Getting Ahead of the Cookie Curve Using AI Relevance Tools

So much has changed in the past two years about the retail space, including customer expectations and demands from retailers. In an analog world where a consumer will walk into a physical store for their shopping needs, whether they are on a mission for a specific item or going in for inspiration, what they expect is that they’ll find what’s relevant to them – often with a knowledgeable store associate to guide them.

Now, in e-commerce, 90% of consumers expect the online experience to be equal to if not better than the in-store experience. This means finding what they need quickly and consistently each time they go to an online retail destination. Without a physical store associate to provide them with guidance, retailers can utilize technologies and advanced search platforms that allow consumers to easily and efficiently navigate, and discover what they need. This is not only true online, but also in multi-channel when people browse in-app in the store.

Companies like Amazon and Netflix have set a high bar when it comes to customer experiences and personalization. Consumers are only one browser away from a better experience – or will even choose a store based on its digital presence. When you have them on your digital property it’s important to keep them there, to help them find what they are looking for quickly, to recommend something you know they will like, to find them the answers to the questions they might have and help them discover products they’re unlikely to want to return. Consumers expectations means that we’re beyond personas, we are now expected to provide true 1-1 personalization.

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Consumers want to protect their privacy by getting rid of third-party cookies

Traditionally, retailers and search platforms have relied on first- and third-party cookies to provide users recommendations that are tailored to their profiles. This practice has its roots in the early days of the web when, in 1994, Netscape introduced the cookie so sites could track visitors’ information and preferences while they are on the page. Cookies are an integral part of why the digital advertising space is now worth $600 billion.

However, in this digital-first era, users have begun to express privacy concerns, tied in large part to the role third party cookies play. In response, top companies like Google, Apple, and Mozilla have gradually begun to phase them out. Without cookies, advertisers will increasingly have a more difficult time tracking the consumer information and preferences that enable them to provide the most efficient and relevant information upfront.

Shoppers demand personalization, but often hide their intent. 40% of customers said they choose to remain anonymous during online shopping and when using guest check out. This “cold start shopper” problem needs to be addressed. Which brings us to the crux of the problem: while consumers want more privacy online, they still expect personalized shopping experiences when they visit online retailers.

With headliners like Google and Apple restricting the use of third-party cookies, retailers will really need to turn to other technologies to determine a shopper’s intent with little to no data at all.

AI and machine learning technologies will meet consumers expectations by delivering relevant recommendations while respecting their privacy

While Google has announced it will delay its plans to phase out third-party cookies until 2024, other browsers are already turning them off. So, now is the time for retailers to get ahead of the cookie curve and address the upcoming challenges that these new circumstances will provide.

The good news is that new technologies can step in to determine and deliver what is relevant to users without relying on the data collected by third-party cookies. Delivering that relevance at scale is absolutely critical to creating a personalized experience for consumers. It’s a matter of moving away from personas to addressing consumers’ needs as people, unique individuals with unique needs. Layering AI and machine learning into site search creates that individualized experience and gives consumers relevant and curated searches while respecting their privacy.

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What will replace the third-party cookie?

In anticipation of its plans to phase out cookies in Chrome, Google has already begun testing machine learning technologies to replace the customer data provided by third-party cookies.

Companies competing with the likes of Amazon can similarly use machine learning technology to power their own site search. In addition to helping retailers augment and enrich content on their sites, machine learning is the key to identifying customers’ intent seamlessly, ensuring they find what they are looking for quickly. The right search platform can serve up product recommendations curated to each shopper’s needs or direct them to other sources to fully facilitate their experience by leveraging only in-session first-party data or zero-party data like answering a website survey.

Wayfair, which has 2,300 developers on-staff to support the online sales of its furniture lines, has successfully demonstrated the use of advanced search technology to create a curated shopping experience. For other retailers that don’t have that scale of operations and inventory, there are still tools for promoting and recommending the right items to customers, replicating the in-store shopping experience.

At a minimum, retailers can leverage contextual personalization which is based on referral data, local weather, and sometimes geo-location as a good starting point when tailoring experiences to the customer. However, it won’t capture the customer’s current intent, only their context.

Shopper intent can also be inferred solely from onsite behavior. By using AI, 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 collected from a single shopping experience can help a website make smart recommendations regardless if the customer is known or anonymous.

With AI-powered insight into their product data and people’s interaction with it, retailers have the opportunity to act like a personal shopping assistant, imitating the best in-store shopping experience and bringing it online. Regardless, of cookies or anonymous shoppers. This does not just solve a problem – it delivers value.