From clicks to conversions: AI strategies that can transform eCommerce

Anja Bojic | 12. 09. 2023.

In the fast-paced digital landscape, eCommerce is continually evolving. This means businesses must work hard to unlock new avenues for growth and customer engagement.

Amidst this transformative journey, Artificial Intelligence (AI) has emerged as a game-changing force, revolutionizing how online retailers operate and interact with their customers.

From personalized recommendations to real-time pricing optimization, AI strategies have become essential tools in driving conversions and boosting revenue.

In this blog post, we’ll explore some innovative AI strategies that can reshape your customers’ shopping journeys and amplify your conversions at the same time.

Join us as we uncover the power of AI and its potential to transform clicks into meaningful conversions, forging lasting connections with shoppers.

Table of Contents

9 AI strategies that transform eCommerce

3 common challenges for ecommerce businesses

Although there are many other strategies, both AI and non-AI, that can be helpful in the context of eCommerce, in this blog post we’ll focus only on the 9 AI strategies that can have a significant impact on the way you personalize the offer for your customers in eCommerce.

To help you understand better how they fit into your customer’s shopping journey, we’ll divide them into three groups based on their unique pain points:

The first pain point is the so-called “cold start” – one of the biggest and the most expensive problems in AI.

Why?

Because it requires businesses to deal with customers they know absolutely nothing about – the ones that land on their website for the first time. Here we can rely on the following AI strategies:

Starter-pack recommender,
Session-based recommender, and
Smart search recommender.

The second pain point is the one involving conversion. Here, your main concerns can include reducing browsing time and encouraging the consumer to make a purchase, personalizing the consumer’s experience, or increasing their basket size. Useful AI strategies in this phase are:

Alternative-products recommender,
Related-products recommender, and
Cross-sell automatization.

Finally, after the customer has made a purchase, the third pain point becomes nurturing – how to behave towards the consumer who’s made a purchase, what to do with the data they left on my website, and how to encourage them to make another purchase once now that you know more about them

At this point, the AI strategies you can rely on include:

Interactions-based recommender,
Personalized recommender, and
Wheel of Fortune.

Let’s explore all of these AI strategies a bit further.

#1 Starter-pack recommender

Although not strictly an AI strategy, this is the first step to getting to know your customers better.

When a person lands on your website, they are probably lost. But you are, too. You’re unsure about what to offer to them or what types of products they might be interested in. So, your best bet is to offer them what your other customers are usually buying or looking at.

For example, you can offer them “The most viewed products” or “Top 10 selling products” – this way, you’re reducing the risk of presenting them with something completely random. If the new customer is anything like your average customer, it’s very likely that they will like the same products as your other users, too.

Moreover, these initial recommendations will help them better understand what other products they might need and what they are looking for.

What you’re actually doing here is basing your actions on pure statistics – you’re offering the products that have proven to be the most selling, attractive, or viewed previously.

Ultimately, the starter-pack recommender is a good way to start learning more about your customer in the long run.

#2 Session-based recommender

The session-based recommender is the most expensive AI model you can use in eCommerce, yet probably the most effective one. This strategy operates based on your customer’s current online behavior.

Let’s illustrate this with an example.

Let’s imagine that the customer is looking at a black T-shirt online. They find a T-shirt they like, so they click the link leading to the website. But then, they find a pair of black pants they also like, so they click another link leading to that product page, too. You get the picture.

So, what’s the role of the session-based recommender in this case?

Well, the session-based recommender is following the behavior of your customer and their session on each product page, so it can start building a “file” on them, their preferences, and perhaps a list of products they like and are willing to buy.

The main catch here is that it’s all happening at the moment of browsing – it’s like you’re behind your customer’s screen looking at what they are doing and writing everything down.

Following what the customer is doing in real-time, this recommender system quickly picks up that they have looked at three black products, so it goes on to recommend more similar products from the same website.

Depending on the current behavior and mood of the customer, this session-based recommender is trying to understand their interests and search intent better. When it learns that, it can quickly generate and recommend other products that they are very likely to buy.

What’s interesting is that the customer’s interests and intent can change from session to session – making this AI recommender system one of the most attractive and adaptable in eCommerce.

#3 Smart search recommender

Did you know that about 61% of website users expect to find what they are looking for in the first 5 seconds after landing on your website?

