This is a post that I drafted when Facebook released their last results, and never got around to publishing. Why publish it now? For a start, none of this is breaking news, so it remains as relevant as it ever was. More importantly, with the ongoing bonfire of Twitter, the questions of whether ad-funded social networks are a good thing or not is more relevant than ever.

My position remains that none of this tracking nonsense is worth while. I have never been served a relevant ad through surveillance-driven adtech. Meanwhile, brand advertising works just fine, simply by virtue of the brand being present in the right context: bike gear on a cycling blog, that sort of very limited targeting that only requires a single bit of information about the audience.

Meta Loses Top-10 Ranking by Market Value Amid Worst Month Ever
Social media company falls behind Tencent in value ranking
Facebook parent has lost $513 billion in market cap from peak
Stock has fallen 46% from last year’s record.

What do the terrible results announced by Facebook — I refuse to give in to their desire that we call them Meta — actually mean?

Zuck blamed Apple's ad tracking prevention features for wiping $10B off their bottom line, and there has been a concerted push since to present this as somehow a bad thing, especially for small businesses. I agree with Nick Heer that this framing is pretty gross on Facebook's part, but what I wanted to do today is to discuss alternatives that are open to marketers today.

I'm not in marketing these days, and I never worked directly in the demand-generation side that would get actively involved with this sort of thing — but I have worked closely with those teams and been in the planning meetings, so I have at least an idea of how that business works.

Everything starts with a campaign: you have a particular message you want to get out, you want it to reach a particular audience, and you want some idea of how effective it is. Given those goals, there are different ways to go about running your campaign — different largely in their ethics, rather than in their actual results. Let's take a look.
Alice and Bob work for ACME Widgets Corp. Both of them are launching marketing campaigns for the coming quarter — but they take different approaches, even though they have the same metrics set by their boss, Eve the VP of Marketing.

Alice goes all-in on the surveillance model: her emails have tracking pixels, the links they point to are all gated behind a form that also signs you up for a newsletter, she places ads that follow users around the web once they have come within her surveillance web. She even messes with the favicon and the hosted fonts on the website in order to be able to track users that way. At the end, thanks to all of this effort, Alice can show Eve attribution metrics with a certain click-though rate for her outreach and a certain acquisition cost per customer, set against their likely lifetime value to ACME.

Bob takes a different tack: his emails are plain text, without even any images — since plenty of people now reflexively block all images in email, or load them through proxies. The links in the email are customised so that Bob can tell which email was the one that triggered the action, but then they go directly to the linked resource. He also buys ads, but instead of direct calls to action, Bob focuses on brand advertising in the sorts of publications that the prospective customers are likely to read. At the end, Bob can also show Eve attribution metrics, click-through rates and customer acquisition costs — but he has got there with without irritating prospective customers, or falling foul of either technical countermeasures or policies such as GDPR or CCPA.

Comparing Alice and Bob’s Results

Effectively, Alice and Bob have access to the same metrics; it's just that one of them is going about the process of gathering them honestly. The only data point Bob is missing is the open rate on those emails — but first of all, how useful is that metric in reality? If the indicator that an email was opened is that a tracking pixel was loaded, Alice doesn't know whether the recipient actually read the whole thing, or paged past her email quickly on their way to something they actually wanted. And even assuming that it's an accurate representation of how many people read the text but don't click on any of the links — what can Alice do with that information that Bob would not also do with the information that he sent out X number of emails and Y% of recipients clicked on the call-to-action link? And no, for goodness sake, the answer is not even more layers of attribution woo that claims to be able to identify whether someone came to the ACME website because they remembered the email, or the billboard ad, or because someone mentioned it to them at work — let alone trying to embed the "read progression" code that far too many websites now include.

Secondly, all of these intrusive metrics now have a firm expiry date stamped on them. On top of the ad tracking prevention, Apple now offers a Private Relay capability in iCloud that hides originating IP addresses. Browsers already no longer report a whole lot of information that they used to, precisely because it was used for creepy tracking stuff. By building her campaigns this way, Alice might achieve her goals today, but soon she will not be able to run campaigns like this, and will have to learn to do things Bob's way anyway.

