When it comes to digital shopping experiences, search personalization is more important today than ever before. If in 2019 about 60% of shoppers expected personalized content, that number increased to 80% a year later.

The general consensus is that in 2021 content personalization will become a necessity in digital commerce. A website that simply spits out a list of all the t-shirts in their catalogue in response to the query “t-shirt,” forcing the user to narrow down hundreds of results, will not last long in the marketplace.

But while most ecommerce companies understand the importance of personalization over segmentation, many believe it a far-fetched goal, achievable only by the likes of Google and Amazon. Their focus is on big data: data from previous online interactions such as Google searches and browsing data, Facebook likes, social media interactions, and even phone conversations.

Of course, unless they’re Facebook, most companies simply don’t have access to that level of customer data.

But what most don’t know is that you don’t need access to the huge amounts of intent data to create relevant and personalized shopping experiences.

Solving Customer Problems With Intent Data

It’s important to distinguish between two types of content personalization: history based and in session.

History-based personalization is when the platform knows the user—either because the user is logged in or because they peruse the site often. In this case, the platform collects information about user intent over time, getting to know them and enriching their profile with every new interaction, and possibly putting this user into a certain engagement category that the algorithm deems appropriate.

While this can be a fantastic way to create a relevant digital experience, the reality for most ecommerce businesses is that this isn’t feasible. Most users, even if they create a profile, don’t return often enough to build a rich profile.

But that doesn’t mean smaller players can’t achieve personalization. AI-powered ecommerce platforms use another kind of personalization—in-session personalization—to offer relevant experiences from the moment a consumer enters your site, without access to big data.

How? By using a smart algorithm that takes into account every action the user performs on the site to learn about them as a person. The idea is that your shoppers reveal preferences and interests when moving from one product to another, all within a single session, revealing purchase intent. The goal is to figure out the user’s intent quickly and guide them to their desired results as smoothly as possible.

Think about it this way: If history-based personalization is comparable to shopping in a neighborhood store where they’ve known you and your family for years, in-session personalization is like going to a welcoming new store on the block and interacting with a very intuitive and attentive seller who makes every effort to learn about your needs and preferences at that point in time.

Both guarantee a rewarding customer experience that makes us feel valued and respected, potentially increasing customer engagement with a brand.

How Do You Determine User Intent?

So how can session-based personalization determine a customers’ intent if you know nothing about them?

In a nutshell, session-based personalization means gaining insight from every action your user takes on your site to learn more about what they’re looking for. As users make choices, their clicks and queries are used to shape their experience, bringing them to the right outcome faster.

There are several ways to gain insight into the user’s intent without any background information.

Time And Location

Right from the start, the system can start personalizing the user’s session with intent data based on the time of their visit and their location.

For example, if it’s December and the user logs into a sporting goods store from Australia, they might be shown summer sports gear. But if another user visits from Vermont at the same time of year, obviously it’s less likely they’re looking for a surfing board. Instead, ski gear is more relevant.

Time of day is also taken into account to guess the user’s intent: for example, if you log into a health store during the evening, you might be shown relaxing, sleep-inducing teas, herbs, and pillows, while in the morning you’d see energizing drinks and yoga gear.

That doesn’t mean, of course, that the user’s shopping path is set in stone from that moment on. After all, it’s quite possible for people to look for relaxing teas in the morning after a sleepless night!

With every new user behavior and signal, the algorithm enriches the user’s profile with more information, increasing the relevancy of their search by serving up personalized content.

Query Auto-completion And Auto-correct

An intent-driven personalization platform should include a good auto-complete system. User’s queries are often cryptic or full of typos, so it is essential that the system knows how to read between the lines to decode intent.

For example, consider a user typing “sw” into the search box. The system immediately considers two scenarios: a) the user is actually looking for something that starts with “sw” (for example, “sweater”), or b) “sw” is a typo and the user is actually looking for something that starts with a similar combination of letters (for example, “sh” for “shoes”).

If it’s a shoe shop that doesn’t offer sweaters, the system should be able to recognize that this is most likely a typo and autocorrect it, leading the user to the right results without them having to correct themselves.

