This week we present a collaborative piece that we’ve been working on with our friends at Lexer. We’re big fans of what they’re building, and their CDP solution solves a lot of first-party data management issues. This piece represents our combined thoughts around the risks that brands will face in the near future, and the opportunity to build first-party data to combat those risks. — Phillip and Brian
Believe it or not, we’re just 40 years into the age of the internet. Despite the indelible mark that the information age has had on the world, and the very real impacts on human psychology, ostensibly, we’re at the very beginning of this new era.
To date, the web has been a new frontier, and largely governed by frontier-style laws. Historically, very few laws managed or policed the practices of the internet, inheriting laws that governed the real world as surrogates for how people or corporations should behave online.
Not anymore. A few significant changes in global and domestic privacy laws have created an environment that is inhospitable to the largest of tech companies, the FAANGs of the world. The largest of which are now going to war with each other, under the guise of privacy, to snuff out competition.
The most notable of these battles — Apple versus Facebook — will have a broad impact on online advertising. With the deprecation of 3rd-party cookies and alternative identifiers, AdTech might be at a turning point—one that could result in an increased focus on transparency and accuracy.
In this piece co-authored with our partners at Lexer, we’ll dig into three major obstacles plaguing marketing teams when trying to find transparency and accuracy in AdTech, and we’ll give you some advice about how this will shift growth marketing to creating space for first-party data — er, we mean communities. 😉
Those three obstacles are:
- The Bots
- The Platforms
- The Fat-Fingers
Let’s start with bots. As Dr. Augustine Fou put it this past January, "digital marketing works; but the vast majority of impressions and clicks are from bot activity currently.” He goes on to say:
When P&G turned off $200 million of their digital ad spending, they saw NO CHANGE in business outcomes. When Chase reduced their programmatic reach from 400,000 sites showing its ads to 5,000 sites (a 99% decrease), they saw NO CHANGE in business outcomes.
The fact is for brands relying on programmatic ad spend, the vast number of clicks you pay for on many channels are probably from bots instead of humans. And we’ve all read way too many articles detailing how platforms prioritize their revenue over being accurate and transparent with advertisers. Facebook is currently in court over the issue, knowing that one of their data metrics around video views was misleading, as the suit alleges. To solve this problem at scale is to create a revenue problem at scale, too.
Why? Platforms’ revenues from bots increase as the negative impact of ad fraud on your campaigns increases. How can we expect them to stop the army of bots helping them achieve record revenues?
On the other hand, small businesses feel greater impact when turning off programmatic and suffer from this effect to a smaller degree than, say, large CPG brands do. The net effect is the leveling of the playing field, and potentially a contributing factor to the rise of challenger DTC brands in recent years. Dollar-for-dollar, the DTC brand is more effective at driving ROAS than the global chocolate-and-water company. They don’t have the scale, or the problems, that arise from being at scale.
The effect of a cookieless future may unfairly harm the small business. The DTC will have less effective, less-targeted audiences to reach; and when they do reach them, the cost for acquisition will be higher. Meanwhile, CPGs will outspend the DTC because unfocused, untargeted awareness campaigns have always been bread-and-butter (pun intended?) for them. Why? Because eCommerce for them is a breakeven marketing channel, at best.
Strangely enough, this is Facebook’s argument against the Apple privacy change, which brings us to point #2.
The second issue is with the major platforms themselves. There are significant, opposing, interests between advertiser and platform as it relates to two key variables: the size of an audience and reported performance.
Modern ad networks aggregate users into audiences. Those audiences can be defined by quantifiable actions — like how old a person is, their income, their gender identity — or even behaviors; say, you’re in the market for a new car or candle shopping.
The greater transformation for DTC businesses, in particular, is the next generation of audience identification: psychographic targeting, which targets a prospective customer based on how they think. This is achieved through sophisticated machine learning algorithms and is generally what people refer to when they have encountered “creepy” marketing.
Changes are coming. In the very near future, Facebook will no longer be able to effectively target audiences who have opted out of cookie-based advertising. This makes a portion of ad spend ineffective, if not wasteful.
What’s causing the misalignment between the platforms’ and marketers’ priorities for audience-building? Here’s an example to help me explain: Let’s say that in year 1 an algorithm “thinks” an audience for a campaign is about 1,000,000 people who truly fit the criteria. Now, if the platform has a goal of increasing revenues 25% in year 2, what’s preventing that algorithm from loosening up a bit and now thinking there are 1,250,000 people who can fit the same criteria even when nothing else has changed?
If you don’t have true transparency into who those people are, your only way to keep tabs on the quality of the audience, whether it’s 1,000,000 or 1,250,000 people, is to look at performance. As long as the performance of the campaign is still acceptable to the marketer, then the campaign can be a success regardless of exactly how the audience is created and who it includes.
That brings us to the second half of this issue which is platforms self-reporting on your campaigns. The process used to be straightforward:'
- Connect your Google Adwords and Microsoft adCenter accounts to your Google Analytics account.
- Set up the conversion points on your ecommerce site where all the ad traffic was being driven and assign dollar values to those end conversions.
- Time to optimize!
It was simple, easy to optimize around, and we trusted that what we were seeing in Google Analytics was accurate, or at least close enough.
Today, conversion tracking has become much more complex as the platforms add in less accurate conversion signals and thus report on less accurate data. If you don’t have transparency into all the attribution signals you can’t clearly see—think view-through conversions, a 60-day purchase window, footfall measurements, or “serve an impression online to someone who eventually purchases in-store” attribution models—it’s hard to know how much of what they’re showing you is real vs. fluff.
