Website personalization means delivering a more tailored and relevant experience to your store’s visitors, moving beyond the generic, one-size-fits-all approach.
Customers associate website personalization with a positive experience and a sense of being valued as an individual and not just a customer.
Not only does it improve customer sentiment towards your brand, but it can also bring in above-average revenue growth of 40%, according to a report by McKinsey & Company.
The reality is, in the extremely competitive ecommerce industry, personalization is quickly moving from a “nice to have” to a “must have” for ecommerce brands.
According to McKinsey & Company, 71% of consumers expect a personalized experience when they land on an e-commerce website. If your store fails to meet this expectation, you risk frustrating 76% of them.
These statistics highlight the significant pressure on e-commerce brands to deliver a tailored experience.
The idea behind personalization is simple: use data to understand and anticipate your shopper’s wants and preferences–and create custom site experiences informed by that data.
This data may include: browsing and purchasing history, demographic information, browser-level data and previous brand interactions.
By leveraging this data for segmentation, brands may provide site content that is tailored to shopper segments, making the shopping experience more efficient and enjoyable for customers.
In fact, merchants who implement personalization have reportedly seen returns as high as $20 for every $1 invested.
In this guide, we’ll discuss the different types of personalization that are available to merchants, as well as strategies and implementation tactics for brands to get started using personalization quickly.
In this section, we will thoroughly explore the various personalization methods merchants use to create tailored experiences for their customers and how to effectively implement them.
We will cover the following types of personalization:
With the actionable examples provided, you can effectively implement behavior-informed personalization in your e-commerce business, enhancing the customer experience and driving sales.
Merchants rarely rely on a single personalization method. Instead, they use a combination of various tactics to engage customers and motivate them to complete a purchase.
This multifaceted approach ensures a more personalized and compelling shopping experience, increasing the likelihood of conversion and customer satisfaction.
Whether you're running influencer campaigns, targeting specific visitor segments, or optimizing for different locations, browser-based personalization will help you create a more personalized and engaging online experience.
The broad categories of browser personalization are the following:
Traffic Sources: Personalizing based on where visitors come from, such as search engines, social media, email campaigns, or referral sites. This allows tailoring content to match the interests and behaviors of users from different traffic sources.
Visitor Data: Personalizing based on user-specific data like demographics, interests, and past interactions with the website.
Page Tags: Using tags on web pages to track specific actions or behaviors of users, such as clicks, scrolls, or form submissions. This data helps in understanding user engagement and optimizing content placement.
Visitor Time: Personalizing based on the time and date of visitor sessions. This includes optimizing promotions or content based on peak visit times or scheduling time-sensitive offers.
Device Data: Personalizing based on the type of device (e.g., desktop, mobile, tablet), operating system, or browser used by visitors. This ensures that the website is optimized for different devices and provides a seamless user experience across platforms.
In the following sections, we will explore each of these personalization techniques in detail, providing practical examples and steps to implement them effectively.
What it is: Traffic source personalization is using the referring website or parameters within the referring URL to deliver a custom version of the webpage.
Why it’s useful: Many marketing and advertising campaigns use custom UTM parameters within the URL to track performance–but those same parameters can also be used to deliver personalized web experiences. Specific messaging and imagery that matches the referring source can help increase the conversion rate of that traffic.
How Merchants Can Leverage It: Merchants can leverage the power of traffic source personalization in any advertising campaign they are running. Examples include:
Actionable Example: Merchants can customize their most popular product page for referral traffic coming from a high value influencer partnership. The UTM URL from the influencer triggers a variant of the product page that has custom imagery with the influencer using the product, along with custom testimonials and messaging.
Steps to Execute: Follow these steps to leverage Shogun for this personalization:
What it is: Visitor data personalization includes anything that relates to a specific attribute about the site visitor. Examples include: location, time on page, site visit duration, page scroll depth and more.
Why it’s useful: Visitor data like location and interactions with your brand’s web pages can reveal a lot about them as a shopper.
Visitors from certain locations in the world may speak other languages or have other cultural differences that require customized content.
