The following article originally appeared in slightly different form in AdExchanger on April 21, 2023.
Privacy is hotter than a habanero right now – and it’s coming for your ads.
The zeitgeist has turned. Despite recent protests to the contrary, the FTC embraces “surveillance” as a metaphor for ad tech, and the cultural elite is even less happy. A recent op-ed in the New York Times, by Julia Angwin, implicated the industry in many dismal practices, including election-rigging, news-defunding and even inflation.
It’s difficult to find anyone wiling to make a case for the defense. If someone were to do it, they might start with these four myths:
1. Advertisers Follow You Around the Web
Only the phone company and your browser can follow you around the web. Angwin claimed: “Tech firms track neverly every click from website to website….” It would surprise her to learn that the vast majority of clicks on the web are neither tracked by advertisers nor available for sale.
Take the best-case scenario (or worst-case, depending on your POV): retargeting. That’s the classic example of the shoes-that-followed-me-around, etc. What does the retargeter know? That (1) your browser loaded a particular item, (2) that browser is on a publishers’ site. That’s it. Better than nothing – but far from a map of your entire web journey.
And by the way, that advertiser has no idea who you are (unless you told them). You’re anonymous. Contrast this with the offline world, where it’s easy to get a file of recent movers – say – from the U.S. Postal Service, and they’re not anonymous.
2. Targeted Ads Hurt Publishers
This is the easiest claim to debunk. Certainly, the internet itself has been hard on print, as cable and then streaming services have bopped linear television. Global revenue for newspapers is down by two-thirds in two decades, and this is not good news for anyone.
Targeted ads did not cause this decline; the internet did. For whatever reason, online ads don’t command the same prices as offline ads per impression. But if you’d like to find a better culprit, look at CraigsList. For decades, classified ads were a cash cow for publishers, particularly local papers. Classified ad revenue for U.S. newspapers fell from $20 billion to $2 billion in the last two decades.
Any publisher will tell you targeted ads are usually worth more per impression than less-targeted ads. Better targeting makes ads more relevant; more relevant ads are more likely to get a response; that response is worth more to an advertiser. It’s just math. There’s a reason ad targeting happened.
Big publishers are the most vocal defenders of targeted ads. Some of them are even going to court to try to save the cookie.
3. Targeted Ads Hurt People
The challenge here is in finding a harm caused by targeting – and not just by ads in general, or by an unethical advertiser who is violating existing consumer or other protections.
For example, a recent study out of Carnegie Mellon concluded that products shown in digital ads were lower quality, compared to search. The study is quite puzzling: display and search work very differently, and it’s difficult to see how a consumer is actually hurt by seeing an ad for a cheaply-made product.
Higher-margin products – from Veg-O-Matic to class-action lawsuits – always advertise. They’re what keep late-night cable TV and the Home Shopping Network in business. As consumers, we’re free to make our own choices.
More seriously, targeted ads are blaimed for tipping elections and spreading falsehoods. The Ban Surveillance Advertising Act proposed last year, said: “It fuels disinformation, discrimination, voter suppression, privacy abuses, and so many other harms..” Again, no specific instances are raised, and there is ample evidence that ads alone don’t tip elections anyway.
If the advertiser is selling a harmful product or lying, that’s the responsibility of the FTC and Truth in Advertising. We all remember smoking ads, or at least saw Mad Men. None of those were “microtargeted.”
4. Eliminating Targeting Eliminates Tracking’
Since Chrome announced the deprecation of the you-know-what three years ago, there’s been a shift toward first-party data. Brands try to get more of it; tech companies build tools to move it along. It all makes perfect sense.
Winners are companies with their own troves of first-party data. Large retailers are loving retail media. These players naturally know what you do on their sites, combine it with other data and insights, and package it up for advertisers. They got an opt-in sometime a while back (it’s in the T&C’s).
Yet that’s not “surveillance.” Nor is search, although it might surprise critics to learn that search data – often more personal than cookie-data – is observed, and sometimes informs ad targeting.
It seems that third-party data collection (retargeting shoes) is a convenient target for angst when the far more potent profiles and power lies with these growing first-parties.
One Bad Thing About Targeted Ads
Third-party cookies need a reset; they were not designed for ad tech anyway. Few defend pixel-synching in its current state. Ironically, the bad thing about targeted ads is that they aren’t subtle. They’re often lower-funnel, so they tend to be less creatively appealing anyway.
And they’re part of a system that doesn’t have a handle on phenomena that bother consumers. Take frequency capping, the scourge of CTV. That’s a symptom of a system not knowing enough about (anonymous) consumers, rather than the opposite.
Nobody’s defending invasion of privacy or unethical ads. But it’s time to put up some kind of defense for basic ad targeting, before it’s too late.
If you’re like most marketers, you’ve been hearing the term “real-time” a lot lately. And you’ve probably been wondering, what is real-time marketing? Are we delivering content in seconds? Milliseconds? Even faster?
It can sound like marketers need to live in the world of the Oscar contender Everything Everywhere All at Once. Not necessarily. What matters is that you reach your customers when they need to be reached, with the right experience. Real-time marketing is not so much having all the answers all the time, but giving customers what they need, when they need it.
What is real-time marketing and how does it use real-time data?
A search for “real-time marketing” reveals a grab bag of definitions. They range from the vague (“systematically responding to your customers”) to the prescriptive (“focusing on … customer feedback”). It seems as though nobody knows what time it is.
Let’s start with the difference between real-time data and real-time marketing. Real-time data is processed and available for use right after it’s captured. That’s milliseconds. For example, the GPS on your phone captures your location and recommends a driving route in real time.
But while it’s important to capture and process data quickly, it’s not always necessary to act on it right away. This is especially true in marketing, when the customer drives the journey. Real-time does not have to mean right now. It’s delivering the information when the end user needs it. That could be seconds or even hours later.
Travel and hospitality is a very time-sensitive business. If a customer’s digital profile isn’t accurate at the moment, it can trigger unfortunate events. When this happens, a passenger misses their flight or doesn’t get the right seat — and airs their grievances on social media.
When a customer changes their seat or flight on the airline’s app or website, they expect it to show up in their experience right away. When they later go to a kiosk or a service counter, or call customer care, they expect — quite reasonably — that the service agent is up to date. The customer also likely assumes the airline won’t send them irrelevant emails or offers.