Thanks to smart search recommenders, you can make sure to give that to them.

Smart search is an AI strategy you can use to provide more accurate, relevant, and user-friendly search results. Its job is to understand your consumer’s intent, context, and preferences to provide them with a more effective and efficient search experience.

So, what exactly makes this system smart? Let’s find out.

What makes smart search so smart?

Smart search can truly support your eCommerce business and help you increase conversions in the long run because it:

Is multilingual: Smart search is available in 300+ languages.
Recognizes synonyms and homonyms: Smart search systems use NLP to interpret the meaning behind users’ search queries. They can recognize synonyms and homonyms, which ensures that the consumers get relevant results even if they didn’t use the exact search terms.
Supports different letters: Smart search supports both alphabetical and Cyrillic letters.
Recognizes the context: Thanks to semantic search, which focuses on understanding the meaning and context of words rather than just their literal interpretation, smart search systems can provide results that match the user’s intent even if the phrasing differs.
Tolerates minor mistakes: In this fast-paced world, fast typing is a must. Luckily for us, smart search lets us find what we need by tolerating our typos.
Supports voice search: Thanks to the growing popularity of voice assistants, users can now speak their search queries and the system processes their speech to provide accurate results.
Enables filters and facets: With smart search, consumers can use advanced filtering options and facets to narrow down search results based on price, category, brand, and more attributes.
Provides real-time updates: Smart search systems can provide real-time updates, especially in dynamic environments like e-commerce where product availability and prices usually change frequently.

Of course, this is not all there is to it but you get the picture.

–

This is where the “cold start” part of your consumer’s journey ends. You’ve already gathered enough data about them so you can take your relationship to the next level.

Let’s explore your options further.

#4 Alternative products recommender

Once you move into the second phase, your main goal is to convert. To do this, you want to:

Make sure that the customer who lands on your eCommerce webshop makes a purchase,
See their basket size going up, and
Ensure they keep coming back to buy some more.

This is where the alternative products recommender comes in handy.

Alternative recommender systems are paramount for eCommerce businesses. Thanks to them, customers can explore a list of products (in the form of a product catalog) and find the thing they are looking for among an overwhelming number of options.

There are two ways in which this AI system supports your eCommerce:

First, a less complex ML-based model uses and analyzes the customer’s input to offer similar products within the same category. If they’re looking for a black shirt, this model will offer similar products that fall into the same product category.
Second, an NLP-based model takes all the details about the product that the customer inserted (color, size, price, brand, etc.) and compares it to other products in the catalog. This way, the customer can get more relevant and more precise hits, ultimately increasing the chances of conversion.

The reason why this recommender system is so interesting for the eCommerce industry is because it keeps the options open for the customer before they’ve made a decision and increases the chances of them making a purchase.

#5 Related products recommender

Similar to the previous recommender system, the related products recommender aims to increase or complete your customer’s basket.

For example, the consumer is looking at a black T-shirt, so this recommender offers other products they are most likely to buy in addition to the black T-shirt – black pants, black jacket, black socks, etc.

Considering its role, this recommender is usually found on the checkout page, but it’s not a generally applied rule.

Related products recommender can be simple and complex:

The simple one operates based on associations. It takes into consideration the products that have previously been in the same basket with the black T-shirt (i.e. a black jacket or black pants) and offers those other products to the customer, too.
The complex one considers the products in your customer’s basket and generates its own combinations of products that it thinks the customer might be interested in purchasing, too. Here, the recommender creates a list of products that are most likely to be found in the customer’s basket, together with the products that are already in it.

Overall, the related product recommender can significantly affect conversion rates and affect how your shoppers interact with your brand in the long run.

#6 Cross-sell automatization

To be able to boost your conversion rates, you simply have to learn as much as possible about your customers and their needs. You can do this by implementing a smart cross-sell strategy.

According to the definition, cross-selling means promoting or selling a different product or service to a customer who has already purchased something from you.

This technique can help you boost revenue by providing customers and clients with an additional product or service that they might find beneficial. As a result, you can increase your customer lifetime value.

Here are a few examples:

A consumer bought a pair of sneakers from you so, you offer them socks at a 20% discount.
One customer bought a very expensive dress, so you offer them a matching purse or shoes.

Although it’s common practice to offer products that are in some way related to the products the consumer already bought, this doesn’t have to always be true.

