Articles by: "Jamie Auslander, SVP Research & Analytics—true[X]"

“People aren’t paying attention to ads.”

“Consumers are seeing too many ads.”

“Ad overload is hurting brands.”

When was the last time you heard someone in the industry make a claim like this?

Probably not too long ago. Ad overload is a staple of conversations at industry conferences and in op-eds. And, anecdotally, we have every reason to believe they’re right. Ad-free streaming services like Netflix and Amazon Prime are on the rise, with newcomers like Disney+ and HBO Max joining them. Ad blockers continue to be some of the most popular mobile apps and browser extensions. Surveys have indicated for the better part of a decade that a large majority of consumers think they see too many digital ads.

But for marketers, precious little attention has been devoted to exactly how overloaded digital advertising is, and what decisions need to be made to ensure that ads are effective without being excessive. In other words, we know consumers think that they see too much advertising. We just haven’t done much as an industry on a granular level to figure out what that means.

The blind spot

This, in my opinion, was a big blind spot. So, the research team at true[X] looked at both standard and interactive video campaigns – over 1,000 in total – that run alongside long-form full episode streaming content on services like those from the Disney-ABC TV Group and other premium publishers.

We also looked at what drove brand lift when you dug deep on these ad campaigns. What were the exact parameters of the campaigns? And how did consumers respond differently depending on where they were in the purchase funnel? Our reason for taking this approach was that, when you analyze over a thousand digital campaigns, you start to see some patterns that might not emerge otherwise, and that starts to help answer questions that are tougher to investigate without the benefits of big data sets.

Here’s one: What’s the difference in brand impact if the only differentiating factor you’re looking at is the length of the ad? After all, consumers tuning out after a few seconds is a well-documented symptom of ad overload. Or is it?

We took a look at 15-second versus 30-second ads to figure out whether an ad twice the length resulted in twice the brand lift or if 30 seconds was starting to reach the zone of annoyance. It turns out that it’s a little more complicated than that.

By normalizing the data and arriving at a “general” brand funnel representative of hundreds of brands we were able to derive average brand lift gains. As it turns out, for the upper and middle funnel, doubling the length does double the brand lift. In fact, it more than doubles it at the awareness stage at the very top of the funnel.

However, when it comes to brand preference and purchase intent in the lower funnel, the improvement in brand lift is much less stark. What we can glean from this is that when a consumer is already familiar with a brand, even a relatively short spot can push them toward becoming a customer. This suggests that perhaps what’s most important is compelling creative, not just time spent with an ad.

Frequency

There’s more we can consider, though. It goes without saying that the digital landscape bears a few key differences to prior mediums. Even in video advertising that bears a close similarity to TV commercials, we can require consumers to opt in or otherwise interact with ads before they advance to their content. This helps advertisers better guarantee that a consumer actually saw the ad, versus whether that consumer was off the couch and taking a snack break while commercials were running. (It’s worth noting that, while we have the ability, sometimes advertisers don’t take advantage of it.) A smart targeting strategy also means advertisers are less likely to need to cast the widest net possible, potentially generating waste by advertising to the wrong audience.

So, that being said, another factor we can look at is frequency of exposures – how many times does an ad need to be deployed in order to be effective? Even in digital, many advertisers are under the impression that more exposure equals more effectiveness. The reality is that it’s far more nuanced than that. When we looked at how frequency of ad exposure impacts brand lift, we found that it differed depending on which stage of the funnel the consumer was in.

At the top of the funnel, brand awareness improved steeply if a user was exposed to an ad twice instead of once, or three times instead of twice. But as the funnel gets narrower, that steep curve flattens to the point that increased exposure barely moves the needle when it comes to purchase, visit or watch intent.

Again, this makes a lot of sense and seems kind of obvious when you think about it. A consumer who’s already well versed in a brand won’t need to be reminded of that brand as much in order to be moved through the brand funnel. But these kinds of nuances aren’t always addressed in media planning, even in digital.

And either way, dialing back on ad frequency won’t help if creative isn’t up to par. We dug a little deeper into one campaign in particular. This retail campaign deployed two versions of its creative, each featuring different furniture products. It was an interesting campaign for a nearly-universally-recognized brand, but a division within it that isn’t as well known. So, we were curious as to what the results would be, and indeed, they surprised us.

The effect of each individual piece of creative by itself, exposed only once, was minimal when it came to driving brand lift. Showing the same creative multiple times, similarly, didn’t do much. But if a consumer was exposed to both of the brand’s ads? That delivered substantial impact throughout the funnel, ranging from an 8-point lift in awareness to 12 points in consideration to 4 points in purchase intent. This is called complementary effectiveness. The sum of the two parts is greater than each individual one on its own.

CTV

When it comes to the power of devices, we sought to understand whether the smaller screen and the intimacy of a personal mobile phone would be more influential at increasing brand sentiment compared to the larger-profile, bigger-screen viewer experience of a connected TV. Traditionalists maintain that TV is king as it commands the living room. “Next- gen” marketers believe mobile devices command our attention in ways no other medium ever has – and that this necessarily delivers more effective branding.

All things being equal, connected TV appears to deliver significantly more impact for brands when it comes to driving behavioral intention: likelihood to purchase, visit online or in-store and watch in the case of entertainment.

The takeaway

What all this tells us is that consumers’ receptivity to the amount of advertising they receive differs depending on where they are in the purchase funnel. It also depends on what the creative looks like and how it comes to them. When they’re early in the customer journey, they’re conducive to receiving the same message more than once. However, as you approach that crucial consideration phase, less is often more.

Ad overload is real. But all too often, advertisers’ and publishers’ reactions have been all-or-nothing. They’ll pull back on ads across the board. Or they crank up targeting in a way that focuses so much on the consumers most likely to buy that it neglects the very real need to build a brand outside of that immediate market.

A smarter and more thoughtful way to address ad overload would take into consideration where a campaign’s target audience lie in the customer journey and whether elevated frequency and full length 30 second spots of a single ad might be better replaced with well-coordinated creative.

While we have evidence that “the bigger the screen, the greater the impact” holds true, this shouldn’t take away from always aiming to deliver the right messaging to the right audience at the right time. In digital, we have the tools to optimize creative messaging for brand impact. We just have to put in the work.

Advertising | Innovation | Perspectives

Ad effectiveness measurement doesn’t scale, and it’s failing advertisers

October 23, 2019 Jamie Auslander, SVP Research & Analytics—true[X]

Market research is big business: The top 50 research firms generated an estimated $11 billion dollars in 2018, with Nielsen and Kantar/MillwardBrown at the very top of the list. But it’s even bigger than that. Adding in expenditures to the other third-party providers that measure fraud, safety, viewability, and outcomes, and any national “ad measurement expenditure” figure increases by hundreds of millions of dollars, if not more.

Given the US spend on measurement, not to mention how many digital innovations have taken root over the past two decades, it is disappointing to know that most branding campaigns are still not directly measured for ad effectiveness and impact in real time. This is not to say that monitoring for fraud and brand safety, evaluating viewability, tracking impressions, and counting clicks aren’t valuable efforts. They are. The issue is that while all brand advertising campaigns are measured for their quantitative impression delivery, only a fraction are measured for their effectiveness at changing consumers’ hearts and minds. The result? Measurement that doesn’t scale.

Rethink the panel

Here’s a modest proposal: We need to critically reconsider our industry-wide reliance on panel-based sampling as a means of measuring brand lift. Conventional lift studies that use panels are expensive, typically do not collect enough data. Thus, they aren’t informative enough early on in the campaign flight to be highly actionable. With access to real-time technologies, we can leverage considerably better alternatives.

