Ecommerce personalisation—trends, tools and examples

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What if the prince in Cinderella didn’t have to go through all the hassle to find who the shoe fits? What if they found him instead, in just seconds? 

That’s the power of personalisation in ecommerce. Rather than making customers sift through endless options, the ideal product finds them. Just like the prince could have saved time and energy with a more direct approach, brands can create seamless, tailored shopping experiences that bring exactly what customers need straight to them.

What is ecommerce personalisation?

Ecommerce personalisation is all about creating a shopping experience tailored to each customer based on their preferences, how they shop, and what they’ve bought before. It’s the digital version of having a personal shopper who knows your style, understands what you’re looking for, and recommends products that fit perfectly.

Instead of overwhelming customers with tons of options, personalisation highlights the most relevant, tailored products, which leads to a better experience and more sales.

You can see this happening everywhere online. On Amazon, for example, you’ll often get recommendations for products similar to those you’ve viewed or purchased. This is thanks to a recommendation engine that tracks your shopping behaviour and suggests items you might like.

It’s not just about what happens on-site either—personalisation works across other platforms like email and social media too. Companies often send personalised emails featuring products based on past shopping habits or reminders of items left in the cart. This kind of targeted approach keeps shoppers engaged and more likely to make a purchase.

Aside from creating a far more luxurious, individual experience for each shopper, ecommerce personalisation hits three core goals: 

1. It increases customer engagement by helping customers find what they want faster.
2. It increases sales because shoppers are more likely to buy from sites that show personalised options.
3. It builds loyalty because shoppers are more likely to revisit a brand that understands them and offers special deals or suggestions. 

How ecommerce personalisation works

Successful ecommerce personalisation relies on understanding your audience, knowing what they want, and delivering information at the right time. A large part of this hinges on data—and we don’t just mean having data, we mean being able to read that data and use it to fire off tailored messaging. 

So, to put it simply, you need: 

• A way to collect relevant data
• A way to segment and categorise customers based on that data
• A way to use that data to create unique experiences

Here’s what this looks like in action. 

Let’s say a customer frequently browses a store for running shoes. This information is logged every time they visit the website, along with other products they click on, how long they stay on a page, and what they add to their carts. 

AI and machine learning then use this data to create a 360-degree view of the customer. This might include their age, their purchasing frequency, and their interests. For example, we can probably assume this shopper likes running or, on a wider scale, athletics in general (because we don’t know for sure they like running, but we do know they need a pair of running shoes). 

Using this data, the website can then spotlight similar products or offer discounts on athletic gear that only this customer can see. 

This can happen on a granular, customer level, or it can happen on a segmentation level. This is where brands categorise customers based on various factors, like demographics, purchasing history, and browsing behaviour. For example, all customers who purchased beauty products in the past might receive emails featuring new skincare lines or exclusive offers related to their previous haircare interests. 

Personalisation can extend beyond product recommendations to include tailored content, like customised landing pages or promotional offers based on a customer’s location or previous interactions with the brand. So, our athletic shopper might see a landing page filled with exclusive shoe deals when they land on the site, whereas someone who’s shown a previous interest in yoga wear might see a different spread of products, like yoga pants and mats. 

How to collect customer data 

To personalise your online store, you need to know your customers. This means gathering information about them. Here’s how you can do it:

Customer behaviour tracking: Watch what people do on your site. Which pages do they visit? What products do they click on? How long do they stay? You can use tools like Google Analytics to see this.

Purchase history analysis: Look at what people have bought before. This can tell you a lot about what they like. Keep a record of every purchase and try to spot patterns.

Demographic information gathering: Find out basic facts about your customers like their age, gender, or job. You can ask for this info when they sign up or make a purchase.

Browsing patterns monitoring: See how people move through your site. Do they always check the sale section first? Do they use the search bar a lot? This can help you understand how they shop.

