Starting with the basics: What is the difference between third-party cookies versus first-party cookies and zero-party data?

Third-party cookies: When you visit a site, a third party leaves a cookie on your browser. These ‘cookies’ then collect browsing data across multiple websites, enabling the tracking of your behavior across the web. 

First-party cookies: These are similar to third-party cookies but are stored directly by the website you visit. First-party cookies in turn generate first party data. This data contains very detailed records of what you do as a visitor when navigating an individual website or e-store. 

Zero-party data: This category introduced by Forrester research refers to explicit data that you, as a customer, intentionally and proactively share with companies. 

Historically, personalization strategies have relied heavily on third-party cookies—pieces of data about a particular user saved to the browser. When you visit a site, a third party leaves a cookie on your browser. These ‘cookies’ collect browsing data across multiple websites, enabling the tracking of consumer behavior across the web. 

Third-party cookies can indeed be great. For many online shoppers, seeing recommendations in tune with what they’re searching for is a plus. If you’re shopping for running shoes for example, then it would be irrelevant for you to receive recommendations regarding a sale on high heeled sandals (unless you’re comfortable running in heels that is!).

However, the privacy revolution has arrived! And as a result, third-party cookies are going away because they’re perceived as a threat. 

While shoppers want their experiences to be highly personalized, they don’t want their privacy to be misused to serve the business interests of corporations. Welcome to the personalization vs privacy paradox

The regulatory and technological landscapes are also evolving fast, raising serious challenges for cross-site tracking. 

Regulatory interventions and privacy acts such as GDPR in the EU and CCPA in  California and the United States now require websites to explicitly ask the first-time/anonymous visitor to agree to being tracked. Browsers like Safari and Google are now also either blocking third-party cookie creation altogether or deleting them within a set number of days. 

What are the implications for brands and retailers?

While the demise of third-party cookies may sound the death knell for old-fashioned approaches to ecommerce personalization, unlike what catchy headlines claim, the death of the third-party cookie doesn’t imply that our future will be really cookieless. After all, first-party cookies—and the data they provide—aren’t going anywhere. 

But what these recent developments do imply is that the future will definitely involve less cross-site tracking—thanks to these increased efforts to block third-party cookies, fingerprinting, and other tracking technologies

As a result, ecommerce companies that seek to address the personalization vs privacy paradox and revamp their personalization strategies must look for a solid, compelling alternative to the third-party cookie.

Will the Real Privacy-First Personalization Please Stand Up?

Brands and retailers don’t need to despair, though. While browsing data from multiple webpages won’t support personalization initiatives, this doesn’t mean that brands and retailers must resort to guesswork or segmentation-based approaches. 

Actually, there’s plenty of relevant data giving brands and retailers clues about a customer’s visit to their e-store. 

Specifically, brands and retailers who wish to embrace privacy-first personalization without compromising on relevance should focus on first- and zero-party data:

  • First-party data: In the context of ecommerce personalization, first-party data is often best characterized as behavior-based data associated with a tracking cookie. This data contains the trajectory of (prospective) clients on a company’s website and provides very detailed records of what visitors do when navigating an e-store. With first-party data, brands and retailers can glean valuable indicators into an individual’s interests and intent, as every click by a user is a potential opportunity for personalization, a chance for a brand or retailer to change the choice context, or provide the user with specially selected information.
  • Zero-party data: This category introduced by Forrester research refers to explicit data that customers intentionally and proactively share with companies. Zero-party data is obtained through short, simple interactions (e.g., surveys, quizzes, forms) that ask a customer to volunteer a few bits of information about themselves (personal needs, preferences, interests, goals, favorites, etc.) in exchange for clear value. Using these responses, retailers can build experiences that don’t require a login and that can better serve both new and returning customers. 

Rather than being antithetical or hardly reconcilable, first-party data and zero-party data should be seen as complementary assets. When combined, these can help brands and retailers deliver highly effective, privacy-aware personalized experiences.