Predictive Category Suggestions

The AI-driven personalization system can use predictive category suggestions whenever appropriate.

If a user browses a wide range of running gear and then types “shoes” in a query box, the system already knows that they are likely looking for running shoes.

It might then prompt them with a drop-down suggestion for “running shoes” to confirm that that’s what they’re currently looking for.

Discovery Tags

To help the user narrow down their search and get to their desired outcome faster, the system can use “discovery tags.”

Discovery tags are a mobile friendly approach to faceted search and navigation. The very features that make faceted search so helpful to users (that is, being able to see filters and results at the same time) are difficult to achieve on a small screen.

 

 

For example, a user might type in a generic query such as “shoes” in a search box.
If it’s their first query and the system doesn’t know anything about the user’s interests yet, it will try to offer a personalized experience by narrowing results down. Just like a good salesperson wouldn’t just bring out all the shoes in a store but instead ask questions first, the system will also try to narrow down the user’s intent, presenting them with quick filter tags such as “running,” “casual,” “formal,” etc.

On the other hand, let’s take a user who has browsed a range of golfing products in the store and then types “Nike.” In this case—again, like a good salesperson—the system will try to narrow their search down based on what it already knows about this particular user: that they like golf.

In this case, they will be presented with quick filter tags leading them to golf-related Nike products, such as “shoes” and “gloves” to help them quickly narrow down the range of products.

Dynamic Facets and Filters

To provide frictionless and relevant personalization, taking clues from user behavior is not enough. The way the search interacts with the product data is equally important.

To put it differently, to provide an optimal customer experience by connecting people with the relevant products faster, search needs to locate those products in the catalog quickly.

nhanced product data in combination with advanced NLP capabilities is what allows the system to find products in the same way people think about them.

For example, if the user’s search query is “square table,” they should not see or have to apply a filter for “shape” as the system should understand their intent and return a list of tables in the desired shape. Similarly, the system understands that users shopping for “pants” and “trousers” in different regions are looking for the same thing.

This is achieved through a fully attributed product catalog that provides relevant facets and filters to help bring users to their goal faster.

User Clustering

Using refined similarity models, the system tries to predict user affinities based on their topics of interest and affiliations that become apparent as they browse. For example, from just a few searches the system might discover that a user is a runner who cares about sustainability and is also a hockey mom. This is possible even for one-time users who are not logged in: in this case, instead of adding the data to a persistent user profile, the system adds it to a temporary user ID and uses it within the session or until the ID expires.

All these attributes will inform what the system shows this user in her next search.

Intent Aware Product Ranking

If two people from the same demographic mold (for example, two 30-year-old urban professional women) type in the same search query (for example, “shoes”), the system will provide very different search results for each.

These results are based on the fine-grained information gathered either in their previous shopping history (for history-based personalization) or based on their behavior and interactions in-session.

If one of the women just browsed professional business attire and the other searched for t-shirts and jeans, the search query “shoes” will lead them to very different results, as the system will take into account everything it has learned about their varying intents in their individual sessions when presenting search results.

 

 

The Importance Of In-session Personalization

Just a few years ago, virtually all marketing was persona-based. Today, that approach will lead to lower conversions, because customers feel mistreated and misunderstood.

As shopping becomes an increasingly digital experience, the more shoppers want online interactions to feel personal.

Intent-driven, session-based personalization not only allows you to create relevant and personalized shopping experiences with minimal data, but also by definition bypasses persona-based marketing by treating every visitor as an individual.

Dig Deeper

If you’re interested in learning more about enhancing the relevance of your shoppers’ search queries, check out the technical details in our piece, Clothes in Space: Real-time personalization in less than 100 lines of code.

Ready for the next step, but wondering how to evaluate different AI driven personalization platforms? Here are 10 Must-Have Features for a Relevance Platform in 2021.

Not sure? Here’s plenty of other information on personalized ecommerce experiences, just for you.

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About Tanya Mozias Slavin

Tanya Mozias Slavin is a content marketing writer with a Ph.D. in Linguistics. She enjoys conveying the value of cognitive technologies to non-technical audiences in an engaging and accessible way. When she is not writing for AI brands she writes personal essays and picture books.

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