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E-commerce / KPIs & Performance

How User Intent and AI Can Drive E-Commerce Conversions

Oct 28, 2021

Understanding what your customer is asking for is one of the biggest challenges of e-commerce. It’s something that all online retailers are constantly striving towards.

However, understanding alone isn’t enough if you want to drive a greater e-commerce conversion rate. The ability to match what the customer wants with what you have to offer is the true differentiator between success and failure in today’s modern commerce landscape.

Accomplishing this requires both a conceptual and contextual understanding of the different types of products in your assortment and how they relate to each other. That adds up to a lot of data.

Luckily, artificial intelligence (AI) is making this process a lot simpler and faster. This means it’s now possible for almost any retailer to serve up better search results and drastically increase their conversion rate on any type of recommendation in any channel.


Why AI Is Vital to Improving E-Commerce Recommendations and Conversion Rate

Search and recommendation algorithms have come a long way since the early days of e-commerce. It used to be that customer searches could lead to recommendations for products that weren’t even remotely relevant to the user’s intent. Thanks to the advancement in search engine algorithms, that has largely changed. 

One of the biggest milestones was the introduction of natural language processing technology. This meant that search engines became clever enough to actually understand the words that customers type in. However, it’s not 100% of the way there yet, and intent can still get muddied results. 

One of the biggest milestones was the introduction of natural language processing technology.

This is where modern AI is making a real difference. Thanks to AI, it’s now possible to understand the intent behind a user’s search request. It can determine whether the user is looking to find information, navigate to a specific website, or find a product or service.

It’s this understanding of user intent that enables the best possible match between what the customer is looking for and the recommended results. Getting this right helps to drive customers through the e-commerce conversion funnel and ultimately increase revenue.

But this evolution doesn’t stop with user intent. It’s also about understanding the nature of the products.

While user intent is a customer expressing their want or need, product understanding is how you serve up the right response to that want or need.

AI has opened up new capabilities in conceptual understanding of a product’s features and usage. This technology can pinpoint what a customer’s intent is and map that with the product database to generate more tailored results.

This is possible because, unlike natural language processing, AI works through knowledge graphs. These contain all of the known information about a product group or type. For example, a knowledge graph will know that a t-shirt is an upper body product.


Over time, these knowledge graphs get more detailed and enriched with information. They also understand the affinity between different product types so they can determine which products are a substitute or complement for one another. For example, a bow tie can be a substitute for a tie, while shoes can complement a dress.

The more enriched the knowledge graphs become, the better the AI can respond in identifying which products best map the user intent. This is what is moving the needle on product recommendations that drive better e-commerce conversion rates and move customers through the conversion funnel.

Using AI for User Intent Benefits Shoppers and Retailers

There are many shopper benefits to getting user intent understanding and product understanding through AI right.

One of the biggest is that recommendations are more relevant. This naturally makes for a better user experience for customers, and at Avensia we’re all about putting user experience first. 

Shoppers want to find products that fit their specific wants and needs at that time. By understanding context better, you can get them to those products faster and in fewer clicks. They enjoy a more convenient shopping experience and engage with your e-commerce conversion funnel more quickly.

This is a real benefit when you consider that customers don’t have a lot of patience for slow or drawn-out online experiences. People are busy, and they don’t want to waste their time finding what they’re looking for. The longer it takes them to find what they want through clicks and filters and keyword searches, the more likely it is that they will exit without converting.

Smarter recommendations also make e-commerce more convenient. There are thousands of potential results for every search a customer makes. It would be impossible for them to look through them all.

As such, they’re looking for brands and retailers to narrow down those options to the very best match. Given that many people turn to online shopping because of the perception of convenience, this can make a real difference to how many shopping journeys progress through digital channels.

The longer it takes them to find what they want through clicks and filters and keyword searches, the more likely it is that they will exit without converting.

Personalization is another potential benefit for shoppers. If you truly understand the customer’s intent when they’re shopping online, then the products recommended to them should be different to those recommended to someone else using similar search terms but with a different user intent.

