A website personalization engine is software that drives relevant, personalized experiences for website shoppers and subscribers.

It uses artificial intelligence (AI) to analyze data supplied at various touchpoints as customers browse, buy, or use a service such as movie streaming.

By analyzing this data on customer behavior, preferences, and past behavior, marketers can use machine learning algorithms to create personalized ecommerce experiences tailored to meet individual shoppers’ unique needs.

How have personalization engines changed in ecommerce?

Personalization engines are fundamentally much more impactful today because of how they leverage artificial intelligence algorithms.

A few years ago, two people might have been looking for “cleats” on an ecommerce site, with one wanting soccer shoes and the other looking for clip-on cycling shoes. This keyword would have given them the same search results, which would have required them to spend a little time sifting through their options to find exactly what they needed.

With modern personalization engines, technology such as natural language processing (NLP) helps discern each customer’s specific intent to quickly display the right information. By noting someone’s unique shopping history and categorizing it in accordance with the user behavior of other shoppers, a company can provide high-quality search results aligned with people’s needs.

Personalization has been winning the hearts and wallets of consumers in droves, and 71% have now come to expect the digital red-carpet treatment.

That means it’s a great time to investigate how personalization tools can help your business strengthen your customer journey and brand loyalty and achieve KPIs.

How a personalization engine works

Personalization engines have six phases:

1. Data collection: Gathering data from sources such as website interactions, purchase history, and social media activity. This might include tracking user clicks and time spent on pages, as well as noting demographic details and which items are added to the cart.
2. User profiling: Creating a detailed user profile for each visitor based on collected data related to their individual preferences, behaviors, and demographic details. For example, a movie-streaming service might build viewer profiles based on people’s preferred genres and actors.
3. Segmentation: Grouping customers based on shared characteristics and behaviors, which allows for better-targeted personalization strategies. For example, an online retailer might segment shoppers in categories such as frequent buyers, seasonal shoppers, and first-time visitors.
4. Experience selection: Choosing appropriate personalized content and product recommendations for each customer or segment. For example, merchandisers for an online movie site might display content on the home page based on each viewer’s history.
5. Delivery: Posting designated content through channels such as web pages, mobile apps, social media, and email. For example, an online retailer might send a personalized newsletter featuring products similar to someone’s past purchases.
6. Measurement: Assessing the effectiveness of personalization through metrics such as customer engagement, conversion rates, and overall customer satisfaction. Have the personalization efforts succeeded? To determine effectiveness, for example, with recommendations, you can run an analysis of click-through rates.

Types of personalization

Personalization engines are used for:

Curated content

Do you feel like the news story topics you’ve been seeing on websites are all super interesting to you? That may be because when you scroll through the headlines, you’re seeing personalized news content.

From political stories to market insights and movie reviews, your reading preferences and previous viewings have been noted, resulting in you seeing seemingly fascinating content.

Product recommendations

Looking to engage your shoppers by promoting products to them that align with their earlier interactions and purchase histories? That’s the power of offering personalized product recommendations, whether the selections relate to books in someone’s favorite genre or cleaning products that align with their eco-friendly values.

Customized email

A shopper responding positively to an email that’s tailored to their interests is personalization at its finest. Personalized email such as “Recommended for you” messages can significantly increase shopper engagement.

Web pages

Shoppers are naturally satisfied when website content, layouts, and recommendations seem to speak directly to their needs. For example, a travel website might display vacation packages related to a customer’s past searches and booked trips, while an ecommerce marketplace site category page tailored for a shopper based on their recent browsing might pique their interest.

Personalized ads

Shoppers may regularly browse your website and then disappear. What if you could then engage with them in another way to follow up? With ad personalization, you can tailor the content to reflect behavior, search history, and demographics. For example, Google shows ads for hiking gear to someone who’s recently searched for information about popular local hiking trails.

In-app personalization

In-app personalization customizes a mobile app experience, serving up relevant content, recommendations, and notifications. For instance, a fitness app might suggest workouts based on the user’s exercise history or healthful eating content.

Location-based content

Location-based personalization provides shoppers with content and offers relevant to their geographic location. This practice uses real-time location data to deliver contextually appropriate information. For example, a retailer could send a notification about a discount from its app that pops up when a shopper is near one of its physical stores.