Additionally, multiple site visits to certain product pages, or a long dwell time on a certain category of products can indicate interest and should influence how you communicate to that shopper.
How Merchants Can Leverage It: Merchants can leverage visitor data in a number of ways, including:
Actionable Example: Translating key elements of your brand’s homepage for visitors from the French-speaking area of Quebec within Canada.
Steps to Execute: Follow these steps to leverage Shogun for this personalization:
What it is: This personalization is based on the day, time and date that the visitor is arriving on your brand’s site.
Why it’s useful: Merchants that tend to run a lot of flash sales throughout the week will find personalization based on the visitor time browser data to be the most useful. It allows for rapid customization of key pages in a scheduled release.
How Merchants Can Leverage It: Here are a couple of examples of personalization that is based on the browser data for site visitor day and time:
Actionable Example: A custom home page experience for site visitors with custom banners and a hero section that promotes the overstocked product of the week. This allows the merchants to cycle through excess inventory on a recurring basis every Monday morning.
Steps to Execute: Follow these steps to leverage Shogun for this personalization:
What it is: Weather targeting allows you to create personalized web experiences depending on the weather forecast in the location of your site visitor.
Why it’s useful: Certain products become more important depending on the weather that your site visitor is experiencing when they are visiting your site. A shopper who is experiencing a week-long deluge of rain is a lot more likely to purchase a raincoat than sunglasses. Weather targeting helps you get predictive information about what types of products the site visitor might be looking for. This is especially useful if you sell items that are seasonal or weather like summer and winter attire.
How Merchants Can Leverage It: Merchants can leverage weather data targeting for:
Actionable Example: A customized “winter” collection that shows thick winter jackets for site visitors with a high temperature of 32 degrees or below, with light jackets shown to site visitors with a high temperature above 55 degrees. Both are winter related–-but emphasize slightly different products based on local weather conditions.
What it is: Device-level data personalization is based on browser data like device type, operating system, browser type and more. These attributes are generally found on the device level itself.
Why it’s useful: Shoppers that arrive on a merchant website with a certain device might influence what products they would be in the market for. It also provides an opportunity to create custom content on site for shoppers that are on smaller screen sizes.
How Merchants Can Leverage It: Systems level targeting has implications for mobile optimization as well as an opportunity to promote personalized products that are related to the site visitor’s device type.
Actionable Example: Merchants selling electronics accessories can create a “featured products” section on their storefront related to HP computers for site visitors coming from an HP laptop.
Alternate Example: Brands selling iPhone accessories can personalize the product featured on the hero section of their homepage based on the model of iPhone the site visitor is using–showing off relevant products to their specific model.
Behavior-informed personalization extends beyond basic browser-level data to reveal not just who your customers are but also why they take specific actions and how they engage with your website.
It leverages a variety of data points to build a comprehensive picture of each customer, including:
Each interaction offers valuable insights about the customer's interests, intent, and at which stage they are in the buying journey. When analyzed together, this data uncovers patterns and trends that can be used to deliver highly personalized content, product recommendations, and marketing messages.
In order to implement behavior-based personalization–merchants need to leverage the power of a customer data platform (CDP).
A customer data platform acts as a repository for collecting and storing the unique attributes and actions of your target audience. These insights can be shared with CRMs, analytics systems, and ad platforms for further analysis and application.
Customer profiles typically consist of information such as demographics (e.g., age, location, gender), interests, purchasing behavior, motivations, and pain points.
Constructing a customer profile involves customer profiling, a process that involves analyzing your broader audience to identify contacts with comparable data points. The goal is to compile a unified profile of your ideal customers based on common characteristics.
A powerful tool that exceeds basic functionalities is Klaviyo, which offers a wide set of features in their customer data platform.
Not only will you manage your customer data, but you will also leverage it to its fullest by uncovering insights about products, customer interactions and overall campaign performance.
Once you have the power of CDP that you can leverage to implement personalization, you can begin to focus on the tactics and strategies to create personalized experiences based on those tracked behaviors and customer data.