How to segment and categorise customers

Once you have all this data, you can group your customers. This increases your chances of sending relevant info to them. Here are some ways to do it:

Demographic segmentation: Group people by things like age, gender, or income. For example, you might have a group for “men aged 25-35” or “women earning over £50,000 a year”.

Behavioural segmentation: Group people by how they act. You might have a group for “frequent buyers” or “bargain hunters who only buy during sales”.

Psychographic segmentation: This is about people’s lifestyles and values. You could have a group for “eco-conscious shoppers” or “luxury seekers”.

Geographic segmentation: Group people by where they live. This can be as broad as “South East customers” or as specific as “people in London”.

How to use data to deliver personalised experiences

To bring this all together, you’ll need some tech help. We cover the tech part of this in more detail below, but here are some examples of technology that use your data points and categories to deliver personalised experiences. 

AI and machine learning algorithms: These are smart computer programs that can spot patterns in your data and make predictions about what customers might like.

Recommendation engines: These suggest products to customers based on what they’ve bought or looked at before. Amazon’s “Customers who bought this also bought…” is a well-known example.

Dynamic content management systems: These change what’s on your website depending on who’s looking at it. So a returning customer might see different things than a first-time visitor.

Predictive analytics tools: These use data to guess what a customer might do next. They can help you figure out things like when a customer might be ready to buy again.

Getting started with ecommerce personalisation 

Ready to go? Here’s a step-by-step guide to getting started with ecommerce personalisation.

1. Define your personalisation goals

Before diving in headfirst, you need to figure out what you want to get out of personalising shopping experiences. This step is pretty crucial because it helps you focus your efforts and measure success. 

First, decide what you want to achieve. Some common goals include:

• Increased sales: For example, “Boost conversion rate by 10% for first-time visitors”
• Improved engagement: Like “Increase average time on site by 30 seconds”
• Higher customer retention: Such as “Reduce cart abandonment rate by 15%”
• Better customer satisfaction: For instance, “Improve customer feedback scores by 20%”

💡Tip: Don’t try to improve everything at once. Pick 2-3 key metrics to focus on. This makes it easier to track progress and see what’s working.

Align personalisation strategies with overall business goals

Your personalisation efforts should support your bigger business objectives. For example, if your business goal is to expand into a new market, your personalisation strategy might focus on tailoring content for that specific audience. But if you’re aiming to increase customer lifetime value, you might personalise based on past purchase history to encourage repeat buys.

Think about how personalisation can help solve your current business challenges. Are you struggling with high cart abandonment? Low repeat purchase rates? Use personalisation to address these issues.

2. Collect customer data  

How will you personalise the shopping experience if you don’t know your customers on a personal level? Understanding who you’re selling to helps you create products your target audience actually want, market to them effectively, and provide a shopping experience that keeps them coming back. 

First, you need to collect information about your customers. You can do this manually through surveys and feedback—ask customers what they think, encourage them to share feedback, and research comments on social media or check out emails to your customer service team. 

Alternatively (and this is the much easier option), use tools like Google Analytics to see how people behave on your site. What pages do they visit most? Where do they drop off? This can tell you a lot about what customers like and don’t like. Pair this with your sales software to understand what products each customer is buying, how much they’re spending on each visit, and how frequently they buy. 

Tip: Klaviyo’s Customer Data Platform consolidates customer data from over 350 platforms, giving you one handy, central place to access all your data. 

Don’t forget to collect returning customer data 

The more times someone returns to your site, the more opportunities you have to learn about them. For example, you’ll know more about Sarah who’s visited four product pages on your site in the past three days than you do about Alex, who’s just visiting for the first time. 

Each time a customer lands on your site, you can add another piece to the puzzle. This rich data then informs your personalisation strategy. If Sarah’s exclusively looking at bicycle parts, you can safely assume that’s what she’s interested in (not, say, running shoes or outdoor jackets) and can serve her content and personalised deals based on those interests. 

Segment your customers based on interests and behaviour

Use the customer data you’ve collected to create customer segments based on shopper interests, behaviour, and demographics. For example, you can group together shoppers who regularly spend £100 or more on your website, those who’ve shown an interest in your haircare line, and those who only purchase when you’re running a sale.