This example shows us the difference between real-time data and real-time marketing. Real-time systems should update customers’ profiles right away. On the other hand, real-time marketing should happen at whatever speed is the right one for the customer — whether that’s today, in five minutes, or next week.
There are implications for the marketers’ back-end data processing systems and resource requirements.
When the customer is on the website or app, they expect their actions to be processed in milliseconds (under a second). But there’s no reason the contact center can’t be updated in seconds and the email system within minutes, right?
Managing response rate requirements can lower costs and complexity, as long as they don’t impact the customer experience.
What do marketers mean when they say “real-time”?
On most occasions, when marketers say real-time, what you really mean is right-time. What is real-time marketing, really? It’s delivering the right data at the right time, to the right systems, to better connect with customers.
Right-time is doing what is needed to make each moment count for the customer
Real-time is collecting and processing data with no delay
The only reason to make this distinction is there can be major technical and organizational costs to imposing real-time requirements on the marketing team. Some teams have resources to handle it and some don’t.
It’s more important to make strategic investments into the systems that need to be real time — for example, your personalization platform and customer data platform (CDP) — and understand what’s required elsewhere.
How can you set your real-time data priorities? It helps to remember that marketing has two basic modes:
Respond: You’re reacting to customers when they’re already engaged. They’re on your website, in your app, poking around on a kiosk in your store.
Inspire: You’re trying to get the attention of customers and prospects when they may not be thinking about you. You send emails with offers, show ads on Facebook and Instagram, etc.
In most cases, it’s the ‘Respond’ mode that needs you to address customer concerns quickly. On the other hand, most ‘Inspire’ activities are pre-planned and benefit from complete and curated data that does not need the hyper-warp-speed investment.
But in some cases, real-time responses can even be counterproductive. Take an abandoned cart email. Not many of us would react calmly to a reminder email — or, even worse, a text message — a mere few milliseconds after we decided to leave. That’s what we mean when we talk about real-time marketing.
What can you do with a CDP using real-time data?
When you’re making decisions based on real-time data, you’re able to respond to customers in ways that make sense to them. Upgrading your customer data platform to one built on real-time data can help make sure that you have the answers your customers want — when they want them.
Doing this can not only make for happier customers, but improve your bottom line in a cost-efficient manner, too. After all, what is real-time marketing but a timely way to meet customer needs?
For example, a customer might make a purchase on an e-commerce website that puts them into a high-value segment. The segment change can trigger — right away — that person’s entry into a journey tailored to high-value customers. You can then target them with the right ad the next time they’re scrolling through Instagram.
Recently, we announced Data Cloud, our CDP that uses real-time data to make real-time marketing easier for companies. Making the most of real-time data can help you improve customer journeys.
Anyone considering a CDP to support real-time data management should ask how well it will support their “right-time” requirements. Just having parts of the customer journey happen in real time may not be enough. For example:
First-party data: Many enterprises already have a trove of first-party data, and it should be easy to make use of it in real time with your CDP.
Data actions: Marketers have different ways to communicate with customers, and these different methods (or channels) need to receive rapid signals from the real-time CDP.
Partnerships: Reliable and easy-to-use integrations with key partners also helps eliminate friction in the data transfer process, where third parties are needed (such as for data enrichment, media activation, and auditing). For example, we recently announced integrations with Snowflake, Amazon SageMaker, Microsoft Azure, and others on the AppExchange.
Any lingering confusion about what is and isn’t real-time fades in importance when we pose a better question: What does the customer really need from us right now?
A version of this purr-fect article originally appeared in The Drum on Feb. 13, 2023, a few days after Super Bowl LVII aired. A picture of my photogenic muse Jerry appeared at the end (as it does below).
While some people say the Super Bowl was a close game, it really wasn’t. Dogs totally dominated cats in the USA Today AdMeter poll.
The Farmer’s Dog came in first with a time-travel tail – uh, tale – that showed just how good dogs are at nuzzling our emotions. Amazon’s bad-dog-turned-angel saga took third.
And where were all the spokescats? Not feline the love. One provided a whisker of comic relief in a Google Pixel ad, where a sour puss pointedly removes a dog from a photo with AI. Probably out of jealously.
It turns out, there’s a good reason for the double-standard. Cats and dogs evoke different emotions in people and can be used to inspire specific actions in marketing.
Dog actors actually inspire us to make connections, take risks, and share our personal space
Cat actors put us in mind to purr-chase more insurance, protect our homes and families, and prep for doomsday
Half of U.S. households have a dog and one-third have a cat – a number that continues to grow. One in five households even found a new furry friend to rescue them during lockdown.
And we are big spenders, not just on our pets. Pet owners tend to be more affluent, healthier, more confident, even better-looking (meow).
Pets appear often in entertainment, from Puss in Boots to every perfect-family-with-an-SUV TV spot, especially if it’s in the snow (cue adorable baby Bernese mountain dog). But the impact of our four-footed friends on our attitudes and behaviors hasn’t really been clawed over until recently.
The current study – a collaboration among universities in the U.S. and Hong Kong – wasn’t yet another meme about cat-people vs dog-people. It showed that dogs and cats actually evoke a chain of emotions in most consumers that is both different and predictable. They prime the message pump.
For decades now, many researchers have adopted an idea called regulatory focus theory, which claims there are two basic consumer mindsets:
Promotion – focusing on gains, success, YOLO, being all that we can be
Prevention – focusing on safety, preserving what we have, personal security, avoiding risks
Now we consumers aren’t all one or the other – except in the case of some fussy outliers – but have a mindset that can be shifted by social cues and marketing messages … which brings us to pets.
”This stream of research suggests that a promotion-oriented eagerness system better captures dogs’ temperaments and behavioral characteristics, whereas a prevention-focused cautious system better describes cats’ temperaments and behavioral characteristics.”
In fact, advertisers already intuited the study’s findings before it appeared. (And they exhibit a subtle anti-cat bias, which cats will remember when they take over the world.)
