser creates an audience including visitors from the past 540 days, only the visitors from the last 30 days would be initially included for targeting, and the list would steadily grow for the next 510 days.
It took many months for newer audiences that took advantage of the longer membership duration to fully accrue as many members as possible, and some of the growth in RLSA traffic we’ve seen since Q4 2016 has been a result of the steady expansion in membership.
Customer Match has also grown significantly, from 2.0 percent in October 2016 to 3.3 percent in June 2018. Increased adoption has played a huge role in the case of this growth, as Customer Match was only made available to advertisers for text ads in September 2015 and expanded to Google Shopping in June 2016.
Another trend that might be at play in driving up Customer Match is that the number of active Gmail users worldwide continues to grow. During its 2017 earnings call, Google announced they had 1.2 billion active users.
Customer Match targeting relies on Google’s ability to match email addresses to users, and the email accounts that Google is most likely to match to users are Gmail addresses. Growth in the use of Gmail helps to increase the share of people searching that can be targeted through Customer Match.
Additionally, Google recently expanded Customer Match to allow advertisers to upload mailing addresses and phone numbers, which also increases the number of users that can be identified for targeting through Customer Match.
So RLSA and Customer Match have both grown with increased adoption as well as changes from Google that helped to expand their reach. Given the significant click share they can now account for, optimizing accounts using these audiences is now a major part of paid search management, though that is often easier said than done.
The complexity of audience management
Plenty of marketers out there will tell you RLSA and Customer Match are magical sources of incredible return on investment (ROI), given that these audiences typically have higher click-through rates (CTRs) and conversion rates than non-audience members.
This makes sense, as the past interactions that audience members have had with a brand indicate affinity and/or recognition that helps to increase the likelihood of an online conversion at a later date. However, as my Mad Scientists of Paid Search panel colleague Andreas Reiffen will tell you, at least some of the orders that come from such audiences would occur with or without the presence of a paid search ad.
While the very high value assigned to these audiences by most attribution schemes might indicate advertisers should be much more aggressive with advertising for these searchers, doing so might be a waste of money when taking into account the actual incremental value of ad clicks in pushing audiences to convert.
Measuring this incremental value requires testing, with results varying significantly from advertiser to advertiser, just as recognition and competitive advantages vary from brand to brand. Such tests can be set up in a number of different ways to test pushing bids, pulling back on bids or turning ads off entirely to audiences in order to estimate the traffic and orders gained or lost with different strategies. Results from such tests can be a bit noisy, and advertisers with limited traffic might never arrive at statistically conclusive measures of incrementality.
Adding to this complexity is that advertisers do have the option of adjusting things like ad copy, landing pages and keyword (in the case of text ads) or product selection (in the case of shopping) in order to increase the incremental value of advertising to audiences. Optimizing and testing for such adjustments requires entirely different tests from those that might measure the effects of bid adjustments and audience exclusions.
To close
In the big scheme of things, the paid search industry is still in the very early phases of truly tackling such testing. But with the explosive growth of the click share coming from these audiences, it’s becoming increasingly important that we consider both incrementality and the potential for personalization in using audiences for paid search management.
Want more info on Paid Search? Check out our comprehensive PPC Guide – Nine chapters covering everything from account setup to automation and bid adjustments!
Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.
About The Author
Andy Taylor is Associate Director of Research at Merkle, responsible for analyzing trends across the digital marketing spectrum for best practices and industry commentary. A primary contributor to the Merkle Blog, Dossier, and quarterly Digital Marketing Report, he speaks frequently at industry conferences and events.