When you’re delivering product recommendations that are personalized for the user, you’re creating a more personal and intimate e-commerce experience. These are the experiences that stick with shoppers and drive loyalty down the road. 

There are also a lot of operational benefits for retailers to using AI for user intent understanding. The headline benefit is how it can improve your e-commerce conversions.

On one level, retailers can increase conversions simply by making better product recommendations to customers. As already noted, by understanding the user intent you can ensure every suggestion is as close as possible to what the customer is looking for.

This is an important distinction because, unless you have the world’s biggest product assortment, it’s impossible that you will always have the exact product the customer wants. 

Or maybe the customer doesn’t actually know exactly what they want, but they have a vague idea. AI-driven recommendations can drive the customer to something that fits within that vague idea and helps focus the user intent on a particular problem. This is where a robust AI program can make a real difference. The ability to understand how different product assortments relate to one another means that you can make the best possible match between what the customer wants and what you actually have for sale. Even just a vague idea of the customer’s intent can lead to product discovery. 

Having a conceptual understanding of each product, how it should be used and its relationship to other product types is how AI can bring customers to the best product search results without you even having to lift a finger. 

This level of understanding could even be used to recommend suitable alternative products, should the specific item a customer wants be out of stock. If you can suggest a high-quality match for the product they wanted in the first place, you have a good chance of making the sale rather than losing it.

AI can also help to make your organization and processes more efficient by automating them where possible. This includes interpreting the different product types and matching them to conceptual data, so you know exactly how they relate to one another.

By improving the e-commerce experience for shoppers, you’re helping to build greater customer loyalty. Customers are more likely to keep coming back to your website or marketplace if they trust that the products you recommend are always going to be relevant to them. This can have a significant impact on your e-commerce conversions, and your bottom line, in the long run.

Commerce Talks

How to Use AI to Match Purchase Intent with the Right Products

The founder and CEO from Apptus Technologies will explain how AI can understand what the online shopper is looking for, and map that purchase intent to relevant products, using a conceptual understanding of the products.

What Are the Challenges in AI and User Intent for E-Commerce Conversions?

Maximizing the benefits of user intent and product understanding comes with a lot of data analysis. It also means expanding product information.

Basic product information that describes attributes such as size and color is not enough if you wish to use AI to make more sophisticated product recommendations. You need additional information that describes the product types and how that is understood by humans. That means using more in-depth categorization, descriptive copy, etc. 

In many retail sectors, where there may be ever-changing assortments consisting of thousands of products, keeping product and conceptual data up to date is a major challenge. For some retailers, it is virtually impossible.

It’s also important to bear in mind that there isn’t one size that fits all when it comes to AI for user intent.

In the same way that an employee in a physical retail store can’t be an expert in every single product on the shelves, one AI program can’t be applied across multiple product types. 

To offer the very best user experience to customers, you need to have an AI that is built for your specific group of products. Otherwise, the conceptual database won’t have the level of detail required. If your product offerings span a number of different product categories, then you may need to utilize more than one AI program in order to get the best results.

It’s also important to bear in mind that there isn’t one size that fits all when it comes to AI for user intent.

However, you don’t have to manage it all alone. Working with expert partners like Avensia can give you access to robust, proven AI-based solutions that have built-in knowledge about the product types within your specific sector. They can also automate the process of matching your assortment to the conceptual data in a quick and efficient manner.

User Intent and Product Understanding Are E-Commerce Conversion Must-Haves

We’re in a modern commerce world now. Customers are expecting a lot more from e-commerce retailers than results that simply have some vague connection to the words they typed into a search bar. Personalized shopping experiences are becoming more of an expectation than just a nice addition. 

Understanding user intent is no longer a “nice to have” for retailers. It is fast becoming a “must have” to remain competitive in the e-commerce market space. 

But it’s not enough on its own. Understanding user intent needs to go hand-in-hand with conceptual product understanding if you want to truly deliver user experiences that make customers buy. AI is a powerful tool for making this happen. Contact us today if you're ready to significantly improve your e-commerce. With our partner Apptus, we help you improve your web using AI-powered personalization.