Leverage First-Party Data to Capture Shopper Intent 

Contextual personalization which is based on referral data, local weather, and sometimes geo-location can be a good starting point when tailoring experiences for your customers. However, it won’t capture the customer’s current intent, only their context. 

Shopper intent can be inferred from your shoppers’ onsite behavior. Brands and retailers can collect records of exactly how users moved around the site, what they clicked and added to their cart, and even how long they let the mouse hover near certain objects on screen. These clues can effectively work as windows into a shopper’s mind.  

Leveraging the many signals that visitors leave behind in the sequential interaction patterns in a session is particularly useful in those cases where visitors are visiting a site for the first time or simply chose to remain anonymous. This is labeled a ‘cold-start’ scenario.  

Make no mistake — cold start problems are more frequent than one might expect

The Many Faces of the User Cold Start Problem

The term “cold start problem” actually originates from cars — when it’s really cold, an engine has difficulty starting up, but once it warms up, it runs smoothly. Similarly, the best recommendation systems excel at guiding us to well-received options, but have difficulty incorporating newness. 

In ecommerce, a classic version of the cold-start problem refers to the difficulty of recommending products to genuinely new users. This is a significant challenge for brands and retailers, as a very large portion of the transactions taking place on their websites involve genuinely new users.

Even when users aren’t genuinely new, users often visit a website very rarely. For example, most people have only one or two vacations per year, meaning that many users may end up visiting online travel agencies and booking infrequently. Similarly, purchasing birthday presents for family members doesn’t happen too often.

Cold start scenarios may also happen when information is available yet no longer relevant. Consider how data about a backpacking trip you took last year may no longer be relevant to the upcoming family-friendly trip with your children. Or how a student looking for a bed may buy a twin format when sharing with a roommate —but a few months  later upon graduating, may opt for a queen to furnish a new apartment instead. 

Finally, cold start problems can also come from users having different interests at different yet closely spaced points in time. For instance, depending on their mood or their social context, users may be interested in watching different movies. 

These are all kinds of cold-start situations where focusing on real-time intent and leveraging in-session clickstream data can prove extremely helpful. 

First-Party Data Personalization at Work 

In all of the above cases, personalization based on first-party behavioral data can prove extremely effective in deriving shoppers’ intent from shopper behavior. 

Because this might still sound abstract, we’ll consider a couple of examples to show how privacy-first personalization can actually work using first-party data.

Session-aware Recommenders

The first example concerns the rise of so-called session-aware recommenders that leverage AI. These provide personalized recommendations by understanding visitor intent based on interactions observed in an ongoing session and analysing the search and browsing behavior in real-time. For example, imagine I’ve searched for some golf pants on an ecommerce website, which I’ve promptly added to my cart. 

Session-aware recommenders may start providing me with golf-related items automatically. This is a key feature and a game-changer that helps ecommerce players deliver personalized shopping experiences without the need for high volumes of data or logged-in users.​ These session-aware recommendations must remain relevant throughout the visitor’s session by also detecting intent changes and adjusting accordingly. For instance, you wouldn’t want to keep seeing golf accessories once you start shopping for your next tennis outfit.

To this end, Coveo has recently developed cutting-edge technology to provide “Personalization as you go,” using shoppers’ signals to tailor and improve the experiences that customers receive in real-time. 

Tailored Experiences Triggered by on site behavior

At a time in which customer intent can change in a heartbeat, it is critical to be able to understand shoppers’ behavior based upon every single click and customer data point and to serve relevant experiences tailored to such intent.

For instance, in the example below we see how our customer Kurt Geiger enabled more seamless visitor journeys by deploying a ‘Save Size Selection’ experience. 

Customers who filter for their size on the Product Listing Page (PLP) and then navigate to a Product Detail Page (PDP) within the same category, will automatically see their size pre-selected for them, making it as easy as possible for them to purchase the product of interest. 