The number of actionable personalization ideas that brands can implement based on shopper behavior is nearly unlimited.
In the following section, we will explore some of the broad categories of website personalization that leverage customer behavior along with guidance on implementation.
What it is: Previous purchase history is the data you collect on a customer based on their purchase history with your brand. This can include information about the particular product (SKU, price, etc) as well as the frequency and date of purchase. All of the data generated during a purchase can be used to personalize the experience of those customers when they return.
Why it’s useful: Previous customers are much more likely to buy another product that is related or complementary to one that they recently purchased from your brand, or to simply purchase the same product again. Many site experiences can be personalized using purchase history data including:
How Merchants Can Leverage It:
Actionable Example: A brand that specializes in footwear can implement a customized “featured products” section on the homepage that promotes sneaker care kits for recent purchasers.
When shoppers who recently purchased footwear return to the brand’s site–they will see this customized section that is directly related and useful as a next purchase to care for their new shoes.
Implementation: See how brands can execute this idea using Shogun and the Klaviyo CDP.
Case Study: Dash camera brand leader Nextbase wanted to provide a personalized experience to past customers. They noticed that generic offers of a product they have already purchased were being promoted to them.
By exploring Shogun’s personalization tool, they leveraged Klaviyo CDP to identify a segment of existing customers who had previously purchased Nextbase's main product, the Dash Cam.
By replacing promotions for the Dash Cam with banners showcasing complementary products like the Rear Window Cam and Rear View Camera, Nextbase aimed to optimize the shopping experience for these customers.
The results were striking, with the personalized page boasting a remarkable 6.34% conversion rate, marking a significant 122% increase from the original page. Additionally, the clickthrough rate surged by 68%, underscoring the effectiveness of personalized content in driving engagement and sales.
What it is: Browsing history data tracks the pages that a shopper visits and associates those URLs with their profile. Personalizations based on browsing history leverage this information to create experiences that are aligned with the products and content that the shopper spent time viewing.
Why it’s useful: Browsing history indicates that the shopper has an interest in the items (and similar items) that they spent time viewing in a previous session. This data can be useful for merchants that want to create customized experiences that align with the browsing history of the shopper to give them more of what they are looking for.
How Merchants Can Leverage It:
Actionable Example: A food brand offers a personalized home page with a customized hero section that promotes a product related to the recipe content the site visitor recently viewed.
What it is: Information that is directly collected from the site visitor can be used to create a personalized experience for the shopper. This can include anything from background information like age and location to product preferences. Any time information is collected directly from the user, this can be used to create personalized experiences.
Why it’s useful: The best source of information about your shoppers comes directly from them. Any time there is a form, quiz or other means of submitting information–that data can be used to refer shoppers to products that match their preferences and interests. This creates a positive shopping experience that is much more likely to convert than a generic product experience.
How Merchants Can Leverage It:
Actionable Example: Beardbrand was able to collect emails from warm leads through an interactive scent quiz, allowing them to follow up with personalized email communications later on.
What it is: Custom events can be collected for most interactions on your storefront. These events can be collected with your customer data profile (CDP) solution and used for creating personalized experiences.
Why it’s useful: For many brands, there are custom interactions that might be useful for creating a personalized experience for their shoppers. Using custom events and triggers can be useful for merchants with a large volume of traffic that want to get granular segmentation capabilities.
How Merchants Can Leverage It:
Actionable Example: The view_item event is the most common event being tracked in e-commerce. This is a great way to track which products are most popular and get added to carts. You can use this event to improve product recommendations.
When we talk about using data for personalization–there are two types of data collection methods that we are referring to: third-party data and first-party data.
Third-party data is collected by sources outside of your brand, while first-party data is collected by you directly.
When it comes to third-party data, It’s no secret that it has been under attack in recent years.
In 2023, Google announced that it would stop tracking third-party data in Chrome. Other internet browsers, like Firefox and Safari, have already been doing this for years.
