The continued advancement of retail technology and eCommerce have set new standards for consumer expectations. When shopping online, customers expect hyper-personalized experiences, with data showing that they are 80% more likely to purchase a business that offers a personalized shopping experience.
But what is hyper-personalization, and how does it compare to legacy audience segmentation tactics?
Let’s start with the basics.
Segmentation is a tactic that most retailers today use to target different sections of their customer base. By separating customers based on factors like age, location, gender, etc. retailers can (in theory) deliver more personalized information, like product recommendations, promotions and marketing campaigns.
Traditional segmentation involves dividing a customer base into groups using demographic information such as age, gender and location for targeting purposes centered on assumptions, often assigning interest to a large group of people, simply based on their age and gender. For example, assuming that a male customer in his 50s is interested in golf, based purely on demographic data like age, rather than shopping behavior.
Advanced segmentation delves deeper into customer data to create more meaningful and targeted segments. This might include looking at data points like customer purchase history, browsing habits, engagement with marketing campaigns and loyalty program participation when creating segments to develop further subset experiences for the individual customer.
While advanced segmentation is a step up from basic demographic segmentation, it still falls short of delivering true one-to-one personalization. It continues to group customers based on similarities and assumptions instead of treating each customer as an individual.
Hyper-personalization is where individual personalization comes into play. Individual personalization aims to provide one-to-one personalized experiences, rather than sending a targeted (but still mass) message to predefined segments.
Hyper-personalization, or individual personalization, leverages customer data to go beyond segments and understand each customer individually. By leveraging advanced data analytics and artificial intelligence (AI), retailers can uncover deep insights about individual preferences, shopping behaviors, and interests. This knowledge empowers retailers to deliver hyper-relevant experiences at every touchpoint – from personalized product recommendations to tailored marketing campaigns.
For example, suppose a customer recently purchased a new mattress and then received an email pushing other mattresses to them. In that case, your targeting is seen as irrelevant and annoying – even if it is “personalized” according to their recent purchase history.
In this example, a hyper-personalized approach would look more like this: after a customer adds a mattress to their cart, the recommendations carousel on the confirmation page updates to show bedding or pillows. And not just any bedding or pillows – bedding and pillows from brands, and in styles that that customer has previously browsed, viewed or purchased. This is true personalization, delivering a unique experience to each customer. And it can be achieved by leveraging AI and machine learning to make the customer feel seen and create “add to cart” opportunities that did not previously exist.
When creating hyper-personalized shopping experiences, it is critical to consider the customer journey. Research from Gartner found that, while customers are unlikely to reward brands for providing a positive experience, they are likely to “punish” them for a bad shopping experience. With this in mind they suggest that, “messages focused on helping the customer accomplish [their goals] increases the predicted impact of the commercial benefit index (e.g., brand intent, purchase, repurchase and increased cart size) by nearly 20%.”
For instance, a customer doesn’t want to be slowed down by most pop-ups when they visit a website unless they need to feel relevant to that point in their shopping journey By example, when entering a website, it might feel intuitive for them to respond to a pop-up identifying the country or region they’re in, so that they see the right product catalog. In comparison however, they’re note likely to want to engage with a pop-up prompting them to share their contact info in order to obtain a discount code - they haven’t even had a chance to search for products yet and may not need that discount code. Instead, a better time to target them with that messaging would be after they’ve expressed an interest in purchasing by adding an item to their cart.
For brands to achieve this level of personalization, they must leverage the power of AI and real-time data analysis. With AI-powered systems, retailers can analyze customer behavior in real-time, generating personalized content at speed. This allows them to deliver offers and recommendations that match exactly where the customer is in their online shopping journey.
An AI-first search and product discovery platform can process and analyze vast amounts of data – far beyond what merchandisers can handle manually – almost instantaneously. This allows AI-first platforms to uncover customer shopping patterns and preferences that are impossible for humans to discover.
When shopping online, consumers expect a Google-quality search experience. However, far too many retailers still rely on antiquated legacy search solutions today. These platforms are built on keyword-matching technology which routinely struggles to generate relevant search results – especially for broad or long-tail queries.
The result?
Customers experience a direct impact on the quality of their product discovery experience, making it harder for them to shop and ultimately hindering the chance for brands to achieve both customer loyalty and a successful sale. Research from Google Cloud has found that search abandonment results in $2 trillion of lost sales globally each year, with 78% of consumers viewing a brand differently after a difficult search experience.
In contrast, AI-first product discovery solutions are built solely on AI. This allows them to learn from a customer’s purchase history to deliver individually curated search results driven by true user intent and organized to maximize revenue.
How do the two compare?
Consider a customer looking for a red plaid shirt. On a site powered by legacy search technology, the third search result might be a red plaid sleeping bag. The technology does not prioritize these phrases by relevancy, but rather by how often that term appears in the product catalog. The rarer a term, the more important it is. In this instance, it’s going to decrease the importance of “shirt” and show more products that match terms like “red” or “plaid” as they likely show up less within the product catalog.
With a next-generation search solution like GroupBy’s Product Discovery Platform powered by Google Cloud Vertex AI Search for Retail, customers would receive accurate results based on their purchasing intent, customer data and past-purchase history. For example, if a customer with a purchase history of buying outdoor equipment is searching for a black sleeping bag, they’ll see search results that showcase sleeping bags first, even if they might be a different color, because it understands that the phrase “sleeping bag” is of key importance for this particular shopper.
With next-generation search, if the product does not match the user’s search intent, it will not be displayed. Both GroupBy’s platform and the next-generation Google engine that powers it are built specifically for eCommerce use cases and can return relevant results from the broadest of queries to the most complex search scenarios, including long tail keywords, part number search, unit conversion and fitment (year, make, model) applications.
GroupBy’s solution is capable of true hyper-personalization. If two customers type in the same search query, the engine will tailor the search results to their individual preferences, while still optimizing for revenue. This generates significant top-line gains, and helps eCommerce wholesalers create the extraordinary digital and omnichannel customer experiences their customers have come to expect.
To build meaningful connections in today’s oversaturated market, retailers must embrace opportunities to go beyond personalization for a segment and instead focus on individual personalization.
But embracing individual personalization is more than a short-term strategy. It is the future of an excellent retail experience. As technology advances, customer expectations will continue to evolve in this direction, and the next generation of consumers will demand even more personalized experiences than we can anticipate today.
Retailers who do not turn to hyper-personalization will fail to meet these expectations and risk falling behind their competitors. By prioritizing individual personalization now, retailers can future-proof their businesses and stay ahead of the curve.
To find out more about how GroupBy’s AI-first Search and Product Discovery platform can help you create individually personalized shopping experiences for your customers, book a demo.
