First party cookies vs third party cookies: what are the differences? And what about zero party data?

  • 3rd party cookie.Third parties leave cookies on a user’s web browser after a site visit. Per the name, these third parties are different than the website that was visited. Third party tracking cookies collect user data enabling the tracking of user behavior across the web. These are used heavily by digital advertising companies to create targeted ads. For instance, Google Ad will create a cookie on a user’s web browser. This allows Google to monitor user activity on multiple websites. Then show those users products that they previously searched for on a different website.
  • 1st party cookie.The website a user visits directly places a cookie on the user’s browser. These cookies, in turn, collect first party data. That is, data about user behavior on that website. This typically contains very detailed records and analytics for each website visitor. These cookies can also be called “performance cookies.” It may be used by the website owner to improve their user experience. For instance, Google Analytics uses these cookies to capture data about web visitors and various aspects of the user experience.
  • 0 party data. Introduced by Forrester research, this refers to data that customers intentionally and proactively share with companies.
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Historically, third party data was a critical piece of any personalization strategy. This wealth of data allowed ecommerce companies to fine-tune each user’s customer experience. It enabled, among other things, digital marketing targeted advertising.

In many ways, it’s great for shoppers too. Seeing a relevant ad that’s in line with what you’re searching for can be a plus. Think of getting recommendations for high-heel shoes when you’ve been looking at running gear. Not very relevant, unless you’re training for a Guinness World Record.

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

Shoppers want their experiences to be highly personalized. But they don’t want their privacy to be misused to serve corporate business interests. Welcome to the personalization vs privacy paradox.

Regulatory Reactions

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

Regulatory interventions such as GDPR and CCPA require websites to explicitly ask for first time/anonymous user consent to be tracked. Until now, it’s been acceptable to be vague about how you use your website uses visitors’ data. (Possibly not the best way to build trust). Today, websites are encouraged to offer a user privacy center. These centers are a place where users can find out anything the site’s privacy protocols and obligations. And whether or not they allow cookies from a third party server.

In 2019, Googles Privacy Sandbox initiative was announced. Its goal is to create new privacy-first data collection standards for websites. It’s also to facilitate digital advertising without the use of third party cookies.

Browsers like Safari and Google are now either blocking third-party cookie creation entirely. Or they’re deleting session cookies within a set number of days. Google Chrome has announced that it will end the use of third party cookies by the end of 2023.

What are the implications for brands and retailers?

Third party cookie deprecation may sound the death knell for old-fashioned approaches to ecommerce personalization. Yet unlike what catchy headlines claim, it doesn’t mean that our future will be completely cookieless. After all, first party cookies — and the data they provide — aren’t going anywhere.

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. Just because personalization initiatives can’t use browsing data from multiple sites doesn’t mean that companies must resort to guesswork.

In fact, companies have still plenty of ways to obtain relevant data about their visitors.

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

What is First Party Data?

First party data is often best characterized as behavior-based data associated with a tracking cookie. This data contains the trajectory of users on a company’s website. They provide 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. Every click by a user is a potential opportunity for contextual advertising. This can include changing the choice context or providing the user with specially selected information.

What is Zero Party Data?

This category introduced by Forrester research refers to data that customers explicitly and proactively share with companies. Customers volunteer zero party data through interactions like completing forms or surveys. This data can include personal information like personal needs, preferences, or interests in exchange for clear value. Retailers can use this information to build experiences that don’t require a login. They can better serve both new and returning customers.

Rather than contradictory, first party data and zero party data are complementary. When combined, they help brands and retailers deliver highly effective, privacy-aware personalized experiences.

How Do You Leverage First Party Data to Capture Shopper Intent?

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

Shopper intent can be inferred from your shoppers’ onsite behavior. Retailers can study how users move around the site, what they click on, add to their cart, and how long they hover the mouse near objects. These clues can effectively work as windows into a shopper’s mind.

Leveraging visitor signals behind in a session is particularly useful when users visit 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.

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What is the 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.

Examples of 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 user 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 analyzing 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

How To 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.

What are the Benefits of Zero Party Data?

  • 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 are the Drawbacks of Zero Party Data?

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 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.

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