As third-party cookies are going away, here’s how retailers can keep targeting customers effectively with first-party data.
There’s no escaping it—the end is nigh for third-party cookies.
While online shoppers concerned with their privacy may rejoice at the thought, eCommerce retailers are certainly less enthusiastic at the prospect of losing valuable insights into their customers’ potential buying habits. What can retailers do to ensure their conversions don’t suffer because of cookies’ demise? And just how effective were third-party cookies at driving engagement and sales, anyway?
Why are third-party cookies integral to personalization and where are they going?
In order to target customers most effectively, marketers have heavily relied on third-party cookies to perfect personalization.
Personalization is the process of customizing an experience or communication for a customer based on information such as name, location, interests, etc. Personalized communication can improve engagement and drive more sales. Marketers often send personalized emails that include the customer's name in the subject line to grab their attention, and recommend products based on the customer's interests or past purchases.
Third-party cookies allow data brokers to provide aggregated sets of consumer demographic information such as age, gender, and location. They’ve long been viewed as the most effective tool for eCommerce businesses to deliver on personalization and consequently, sales.
However, both consumer and government concerns around privacy have tainted this method as an undesirable one, leading to browser and software abandonment, and even privacy laws limiting the practice.
Google, whose Chrome browser accounts for nearly two-thirds of the world’s internet usage, plans to phase cookies out completely in 2024, while Mozilla Firefox and Apple Safari have already done so. This poses a real concern for retailers that need to quickly prepare for the demise of cookies and still be able to effectively recommend items to customers. Marketers that get personalization wrong can pay the price. According to a Gartner survey[1], more than half report they will unsubscribe from a company’s communications and 38% will stop doing business with a company if they find personalization efforts to be “creepy.”
The rise of first-party data as a solution for personalization
While third-party data is being phased out because of its privacy concerns, there are still ways to leverage valuable signals from customers in order to drive sales. First-party data refers to the information a business gathers from a user through direct interactions with its own website or catalog. This information is gathered by informing the user that it is being collected and what for, something that 84% of consumers indicate they are willing to do.
For retailers, there are inherent benefits to first-party data over third-party cookies. Firstly, the information is obtained through an open exchange with the consumer, which gives the customer a sense of security and trust in the brand.
Secondly and more importantly for the business, first-party data reflects the tastes and preferences of a user within the context of a given eCommerce store, as opposed to an agglomeration of other stores similar to it.
Applying a brick-and-mortar scenario to eCommerce personalization
While third-party data has served as the primary customer data used to make eCommerce recommendations to customers, it has never been the most effective way to achieve true personalization. That’s because it is too concerned with who a person is versus what they want or are interested in.
Consider the following brick-and-mortar retail store scenario. Imagine you, as a salesperson in the store, have one of two options when a customer walks in. You can ask them to disclose personal information such as their age, gender, and where they live. Or, you can allow the customer to browse independently and observe which items they interact with within their first few minutes in the store.
Obviously, the latter option would allow you to more accurately make a recommendation based on the customer’s actual tastes and preferences, as opposed to making assumptions about what they might want based on how they identify as a person.
Advancements in AI make first-party data especially valuable
The brick-and-mortar scenario holds true for customers online, as well. The problem is that it can be difficult to accurately interpret website visitor interests in eCommerce settings in a timely enough manner to keep them from bouncing from your website. Recent advancements in artificial intelligence (AI) and machine learning (ML), however, have made behavior-based recommendations online a reality.
By leveraging cutting-edge AI and ML techniques, businesses can adopt an approach that focuses on customer behavior—things like cart adds, likes, page scrolls, etc.—in a matter of clicks, all without concerning themselves with personal data.
This means that not only are third-party cookies less relevant than first-party data when it comes to effective personalization, but also that conversion-driving recommendations can be achieved even in the absence of prior browsing history. This is particularly important because of the high amount of website visitors that are new or anonymous.
Behavior-based recommendations as a way to supercharge engagement and conversions
Methods like recommending “best sellers” or “previously viewed” are standard yet mediocre ways to approach personalization because they limit discovery to storewide purchase data that may be irrelevant, or past browsing history. This is especially limiting for new and anonymous users.
By using behavior-based recommendations[2] to provide highly-personalized experiences to consumers, retailers are able to surface items a consumer loves in a shorter amount of time. This increases both the likelihood of a conversion as well as brand loyalty, since the consumer will feel that the business understands them and quickly surfaces items they are truly interested in.
Additionally, the first-party data that enables behavior-based recommendations while browsing can be used to enhance the checkout experience. Opportunities for bundling items or upselling can be more effectively presented to customers based on their habits.
Only by leveraging first-party data with the right recommendation platform can retailers take full advantage of these methods. Third-party cookies and less advanced recommender systems are ill-equipped to provide such a rich browsing experience for customers.
Use first-party data to meet customers where they are
First-party data from a customer’s time on an eCommerce site is extremely valuable, even after the consumer navigates away from the site. Being able to re-engage with customers where they are within your brand’s larger ecosystem is critical. An effective omnichannel approach can be the difference between an abandoned cart and a sale—and leveraging on-site behavior for emails, SMS, in-app, or in-store interactions will get you there.
Traditional omnichannel methods include recommending what was left behind in a cart, previous purchases, or new and popular items. While these can all be somewhat effective in terms of re-engagement, being able to hone in on user behavior to make recommendations is more likely to pique interest and get the customer back in a shopping mindset.
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