Let’s look at the cost and examine whether we’re really getting our money’s worth. It is unheard of to purchase a completed survey from a sample vendor for less than $2 per interview. And we’d need to collect 500 control and 1000 exposed survey observations, which enables researchers to address different types of error while still empowering them to slice and dice the data. So, this would cost ~$3,000 per study. The reality, however, is that most brand lift studies cost magnitudes more. And yet responses are still very slow to field, and collection levels typically hover at 100 control and 100 exposed.

Some issues

Collection cost, yield, and timeliness aren’t the only barriers to progressing sample-based effectiveness measurement. Actually, representative sampling and quality can be even thornier issues. Surveys have a big problem with straightliners (people who select the same response option at each question), speeders (who take the survey too fast to be credible), and cheaters (who do not respond truthfully). You’ve probably done this yourself, but you can’t be faulted, as “good” panel-based surveys take ~8 minutes to complete. However, most sample providers don’t even blink when asked to fulfill 20-minute studies or longer. Considering the challenges of holding consumer attention these days, if you were in the 7th minute of a survey about the Gap, what type of quality response would you likely to provide?

Quality control concerns are bigger still when you ask the question: Who are these “panelists”? Do you personally belong to a panel? Did you click a banner ad to “get paid mad cash to take online surveys”?

The reality is that even though the “best” panels market their pools as containing millions of Americans, there is very little transparency around these and who actually participates in them. Given the cash approach to attracting panelists, it feels fair to question their practices and how representative these panels are of the populations they aim to mirror. How panels engage, recruit and incentivize respondents matters, as it is highly likely to affect data quality and the resulting inferences.

Measurement matters

When academics contribute to the scholarly literature around ad effectiveness measurement, convenience sampling from panels is rarely, if ever, portrayed as a best practice. A 2006 comScore study in the US concluded that fewer than 1% of panelists in the 10 largest panels were responsible for 34% of completed questionnaires.

Things haven’t necessarily gotten better since. Most sample providers today cannot use a single panel to field a study. That’s because the findings aren’t sufficient. Instead, they stitch together a network of suppliers to source respondents. This introduces serious sampling challenges.

Moreover, in the last few years sample suppliers have energetically embraced programmatic technology. This means that while sourcing sample is more efficiently connected to demand than ever, the promise of randomness is critically upended by the economic benefit of finding the cheapest possible respondent to answer your brand study.

Explore the alternatives

In short, there are many issues with panel-based ad effectiveness measurement that should compel us to explore better alternatives. Thanks to digital technology, these alternatives exist. While it might sound surprising, the next generation of brand lift measurement should consider returning to the tradition of random sampling. It is time to reinvest in sampling technology that enables respondents to be drawn from audiences that are truly representative of the populations exposed to brand advertising campaigns and of those who could have been exposed (control holdouts).

Imagine if large random samples could be drawn quickly from the very same sites, channels, devices, and platforms where advertising occurs. So, if a Toyota campaign runs while streaming an episode of the Bachelor, the surveys used to measure and optimize this campaign also run during episodes of the Bachelor.

Here are some ideas to make measurement better:

Make survey placements match the placements where the ad campaign is flighted.
Surveys should be served according to the same logic and targeting that steers towards a specific addressable campaign audience.
Surveying should be completed with one or two questions by dual-purposing the very same ad and targeting tech that underlies how we serve ad campaigns today.

Just imagine how much better measurement would be if survey serving spoke to ad serving in real time.

Yes, we can advertise better. We can also measure better.

Perspectives

Why brand lift is the advertising industry’s most misunderstood and underutilized metric – and why it should reign supreme

July 15, 2019 Jamie Auslander, SVP Research & Analytics—true[X]

Marketers, I want to ask you a not-so-hypothetical question: How do you decide whether one of your digital brand advertising campaigns was successful? Here are a couple of things that I’m guessing might instantly come to mind:

We drove X dollars in attributable sales.
We achieved X number of viewable impressions.
X people clicked on our ads.

But does this really mean your ad was as successful as it could be? None of these metrics, not even sales, have much to say about the long-term perception of your brand. Clicks and views, for example, are proxies for attention. They don’t tell you about how your creative actually impacted a consumer.