Customers that fall into these three categories (and any others you deem important) have different wants and needs. Someone who only buys during a sale isn’t going to be interested in your latest, premium offering. Just like someone interested in haircare may not be as interested in hats, scarves, and gloves. 

By segmenting shoppers like this you can deliver relevant communications based on their unique needs. It’s not quite on an individual level, but you can assume that the majority of customers in each segment have similar needs. 

The Castore team makes use of Klaviyo’s segmentation features to effectively reach customers with products that match their interests. They do this through targeted campaigns, paid advertising, and Klaviyo’s AI, which recommends items based on customers’ shopping and browsing behaviours. By using these tools together, Castore can create seamless shopping experiences right from the start.

Max Holland, Castore’s Senior CRM Manager, explains that with Klaviyo AI, they can deliver content that customers genuinely want to see. This approach leads to higher conversion rates, increased sales, and better customer loyalty over time.

3. Decide what to personalise 

There are almost too many opportunities for personalisation throughout the customer journey. Practically every touchpoint can be made a little more personal, from a retargeted ad showing previously looked-at products to a price adjustment for a loyal customer. 

The key is to focus on areas that will have the biggest impact on customer satisfaction and your bottom line. 

Map out your customer journey

First, sketch out the main steps in your typical customer’s journey. This might include: 

• Discovering your brand through ads, social media, or search
• Landing on your homepage
• Browsing product categories
• Viewing individual product pages
• Adding items to the cart
• Checking out 
• Post-purchase communications 

For each stage, ask yourself: where do customers often get stuck or drop off? What information do they need to move forward? How can we make their experience easier or more enjoyable? 

Prioritise high-impact touchpoints

Once you’ve mapped out the customer journey, identify areas where personalisation can make the biggest difference. Here are some ideas to get your creative juices flowing. 

• Homepage personalisation. Customise the featured products based on a visitor’s browsing history or location. For example, a returning visitor who previously looked at winter coats sees a banner for “new winter styles” instead of a generic promotion. 
• Product recommendations. Show “you might also like” sections with items similar to what the customer has viewed or purchased. A customer looking at running shoes might see recommendations for running socks and water bottles. 
• Search results. Adjust the order of search results based on the customer’s preferences or past purchases, so a customer who often buys organic products sees organic options first when searching for fruit. 
• Email marketing. Send personalised product recommendations or reminders based on browsing history—e.g. Send an email featuring complementary items to a recent purchase, like a laptop case for someone who just bought a laptop. 
• Abandoned cart follow-ups. Customise reminder emails with personalised offers. For example, you can include reviews for items left in the cart to boost confidence or offer free shipping if that is a potential barrier. 

💡Tip: You don’t have to personalise everything at once. Pick one or two implementations to start with. You can then set up A/B tests to compare personalised vs non-personalised experiences and monitor key metrics like click-through rates, conversion rates, and average order value. 

4. Automate where you can 

Obviously, you can’t manually create personalised experiences for each individual customer—unless you have just one or two. It’s not realistic. Automation lets you scale your personalisation efforts so each shopper feels special without you needing to work round the clock. Automating what you personalise can also make it more accurate. Humans make mistakes, but algorithms can analyse massive amounts of data at lightning speed. 

Even better, the machine learning algorithms that power ecommerce personalisation get smarter over time, so you can continually refine the process until you’re creating exceptional, tailored experiences. 

💡Tip: See the section on ecommerce personalisation platforms and tools below. 

Set up triggers

Sometimes, you won’t know a customer’s next move, but you can take a guess based on what previous customers did at that point in the sales cycle. This is where triggers come in handy. For example, you can trigger an email to send to customers who haven’t made a purchase in six months or who recently purchased a bottle of lotion that’s probably run out. 

These triggers predict what a customer will do next based on their last known action. So, if they’ve left an item in their cart, you can trigger an email after 24 hours that reminds them the item is in the cart. You might even add relevant product recommendations to the email to increase AOV. 

