Co-founder & CTO at Algolia
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Data is one of the most critical assets thriving companies of all sizes possess. Unfortunately, that doesn’t always translate into good practices, especially for the users who can be subject to an outrageous abuse of their personal data without being able to do much about it.
That’s one of the many reasons users regard online companies that are using AI systems to personalize their experiences as disingenuous. However, this is not always the case, and it certainly isn’t the rule of thumb for companies across the board, mainly because there are a few essential forces at play that are preventing them from doing it:
Just recently (Jan, 2022) Austria’s data regulator has found that the use of Google Analytics is a breach of GDPR. While half of the solution might be in the form of a new EU-US data transfer pact, it does bring attention to the other half: how can end-users have more control over their data?
Yes, 83% of consumers indeed expect personalization within moments and hours. At the same time, up to 80% of consumers are sensitive to companies’ security and privacy practices regarding their online data.
At first sight, this might seem like a paradox: personalizing users’ experiences while protecting their data privacy?
In our experience, as long as we’re carefully executing to provide value and convenience to users and do not become intrusive, AI-based personalization is a practice that can be encouraged and widely adopted.
When we measure the effectiveness of these personalization techniques, there are a few aspects to take into consideration:
The inconvenient truth is that, in the online realm, the effectiveness of personalization techniques is mostly correlated with the bottom line: “The recommendation engine must be a hit for our users because our revenues skyrocketed!” That logic is partially valid, as it doesn’t paint the whole picture. An increase in revenue doesn’t necessarily mean an increase in user happiness and metrics that are difficult to gauge, such as user satisfaction, often fall to the bottom of the priorities list.
Plus, it reveals a vital blind spot – the answer to the question: is our user-centric AI system privacy-aware?
Privacy-aware AI systems understand that an individual has her personal data scattered across various accounts, which can be accessed only by the individuals themselves.
Hence, to get value from personal data in interacting with a third-party AI system, the individual must “activate” that personal data. In other words, the individual should decide the conditions for how their user data profile can be used by third parties, including AI systems.
Inspired by two papers that go deep into the topic, each having their own approach, but ultimately attempting to solve the same problems (My data, my Terms: a Proposal for Personal Data Use Licenses and Solid: A Platform for Decentralized Social Applications Based on Linked Data) and in the light of existing data usage practices by consumer-facing companies, we’re envisioning that when it comes to third-party AI systems, individuals should be able to set the following conditions for access to their user data profile:
Understanding the implications of such user data profiles today can give companies a substantial competitive advantage tomorrow by becoming early adopters, leading this wave of change rather than falling behind and resisting it.
Our vision for adopting user data profiles (which can be integrated by online retailers, marketplaces, or even media companies) consists of a 3-layer implementation strategy in a user-centric AI ecosystem.
There are at least 3 user data types that most online businesses deal with:
Behavioral User Data (Implicit; External). Behavioral user data is the most shallow type of data stored since it’s anonymized or at least semi-anonymized most of the time. Think of all the online businesses that use 3rd party analytics platforms (such as Google Analytics) to track visitors on their website/apps – hence its external nature. Because visitors interact with the website in a “guest” mode without disclosing any personal information, this behavioral user data can be characterized as being implicit.
A classic ecommerce funnel is composed of the following steps:
If a user manifests a certain intent at each step of the funnel, we can imagine that certain permissions to the individual data profile can be granted:
If we put it all together, the image we uncover is quite different from the current status quo. This image can be disruptive for both online businesses and users alike. User-centric and potentially user-held data models liberate service providers from collecting data from third parties (data brokers) and give them tools to get the most accurate data directly from their customers (with customer consent).
Such new data models would also help companies create more personalized experiences for their customers and increase competition among companies trying to offer more customer value. Moreover, individuals will benefit from having better control of how their personal information is used and receiving better, more relevant products and services.
If we look at the core concept we’re proposing in this article and analyze how users will manage their identity in a user-centric paradigm we quickly realize that each website or application can be granted different permissions.
The potential for automatically identifying and managing permissions based on the customer’s e-commerce preferences is shaping up. Say the user lands on an online furniture store to look for an office chair – just to browse and get some inspiration as she’s not yet ready to purchase. In that case, the “view profile” can be switched on, which means that the store’s AI system can track and use only the data profile that’s being granted access to.
Or, if they’re a heavy online fashion buyer and set their “interest profile” on, they allow the website or app to prompt a more personalized experience.
We can call this the Human-AI privacy handshake! Think about it, with minimum human intervention and in a seamless way, third-party AI systems can be more aware of users’ privacy settings and act accordingly, even emphatically.
One way online retailers could enable user data profiles is in the “My Account” section of their website/app. From there, end-users can grant access to their user data profile, depending on their specific intents: view, interest or shopper.
While navigating on the website/app, the AI system would ingest and process only the data that it has been given access to. If we’re talking about personalized search results, it will return a list of items ranked by the relevancy score that can be inferred from the shared profile.
In the case of recommended products, those can be displayed in different formats across multiple pages, sometimes associated with a personalized message, depending on the transaction probability of the user.
There is no doubt that technology is a powerful drive for better customer experiences, no matter which side of the fence we’re on. The proposed user data profiles can be an elegant solution to the privacy-aware personalization problem. Still, the most crucial distinction is that the user grants an AI system access to a certain level of personalization, which is deemed comfortable by the user. For that to happen, the number one aspect that needs to be true is trust. What it takes to get there is an open mind and a willingness to experiment with new ways of looking at the data.
What’s your take on it? Are you willing to take the leap and prepare for using user data profiles in your organization? Contact us and let’s start discussing the possibilities.