Plus, brand marketing is about more than a direct sale. You’re investing your budget – in some cases, a considerable budget – into building long-term perceptions of your company and what it can bring to consumers. Not every consumer is going to turn into a customer upon seeing an ad, considering not every consumer is constantly in-market for a product. That doesn’t mean the ad doesn’t have value. At it doesn’t mean that they can’t be converted to a customer somewhere down the road.

Brand lift

The metric that matters is brand lift. Brand lift is what tells you if a consumer came any closer to becoming a customer – in short, if your ad worked. And in the pre-digital days, it was celebrated as the industry gold standard for determining ad effectiveness.

But today, easier-to-collect acquisition metrics like conversions and behavioral metrics like clicks are much more actively being pursued by brands and their agencies. And this is a problem, because it means our standards aren’t as high as they could be. If the objective of brand advertising is to generate attention, inform, persuade and drive intent, why is the industry moving away from brand lift towards measuring for outcomes?

Accurate vs. actionable

The answer to the question of why brand lift fell from favor is a pretty easy one. Pre-digital, while brand lift measurement was accurate, it wasn’t actionable. Marketers typically had to wait until a campaign had wrapped before brand lift could be measured, which meant they had no way to potentially optimize their strategy or change course while the campaign was actually running because they didn’t have a statistically stable projection. Further, brand lift reporting should really illustrate how well consumers are moving down the purchase funnel, from brand awareness through to brand intent and virtually none do this well.

Measuring brand lift has also historically been expensive and complicated. The methodology isn’t an ad tech creation. It is a scientific method. It requires taking two random sets of survey observations, one from a “clean” control audience and one from a test or ad-exposed group, both from within a campaign’s targeted audience.

Measuring brand lift requires complex sampling to ensure randomization and representation, survey instrument development, rendering questionnaires online, generating responses, estimating for biases, data cleaning, sample weighting and statistical testing to name some of the efforts required.

Difficult but effective

As a result, brand lift is difficult to deploy and potentially much more so than coding for an online click-based behavior such as registering a sale or logging a site visit. And there were even more variables: Methodologically, who are these people who participate in paid, panel-based studies and are these respondents truly representative of the consumer base exposed to the ad? Most brand lift studies, rarely, if ever, provide enough data for stable and sliceable analyses.

However, brand lift measurement works. And you can’t game it. Clicks, sign-ups, and other digital-first metrics are all subject to skews related to the type of media. Even worse, they are sitting ducks for fraud and bad actors. Brand lift is a fundamentally human metric. And bringing it back to front-and-center is just a small part of what we as an industry need to do as we refocus on the fact that humans, not machines, are at the heart of brand storytelling.

So, what do we do? In short, we’ve got a ton of new digital tools available to us that make brand lift measurement affordable, scalable, and actionable in real time. We’re not using them at an industrywide level. And we should be.

Give brand lift a lift

To give brand lift new relevance in the digital age, the industry should explore gauging brand sentiment with innovative surveys that are far more streamlined than we’re accustomed to. Incorporating consistency and even questionnaire standards could develop real benchmarks as well as ensure brand lift is an “honest” metric that can’t fall into the shady math situation that characterizes so many other digital analytics practices.

These surveys can be structured using a single question, keeping the experience lightweight for consumers. This is important because it broadens the base of consumers who can be surveyed. No longer are you limited to paid participants or to people who are willing to sit through multiple questions.

And perhaps most importantly, collecting large samples as a campaign is still running has the potential to shift the collection brand lift data from after the campaign ends to real-time – producing truly actionable insights. If something isn’t working, you can adjust your creative or targeting.

A consumer in your target demographic might not be ready to buy your product. That’s fine. However, the metrics we’ve been using in digital are both giving us an exaggerated sense of urgency – because they can’t measure anything more than a direct conversion – and keeping our standards low. It’s time to bring brand lift back to front-and-center, because finally, we’re ready for this tried-and-true metric to reach its full potential.