Cats are often used in dark and dyspeptic scenarios:
Wells Fargo demonstrating “suspicious activity” on cards, pushing alerts
Sainsbury’s (U.K.) Mog the Cat, disappointed by an empty dinner bowl at Christmas, warning us to shop early
That alarming All-State ad where Mayhem cat-thropomorphizes from human to feline while his home decomposes around him
Meanwhile, of course, dogs tag along with kids in the sunshine and spread nothing but golden light and joy:
Wells Fargo, this time with Regina King and a golden retriever promoting a cash-back rewards card
Subaru Ascent … which featured no fewer than seven adorable goldens outside a condo sign-posted “The Barkeleys” and … I rest my case.
Cats, Fight Plaque!
The researchers got their human test subjects primed with dog (or cat) questions and images, putting them into the promotion (or prevention) mindspace. Then they asked them if they would buy toothpaste that would “freshen breath” (or “fight plaque”).
Not surprisingly, the dog-primed promotion-focused pack preferred the fresh-breath feature. The cat-primed prevention-focused pride wanted to take a swipe at that plaque.
Other scenarios found that the dog-primed pack was significantly more likely to:
Participate in a lottery
Buy stocks (vs funds)
Buy vitamins that made wellness (vs prevention) claims
On the other paw, the cat-primed group was more likely to:
Stay away from high-flying but risky stocks
Put a higher value on preventative health services
Spend more for products with prevention claims
The experiments controlled for factors like personal pet ownership, preferences and even mood. So again, it’s not just more cat-people-are-shy propaganda. Rather, it’s evidence that there is some stereotypical behavior in animals that triggers semi-unconscious associations in people. These associations in turn nudge consumers into a particular general mindset, which can bolster certain messages.
So now we have some guidelines for our feline- and canine-themed campaigns. That’s something to howl (or meow) about.
We’re at the Rosewood Sand Hill, optimally close to Kleiner Perkins and the banks that whisper out our future like the oracles of air. No conspicuous consumption; in fact, we’ve moved beyond consumption into non-consumption, all electric and non-meat, so faithful are we to our own shared abundance.
And onto the stage comes a slightly seedy character, no longer young, ironic in the way of the 1990s, inhabiting a gestalt that’s gone. It’s me, in fact: about to argue yet again the importance of forgotten history to a room full of futurists. This is what you may have missed …
1. We Start with Netscape
The internet predates the browser, but the browser was the box on which we built the web. Before Mosaic and – more significantly – its virtual spin-out Netscape, launched in 1994, the internet consisted of academic cultists and gardens like AOL and CompuServe with walls so high they blocked out the sun; they weren’t the answer.
Mosaic was “achingly beautiful,” in the words of Vint Cerf, a project emerging from protocols developed by the British physicist Tim Berners-Lee (HTML, HTTP). Mosaic put images on the web, after a battle. It made hypertext easy. Developed by unpretentious hackers at the University of Illinois’ National Center for Supercomputing Applications (NCSA), it was profiled in the New York Times, which failed to mention its actual makers, Marc Andreessen and Eric Bina.
Thus stung by lab politics, Andreessen and then Bina and others agreed to join Silicon Graphics’ founder Jim Clark in the Bay Area to build a better Mosaic. It went public in 1995 and started the dot-com boom, through no fault of its own. Everybody was excited by Netscape; especially Microsoft, which promptly tried to bury it.
And while the academic internet was explicitly anti-commercial, Netscape was not. Nor were many of the publications that bravely stood up online, such as HotWired.com, the digital porting of the digiterati’s lifestyle manual: Wired magazine. Portals like Jerry Yang and David Filo’s link roster Yahoo! were initially anti-ad – as was Google at launch, five years later, — but ads got inevitable. From the beginning, nobody seemed to want to subscribe to bits on a screen or use a credit card online.
Ads were the only way to pay.
On the Netscape homepage was an early “live cam” (updated every few seconds) of a fishtank. It sat on the desk of a 24 year-old engineer named Lou Montulli. Now Lou was not part of the original NCSA cadre but was merited in due to his popular text browser Lynx, written while he worked the help desk part time in the Computer Lab at the University of Kansas, between racquetball games.
And Montulli of course – as everyone who has followed me since my Gartner days knows – invented the browser cookie. As he told me, it was to enable a shopping cart in the stateless-by-design internet, and immediate adoption by ad workers was not in the plan. Montulli decided to enable third-party cookies by default not because he wanted to invade anyone’s privacy but quite the opposite: he wanted the whole thing to be unobtrusive and transparent.
In other words, he made the engineer’s mistake of assuming that because a feature exists (we have always had the power to turn off our cookies), it actually exists. In fact, nobody bothered to look.
Lesson #1: Whoever owns the browser software, controls the Internet (i.e., Apple, Google).
2. The Dot-Com Ad Network Boom
In the 1990s, everything was an ad network. There was no real-time bidding on anything, and even big publishers didn’t have a big online ad business. Scaled large websites like NYTimes.com and WSJ.com weren’t scaled enough to attract TV money, at first, and smaller websites (i.e., everyone else) had neither staff nor knowledge, buyers nor technology, to extract rents from the web on their own.
So we have the ad networks, which do the work of signing up lots of publishers so they can go to ad buyers and offer them something they might actually want – millions of impressions across thousands of websites, never mind where. A natural extension of this middleperson model is the software that sits between publishers, ad buyers and agencies, deciding which ads to run, when, and counting what occurs.
This software is called an ad server, and many ad networks built their own ad servers, or tried, until the leaders emerged. This emergence happened quickly: by 1996, DoubleClick was dominant, and when it acquired on-premise rival NetGravity in 1999, it was super-dominant. People forget DoubleClick started as an ad network, and its ad server was a tool for the sellers. SaaS as a margin machine wasn’t real until the Y2K.
DoubleClick had the virtue of swagger; it was unapologetic in its quest for excess. Its hyper-charismatic, raven-haired co-founder Kevin O’Connor threw out business plans that were too cautious: “How does that lead to total domination?!” DoubleClickers loved him, and everyone in New York media wanted to work at DoubleClick in 1999. I tell you this from memory.
Silicon Alley – triangulated roughly around the Flatiron region, cheaper than midtown and convenient to the R and 6 – was a publicists’ pitch pushed by DoubleClick and the city of New York. The company’s logo was on just about every lamppost in lower Manhattan. Skywriters flew over beaches in the Hamptons telling ad buyers their campaigns were safe, the machines were on it.