The Google announcement didn’t sit well with marketers because they weren’t prepared in advance for a future without third-party cookie tracking. Google made this decision in response to user concerns about data protection, which had ultimately eroded trust in the search engine.
A Pew Research study reported that over half of Americans feel as if they don’t have any control over who can access their online searches. They felt the most concerned over information about their physical location.
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The third-party data ship has sunk, but this doesn’t mean marketers have to go down with it.
In fact, 92% of marketers consider first-party data essential for growth, with 58% strongly agreeing it’s a strategic asset. Top marketers are planning to boost their investment in machine learning for predictive analytics by 1.5 times.
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Having all this in mind, here are some strategies for how leading e-commerce brands are collecting their first-party data:
No tracking tool can rival the effectiveness of direct communication with your consumers. This is why online surveys and receiving feedback can really leverage your website personalization strategies.
You can see the power that consumers hold, especially after negative reviews. It's been found that a whopping 87% of consumers haven’t purchased from a brand after reading negative reviews or bad news on the internet.
That’s why leading e-commerce brands collect user feedback through online surveys. Getting consumers to fill them out 100% is the biggest issue. To overcome this, try to offer incentives that would encourage your participants, like coupons, free merch, loyalty points, charity donations and more.
In a recent study by Small Business Executives’ Online Survey Response Intentions, it was found that certain incentives perform better than others based on the length of the survey.
Incentives such as $20 cash, $20 gift cards, and $20 donations to charity almost equally highly performed and significantly outperformed no incentives or sweepstakes.
Strategically incorporating subscription and sign-up forms will boost your acquisition of first-party data. But you shouldn’t expect consumers to do this for nothing. In order to encourage them to subscribe to your email list or create an account, you need to make it worth their while.
The formula is simple: the more enticing the reward, the more sign-ups you'll attract.
Some commonly used strategies to increase newsletter sign-ups include providing daily deals, free-shipping, coupons and more. Utilize compelling words like "save," "free," "best," "special," and "exclusive" to effectively convey your message of cost savings to potential subscribers.
Doing what others are doing isn’t going to get you very far. A study by eMarkerter found that subscriptions aren’t growing at such a fast rate due to inflation and subscription fatigue. This data proves the point that consumers are tired of reading the same offers and are hungry for a more creative approach.
Loyalty programs are powerful customer retention tools that keep your customers engaged, ensuring they choose to buy from you first whenever they need a product you offer. Let’s also not forget that 65% of all purchases come from repeat customers. Increasing your brand's retention rate by only 5% leads to a 25-95% increase in profits, according to Finances Online.
Yet, creating a meaningful connection with consumers proves challenging when the sole interaction revolves around transactions. Therefore, e-commerce loyalty programs should transcend mere 'points for purchases' and also reward other engagement.
For example, incentivizing user reviews with bonus points fosters positive word-of-mouth, and introducing an online treasure hunt promotes product exploration.
By also enabling first-time customers to collect points through non-transactional activities in an online loyalty program, they are more motivated to remain engaged and use their collected points to make their first purchase.
Live-selling (also known as live-commerce) enables real-time product purchasing and interaction via livestream, most commonly through a social media platform. In China, this trend has completely changed the retail game, becoming a huge sales channel in less than five years.
While Western retailers are still catching up, those who are early adopters are starting to see impressive sales results. A recent study by McKinsey found that clothing was the most popular category in Europe, US and Latin America during live-selling videos.
The key question is: What's keeping down the growth of live-selling video streams outside of China? According to the same study, 32% of U.S. audiences were skeptical about the product value, making it difficult for them to decide on a purchase. Additionally, many found the live-selling content to be too lengthy and overly sales-focused.
What can e-commerce brands in the United States do? They should focus on transforming their websites or apps into prime destinations for live shopping.
This approach can capture their audience's attention, enhance the effectiveness of live streams, and boost customer engagement. Hosting live shows on their own platforms allows e-commerce brands to gather richer data and gain deeper behavioral insights.
This understanding helps them better meet consumer needs, optimize the live shopping experience and overall customer journey, and foster product and marketing innovation.