Hyper-personalisation and scaling ecommerce with AI 

Hyper-personalisation takes regular personalisation and cranks it up a notch. Instead of just showing customers products based on broad categories they like, you can tailor every single interaction to that specific person—kind of like the difference between saying, “Hey, you like shoes” and “Hey, we noticed you’ve been looking at red running shoes for your upcoming 5k race next month.” 

To do this, marketers need the help of AI.

First, AI can process unthinkable amounts of data in milliseconds. It can look at everything a customer has ever done on your site—what they’ve bought, what they’ve looked at, how long they’ve spent on each page, what they’ve put in their cart but didn’t buy—and use all that information to make smart decisions about what to show them next. 

And, unlike humans, AI doesn’t sleep. Every click, every purchase, every abandoned cart teaches the AI something new about your customers—but it can do this for literally millions of customers at once. Here’s what this might look like in practice: 

Imagine you run an online sports store and Sarah visits your site. The AI knows Sarah is a runner who usually buys new shoes every six months, and her last purchase was five months ago. It knows this because it’s been collecting data on Sarah every time she visits the site. So, when Sarah lands on your homepage, the AI might: 

• Show her a banner for new trail running shoes
• Adjust the product recommendations to focus on trail running gear
• Highlight a blog post about the best local trail runs
• Offer her a personalised discount on hydration packs

All of this happens instantly, creating a unique experience just for Sarah at the same time that it’s creating a unique experience just for Jess, Tom, Paul, Carla, and thousands of other shoppers.  

Ecommerce personalisation tools 

Level-up your personalisation game – these handy personalisation tools make the process much, much easier. 

Customer Data Platform (CDP)

A CDP collects and unifies customer data from multiple channels, including your website, mobile app, email, and even in-store. 

Klaviyo’s CDP makes it easy for brands to gather data from these different sources and create a complete picture of each customer. Once the platform starts populating each customer profile, brands can analyse interactions to understand what actually drives purchasing decisions and use that info to deliver relevant experiences. 

Klaviyo also helps brands automate personalised messaging across different channels like email and SMS. So, when a customer shows interest in a specific product or category, they can receive timely reminders or exclusive offers related to their interests. 

⚒️ Learn more about Klaviyo’s CDP. 

AI-powered recommendation engine 

This is what Amazon uses to suggest products “you might also like”. A recommendation engine learns about each customer by keeping track of what products they look at and how long they spend on different pages. Based on all that info, it can guess what else a customer might like. For example, if someone buys a camera, it might suggest a camera bag or extra memory cards. 

The great thing is, that you can populate almost any part of your website with the results of a recommendation engine. On the homepage, it might show “picks for you” based on past visits, while on product pages, it might show items “people also bought”. 

⚒️ Learn more about Klaviyo’s AI technology. 

Automated email flows 

An email marketing platform makes it possible to set up email sequences triggered by specific customer actions. You can set up and automate welcome email series, abandoned cart reminders, post-purchase follow-ups, replenishment reminders, and win-back campaigns to meet customers wherever they are in the buying journey.

⚒️ See how UK brands have used Klaviyo to automate ecommerce personalisation.  

Chatbots for personalised support 

When shoppers are in the zone, they don’t want to break away to find information that should be readily available. Installing an automated chatbot can answer customer queries in the moment and create unique journeys for each shopper. You can send customers to relevant blog posts, product pages, and sign-up forms based on their needs and interests, as well as deliver tailored product recommendations based on their interactions. 

Examples of ecommerce personalisation in action 

Let’s take a look at how brands can implement ecommerce personalisation. 

1. Product recommendations

The most common type of ecommerce personalisation is tailored product recommendations—we’ve all seen the “you might also like” widgets on Amazon and other sites. Smart product recommendation engines use customer data to suggest items that align with a shopper’s interests and past behaviour. For example: 

• If a customer frequently views artisanal soap sets, you can highlight similar collections.
• When someone adds a soap set to their cart, you can suggest matching body wash or bath oils. 
• You can show items that are frequently purchased alongside the product being viewed. 