And they were. Business was built – again, absolutely openly – on the compilation of a file against every DoubleClick cookie and I.P. address, appended with third-party data like location. With almost every premium publisher in its network, DoubleClick had a cookie on all our browsers, and so read much (not all) of our etheric adventures.
It was only when O’Connor acquired Abakus to insert real names and addresses into the pseudonymous cookie profile did USA Today, and then everyone else, raise a hand. But this was in 2000-01, when we all had other problems.
There was one outlier, a brave ad network that took a principled stand against dropping third-party cookies on browsers, for privacy reasons. It was a sortie that did not connect. In the 1990s, WebConnect was most definitely on the wrong side of history.
Lesson #2: Ad dollars flow to places with more information, not better ethics.
3. The Rise of Retargeting
Retargeting is the killer app for digital ads.
There is a very simple reason for this: it works.
In the 2000’s, ads targeted based on specific things we did in our browsers got at least 3X better engagement than non-retargeted ads, however we measured. Of course, many people claim to have invented this miracle-grow: Advertising.com, TACODA, MySpace, Criteo, BlueLithium/Yahoo. But it seems to have been invented quite naturally by DoubleClick, sometime around the dot-com crash.
Retargeting works, but it has a signal flaw. It is very easy to notice. In fact, we might say most civilians did not even suspect the persistence of cookies and targeted ads until retargeting gave them a clue.
Slowly at first, and then with more persistent vigor and rage, the ad-that-followed-me combined with a generalized uptick in paranoia to create a climate of conspiracy around the ad business. We went from being Mad Men to madmen with malodorous intent.
We have the spectacle, in the later 2000’s, of otherwise circumspect journalists reporting their businesses closer to the grave with richly-researched deep dives under ominous titles such as “The Privacy Project” (“I Visited 47 Sites. Hundreds of Trackers Followed Me” – New York Times) and “What They Know” (“They Know What You’re Shopping For” – Wall Street Journal).
Web surfing for the cultural elite assumed the soundtrack of a horror film. Yes, they probably do know what you did last summer.
Looking at Google Trends data over the past two decades, interest in ‘tracking’ has indeed swelled – even as interest in a randomly-selected fad (in this case: ‘Bieber,’ in red) has fallen off the cyber-cliff.
Lesson #3: In the long run, it is more profitable to be gentle with your superpowers.
4. The Epochal Moment Called 2018
Everything changed in 2018. Certainly Snowden had an impact, five years earlier, but his concerns were largely abstract, existential and political. He and his outraged adherents had much more important things on their minds than mere ads.
Which brings us to Mark Zuckerberg. He appeared in front of two Senate committees two years after the 2016 presidential election and some time after a German magazine revealed Facebook had given unredacted network data to a Cambridge researcher, who’d sold it to a conservative consulting firm called Cambridge Analytica. (Meta settled this case only last month, by the way.)
Nothing happened out of the senators’ naive posturing, but an estimated 80 million people saw part of the hearings – and everyone knew they were on. This was a yellow-card moment in digital; another ping at the populus. This time, for the first time, we started to look at our phones with suspicion. Our apps were watching us too.
By no coincidence, 2018 was also the year that digital ad spend overtook offline – that is, digital won – and the share of the triopoly (Google, Meta, Amazon) exceeded 80/20, including search. Digital became the dominant dimension in ads, and a handful of mastodons maneuvered for control.
Zuckerberg’s appearance in 2018 almost forced Apple to do what it did, two years later.
Intelligent Tracking Prevention (ITP) and the App Tracking Transparency framework (ATT) flexed the power of the browser and OS owner. We noted this with Netscape, and we see it again. Google was peer-pressured into blog posts and a Privacy Sandbox of professorial interest, while Apple filled a vacuum left by our Congress, appointing itself Neighborhood Watch for the World Wide Web.
Two things to mention here:
One, Apple’s specific tactics aren’t as important as its heuristic decision to make data collection contingent on an explicit opt-in by the user. We need to say “Yes.” Which makes sense until we ask the logical question: “Yes to what?” And we’re caught in the sepulchral eddy of the Privacy Paradox, which states that it’s almost impossible to make an intelligent decision about whether we do or do not want to “opt in” to collection of … what? … for what purpose? … and by whom?
Most of us don’t care. We click yes or no depending on our prejudice. (I always click yes.) Also, whether we’ve heard of the brand. And we’re grandfathered into the big apps like Amazon, Instagram, Facebook, TikTok. In fact, we have no idea what they know, and they don’t really have to tell us.
Two, nobody wants untargeted ads. Trust me on this. We might romanticize an experience of anonymity, but I challenge you to live it – as I did, — disable your trackers, obscure your I.P., use a VPN, and just look at the ads you’re served up: a late-night-cable-TV hodgepodge of personal pizza, car caddies, weight loss lies and class-action lawsuits.
Irrelevance has never been so loud.
Lesson #4: Apple is taking the place of a well-ordered Government today.
5. What’s Coming Next
As much as we crave good direction, howling in despair, the ad market still obeys its formulas. We need addressability and accountability; targeting and measurement. We target based on real information, inferred information, at a person- or a cohort-level, and then there is context (time, publisher, page, device). Measurement is an art form in itself and has never been exactly precise.
Building a campaign, we start closest to the point of decision – if we can – and build out from there; again, starting with those places where we have the most control, or think we have. So many times we start with paid search because it’s (1) close to the point of decision, and (2) in our control. We’ll overpay to meet these criteria. And so on, out into the wild terrain of unknown ad networks and disconnected TV.
Given the principles of proximity to a sale and greater information, where do we look?
Until it perfects its in-app storefront, Facebook/Instagram is hampered by its distance from the end zone. That’s why it needed the mobile ad ID (MAID). So-called retail media – which is living its own wonder years right now – can place ads close to the point of purchase and record a sale. It’s an ideal environment limited only by immature tech (in most cases) and the narrow data sets.
And it seems very safe to me to say that we are in desperate need of new ad formats.
Paid search is the most successful, and it was entirely unpredicted. Banner ads feel like a retreat to the mean. McLuhan tells us the content of any medium is always another medium. Digital is growing up and out of that, at last.
