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This article was contributed by David Magerman, managing partner and cofounder of Differential Ventures.
Have you ever had one of those creepy experiences where you’re talking about something — a product or service like skinny jeans or car rentals — and then ads start showing up on Amazon, Google, and Facebook related to your conversation? No, big tech isn’t listening to you, or at least not all the time. You might not realize it, but your communications often leave trails of breadcrumbs behind — in your email, search history, credit card purchases, and other places within the digital universe. Whether its third-party cookies or data sharing arrangements between technology companies, it’s effortless for advertisers and ecommerce sites to design targeted ads eerily relevant to your day-to-day life.
Until recently, the customer journey has been heavily influenced by this kind of data — breadcrumbs — usually without explicit consent (not counting the 100-page legal documents we pretend to read when we start using a website). Now, stricter regulations, such as Europe’s GDPR and California’s CCPA, are changing how companies capture, use, and share customer information. Consumer attitudes are also shifting. More and more consumers are being turned off by the feeling their personal information is driving how advertisements are reaching them.
To understand the problem, as well as potential solutions, it helps to understand how artificial intelligence (AI), driven by machine learning (ML) models fueled by customer data, is being deployed to influence advertising decisions throughout the customer journey. Modeling behavior involves two major components: classifying consumers into categories and modeling the behavior of the collections of people in those categories.
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How we’re categorized
Human beings are often categorized by demographics including age, sex, religion, income, and marital status, as well as interests based on one’s education, job title, known hobbies, and so on. Many of these factors can be determined or inferred by online data including LinkedIn profiles, public social media posts, and public (and not-so-public) databases. To AI/ML-driven advertising systems, the universe of potential customers are points in a multidimensional space, where everyone has a weighting in each dimension. Using all the data they can collect about an individual consumer, digital advertising companies classify each consumer to a point in that space.
Advertisers can’t model each person individually. Instead, they model behavior based on the different factor groups. Each factor on its own might lead to a prediction. Let’s say you have a sweater you want to advertise to customers. Data-driven models might determine that a 40 to 45-year-old would have a 25% likelihood of wanting the sweater, dropping to 15% if that person was a woman. In reality, Jane Smith, a 42-year-old woman living in Calais, Maine, might have a 45% chance of wanting the sweater, while her 43-year-old husband, John, might have no interest at all.
Advertisers describe people according to hundreds of factors and build models that try to predict buying behaviors and outcomes. Their goal is to spend their advertising dollars putting ads in front of the people most likely to purchase the products or services. These data-driven factor models, trained using ML and AI, help them do that with remarkable accuracy. But times are changing.
Big changes to the customer journey are on the way
The big change happening across the internet, and for advertisers in particular, is that regulators are becoming more restrictive about what consumer data can be shared without explicit permission. So, when you email friends about your upcoming ski trip and start making reservations online, you are less likely to have ads showing up for ski equipment. That’s because your email provider, credit card companies, and other online vendors are no longer as freely sharing the information they’ve gathered about you with third parties, which these third parties often sell to advertisers.
This change impacts online targeted advertising in two ways. First, the change makes it harder for advertisers to build models of customer behavior. They already have models, and those models will continue to work for a while. Over time, they will become outdated and without new data to train them, they will become less effective. Second, it’s now harder for advertisers to describe their customers in terms of their multifactor model. Many websites previously used third-party cookies and other metadata from your browser to determine or guess your identity, or at least tie you to some of your previous online behavior. Under new regulations and changes being made in browsers, it is now harder for sites you aren’t logged into to identify you and model your behavior based on that information.
Today, more than ever before, customers are controlling their online consumerism journey. More sites will be incentivizing them to opt in to data sharing, either by paying them for their data or giving them access to better deals or more online functionality in exchange for their information. However, if the majority of customers opt-out, the ability to model customer behavior and to conduct targeted marketing will be diminished, even for those customers who opt-in.
If you like things as they are, don’t be too concerned. Several startups are developing and releasing new products to help ecommerce sites and advertisers navigate this changing world. Some are providing tools to help encourage more customers to opt in to data sharing. Others are proposing new technologies to overcome the elimination of third-party cookies. In some ways, the problem of protecting the privacy of online consumer data is a cat-and-mouse game. The new regulations will protect the mice for a while, but the cats will keep trying to come up with ways to capture their prey. Ultimately, the regulatory climate for protecting consumer data should lead to a better environment for consumers in general.
Companies using customer journey data should adopt these new solutions for targeted advertising that are compliant with new regulations and customer preferences for data privacy. Companies like Konnecto encourage customers to share data with an opt-in strategy and then connect brands to information about customers based on models trained on that data. Startups like Kahoona analyze customer online behavior to help targeted marketing without needing third-party cookies or other regulated private data. Companies should find ways to incentivize customers to agree to share their data for targeted advertising using rewards systems, discounts, or even direct compensation for opting in to data sharing. Most importantly, companies should start implementing solutions now, even though third-party cookies are still available in most browsers and regulations are still in nascent stages. Any data-driven solution will require a critical mass of data to train ML-driven systems, and if competitors collect enough data first, they will have a competitive advantage.
The message from customers and regulators is clear. People should have the ability to dial up and down the availability of their data. For those who do share their information, advertisers and ecommerce sites must do a better job presenting these customers with products they want advertised to them, hopefully with discounts and other advantages. People who value their privacy over a more targeted customer experience should see more generic advertising, or none if they choose to opt-out and block ads. This evolution may take time, but with many governments shifting the power to those who generate online data, the progression to a better, more private, and breadcrumb-free digital universe seems inevitable.
David Magerman is a cofounder and managing partner at Differential Ventures.
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