At the core of Bob's method is turning tracking inside out. Instead of trying to stalk users around the Web, engaging in a constant arms race and violating their clearly expressed preference, Bob simply figures out where his most valuable prospects gather and advertises there. First-party data is enough for his purposes, and while individual ads might be more expensive in CPM, he avoids engaging with an ecosystem that is ridden with fraud. He also does not need to worry that the ACME ad might show up beside some tin-foil-hatter YouTube channel and get bad press that way — and the time he doesn't spend micro-managing ad placement can be spent more productively on creating better copy, or an entire other campaign.

Context matters in other ways, too: when a prospective customer is reading about the latest political crisis, famine, or natural disaster, they are not in a widget-buying mood, so showing them a widget ad is counter-productive anyway. Instead, Bob puts his widget ads in widget blogs, places them with streamers who test widgets, and gets hosts of widget-focused podcasts to read out his ads. All of these channels have very limited tracking; podcasts offer none at all, unless Bob creates a special landing page or discount code for listeners of each podcast. And yet, those are some of the most expensive ad slots around, because the context makes them very strong indicators of desire to buy.

Eve looks at the campaign performance numbers presented by a haggard Alice and a relaxed Bob, remembers the news stories about Apple and Google clamping down further on ad tracking, and suggests gently to Alice that maybe she should sit with Bob and figure out how to get the job done without the crutch of surveillance ad tech.

🖼️ Photos by Charles Deluvio and Headway on Unsplash

As the token European among the Roll For Enterprise hosts, I'm the one who is always raising the topic of privacy. My interest in privacy is partly scarring from an early career as a sysadmin, when I saw just how much information is easily available to the people who run the networks and systems we rely on, without them even being particularly nosy.

Because of that history, I am always instantly suspicious of talk of "personalising the customer experience", even if we make the charitable assumption that the reality of this profiling is more than just raising prices until enough people balk. I know that the data is unquestionably out there; my doubts are about the motivations of the people analysing it, and about their competence to do so correctly.

Let's take a step back to explain what I mean. I used to be a big fan of Amazon's various recommendations, for products often bought with the product you are looking at, or by the people who looked at the same product. Back in the antediluvian days when Amazon was still all about (physical) books, I discovered many a new book or author through these mechanisms.

One of my favourite aspects of Amazon's recommendation engine was that it didn't try to do it all. If I bought a book for my then-girlfriend, who had (and indeed still has, although she is now my wife) rather different tastes from me, this would throw the recommendations all out of whack. However, the system was transparent and user-serviceable. Amazon would show me transparently why it had recommended Book X, usually because I had purchased Book Y. Beyond showing me, it would also let me go back into my purchase history and tell it not to use Book Y for recommendations (because it was not actually bought for me), thereby restoring balance to my feed. This made us both happy: I got higher-quality recommendations, and Amazon got a more accurate profile of me, that it could use to sell me more books — something it did very successfully.

Forget doing anything like that nowadays! If you watch Netflix on more than one device, especially if you ever watch anything offline, you'll have hit that situation where you've watched something but Netflix doesn't realise it or won't admit it. And can you mark it as watched, like we used to do with local files? (insert hollow laughter here) No, you'll have that "unwatched" episode cluttering up your "Up next" queue forever.

This is an example of the sort of behaviour that John Siracusa decried in his recent blog post, Streaming App Sentiments. This post gathers responses to his earlier unsolicited streaming app spec, where he discussed people's reactions to these sorts of "helpful" features.

People don’t feel like they are in control of their "data," such as it is. The apps make bad guesses or forget things they should remember, and the user has no way to correct them.

We see the same problem with Twitter's plans for ever greater personalisation. Twitter defaulted to an algorithmic timeline a long time ago, justifying the switch away from a simple chronological feed with the entirely true fact that there was too much volume for anyone to be a Twitter completist any more, so bringing popular tweets to the surface was actually a better experience for people. To repeat myself, this is all true; the problem is that Twitter did not give users any input into the process. Also, sometimes I actually do want to take the temperature of the Twitter hive mind right now, in this moment, without random twenty-hour-old tweets popping up out of sequence. The obvious solution of giving users actual choice was of course rejected out of hand, forcing Twitter into ever more ridiculous gyrations.