An illustration shows optimizations made on a product landing page

Use Zero-Party Data to deliver personalized shopping journeys

Inferring preferences, goals, and needs from the traces left by your shoppers can be great. But you can complement your first-party data with zero-party data shared by your customers, expecting that it will be used for their benefit. 

With zero-party data, brands and retailers might better understand why consumers behave in certain ways and in which contexts. 

In fact, zero-party data offer several benefits, including:

  • Quality and accuracy: Third-party data isn’t always that accurate: it’s in fact often outdated or incomplete. Since zero-party data comes directly from the customer, it tends to be way more accurate. But you still have to make sure you ask the right questions, as people sometimes have limited insights into their abilities or even preferences. For example, while most people say they are better than average, it’s statistically impossible that more than 50% of a population is better than average in any ability. So, asking customers to choose whether they are reasonably experienced snowboarders, seasoned snowboarders, newbies, etc., might not lead to the most accurate and meaningful self-segmentation.  
  • Compliance: Being compliant for zero-party data collection has little to no risks because you know the source of data and how it was collected. 
  • Transparency: Zero-party data disclosure is based on an existing consumer–retailer agreement. Consumers know what information they are sharing and why, so ultimately they can decide how much to reveal. 

What’s not to love about zero-party data? One obvious downside of zero-party data collection is that if you ask customers for too much information at once, you risk making them feel overwhelmed. 

More generally, a strategy based on zero party data can inevitably introduce friction in the shopper’s journey. 

There are ways of overcoming these concerns, though.

First, always try to spread out your requests. The recommendation is therefore to infer preferences, needs, and goals as much as possible and ask customers to self-report those only sporadically—such as, when it makes most sense and they can easily identify the value they will gain from sharing information about their preferences. 

Second, while it’s true that consumers get better personalization, product recommendations, and service suggestions by providing zero-party data, brands and retailers can encourage disclosure also with financial incentives and rewards. For example, offering a tiered loyalty program. 

How to Capture Zero-Party Data 

Collecting zero-party data is typically conducted  in a conversational format, allowing consumers to offer only the information they want in a format they agree with. In fact, you may already have several tools at your disposal for collecting zero-party data. 

Self segmentation tools are one example, where you ask your customers to tell you which audience segment they belong to. By carefully tailoring your questions and suggested segments, this can be a very effective strategy for personalizing the buying experience for personas.

Self-segmentation can be as simple as asking visitors whether they are home owners, renters, or looking for a new property so that they receive relevant information. Avoid ambiguous language, fuzzy categories, or overly subjective assessments. 

Surveys can also be powerful tools to gather information about people’s preferences, wants, and needs. In the example below, customers can express their preference for a specific brand.

An illustration shows survey data being collected on mobile and on desktop

Similarly, as illustrated in the example below, customers can express preferences about the categories and types of products they are interested in. 

Being able to segment your visitors according to their survey responses  will be critical to allow you to target them with the most relevant experiences.

Coveo-Qubit offers powerful tools to add context to site behavior, asking your customers more about themselves, and listening to the feedback. 

In Summary

Retailers who wish to embrace privacy-first personalization without compromising on relevance should look for technology vendors that can create personalized shopping journeys with first- and zero-party data. They should especially look for vendors with machine learning that can quickly detect intent based on a visitor’s interactions and tailor experiences for “cold start” shoppers.

Dig Deeper

Looking for deeper insights on how to optimize your conversion rate? 

In our Ultimate Guide to Conversion Rate Optimization in Ecommerce, you’ll learn how to leverage synergies between touchpoints, harness the full power of analytics, and a three-step testing process for encouraging outcomes. 

Download your copy todayEbook: Ultimate Guide to Conversion Rate Optimization
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About Andrea Polonioli

Andrea is a Product Marketer for our Commerce line of business. Prior to joining Coveo, he was at Tooso, the acquired AI search ecommerce startup. He has a passion for innovation-driven companies and a research background in cognitive science. When he is not working, he is likely to be experimenting new dishes, travelling or hiking.

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