Since the platform gets your dollars whether or not the signals are real or fluff, we can’t expect transparency to be a significant part of the picture. It’s like asking high school kids with their focus set on college admissions to grade their own exams, and not allowing any adult to double-check their grading. Some kids will be honest when grading their own work and some won’t be. Who is truthful and who isn’t could easily change from week to week. And we would never know the difference.
The platforms make decisions to maximize their profits and shareholder value, not to serve you better. If you’ve ever felt that you have multiple platforms taking credit for the same conversion, you’re not crazy and you’re certainly not alone. This is also why some of your campaigns might be showing huge ROI that doesn’t seem to translate into actual sales growth for your business.
Now, imagine a future where the data becomes more opaque, and your spend is less effective. That’s the future we’re trending towards with the new Apple iOS 14 changes, and when Google rolls out its new 3rd party cookie default.
The cookieless future will make it difficult for platforms to prove success, and more difficult for brands to provide relevant content for their prospective customers.
We all make fat finger taps when using mobile apps. There’s nothing malicious about it. When we fat-finger tap on an in-app banner ad or a video play in the middle of an article about the 10 tastiest pizza places in our city, it’s purely an accident that’s hard to avoid. Despite no mal-intent, however, the tap is still costing advertisers and can be reported as if it were an act of genuine interest.
To show just how big of an issue this is, Let’s look at a programmatic ad campaign through one of the most prominent trade desks.
Consider the launch of a programmatic B2B campaign at a well-known trade desk, encouraging the audience to read a new piece of ungated content. The corporation wanted to base the campaign's success on how many people clicked or tapped on the ad to view the PDF. The team’s one requirement was that the audience had to click or tap twice in order to be counted as a reader of our content. One click or tap on the ad, and a second click or tap on a big CTA button in the center of a mobile-optimized landing page that included one sentence prompting the audience to click the button and view the content.
The ad had over 200 unique clicks. The landing page had 3. Let’s let that sink in...
Only 3 people were interested enough in the content to tap their phones twice out of 200+ people (and maybe some bots but we’ll never know) who made a first click or tap, but the business paid for all 200+. So over 98% of our ad spend in the programmatic campaign could be considered wasted. How much are you wasting in those channels?
What’s worse is how these unintended fat-fingers act as intent signals to platforms. Some users already experience an ineffectual marketing experience due to poor prediction by the algorithm. Because platforms learn by observing behavior, enough of these errant clicks can bucket people into the wrong behavioral audience.
And it’s not just clicks! Dwell time, reading comments, and even the act of stopping and scrolling back to look at a post — any post, whatsoever, not just an ad — can be interpreted as interest signals to a platform. Machine vision will try to understand the context of what caught your attention, and seed in lookalikes to test whether its hypothesis was true or not. This has chilling implications: first, the ability to groom behavior by grooming the content, and second, the inability of the user to understand which of their actions are being monitored or harvested for ad targeting purposes.
Again, this all will change in a future where cross-platform cookies cannot stitch behavior across the web into ad networks.
What can we do? Astute brands have decided it’s best not to build on rented land. Building a first-party relationship with the customer is a must for any modern brand. Once you have this relationship, you can use your first-party data as the basis for all customer insights, segmentation, audience creation, and performance measurement.
We’re partnered with Lexer, a Customer Data Platform that helps brands and retailers do just that so they can take advantage of all the benefits the major ad platforms have to offer, while significantly reducing the extent to which these platforms can take advantage of the lack of transparency. Since we’re biased on this front, let’s focus on the second solution now emerging that will get us closer to transparency and accuracy in AdTech.
The second solution is the rise of some of the biggest brands and retailers allowing others access to their own first-party data for advertising purposes. If you work for a consumer brand, Walmart’s data is a goldmine. If you’re in media, entertainment, or children’s anything, think about how valuable Disney’s data would be for your marketing activities. Who has better mobile device data than the mobile carriers like T-Mobile?
Will those data providers and emerging platforms want to make money from their AdTech offerings? Of course. The difference is that AdTech is not likely to ever be their core business. It stands to reason that whatever AdTech offerings retailers will provide will not be allowed to hurt their core retail business, and thus there is an inherent mechanism that will pull their offering in the direction of providing more transparent audiences and accurate reports to the marketers they serve. They are much less likely to deceive their marketing customers when those marketing customers are also grocery, household products, and sporting goods customers. They don’t want bad press from their small AdTech businesses damaging their nation-leading consumer businesses. And thus, being transparent in their AdTech offering is much more important.
At Future Commerce it has often been said that one cannot just “build a community”, but you can set the table and send out the RSVPs, but you cannot force people to eat your meal. Building a forum where community can happen — newsletters, podcasts, or any other form of direct engagement and media — will allow you to have a first-party relationship with the customer so you can ensure that every meal you make is worth eating, so to speak.
So if you’re worried about 3rd-party cookies and alternative identifiers becoming a thing of AdTech past, remember all the flaws that came along with them. Then look forward to these massive and probably much more accurate first-party datasets that appear to be emerging as your 2nd-party data options. These big brands and retailers offering new advertising options:
- Have data that is not as wide in coverage in the number of people but that offers much deeper insight into those people.
- Are much less prone to situations where the incentives of the buyer and provider are at odds.
- Will likely give marketers a more accurate view of audiences and performance.
Better data and better alignment of incentives may make these budding advertising services from the massive legacy consumer business the new standard in honest AdTech. As transparency becomes increasingly disassociated with the prominent digital ad platforms that have surged in prominence over the past decade, it will likely become associated with the traditional retailers who are lowering their own walled gardens.
Article by Phillip Jackson and Will Cooper - Director, Demand Generation, Lexer