For example, if you have a product that hasn’t been selling well, you can offer it to a customer who’s already bought your best-selling product.

How does AI fit into this part?

Well, once your customer has made a purchase, AI automatically analyzes their data thoroughly so it can provide additional recommendations for that particular customer and increase the likelihood of another purchase.

For this step, you don’t wait for the customer to come back to your website but you take the next step and reach out to them via e-mail or a text message.

#7 Interactions-based recommender

Interactions-based recommender gathers and analyzes data about everything a consumer does online (not only on your website) – their clicks, their likes, their previous purchases, etc.

Based on the data it gathers about the consumer, interactions-based recommender can offer similar or related products to a consumer that goes back to your website.

Once they go back to a certain website, this recommender system already has “a file” on the consumer with details about what they bought, searched, or viewed.

Thanks to the consumer’s history, this recommender system can and will offer similar or related products, or even show them what products they viewed the last time they were here to increase the chances of another purchase.

This brings you (eCommerce) one step closer to providing your customers with a completely personalized experience.

#8 Personalized recommender

Personalized recommender systems represent the highest level of personalization.

Unlike the interactions-based recommender that operates based on the previous web activity of your consumers, a personalized recommender operates based on the previous purchases of your consumers.

Based on the data this recommender system takes into consideration, we can have three different personalized recommender systems:

Time-based: This recommender system covers all the patterns in your consumer’s previous purchases. For example, it takes into account when they buy certain products – toilet paper once a month, detergent twice a month, etc. – and based on these patterns, it knows when to recommend these products again.
Collaborative filtering: The social recommender system looks at similarities between its users and consumers so it can provide a more personalized experience to all of them. An example of this recommender is Netflix, whose recommender system finds similarities in users’ preferences. It then goes on to recommend similar or even the same series of movies to the users, especially if they haven’t seen it before.
Content-based: This recommender system usually considers a user’s history and identifies similarities between products based on their descriptions, such as product descriptions or lyrics. The similarity metric can be anything – product description, product review, or technical documentation about the product.

Essentially, personalized recommenders are a must-have for any eCommerce whose idea is to move their business forward and continuously keep providing an exceptional experience to their customers.

#9 Wheel of Fortune

Although not strictly an AI strategy – the Wheel of Fortune recommender represents the icing on your personalization cake.

This recommender is becoming more popular thanks to its ability to gamify the experience for your consumers. It allows you to provide a more diverse experience to your customers past the point of their first purchase.

Moreover, it’s a great way for you to collect valuable leads and boost your cross-sell approach.

How does it work?

The first step can be one of the following:
- The user has to register (create an account) on your website to participate in the Wheel of Fortune.
- The user wins an exclusive right to spin the Wheel of Fortune on your website after spending a certain amount of money, let’s say $100.
The next step is to spin the wheel in an attempt to win something – a discount for another product or a special/limited offer.
Usually, every other slot is empty and every other slot offers a special offer/discount, making the entire experience more random.

However, working with one of our clients, our team at Things Solver managed to develop a personalized Wheel of Fortune recommender with the help of machine learning.

This system was designed to take into account the historical data of each user and make use of data gathered by the personalized recommender to generate a completely personalized Wheel of Fortune experience.

Based on data it has on the users on the website, this Wheel of Fortune was able to offer categories of products and brands that the user has already bought before or shown interest in.

Just think about it – who doesn’t love to go back to an online shop and win a special discount on their favorite brand?

Embracing AI strategies paints a brighter future for eCommerce

In the ever-evolving landscape of modern eCommerce, the adoption of AI strategies has become a necessity.

As digital transformation continues to shape the industry, Artificial Intelligence continuously proves to be a transformative force that allows you to:

Stay ahead of the curve to grow and keep customers engaged.
Reshape the way you connect with customers and drive conversions.

By strategically addressing unique pain points in the consumer experience, the AI approaches we’ve outlined here hold the promise of bridging clicks to meaningful conversions.

These strategies, rooted in AI’s power to glean insights from user interactions and past behaviors, can elevate your personalization efforts to new heights.

Don’t hesitate to reach out to us at ai@thingsolver.com if you need help offering relevant recommendations, understanding your consumers’ intent, and enhancing your users’ engagement.

Together, we can work on nurturing your relationships and encouraging your loyal consumers to repeat purchases with suitable AI strategies.