Look at the #Influencer phenomenon, reaching perhaps $13 billion this year. Those are blatant product pushes, in our pockets, from figures not too proud to beg – and they work. That’s digital for you. What about product placement? Imagine tools that paint products into sets (and eventually dialogue) without a seam. They’re coming soon. I call these “non-ad ad formats.”
Soon we’ll all just call them ads-as-usual.
So where does that leave the hard-working brand? You know that you need first-party data, but it doesn’t have to be your own. If you have it, scale it; get a Customer Data Platform (CDP). If you don’t, you’ll just need to ride along with someone else’s, once Montulli’s cookie’s gone. Don’t worry, though: those ‘someone else’s’ (Amazon, Disney, NBCU, Uber) – they all know you’re coming.
They’ll be ready. There will always be a way to advertise.
If you didn’t know, for the past 18 months or so I’ve been hosting a podcast with the inimitable Jill Royce on the topic of ad-tech history. It’s called #PaleoAdTech and we’ve been blessed with a roster of ad-tech titans, from the pinballing pioneers of the dot-com days — now-forgotten blazers like AllAdvantage, AdAuction and FlyCast; and well-remembered disrupters like DoubleClick and Advertising.com — to the makers of a more recent past, like AppNexus and MediaMath.
Wondering where ad servers, DSPs, DMPs, SSPs and all the other Ps came from? Join us as we regularly offer 30-minute chats with fascinating founders, co-founders and otherwise intriguing entities. Find it:
Taylor Swift has a way of breaking things: records, superlatives, hearts, and even – when she committed the previously inconceivable magic of occupying all ten spots on the Billboard Top 10 with tracks from her 10th album Midnights – herself:
She also broke Ticketmaster, apparently startled by demand for the 2.5 million tickets available for her 52-city ‘The Eras’ tour, her first since 2018.
“It’s a function of Taylor Swift,” said the CEO of the largest shareholder in Live Nation/Ticketmaster’s on CNBC. “We had 14 million people hit the site, including bots….” Even non-humans love Swift.
What’s her secret? After Midnight dropped at, of course, midnight on October 21st, Swift became the most-streamed artist with the most-streamed album in a day on Spotify, breaking records set by her own Red (Taylor’s Version) and 2020’s folklore.
By now, it’s clear Taylor Swift’s only rival is herself.
“Taylor Swift is nearly unimpeachable as a human, role model and brand,” Aaron Kwittken, Chief Executive of the PRophet, told The Drum recently.
As a brand – setting aside her artistry for a moment – Taylor Swift is a global phenomenon. Capital One, Target, Starbucks, Keds, CoverGirl, Diet Coke, Apple, Comcast, American Greetings – just a partial list of partners who have contributed to her estimated $400 million net worth in recent years.
Make no mistake; she’s a marketing engine. And it turns out brand Taylor Swift has a definite playbook, refined over her fifteen-year career. She still has a lot to teach marketers.
1. Be an Anti-Brand
The first single from the 13-song Midnights was called “Anti-Hero,” which Swift claims is about her “insecurities.” (Remember that number 13: it’s important.) In many ways, Taylor Swift is an Anti-Brand.
Traditional branding calls for a definite identity, promise and voice. It requires research-driven lines in the “brand space.” But Taylor Swift doesn’t have these things – she’s more of a “Blank Space” on which any of us, no matter how different, can see anything we need to see, especially ourselves.
Earlier this month, Midnights sponsor Capital One revealed two spots for the World Series called “Multiple Taylors,” featuring versions of Swift from 1989, Speak Now and others. It recalled the cryptic video for her song “Look What You Made Me Do,” from Reputation, unleashed at the 2017 MTV Video Music Awards, which featured fifteen versions of Swift, from the Red ringmaster to the stunned victim of Kanye’s notorious trophy-snatching.
She dared to ask us: “Who is the real Taylor Swift?” And the answer: We all are, pick the one you want.
She’s an oddly malleable brand, ideally suited to an age of creators, remixes and memes. It makes sense that there are multiple versions of two of her albums, with at least four more to come. Another celebrity, Ryan Adams, famously rerecorded her entire 1989 himself.
And then there are the lookalikes: it’s possible to become TikTok famous just for looking a little bit like Swift. And the oddly Teflonish quality: famous people who seem to want to feud with Swift somehow end up fading away (like Katy Perry) or on stage at one of her shows singing a duet (like Hayley Kiyoko), best friends forever.
Sociologist Emile Durkheim talked about totems as supernatural objects within which tribes can see themselves. Brands like Taylor Swift are totems for the age of TikTok.
2. Make Your Fans Do Work
It’s not easy being a fan of Taylor Swift; just ask us. Between buying $75 pajamas at her Official Store, waiting on virtual lines that break, and pre-ordering 13 copies of the expanded 20-song Midnights (3am Edition), there’s barely time to decipher all the clues she’s left in her TikToks, lyrics and Insta captions.
Mainstream fans might not know it, but Swift has long embedded Baroque ciphers into her marketing materials. She does this to encourage social media action, conversation, digital engagement – and of course to repay our attention.
Swift’s code-work started early. Her first album, Taylor Swift, released in 2006 when she was 16, featured liner notes with random capital letters, embedding messages like “Date Nice Boys.” She told the Washington Post: “That’s how it started, and my fans and I have since descended into color coding, numerology, word searches, elaborate hints, and Easter eggs.”
Examples abound – so many, in fact, that at least one commentator has ranted that “people treat Taylor Swift’s albums like they’re the damn Da Vinci code!”
Many of these Easter eggs are simply rewards for the faithful, references to outfits and props from previous albums. In the video for “Anti-Hero,” a stand-in breaks a guitar from the Speak Now tour, and a character wears a dress from Fearless. Others are aimed at completists: there are four versions of the vinyl album cover for Midnights that can be tiled to build a clock.
In her more Gothic Reputation phase, Swift packed the video for her first single with references to Mean Girls, her “Out of the Woods” video, a dollar bill she won in a notorious lawsuit, snakes and tea referring to various Kardashians, and so on.