Beauty Pie shares items that are frequently bought together on individual product pages. The chances are that if multiple people have bought the same combo of products, the shopper currently on-site may wish to do the same. Beauty Pie also makes use of its recommendation engine at the checkout. When shoppers visit their cart, they can see a selection of products that they might also be interested in. 

2. Customised homepage and category pages

Show different pages to different people. You can adapt the layout of your homepage to reflect customer preferences. For example, you can highlight product categories the user has shown an interest in, showcase new arrivals in the customer’s preferred styles, and adjust the order of navigation menu items based on previous browsing patterns. 

3. Personalised content suggestions 

Not all shoppers go straight to checkout. Some want to learn more about your brand and products or just want a little TLC before they hand over their cash. In these instances, you can deliver tailored content recommendations, including blog posts related to recent purchases, video tutorials on product use or care, and user-generated content featuring items similar to those the customer has viewed. 

4. Tailored promotions and offers

Use existing customer data to create personalised promotional campaigns. You can offer discounts on items a shopper frequently browses (but hasn’t yet purchased), send email campaigns with deals on favourite product categories, and provide loyalty rewards tailored to individual shopping habits.

Loveholidays sends a special curated selection of deals based on holidays I’ve viewed and booked in the past. 

5. Interactive product finders and quizzes

Everyone loves to learn something about themselves—it’s the reason BuzzFeed quizzes to find out which vegetable you closely resemble are so popular. You can package up this interactive fun and use it to personalise shopping experiences.

For example, you might: 

• Create a quiz that suggest products based on style preferences, budget, and intended use.
• Offer virtual try-on tools for clothing or accessories.
• Use product configurators for customisable items. 

IPSY’s quiz helps shoppers find the right products for their skin tone. The brand then uses this information to send personalised product recommendations and content related to the results. 

6. Personalised search results 

69% of shoppers head straight to the search bar when they land on an ecommerce site—and 80% leave because they have a bad experience. Give shoppers a good experience by optimising your on-site search so it prioritises results based on user behaviour. 

For example, you can adjust product rankings in the search results based on past purchases and views, offer autocomplete suggestions tailored to individual customer interests, and provide personalised category filters based on common search patterns. 

Oliver Bonas’ search results feature “suggested items” based on a shopper’s previous searches. 

7. Personalised email marketing campaigns 

Use customer data and powerful segmentation to create highly-relevant email campaigns. These might include:

• Abandoned cart reminders with personalised product recommendations.
• Birthday or anniversary offers featuring favourite product categories.
• Re-engagement campaigns highlighting new items in preferred styles. 

Dermalogica uses Klaviyo to continuously deliver 82 live email and SMS flows that automatically help build smarter, deeper digital relationships. These flows include a personalised abandoned cart flow, an abandoned browse flow, and SKU-specific post-purchase sequences.

The brand has also breathed new life into its AI face-mapping tool by using smarter customer grouping. Now, when people scan their face, they get personalised skincare recommendations that really fit their needs.

Using Klaviyo, Dermalogica creates custom learning journeys for each user based on their specific skin issues. This gives customers more detailed and personalised advice. All this data helps Dermalogica tailor their marketing, from new product launches to email campaigns, making sure customers hear about products that match their skin type.

Get personal with ecommerce personalisation 

Don’t be like the prince in Cinderella. Instead, create unique customer journeys for each shopper based on their purchasing behaviour, interests, and characteristics. Ecommerce personalisation isn’t just a trend—it’s a fundamental shift in how brands engage with customers. By using data and advanced technology, you can create experiences that resonate with shoppers on a truly granular level. This increases customer satisfaction levels and drives sales and loyalty. 

Over the next few years, the integration of AI and machine learning will continue to refine personalisation strategies, making it easier for brands to connect meaningfully with their audiences.