The latest turn is that for a brief shining moment they got it mostly right, but hilariously and ironically, completely misinterpreted user feedback and reversed course. So much for learning from the data… What happened is that Twitter briefly gave users the option of adding a "Latest Tweets" tab with chronological listing alongside the algorithmic default "Home" tab. Of course such an obviously sensible solution could not last, for the dispiriting reason that unless you used lists, the tabbed interface was new and (apparently) confusing. Another update therefore followed rapidly on the heels of the good one, which forced users to choose between "Latest Tweets" or "Home", instead of simply being able to have both options one tap apart.

Here's what it boils down to: to build one of these "personalisation" systems, you have to believe one of two things (okay, or maybe some combination):

• You can deliver a better experience than (most) users can achieve for themselves
• Controlling your users' experience benefits you in some way that is sufficiently important to outweigh the aggravation they might experience

The first is simply not true. It is true that it is important to deliver a high-quality default that works well for most users, and I am not opposed in principle to that default being algorithmically-generated. Back when, Twitter used to have "While you were away" section which would show you the most relevant tweets since you last checked the app. I found it a very valuable feature — except for the fact that I could not access it at will. It would appear at random in my timeline, or then again, perhaps not. There was no way to trigger it manually, or any place where it would appear reliably and predictably. You just had to hope — and then, instead of making it easier to access on demand, Twitter killed the entire feature in an update. The algorithmic default was promising, but it needed just a bit more control to make it actually good.

On the other hand, I quite like “while you were away” and wish I could access it on demand, instead of being reduced to hoping it shows up, like a peasant praying for rain.

— Dominic 🇪🇺🇺🇦🏳️‍🌈 (@dwellington) June 29, 2021

This leads us directly to the second problem: why not show the "While you were away" section on demand? Why would Netflix not give me an easy way to resume watching what I was watching before? They don't say, but the assumption is that the operators of these services have metrics showing higher engagement with their apps when they deny users control. Presumably what they fear is that, if users can just go straight to the tweets they missed or the show they were watching, they will not spend as much time exploring the app, discovering other tweets or videos that they might enjoy.

What is forgotten is that "engagement" just happens to be one metric that is easy to measure — but the ease of measurement does not necessarily make it the most important dimension, especially in isolation. If that engagement is me scrolling irritably around Twitter or Netflix, getting increasingly frustrated because I can't find what I want, my opinion of those platforms is actually becoming more corroded with every additional second of "engagement".

There is a common unstated assumption behind both of the factors above, which is that whatever system is driving the personalisation is perfect, both unbreakable in its functioning and without corner cases that may deliver sub-optimal results even when the algorithm is working as designed. One of the problems with black-box systems is that when (not if!) they break, users have no way to understand why they broke, nor to prevent them breaking again in the future. If the Twitter algorithm keeps recommending something to me, I can (for now) still go into my settings, find the list of interests that Twitter has somehow assembled for me, and delete entries until I get back to more sensible recommendations. With Netflix, there is no way for me to tell it to stop recommending something — presumably because they have determined that a sufficient proportion of their users will be worn down over time, and, I don't know, whatever the end goal is — watch Netflix original content instead of something they have to pay to license from outside.

All of this comes back to my oft-repeated point about privacy: what is it that I am giving up my personal data in exchange for, in the end? The promise is that all these systems will deliver content (and ads)(really it's the ads) that are relevant to my interests. Defenders of surveillance capitalism will point out that profiling as a concept is hardly new. The reason you find different ads in Top Gear Magazine, in Home & Garden, and in Monocle, is that the profile for the readership is different for each publication. But the results speak for themselves: when I read Monocle, I find the ads relevant, and (given only the budget) I would like to buy the products featured. The sort of ads that follow me around online, despite a wealth of profile information generated at every click, correlated across the entire internet, and going back *mumble* years or more, are utterly, risibly, incomprehensibly irrelevant. Why? Some combination of that "we know better" attitude, algorithmic profiling systems delivering less than perfect results, and of course, good old fraud in the adtech ecosystem.

So why are we doing this, exactly?