It’s all harmless fun but can get hyperbolic in an overwired age. Swift’s fans often work harder than required, locating clues that aren’t actually there. Last September, the NFL made the mistake of issuing an announcement at midnight. Immediately, Swift’s conspiracy-minded cadre built a widely-reported rumor that Swift herself was going to be the half-time show at the Super Bowl because – well, of course – Taylor Swift owns midnight.
The announcement had nothing to do with Swift. Sometimes midnight is just midnight. (Rhianna is doing the half-time show.)
There’s a sociological theory that the successful cults are those that make their adherents work harder. Brand Taylor Swift is annealed by all the work we put into it.
3. Master the Art of Suspense
On October 7, thirteen days before Midnight‘s release, Swift started posting videos on TikTok under the title “Midnights mayhem with me.” The singer pulled titles from a bingo cage at random, announcing them and providing backstories. At the same time, Spotify co-branded billboards appeared in New York City and London with enigmatic snippets of song lyrics.
Swift has always made the most of withholding and releasing facts most prized by fans: release dates, album and song titles, co-stars. Last year, for Fearless (Taylor’s Version), she combined suspense with her penchant for puzzles, tweeting a video of a vault filled with scrambled letters. These were unscrambled by the intrepid to reveal the names of collaborators Phoebe Bridgers, Chris Stapleton and Ed Sheeran, and song titles such as “All Too Well.”
For 2019’s Lover, Swift provided both a Monday and a Saturday version of the mystery. She admitted the video for “ME!” contained the (unknown) title of her next album, but fans rejected “Lover” because it was too obvious, appearing in huge pink neon letters on the top of a building. The album was called Lover.
Then some sharp-eyed owners of her official calendar noticed a butterfly stamp on April 13 (there’s 13 again), and thirteen days later, Lover‘s first single debuted.
This kind of suspense makes her releases more poignant. Combined with a sense of scarcity, carefully cultivated through the ticket-buying (or not-buying) process, it puts Swiftys into a state of near-continual brandemonium around these key launch windows.
So brands, be like Taylor: malleable and flexible, demanding in a way that rewards close attention, and above all unpredictable. Taylor Swift is in show business, of course, but now so is every brand.
Elon Musk does not have a love-hate attitude toward advertising: he hates it. At least, that’s what he said – on Twitter, of course – back in 2019: “I hate advertising.”
This may be a curious attitude for a new media mogul whose most recent acquisition – that same Twitter – is almost entirely ad-supported.
But it turns out that Musk is not alone. The history of major ad platforms is littered with righteous founders who did not like the business they now own. In fact, it’s difficult to find a founder who expressed any fondness for ads, let alone ad tech.
But when revenues are required, attitudes change; call it a pivot to reality.
Here – in alphabetical order – are the origin attitudes of the great modern ad businesses. What’s the lesson? We change as we grow? Maybe. Or maybe, like Musk, we all need to learn a little respect.
“Advertising is the price you pay for having an unremarkable product or service.”
Advertisers only wish we knew as much as people seem to think we do. “Surveillance” is a mysterious term and surely overstates the case. But it is undeniable that the rules are changing, perception is a mounting problem, and it’s time to think ahead. How?
One company leads the field with its provident tactics. Already a significant — if not yet dominant — media player, it is assembling the components of a powerful offering and has much to teach us all.
(1) Start with Your Brand
This won’t be easy. The ad business has long had trust issues, which did not start with GDPR and CPRA. Celebrations last year that advertising was now merely the second least-trusted profession (after politics) were just sad.
But there is an exception: a $400 billion-earning company that has managed to persuade most of us that it’s not part of a menacing “data-industrial complex.” Apple is the world’s most valuable brand, according to Interbrand, up 26% since 2020, well ahead of its competitors.
How? They raised awareness for problems most did not know existed — such as mobile app ID, I.P. address and email “tracking” by marketers — and then provided a solution. In a single release (IoS 14.5 for ATT), Apple went from enabling to solving a problem. Its brand as neighborhood watch for the web extended to TV spots showing creepy characters stalking us on our phones until a superheroic ATT intervened.
Lesson: Counteract trust erosion by investing in a brand of law and order.
(2) Make the Opt-In Positive
Apple has championed the opt-in model of consent, arguably influencing pending national privacy legislation. Opt-in presents the consumer with a mandatory choice to accept or reject (opt-in or -out) whatever is requested.
The challenge here is well-studied, although not by advertisers. It even has a name: “The Paradox of Privacy.” As it turns out, there are a lot of cognitive biases that affect a person’s decision in that magic opt-in moment.
We are lazy (“can’t think about this right now”); overestimate how much data is collected about us (“tracking me around the web”); underestimate the benefits (“what is ‘relevance,’ anyway?”). In short, quite logically, we lack both time and data to make a good decision.
Very few people watch ads by choice, and nobody endorses tracking. The most successful modern platforms — paid search, social in-stream, retail media — are not optional. Opting-in to ads is a default on sign-up.
A meta-study of dozens of academic studies of the privacy paradox concluded: “Privacy attitude was best predicted by internal variables likes trust ….”
And that’s why the language on the opt-in box itself — the experience around the decision window — is critical. Apple knows this.
As we all know by now, the required headline for the required ATT prompt read: “[‘Brand’] would like permission to track you across apps and websites owned by other companies.”
We note that tracking is not a benefit, and no rational actor would agree to it, even with time to think.
When it came to describing the rewards of targeting in its own environment, Apple provided more benefit-centric headline text and copy: “Personalized Ads … help you discover apps, products and services that are relevant to you.”
Lesson: Frame the benefits of behavioral data collection in positive terms.
(3) Focus Down the Funnel
The cookie and mobile IDs may be engaged in one of the longest death scenes in history. But pointed targeting and powerful measurement are more possible than ever — in controlled (“opted-in”) environments, sometimes called gardens.
Digital ad money was always further down-funnel than linear, and it’s getting more so. People forget that paid search is still half of digital ad dollars, and it’s a strong signal of intent. Retail media is more than a trend: connecting ads with purchases is as outcome-based as ads can get.
Building up a campaign, smart marketers start with (1) moments closer to the point of sale, and (2) outcomes they can track. They build out from there, into intent and targeted awareness (like CTV) and then less controlled environments like late-night cable for reach.
