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Online Ad Effectiveness Research Grows Up

 This article is
brought to you by Survata.

The days of giving
digital a pass are over. It’s time to grow up.’- Marc
Pritchard, Chief Branding Officer, Procter & Gamble, January 2017
When the CBO of P&G tells us to grow up,
we listen. And after speaking with clients at last month’s Media Insights
Conference, it’s clear that there’s consensus: online advertising research
needs to get more sophisticated.
We’re here to help. IAB breaks research down into phases: design, recruitment & deployment, and
optimization. We’ll walk through each phase and determine what’s most in need
of ‘growing up.’ We’ll also include questions to ask your research partner to
help increase the sophistication of your ad effectiveness research.

Let’s start by acknowledging that
statistically sound online ad effectiveness research has not been easy to
implement at reasonable cost until recently. As IAB notes, ‘Questions around recruitment, sample bias and deployment are
hampering the validity of this research and undermining the industry as a
Just because perfect research design is
challenging to achieve doesn’t mean that advertisers should settle for studies
with debilitating flaws, leading to biased, unreliable results. In addition to
challenges inherent to good research design, most ad effectiveness research
partners have systematic biases due to the way they find respondents, which
must be accounted for in the design phase. There has been innovation in this
space within the past year using technology to reduce or eliminate systematic
bias in respondent recruitment. 
Assuming you’re able to address the systematic
bias of your research partner’s sampling, the major remaining challenge is how
you approach the control group. At Survata, we think about this as a hierarchy: 
Using a holdout group is best practice, but
implementing it requires spending some portion of your ad budget strictly on
the control group. In other words, some of your ad budget will be spent on
intentionally NOT showing people an ad. A small portion of people in the ad buy
will instead be shown public service announcements to establish the control
group. We love the purity of this approach, but we also understand the reality
of advertising budgets. We don’t view holdout as a requirement for sound online
ad effectiveness research. Smart design combined with technology can achieve
methodologically sound control groups without ‘wasting’ ad budget.
Along those lines, the Audience Segment
approach has become de facto best practice for many of our clients. Basically,
you create your control group from the same audience segment that you’re
targeting in the ad buy. This isn’t perfect, as there could be an underlying
reason that some people in the segment saw the ad but others didn’t (e.g., some
people very rarely go online, or to very few websites), but it’s still an excellent
approach. It’s the grown-up version of Demographic Matching.
Demographic Matching, in which the control
group is created by matching as many demographic variables as possible with the
exposed group (e.g., gender, age, income), is still a very common strategy.
It’s straightforward to accomplish even using old online research
methodologies. As online data has allowed us to learn far more useful
information about consumers than demographic traits, this approach is dated.
Simply sampling GenPop as a control is
undesirable. The results are much more likely to reveal the differences between
the exposed and control groups than the effectiveness of the advertising.
Questions for your research partner:
  • What are known biases among
    respondents due to recruitment strategy?
  • What is your total reach? What
    percentage of the target group is within your reach? Is it necessary to
    weight low-IR population respondents due to lack of scale?
  • What’s your approach to creating
    control groups for online ad effectiveness research?
  • For Demographic Matching, how do
    you determine which demographic characteristics are most important to
  • How do you accomplish Audience
    Segment matching?
Recruitment/ Deployment

Historically, there were four methods to recruit respondents / deploy the
survey: panels, intercepts, in-banner, or email list. To stomach these
methodologies, researchers had to ignore one of the following flaws:
non-response bias, misrepresentation, interruption of the customer experience
or email list atrophy. In our view, these methodologies are now dated since the
advent of the publisher network methodology.

The publisher network works by offering
consumers content, ad-free browsing, or other benefits (e.g. free Wi-Fi) in
exchange for taking a survey. The survey is completed as an alternative to
paying for the content or service after the consumer organically visits the
publisher. In addition to avoiding the flaws of the old methodologies, the
publisher network model provides dramatically increased accuracy, scale, and speed.
Questions for your research partner:
  • What incentives are offered in
    exchange for respondent participation?
  • What are the attitudinal,
    behavioral, and demographic differences between someone willing to be in a
    panel versus someone not interested in being in a panel?
  • What are the attitudinal,
    behavioral, and demographic differences between someone willing to take a
    site intercept survey versus someone not interested in taking a site
    intercept survey?
  • How much does non-response bias
    affect the data?
  • Are you integrated with the
    client’s DMP?
  • How long to get the survey into
    the field, and how long until completed?
  • How does the vendor ensure that
    exposure bias doesn’t occur?
  • How does the vendor account for
    straight-liners, speeders, and other typical data quality issues?

An optimal ad effectiveness campaign returns results quickly, so that immediate
and continuous adjustments can be made to replace poorly performing creative,
targeting, and placements with higher performing ones. We call this real-time
spend allocation. It’s analogous to real-time click-through rate optimization,
as it relies on solutions to the same math problem (known as the multi-armed bandit).

By integrating with DMPs, ad effectiveness
research can be cross-tabbed against even more datasets. The results will yield
additional insights about a company’s existing customers.
Questions for your research partner:
  • Are results reported real-time?
  • How much advertising budget is
    wasted due to non-optimization?
  • How can DMP data be incorporated
    to improve ad research?

Flawed research methodologies can’t grow up,
they can only continue to lower prices for increasingly suspect data. For
online ad effectiveness research to grow up, new methodologies must be adopted.

To learn more about
conducting your own ad effectiveness study, visit Survata