It comes back to the same issue as with engagement: because something is easy to measure and chart, it will have goals set against it. Our lives online generate stupendous volumes of data; it seems incredible that the profiles created from those megabytes if not gigabytes of tracking data have worse results than the single-bit signal of "is reading the Financial Times". There is also the ever-present spectre of "I know half of my ad spending is wasted, I just don't know which half". Online advertising with its built-in surveillance mechanisms holds out the promise of perfect attribution, of knowing precisely which ad it was which caused the customer to buy.

And yet, here we are. Now, legislators in the EU, in China, and elsewhere around the world are taking issue with these systems, and either banning them outright or demanding they be made transparent in their operation. Me, I'm hoping for the control that Amazon used to give me. My dream is to be able to tell YouTube that I have no interest in crypto, and then never see a crypto ad again. Here, advertisers, I'll give you a freebie: I'm in the market for some nice winter socks. Show me some ads for those sometime, and I might even buy yours. Or, if you keep pushing stuff in my face that I don't want, I'll go read a (paper) book instead. See what that does for engagement.

🖼️ Photos by Hyoshin Choi and Susan Q Yin on Unsplash

Some of the most insightful and succinct commentary on the whole Antonio Garcìa Martìnez debacle comes from an ungulate with a Classic Mac for a head:

the Macalope believes Apple should not have hired García Martínez only to fire him. He believe it never should have hired him in the first place.

I'm not going to go over all of the many (many, many) red flags about this person's opinions that should have at the very least triggered some additional scrutiny before hiring him. The reaction from Apple employees was entirely predictable and correct. Even if the misogynistic opinions expressed in his public writing were exaggerated for effect, as he now claims, there would always be a question mark around his interactions with female employees or those from minority backgrounds. At the very least, that would be enormously disruptive to the organisation.

Leaving that aspect aside for a moment: even if this had been someone with the most milquetoast opinions possible (and no NYT bestselling book in which to trumpet them), it's still not great that Apple was looking for someone with his specific professional experience — honed at Facebook.

This to me is the most concerning part of the Apple ads situation. That they would even consider recruiting someone like Antonio García Martínez is very concerning for the future of Apple’s platform. I pay a premium price for Apple products for a pleasant experience, not ads. pic.twitter.com/gt6tHP51TQ

— Dominic 🇪🇺🇺🇦🏳️‍🌈 (@dwellington) May 15, 2021

This particular hire blew up in Apple's face — but it's extremely concerning for Apple users that they were actively recruiting for this type of experience in the first place.

I'll lay my cards on the table: I dislike the idea of search ads as a category, especially in the App Store. We can argue the merits of allowing apps to "jump the queue" of results for generic searches, but as it is today, you can buy yourself into a position ahead of your competitor even for direct searches on that competitor app's name. Where is the value to users in that?

Display ads in Apple News or Stocks, which are the other two Apple properties discussed, might be acceptable — as long as they are not too intrusive. I don't have as much of a philosophical issue as some do with Apple using first-party tracking data within iOS, precisely because those data are not available to other parties or to other platforms. It's easy to opt out of Apple's tracking, simply by not using those apps, and ads from there won't follow me around the rest of the web.

The lesson I hope that Apple takes away from this whole situation is not "don't hire people with big public profiles" but "users really hate sleazy adtech". I would hate for Apple to go the way of YouTube, which is becoming unusable due to ad load. I understand that Apple is trying to boost its Services revenue, and App Store search ads are a way to do that, but if it makes my user experience worse, that's a problem. Apple products command a premium in large part because of how nice they are for users; anything that undermines that niceness weakens the rationale for staying in the Apple camp.

The conversation about the proposed Australian law requiring Internet companies to pay for news continues (previously, previously).

Last time around, Google had agreed to pay A$60m to local news organisations, and had therefore been exempted from the ban. Facebook initially refused to cough up, and banned news in Australia — and Australian news sites entirely — but later capitulated and reversed their ban on news pages in Australia. They even committed to invest $1 billion in news.

So, I asked my Australian friends what they thought of the News-free News Feed and, uh.... pic.twitter.com/iVDG81zNkz

— Evelyn Douek (@evelyndouek) February 24, 2021

One particular thread keeps coming up in this debate, which is that news publications benefit from the traffic that Facebook and Google send their way. This is of course true, which is why legislation that demands that FB & Google pay for links to news sites is spectacularly ill-conceived, easy to criticise, and certain to backfire if implemented.