Apple’s media business starts with Apple Search Ads for app installs in the iTunes store, an estimated $5 billion business. Exempt from ATT, Apple can tie these ads to app installs and in-app activity. (Compare this with the brand-focused, abandoned iAds project.)
What comes next? It added hero placements on the App Store’s opening screen. It also offers ads in its News, Podcasts and Maps apps. Maps in particular offers direct-response opportunities tied to location.
That long-rumored DSP and network could allow Apple to bring some of this helpful intent information to ad targeting in apps it doesn’t own.
Lesson: Future-proof ad businesses start with direct response ads.
(4) Focus on “Non-Ad” and Peripheral Placements
There are ads that are ads that don’t seem much like ads. They’re more likely to be acceptable even to more paranoid web surfers and even to regulators. Paid search is an example, I think: despite Google ingesting some of the most private material on earth, consumers don’t seem to think it’s a problem.
Contrast search with a retargeted banner, which appears out of nowhere indicating it was watching me elsewhere. The retargeter knows much less about me than my search engine but seems to know more. Why? It’s overestimated. And it was widely noted that Apple Search Ads flourished after ATT limited retargeting last year.
What does this mean for your future-proofing ad player? What I’m calling “non-ad” ad formats are those that are ads but don’t feel like them. Look at product placement. These ads are growing almost as fast as CTV, are a $23 billion business, and are not even mentioned in the 11 Chapters of the of the GDPR text — which mentions just about everything else.
As AI gets better, weaving products into shows on Apple TV and network apps becomes appealing. Other trending formats don’t interrupt but marginalize ads, literally placing them around content. TikTok Pulse and Meta’s Reels released new “multi-advertiser” formats like this.
The real opportunity may be in shoppable commerce. These are non-ads in that they put a buy-now button on other content, like a show (or product placement). As mobile ad expert Eric Seufert has noted, commerce, retail and CPG are Meta’s largest verticals. Innovative units could shift some of that spending around.
Lesson: Future ads look more like buy-now buttons.
Without appropriate cultural relevance, data analysis and distributed resources and knowledge, centers of excellence can undermine global success, writes Salesforce’s Martin Kihn as part of The Drum’s Globalization Deep Dive.
/ Leon Seibert
Many of us remember where we were during the great chopstick scandal of 2018, when a storied luxury fashion maison launched three highly-produced videos on YouTube in China to support an upcoming show.
In them, models tried to eat traditional Italian food like cannoli and pizza using chopsticks, with predictable results: a hot mess for both the utensils and the brand, which faced a chorus of complaints from Chinese consumers on Weibo and other platforms, claiming cultural tone-deafness.
The brand quickly pulled the videos, canceled the show and has since lost share in the Chinese market. Part of the problem was a lack of localization in global brand messaging – or what one study of social marketing fails politely called “inadequate research.” The brand had a world-class marketing team, but it was back in Milan, the company’s de facto ‘center of excellence’ (CoE).
During a time of cultural sensitivity, global marketers must balance the imperative to build out such centers with a growing need for in-market nuance. There are new forces facing global marketers that call into question the conventional rush to build centers of excellence across almost every functional discipline.
It turns out, excellence may increasingly be found at the edge – not the center.
Four horsemen of the CoE-pocalypse
There is much logic and some evidence that CoEs can add value. Firms such as Gartner have long championed CoEs for marketing functions such as analytics and data science. They generally define a CoE as a discrete cadre of coordinated professionals with specific, uncommon expertise, who cross-reference ideas, disseminate practices and templates and function as a skilled resources for dispersed global operations.
So compelling is the impulse to the CoE model that it is difficult to find any doubters. Consultancies such as McKinsey routinely recommend establishing a CoE for advanced functions. For example, a recent McKinsey report on automation says: “A center of excellence is vital both as a source of expertise and to define priorities.” Meanwhile, the US Army has at least 15 CoEs for functions from missile defense to human resources.
Global marketers have taken the advice and adopted CoEs. A Gartner survey indicated that two-thirds of enterprise marketers already had an analytics CoE five years ago – yet this year, 26% of CMOs identified analytics as an ongoing capability gap. The same research revealed global marketers had a lot of swagger about their ’operational excellence’ (only 15% cited as a gap), due in part to CoEs.
Yet as the chopstick incident implies, not all wisdom can be centralized. And there are a number of rising forces that point toward the need for global marketers to question the march toward CoEs.
The four horsemen of the CoE-pocalypse are:
Cultural relevance: Local consumers require local nuance – and will take to social media if it’s missing.
Data bias: AI and machine learning models can inherit bias from data collection and processing methods, both of which can have a cultural dimension that is only now being recognized.
Knowledge resources: Many formerly ‘specialized’ disciplines – including reporting and campaign automation – are more common, with widespread learning resources.
Distributed workforce: More dispersed and hybrid employment models undermine some of the CoEs’ neo-Xerox Park ‘skunkworks’ premise.
To CoE or not to CoE?
How is a marketer to assess whether or not a CoE makes sense for a particular situation? As an ex-consultant, I’d be surprised if I didn’t propose a solution in the form of 2×2 framework.
CoEs are supposed to support: (1) availability of specialized skills; and (2) processes that can be run globally rather than locally. So by implication the axes of our 2×2 are:
Skill level: specialized v unspecialized
How hard are the skills to find in most global markets? If they’re rare and uncommon, or less rare in some regions than others, a CoE may be in order. As marketing teams get more sophisticated over time, fewer skills may fall into this area. (According to Gartner, the hardest marketing skills to find in the current environment are data and analytics, customer experience management and marketing technology; the easiest is social marketing).
Cultural proximity: embedded v not embedded in local culture
Is the function something that requires an awareness of how actual human beings talk, think and work in a specific context or not?
Another way to evaluate this requirement is in symbolic terms: How much does the function use global symbols, such as numbers, versus more culturally loaded symbols like words and images? If it’s mostly about numbers, a CoE could work – and if not, it may be time to reevaluate.
So the CoE is most useful for marketing challenges related to data modeling and predictions, such as next-best-action and -experience – and for data operations and automation projects that can be standardized across regions. It is less useful for developing creative artifacts for specific regions and building media plans.
Let’s apply the framework to our original example. We see the capabilities required are video production and creative development, skills that are both locally available and deeply embedded in the culture – so, not amenable to the CoE treatment. On the other hand, were that same brand to implement a marketing automation system, it would be entirely reasonable to spin up a CoE for that.
As we go forth into our new world of global marketing, we should take care to discriminate non-human processes from very human communications and recognize that both standardization and globalization have their limits.
This article first appeared in The Drum US on August 31, 2022
We’re all under the influence.
Influencer marketing is the fastest-growing paid channel this year, after connected television (CTV), resilient even in the face of recession. As companies plateau their use of social media, 75% of US marketers plan to invest in influencers this year – up from 66% in 2020, according to eMarketer.
And it’s not about products-for-posts anymore – it’s big business. Global marketers spent about $14bn on influencers last year, including media. B-listers such as Joanna Gaines and Addison Rae enjoy multi-figure deals, while real-life stars including footballer Cristiano Ronaldo get an estimated $500,000 per post. And there are thousands of creators in niches from travel to beauty to – of course – cats who are paid an average of $100 per 10,000 followers per meow.
In a world where 50 million people call themselves ‘creators,’ there are a lot of options for brands to partner their way into feeds, tweets and videos. Influencers can provide creative content, access to elusive audiences, higher engagement and compelling social proof.
But there’s a problem. Brands using influencers, surveyed by the Association of National Advertisers (ANA) in 2020, admitted their top challenge was measurement. The situation is no better now. How do you know if you’re getting a worthwhile return-on-influencer (ROIn)?
Channels are not created equal
Measuring the impact of an influencer program is notoriously sketchy. It’s an emerging channel without industry standards. Although the Media Rating Council (MRC) has established guidelines for paid social measurement, most of the value of influencers comes from organic engagement – all those likes, shares and comments from followers and friends of friends that turn a snippet of video into cultural cachet.
Challenges with measuring ROIn include:
Data collection: Brands without API access to influencer accounts rely on methods such as emailed screenshots for metrics
Reach: It is difficult (read: impossible) to deduplicate audiences across platforms
Engagement: Different platforms present different options (where TikTok garners likes, Pinterest culls clicks) and define ‘engagement’ in different ways
Consistency: Agency partners often use proprietary roll-up metrics that can be opaque
Earlier this summer, the ANA released the first ‘Influencer Marketing Measurement Guidelines,’ taking a step toward standardizing organic measurement. Developed by the Influencer Marketing Advisory Board – formed in 2020 with reps from brands such as Puma and Target – it was based on meetings with 25 agencies and the eight major platforms (Facebook, Instagram, LinkedIn, Pinterest, Snapchat, TikTok, Twitter and YouTube).
Brands that have been there will tell you that working with influencers is special – more like hiring an improv troupe than deploying a bot. Companies like control, but creativity is part of influencers‘ charm. So it makes sense to start by asking them how they measure success. A beauty star such as Huda Kattan might value video engagement, while a photo influencer such as Murad Osmann might care more about shares.
Most brands measure ROIn based on ‘engagement,’ a blunt sum of actions divided by exposures, aggregated across all the platforms in the campaign. But this method assumes every creator aims for the same responses, and it ignores the platforms‘ real inconsistencies.
Many roads to the rainbow
Using basic discipline, the hard-working marketer with an influencer program wants some combination of three KPIs:
Awareness: This is driven by reach and frequency, generally available for each platform in isolation, but not across platforms; video views are usually counted here
Engagements: These are measured interactions with the influencers‘ content, including likes and shares – often expressed as an ‘engagement rate’ (ER) or engagements per reach
Conversions: Often the ultimate goal, this is likely undercounted and based on direct clicks through to the brand’s commerce site or other destination
Now, the ANA performs a public service in teasing out the vagaries of the platforms‘ self-reported metrics. Anyone who’s spent time parsing reports from social networks will appreciate this effort. Key differences among the platforms‘ influencer reporting include:
Facebook and Instagram: For Meta-owned platforms, ER is total engagements divided by impressions, not including video views
TikTok: ER is total engagements divided by video views, excluding replays
YouTube: ER is the same as TikTok; however, TikTok counts any video that’s started as a view, while YouTube only counts a view after 30 seconds or 100% for its short-form ‘Shorts‘
Twitter: Twitter is similar to Meta, but quote-tweet counts aren’t available via the API
LinkedIn: ER does not include video views, which are counted after two seconds with 50% viewable
Snapchat: Interestingly, Snap doesn‘t yet provide organic influencer reporting
Understanding the components of the platforms‘ reports unlocks comparisons. Obviously, an autoplay video view on Twitter isn‘t as meaningful as a video view on YouTube, and a retweet on Twitter is not exactly equivalent to a pin on Pinterest.
For awareness and conversion measurement, reach by platform and direct attribution are useful. They aren‘t perfect, since the former misses duplicates and the latter indirect attribution (ie people saw the content and converted later, or offline). But they‘re reasonable baselines.
The problem comes with the most important influencer metric: engagement rate. How can it be improved?
Worth the weight
The answer is by weighting the different components of engagement. Intuitively, we know that a like isn‘t the same as a share or a comment. It‘s easy to like a post – you just tap the heart, right? But sharing to your network is a kind of endorsement, and a comment – with the right sentiment – indicates more visceral involvement.
A principle I used when measuring the impact of social media for brands was one I took from the self-help guru John Bradshaw: “We give time to those things that we love.” Simple enough. Extending it to social platforms, I‘d argue that actions that take more time and effort should count more toward ROIn.
For example, the marketer can create a consistent weighting factor for different actions based on the time they take to complete. Say it takes a second to commit to and tap a like. Even a short, positive comment takes at least five seconds. And a share with a comment might take longer. Typical viewer patterns should be considered, and they will vary considerably based on the influencer and type of campaign.
The ultimate ROIn plan might include breakouts for awareness and conversion, and an approach to ER that considers weighting actions by their level of effort. (The ANA guidelines don‘t address weighting.) Of course, a detailed formula requires access to the platforms‘ API and permission from the influencer. Art, science and some social engineering are required.
But that’s what puts the ‘sure’ in measurement.
Martin Kihn is senior vice-president of strategy, marketing cloud at Salesforce.