Examples of personalization engines

Online retailers that have mastered the art of using a personalization engine include:

Amazon

Amazon is famous for its sophisticated cutting-edge personalization. To enhance the customer experience, the ecommerce giant uses:

• Product recommendations: The site analyzes shoppers’ browsing history, purchase behavior, and items in shopping carts to suggest products they would be likely to buy.
• Personalized email: Shoppers receive email with product recommendations based on their past purchases and browsing behavior.
• Dynamic home pages: The site’s home page and product pages are tailored to cater to people’s preferences, displaying recently viewed items, relevant deals, and categories likely to be of interest.

Levi Strauss & Co.

This jeans retailer creates an engaging shopping experience both online and in stores by applying personalization in strategic ways.

Here’s how its approach helps drive repeat buys:

• Personalized fit recommendations: Using data from online shoppers’ preferences and prior purchases, they offer fit and style recommendations.
• Tailored marketing campaigns: They use customer data to send targeted email featuring products and promotions that align with people’s preferences and past behavior.
• In-store personalization: Levi’s taps online data to enhance onsite store experiences, allowing sales associates to provide guidance informed by shoppers’ online browsing and purchase history.

Walmart

Walmart leverages personalization to improve the shopping experience across its digital platforms and physical stores alike.

Here’s how their efforts contribute to their competitive advantage:

• Product recommendations: Like other retailers, Walmart uses customer data to suggest products tailored to people’s shopping habits and preferences.
• Personalized promotions: Shoppers are shown personalized offers and discounts based on their purchase history and browsing behavior.
• Customized in-store experiences: Walmart’s mobile app provides a personalized experience in their physical stores by assisting shoppers with their shopping lists and past purchases in mind.

Netflix

Netflix personalizes its online user experience by recommending shows and movies based on subscribers’ viewing history and ratings. To increase user engagement and retention rates, the platform offers:

• Personalized recommendations: Suggestions for TV shows and movies that are similar to what people have watched and enjoyed.
• Dynamic thumbnails: The platform attracts users’ attention with thumbnails customized based on what Netflix knows about their preferences.
• Customized genres: Netflix creates unique genre categories for viewers based on their preferences, such as ” critically acclaimed dark comedies” and “Sci-fi movies with a strong female lead.”

Spotify

Spotify uses personalization to curate music suggestions based on listeners’ habits. Its recommended playlists include:

• Discover Weekly, which offers recommendations tailored to the listener’s tastes.
• Daily Mix, which combines the subscriber’s favorite tracks with suggestions for new music, segmented by genre or mood.
• Release Radar, which presents new songs from artists the subscriber listens to or follows.

How to choose the right personalization engine

If you’re in the market for a personalization engine, here are five things to consider:

• Data capabilities: You want to ensure that you can integrate your personalization platform with, and process data from, your CRM systems, websites, and social media platforms.
• Supported channels: Your personalization engine should be able to support all the channels you use to interact with visitors, including your website, mobile app, social media presence, and email.
• Pricing: The pricing structure must align with your budget; you’ll want to budget for implementation, licensing, and ongoing support.
• Services: Does your prospective personalization engine include training and support? These extras can be key for successful implementation and optimization.
• Scalability: You want a solution that can handle an increasing amount of data and more complexity as your business grows.

How to start using personalization

Here’s how to take the first steps:

• Define your objectives: What do you want to achieve by using personalization? The more specific, the better. Your goal might be to increase sales, improve customer satisfaction, or enhance user engagement.
• Build a data foundation: Integrate your systems so you can organize all the data from available sources and provide a unified view of the information.
• Start simple: Begin with a single project, such as launching a personalized email campaign or making product recommendations. Test your process (e.g., with A/B testing) and refine your approach.
• Give it some time: Continuously analyze your performance data and customer feedback, then improve and expand your personalization strategies.

Drive up your revenue

With the right personalization engine, you can optimize your marketing strategies and deliver the exceptional retail experiences your customers are demanding.

To learn more about personalization and implementation, check out our personalization benefits guide and personalization mastery guide.