Many cite the example of Spain, where Google shuttered the local Google News service after a sustained campaign — only for newspapers to call on European competition authorities to stop Google shutting its operation. However, it turns out that since the Google News shutdown in Spain, overall traffic to news sites went largely unchanged.

Getting the facts right in these cases is very important because the future of the web and of news media is at stake. The last couple of decades have in my opinion been a huge mistake, with the headlong rush after ever more data to produce ever more perfectly targeted advertising obscuring all other concerns. Leaving aside privacy as an absolute good, even on the utilitarian terms of effective advertising, this has been a very poor bargain. Certainly I have yet to see any targeted ads worth their CPM, despite the torrent of data I generate. Meanwhile, ads based off a single bit of information — "Dominic is reading Wired" (or evo, or Monocle) have lead me to many purchases.

The worst of it is that news media do not benefit at all from the adtech economy. Their role is to be the honeypot that attracts high-value users — but the premise of cross-site tracking is that once advertisers have identified those high-value users, they can go and advertise to them on sites that charge a lot less than top-tier newspapers or magazines. The New York Times found this out when they turned off tracking on their website due to GDPR — and saw no reduction in ad revenues.

Of course not every site has the cachet or the international reach of the NYT, but if you want local news, you read your local paper — say, the Sydney Morning Herald. Meanwhile, if you're an advertiser wanting to reach people in Sydney, you can either profile them and track them all over the web (or rather, pay FB & G to do it for you) — or just put your ad in the SMH.

Hard cases make bad law. The question of how to make news media profitable in the age of the Web where the traditional dynamics of that market have been completely upended is a hard and important one. This Australian law is not the right way to solve that question, even aside from the implications of this basically being a handout to Rupert Murdoch — and one which would end up being paid in the US, not even in Australia.

Let us hope that the next government to address this question makes a better job of it.

🖼️ Photo by AbsolutVision on Unsplash

Even though I no longer work directly in marketing, I’m still adjacent, and so I try to keep up to date with what is going on in the industry. One of the most common-sensical and readable voices is Bob Hoffman, perhaps better known as the Ad Contrarian. His latest post is entitled The Simple-Minded Guide To Marketing Communication, and it helpfully dissects the difference between brand advertising and direct-response advertising (emphasis mine):

[…] our industry's current obsession with precision targeted, one-to-one advertising is misguided. Precision targeting may be valuable for direct response. But history shows us that direct response strategies have a very low likelihood of producing major consumer facing brands. Building a big brand requires widespread attention. Precision targeted, one-to-one communication has a low likelihood of delivering widespread attention.

Now Bob is not just an armchair critic; he has quite the cursus honorum in the advertising industry, and so he speaks from experience.

In fact, events earlier this week bore out his central thesis. With the advent of GDPR, many US-based websites opted to cut off EMEA readers rather than attempt to comply with the law. This action helpfully made it clear who was doing shady things with their users’ data, thereby providing a valuable service to US readers, while rarely inconveniencing European readers very much.

The New York Times, with its strong international readership, was not willing to cut off overseas ad revenue. Instead, they went down a different route (emphasis still mine):

The publisher blocked all open-exchange ad buying on its European pages, followed swiftly by behavioral targeting. Instead, NYT International focused on contextual and geographical targeting for programmatic guaranteed and private marketplace deals and has not seen ad revenues drop as a result, according to Jean-Christophe Demarta, svp for global advertising at New York Times International.

Digiday has more details, but that quote has the salient facts: turning off invasive tracking – and the targeted advertising which relies on it – had no negative results whatsoever.

This is of course because knowing someone is reading the NYT, and perhaps which section, is quite enough information to know whether they are an attractive target for a brand to advertise to. Nobody has ever deliberately clicked from serious geopolitical analysis to online impulse shopping. However, the awareness of a brand and its association with Serious Reporting will linger in readers’ minds for a long time.

The NYT sells its own ads, which is not really scalable for most outlets, but I hope other people are paying attention. Maybe there is room in the market for an advertising offering that does not force users to deal with cookies and surveillance and interstitial screens and page clutter and general creepiness and annoyance, while still delivering the goods for its clients?

🖼️ Photo by Kate Trysh on Unsplash