In a recent study by Google, it was found that poor site search costs brands over $300 billion each year. And around 43% of customers are using a website's search bar, making it a strong deciding factor for either gaining lifelong customers or losing them to competitors.
Enhanced product recommendations, top picks, targeted banners, search bar suggestions, and personalized AI search results are key personalization practices that help customers find and purchase exactly what they're looking for.
What’s interesting is that personalization can get even more personalized - a term commonly referred to as micro-segmentation. Micro-segmentation refines predefined customer groups by identifying new methods to enhance customer retention based on purchasing behavior combined with various classifications, such as demographics. We’ll explore use cases of brands that are using site search and micro-segmentation successfully and how you can leverage them for your brand.
The search bar is one of the most frequent features that visitors use when they land on your website. So you can only imagine how much personalization can help improve accuracy and relevancy, showing customers exactly what they need.
Sometimes customers don’t know what exactly they’re searching for or misspell search terms. By offering several options in the search bar, you can help nudge them a little toward products that they would like to explore.
The more information you have about a customer, the better you can personalize the search bar. However, every customer begins as an unknown visitor. Even without details like age, gender, or purchase history, you can still personalize the search bar based on their IP address, location and search queries.
Once a customer creates an account, you'll have more data to offer a personalized experience tailored specifically to them. Here are some common practices for customizing search queries for your customers:
With data based on a customer's past search queries and interactions, you can provide product recommendations that are precise to what they’re interested in.
For example, if a user is searching for a new TV, you can use their past search queries to show them TVs in the price range and size they prefer.
Adding a section highlighting popular products based on a user’s search preferences is a powerful personalization method that helps create credibility and trust.
If the user searches for “TVs” in an online electronics store, the store will then display a “Top Picks for You” section of best-selling TVs based on their previous search history.
Based on a user's search queries, you can show them targeted banners that precisely match their interests, thereby increasing the likelihood of conversion.
Suppose a user searches for "inflatable pools" on an e-commerce site. The search personalization system identifies this interest and displays targeted banners for related products, encouraging the user to make a purchase.
One of the key aspects of personalization is suggesting products to users based on their search queries. While they are typing their search query, an e-commerce website can intelligently streamline their search experience with more products to choose from that would interest them.
Search personalization with recommended searches involves providing autosuggest and autocomplete features to help customers quickly find their desired products.
As users begin typing, the system instantly offers suggestions based on popular or related search terms, refining their search and presenting alternative options they might not have initially considered.
Personalized AI search results can take your shopping experience to the next level by offering customized search outcomes for each user.
By analyzing past searches, website interactions, and purchase patterns, the system delivers the most relevant products tailored to each individual.
Online cosmetics retailer Sephora uses AI algorithms to highlight brands their customers love and curate similar products that match their preferences. This personalization is unique to each user and significantly enhances their shopping experience, making it more seamless and enjoyable.
For instance, if you search for “Fenty Beauty” and browse their products before leaving the website, Sephora's AI algorithm will recognize this. When you return, it will display products from Fenty Beauty along with other brands that align with your preferences.
If you thought that segmentation couldn’t get any more detailed, think again. As previously discussed, traditional segmentation involves dividing consumers into general categories such as age, gender, income, and more.
Micro-segmentation further refines these broad categories, dividing them into smaller, more targeted groups within each segment. This way, e-commerce brands can have highly-targeted campaigns that cater to the specific needs of each segment.
Here are the leading brands that use micro-segmentation as their bread and butter to drive conversions:
Did you know that more than 80% of people watched Netflix’s shows through their recommendation system? Netflix uses advanced AI algorithms and machine learning to make predictions on what shows to recommend to its hundreds of millions of users through micro-segmentation.
Your e-commerce brand might not have Netflix-level resources to invest in machine learning and extensive research teams, but recognizing the value of deep personalization is crucial for reducing churn.
Amazon is the leading e-commerce giant we can all take notes from. They use micro-segmentation in so many aspects.
