The Internet is having an autoimmune reaction. The basic business plan that supports much of it (targeted ad revenue) has become anathema to users and has led to a progressive arms race of ad blockers, privacy laws, and user outrage while the industry itself has doubled down on the model, finding ways to accumulate yet more user data for yet more targeted advertising.
What may be most surprising about this is that it is happening in an industry that is known for questioning assumptions, innovation, and generally giving users what they want. Yet the basic orthodoxy of the current model has remained largely unquestioned since the 1990s even though it is only now finding its apotheosis in the form of Facebook. It’s worth unpacking some of it to see how we got here.
The Internet Must Be Free
In his great article, The Internet’s Original Sin, Ethan Zuckerman traced the history of the Internet business model and previewed a lot of the problems we now face. In short, to defeat and deter competition, build network effects (and, more recently, outmaneuver regulators), internet companies must grow exponentially. And in order to grow exponentially, they must remove all friction, including that most fundamental and pernicious friction: asking users to pay for their services. But they must still generate revenue and so they turn to sponsorship and advertising. Since very little blanket digital advertising actually works, they must promise more and more targeted advertising and this, in turn, is what drives the pressure to collect more and more user data.
In some ways this model has succeeded brilliantly. We have free services for so many things that used to cost money. News, phone calls, music, and so much more. At one level, you’d think we must be better off. But clearly that is not true since for the most part both advertisers and end users are profoundly dissatisfied. Only the big internet companies themselves have truly benefited.
The Ultimately Targeted Ad
Zuckerman refers to the myth of the ultimately targeted ad and it is a seemingly compelling one: if only I could properly define the right universe for my message I can be successful. The parameters may be demographic, psychographic, or behavioral and can be defined as narrowly as I’d like. Surely this should allow me to overcome John Wanamaker’s perennial challenge: “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.”
But is that really what’s happening? Are the benefits of the each progressive layer of targeting really that clear or are Facebook et als really just successful because of the breadth of their audience? People expect everything in their internet experience to benefit them in some way and, especially as more and more of us experience the internet on the small screen of a mobile device rather than the big screen of a desktop, each and every pixel needs to be justified.
Give The Customer What They Want
As a simple contrast to the behavioral targeting model, consider Google’s now venerable search business. Instead of targeting “24-38 year old women interested in fitness and Christianity living near St Louis” (or whatever), you could simply offer to sell your product to someone who is searching for something very much like it. This contextual advertising doesn’t necessarily require much if any user data but can be much more effective that even the most targeted ad since it is in fact giving users what they want.
The problem with paid search is that while it works for users, it isn’t nearly as good for advertisers. This is just because of supply and demand: search volume isn’t growing as quickly as social media volume so there’s only so much inventory to buy while demand from advertisers has continued to grow. Since Google works as an auction, that means the price to buy an ad on your preferred terms goes up to the point of efficiency – that is to say when the gross margin for the product being sold net of advertising approaches zero. In this case the only one making money is Google.
Get The Best of Both
But why isn’t is possible to give users what they want (a largely ad free internet that only includes calls to action that are relevant to what a user actually wants to buy or do) while still creating volume for advertisers? Since what a user wants to accomplish can almost always be inferred by immediate context (e.g. the search term I entered, the content I am viewing), this can be done without needing to store users’ personal data.
The key to doing this is in a closer integration not between user data and ads but between content/context and ads. A personalized experience can be derived from the content, but it is not going to be as simple as searching content for keywords. A semantic understanding of context is necessary with calls to action that match. That requires intelligence, but machine learning has gotten to the point where such as thing is possible.
A Case In Point
We live these ideas at Public Good. Our technology uses machine learning to derive context from a news story or other piece of digital content. It then uses that context to create a personalized call to action. For example, if you are reading a news story about homelessness in San Francisco, you would be served a unit allowing you to volunteer with or donate to a Bay Area homeless shelter. But we also consider a deeper understanding of the context: if the article you were reading related to a homeless person with a severe substance problem you might see calls to action about substance abuse or if they had been attacked it might be about how you can help reduce violence.
We can create enough scale to get meaningful change by analyzing all the content produced by many of the nation’s biggest news publishers in real time as they are produced, a task that requires substantial automation through AI.
But ours is just a narrow use case of the broader possibility. The same thing could (as is starting to be) done for a variety of other contexts and products in places like Pinterest and Instagram and, for the most part, users really like it. As more platforms adopt this “deep contextualization”, the volume can start to grow sufficiently to make the pricing work for advertisers.
The internet doesn’t need to be at war with itself. Users don’t need to sacrifice their personal data to enjoy ad supported services. And advertisers can get unstuck from the myth of the perfectly targeted ad in favor of the perfectly placed one.