The following article was initially published in Calcalist.
In today's digital age, data analytics plays a crucial role in creating awareness and driving demand around new products and services. Understanding customer needs, intentions, and behaviors and then tailoring messages to target specific audiences has become a fundamental practice.
However, there is a growing concern surrounding the trade and exploitation of personal data, which was coined by Harvard professor Shoshana Zuboff in her excellent book, “The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power.” Surveillance capitalism is based on the idea that personal data is a valuable commodity that can be sold to advertisers, marketers, political organizations, or anyone else who wants to influence your decisions.
This vast amount of data provides valuable insights into behavior, preferences, and trends, which can then be converted into highly targeted advertising. In B2B marketing, this data is used to target specific personas - typically decision makers, stakeholders, and influencers, who (as a whole) are exhibiting intent around their buying decisions. In large enterprises, for example, many IT and cybersecurity decisions are investigated and researched months and even years ahead of a final purchase decision. This multi-layered “digital exhaust” can be enormously valuable to marketeers seeking to make effective decisions with increasingly tight marketing budgets.
The new frontier in personalized marketing
The nature of surveillance capitalism opens the door for exploitation by marketers in many ways. The underlying principle is that user data collected from multiple sources is used to build detailed profiles that allow for highly targeted advertising. Let's explore some hypothetical examples that further illustrate this point:
Hyper-Personalized Advertising: Imagine a large online retailer using advanced machine learning algorithms to predict not just what you want now, but what you will want in the future. The company uses data about your browsing habits, purchase history, and even the time you spend looking at particular items to build a complex model of your desires and interests. This model is then used to push ads for products that you may not even realize you want yet.
Location-based Advertising: With the proliferation of location-tracking devices and apps, marketers have found new ways to target consumers. For instance, a popular coffee chain might use your location data to push a special offer notification when you're near one of their locations. They could even monitor the locations you frequent most often to determine the best places to open new stores.
Cross-platform Data Collection: Consider a scenario where a fitness tracking company is bought by a larger tech conglomerate. This conglomerate then integrates the fitness data with its existing user profiles, allowing for highly targeted health and wellness advertisements. For example, if your tracker shows you've been running more, you might start seeing ads for running shoes or ads for health insurance.
Behavioral Prediction and Real-Time Bidding: Some digital ad exchanges have developed the capacity to use real-time data to anticipate user behavior and adjust advertising accordingly. If, for instance, their system predicts that you're likely to book a vacation soon based on your browsing patterns, travel agencies or airlines might engage in real-time bidding for the ad space on the website you're currently viewing.
Data Brokering: The rise of data brokering firms has made it possible for businesses to buy immense amounts of data on potential customers. This data is then used to create exhaustive customer profiles, which in turn enable ultra-specific ad targeting. For example, a car company might purchase data on people in a specific income bracket who've recently searched for car reviews online.
Social Media Advertising: Social media platforms continue to refine their algorithms to better predict user interests for targeted advertising. For instance, if you follow several home improvement brands and like posts related to home decor, you might start seeing ads for home renovation services or interior design firms.
Smartphones have become the primary source of data for such marketing tactics. Many apps won’t even work unless users permit access to their microphone, camera, location, and even personal contacts. There are many cases where apps collect more data than they actually need; often hidden behind privacy settings that are not intuitively accessible.
A marketeer’s moral dilemma
As a marketing professional in the cybersecurity field, I find myself grappling with the ethical implications of surveillance capitalism. While data collection and analysis have long been the core of understanding audiences and developing effective marketing campaigns, the manner in which personal data is often acquired raises difficult questions of acceptable business conduct.
Even in the business world, individuals are often unaware that their online activities are being collected to obtain a broad picture of the company’s intents and purchasing plans. Questions of integrity, transparency, or authenticity arise when you track and analyze data coming from an audience that doesn’t realize it is being tracked. In some ways, this is the inverse of what’s behind the GDPR double opt-in regulations: Ideally, you want your audience to give you express permission, before you do anything that connects to their personal data.
Finding the Ethical Middle Ground
So, how can marketing professionals build campaigns without leaning on surveillance capitalism? Here are some possible alternatives:
Use first-party data sources: Instead of relying on third-party data brokers, analytics firms, ad networks, or platforms that collect or sell personal data without consent or transparency, use first-party data sources that you collect directly from your customers with their permission and awareness.
Adopt privacy-by-design principles: Instead of collecting or using more data than you need or storing it longer than you should, adopt privacy-by-design principles that minimize the amount and duration of data collection and use, and implement appropriate security measures to protect it.
Embrace outcome-based business models: For companies providing web-based services that have access to large amounts of personal data, use this power with care. Instead of depending on advertising revenue or data selling as your main source of income, embrace subscription-based models that provide value to users by offering high-quality content, convenience, and personalization, without relying on advertising or data selling. Companies like Netflix, Spotify, or Medium are great examples.
Focus on quality over quantity: Instead of chasing large numbers of impressions, clicks, or conversions based on data-driven targeting or personalization, focus on creating high-quality content, experiences, or solutions that resonate with your core audience based on value-driven segmentation or positioning.
In short, syphoning data about employee behavior to generate insight into purchasing intent raises a red flag: both with regard to a company’s long-term relationship with a targeted base of potential customers, and with regard to societal privacy thresholds. The twin values of corporate transparency and customer consent may be compromised, if you choose to leverage data obtained through third-party sources.
Life isn’t black & white, and there’s complexity behind most business decisions. Yet, it’s a question of adopting an approach to marketing that leaves room for a healthy awareness of the inherent privacy issues at hand. By building campaigns without leaning on surveillance capitalism, marketing professionals can avoid ethical dilemmas, legal risks, and strategic pitfalls. They also can create more meaningful value for themselves and their customers, and can contribute to a greater awareness of privacy issues throughout society as a whole.