One that stands out instantly are their product recommendations, which are tailored based on collecting data from browsing history, purchases and even wish lists.
Home goods retailer Wayfair uses micro-segmentation methods in order to enhance their customer experience. Their entire strategy is developed around one single goal, which is to help their customers make their dream home a reality.
By analyzing their customers' browsing and purchase histories, behavior and other preferences, they are able to provide tailored-product recommendations, marketing messages and an overall personalized shopping experience.
Below, you can see Wayfair’s data pipelines that drive micro-segmentation.
Back in 2008, Subway created a promotion that would be forever remembered in marketing history. Their 5-dollar footlong brought in a whopping $3.8 billion in revenue during the first year. This launched Subway to become one of the biggest food-chains in the world.
Despite the promotion's success, the campaign was ultimately discontinued. Why? Well, the margin loss for Subway franchisees cost them big time.
Offering the deal to all customers, even those who previously paid more, undermined the goal of promotions to attract shoppers who were on the fence. New versions of the promotion failed quickly, just proving that high sales volume can be more damaging than good when done at the wrong price point.
Let this case study serve as a reminder that promotions need to be optimized, or else you’ll be increasing conversions at a profit loss.
The good thing is that there’s actually a science behind promotions called promotion optimization. Promotion optimization is the process of optimizing your promotions in order to maximize their effectiveness in achieving higher conversions.
Here are some ways e-commerce brands are applying promotion optimization to their personalization strategies.
Around 70% of all online shoppers abandon their shopping carts. There are various reasons for shopping cart abandonment, but if customers have spent a considerable amount of time on your website, offering a discount or special offer could encourage them to complete the purchase.
88% of consumers are willing to spend more money if they have an enjoyable experience. Apart from the interactive ”Spin the Wheel” that we’ve encountered on many websites, there are even more personalized methods to interact with users.
A personalized quiz allows you to gather valuable data, enabling you to recommend products tailored to a consumer's preferences. Here’s a perfect example of a personalized quiz that suggests watches based on a series of questions on lifestyle, budget, gender, and more.
A bundle is a great way for customers to feel that they’re getting more for their money. For instance, if you sell smartphones, create a bundle that includes the phone, a protective case, a screen protector, and a portable charger. This approach not only increases sales of the smartphone but also promotes its accessories, maximizing revenue per customer.
CleanCult’s complete home bundle makes it easy for new eco-cleaning consumers to switch to sustainable cleaning by having all the products they need while also saving money.
Remembering your customers' birthdays is the essence of personalization, and by offering something special like a discount or free gift, you'll turn them into loyal fans.
Tailor your gift offerings based on their purchasing history. For top spenders, consider offering a complimentary gift, while for new customers, a discount could be just the thing to encourage repeat purchases.
If personalization is new to you, there are key terms you'll need to understand. Below, we've compiled common terminology used in e-commerce website personalization, along with the important role of data.
Data is the bare necessity of website personalization. Without it, there’s nothing to personalize. This leads us to the question; which types of data lead to effective personalization?
Before going into data types, it’s important to highlight the types of dataset engines that work in the background and fuel website personalization.
Dataset engines contain the following capabilities:
This dataset engine unifies (hence the name) data from multiple sources into a single view. This helps businesses have a general view of all the data in one place and helps them with decision-making.
In order to provide businesses with a more holistic view of their consumers, unified dataset engines analyze data from web analytics, CRM systems, social media and third-party sources.
An open architecture dataset engine has the capability to integrate with other platforms. This way, it can support different data sources and businesses can import other data from different sources.
The open architecture dataset engine is a flexible way to stay up to date with evolving data needs while facilitating collaboration among different teams, in order to maximize personalization efforts.
Decision logic comprises the algorithms and rules operating behind the scenes to analyze the data that drives personalized decisions. These capabilities are achieved through predictive analytics, machine learning models and decision-making frameworks that run in real-time, and decide which actions to take next.
In order to have the most effective web personalization, we need to understand various data points for each customer. Here are the most common ones:
