We live in a world where companies pretty much own the data. Whether that’s Facebook, Google or Amazon, data is one of the most important assets they possess. Unfortunately, that means bad news for the users who are subject to an outrageous abuse of their data, without being able to do much about it.
That’s why they often connect the dots between business operations that employ large amounts of data and posts they see in their social media feed or the ads they may see online.
When most online retailers are trying to customize the shopper’s experiences, making it more personal, increasingly more users see this as disingenuous. However, this is not always the case and it certainly isn’t the rule of thumb for all companies.
In our experience, as long as it’s carefully executed to provide value and convenience to users and to not become intrusive, tailoring customer experience to specific user needs is a practice that should be encouraged and widely adopted.
But how do we measure the effectiveness of these personalization techniques? How can we tell that effort invested in both customization and the communication that supports this process (support, marketing, compliance, etc.) is worth the effort?
There are a few aspects to take into consideration:
- The technical component, which, for the most intelligent recommendation systems, involves machine learning
- The evolution of the business KPIs, namely conversions, average order value, revenue growth
- User satisfaction, which is often considered implicit: if the users convert, it means that they are satisfied.
The inconvenient truth is that, in the e-commerce industry, the effectiveness of personalization techniques is mostly correlated with business objectives and KPIs: “the average order value increased, hence the recommendation engine must be a hit for our users.” Metrics that are difficult to gauge, such as user satisfaction, often fall to the bottom of the priorities list.
That logic is partially valid, as it doesn’t paint the whole picture. Plus, it reveals a very important blind spot: the answer to the question “did the AI that built into the recommendation engine respect the personal data license?”
What’s that now? Personal data licenses?
User-held data means that an individual has her personal data in her personal cloud account which can be accessed only by the individual themselves. Hence, in order to get value from personal data in interacting with third parties, the individual has to have tools that enable them to “activate” that personal data. In other words, the individual should be able to decide the conditions for how their personal data profile can be used by third parties.
In the light of existing data usage practices by consumer-facing retailers, one may envision that, when dealing with such third-party service providers, individuals should be able to set the following conditions for access to a personal data profile:
- Full/limited anonymity. For example, an online shopper may choose to remain anonymous and not disclose any personal information about themselves to the eCommerce website.
- Permission to track. By granting access to their personal data profile, an individual can impose an obligation on the service provider to not follow that particular user (i.e., not to track an individual’s activities during or after that particular session).
- Permission to store data. This means that, even if the service provider is given access to the personal data profile of a particular individual, the service provider is not entitled to retain the personal data profile in its system.
- Permission to bundle data. Individuals should also have the right to prevent third-party service providers from aggregating that particular individual’s personal data profile with personal data profiles of other individuals.
- Permission to share data. Individuals should be able to impose a requirement that the service provider does not share that individual’s personal data with other third parties.
- Permission to sell data. One of the most controversial issues currently relates to the fact that personal customer data is sold among companies without customer consent.
This is the concept being championed by Prifina, loosely inspired by the Creative Commons licenses and thoroughly described in their paper published in the Harvard Journal of Law & Technology.
And if you think this isn’t something that will happen overnight, you might be correct, but let me remind you about the forces at play:
- General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA)
- Google to ‘phase out’ third-party cookies in Chrome
- Artificial Intelligence and Ethics
Understanding the implications of personal data licenses on your eCommerce businesses today can give you a huge competitive advantage tomorrow when the user data profiles will become a reality.
This is why we champion the adoption of user data profiles in eCommerce by proposing a 3-layer implementation strategy in a user-centric and user-held data ecosystem.
1. Understand User Data Types
There are at least 3 user data types that most online retailers deal with:
- Personal Data. Details such as age, name, gender, location, identification document numbers, and others.
- Generated User Data (Explicit; Internal). In addition to the default personal data that every user possesses, other data points are also collected through order forms, contact forms, feedback forms, interacting with chat agents, email messaging, etc. This information is explicitly given by the user when interacting with the eCommerce company and it’s mostly considered internal data since online retailers usually store that information in their data warehouses.
- Behavioral User Data (Implicit; External). Behavioral user data is the most shallow type of data that can be stored, since it’s anonymized or at least semi-anonymized most of the time. Think of all the eCommerce companies that use 3rd party analytics platforms (such as Google Analytics) to track visitors on their website – 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.
2. Build The New Ecommerce Funnel
A classic e-commerce funnel is composed of the following steps:
- Product Details
- Add to Cart
For each step of the funnel, certain permissions to the individual data profile can be granted:
- View Profile. For users that have no intention of buying and are only there to browse. This means the eCommerce company won’t have access to the consumer’s personal data (age, occupation, location, and more) when they view the homepage, product categories or even product details.Does this mean that the user won’t benefit from a high level of personalization? Yes, IF the user chooses to remain anonymous and grants access only to his view profile. Under this profile, the data type we’re operating with is behavioral.
- Interest Profile. This kicks in when the user decides to add one or more items to their cart. Why? Well, they might need to choose the t-shirt size (or gender), if that’s what they’re buying, disclosing not only the interest to purchase that item, but also a generated type of data. Or they might start using the live chat feature on the website. At this point, users can receive more personalized (and relevant) recommendations due to the new data points that have been shared with the eCommerce platform.
- Transactional Profile. Once the user is ready to purchase, they will inevitably put in their credit card information. When a user reaches this point, chances are their level of trust in the eCommerce company operating his data is close to 100%.
3. Enable The AI Handshake
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 companies and users alike.
User-centric, 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 decentralized 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, as well as from receiving better, more tailored products and services.
If we look at the core concept proposed by Prifina and analyze how users will be able to manage their identity in a user-centric and use-held paradigm, we quickly realize that each eCommerce application can be granted different permissions.
We can immediately recognize the potential for personal AI capabilities to automatically identify and manage permissions based on the customer’s eCommerce preferences.
Say the user doesn’t usually visit online furniture stores to purchase, just to browse and get some inspiration. In that case, the AI attached to their account can recognize when accessing a new eCommerce website that’s selling furniture items and can automatically trigger your “view profile” mode. Or if they’re a heavy online fashion buyer and they set their personal data manager on autopilot, it may kick their “interest profile” on and allow the eCommerce app to prompt a more personalized experience.
I call this the AI handshake! Think about it, without human intervention and in a seamless way, personal AI is capable of automatically granting permission to the eCommerce app that integrates an AI-driven recommender system.
How We’re Helping Ecommerce Companies Prepare For Personal Data License Adoption
How would MorphL work for eCommerce organizations in this futuristic but extremely likely scenario?
- The user goes to your eCommerce app and grants access to their personal data profile: view, consideration or transaction.
- The eCommerce app calls the MorphL API for one of our ML models, let’s say Product Recommendations.
- MorphL returns a list of items that the user is most likely to enjoy based on the shared profile.
- The eCommerce app displays those products in different formats across multiple pages, sometimes associated with a personalized message, depending on the transaction probability.
As you can see, as far as we’re concerned, user data profiles are an extra data source for MorphL. The most important distinction is that the user grants MorphL access to provide predictions to the eCommerce app, which are displayed as recommendations. In order for that to happen, the number one aspect that needs to be true is trust.
There is no doubt that technology is a powerful drive for better customer experiences, no matter which side of the fence we’re on. What it takes to get there is an open mind and a willingness to experiment with new ways of looking at the data you already have.
What’s your take on it?
Are you willing to take the leap and prepare for using personal data licenses in your eCommerce organization? We’re here to help.