eCommerce Personalization Trends: How to Stay Ahead in 2024
Adopting an ecommerce personalization strategy in 2024 isn’t just a nice-to-have – it’s a necessity. Brands that fail to deliver tailored experiences and personalized recommendations fail to meet core customer expectations and risk losing a significant portion of revenue.
A study by researchers from McKinsey & Company found that 71% of consumers expect personalized interactions from the companies they support. 76% of them feel frustrated when they don’t receive personalized experiences. The same study found that companies that adopt personalization grow faster and derive more than 40% of their revenue from personalization.
Clearly, brands that get personalization right from the start will perform better, build longer-lasting relationships, and retain their customers longer than their generalist counterparts.Let’s take a closer look at personalization and the trends shaping personalization technology and strategies in 2024.
What Is eCommerce Personalization?
ECommerce personalization refers to the practice of customizing the shopping experience to meet the specific needs of individual customers. Personalization may involve presenting tailored product recommendations or targeted messages based on a customer’s browsing history, purchase history, and behavior.
Businesses use personalization to reach a number of goals, including enhancing brand awareness, improving the overall customer experience, and increasing customer satisfaction. By gaining insight into the preferences and desires of customers, companies can create a more seamless and relevant customer journey, which leads to higher conversion rates, increased average order value, and improved customer loyalty.
Customers are more likely to return to a business that understands their needs and preferences and provides a shopping experience tailored to their interests. Personalization can also help reduce cart abandonment rates by providing customers with targeted messages or incentives encouraging them to complete their purchases. By addressing any concerns or hesitations that customers may have, businesses can increase the likelihood that customers will complete their purchase.
It’s no surprise that online retailers view personalization as a strategic imperative for 2024 and beyond.
Personalization Trends in E-commerce
2024 will be a game-changing year for e-commerce as businesses face emerging customer expectations and global economic challenges. Businesses that want to push ahead of the pack must consider changing consumer preferences, privacy concerns, and emerging technology. Some of the trends to pay attention to include the following:
Headless ecommerce personalization refers to delivering personalized content or experiences to users without relying on a traditional web content management system (CMS) or presentation layer.
In a traditional CMS, the front-end presentation layer and back-end content management system are tightly coupled, which can limit the ability to customize the user experience. In contrast, headless personalization involves decoupling the CMS from the presentation layer and using APIs to deliver content to any device or platform, such as mobile apps, chatbots, or virtual assistants. Customer data from the back-end system is used to personalize the user experience on the front-end system, e.g., using data from an existing CRM or data warehouse to deliver exclusive discounts, relevant products, or personalized messaging to customers.
Headless personalization allows organizations to create more flexible, customized, and dynamic experiences for every user without changing their website’s design while also allowing for greater scalability and faster time-to-market.
AI-Driven, Dynamic Personalization
AI has been on the radar of most ecommerce platforms for several years. As AI became more accessible, retailers scrambled to deploy AI-driven recommendation engines and other personalization tools.
AI-driven dynamic personalization refers to using artificial intelligence (AI) and machine learning (ML) algorithms to dynamically tailor content, products, or services to the specific needs, preferences, and behavior of individual users.
This approach collects and analyzes data from multiple sources, such as user interactions, historical behavior, social media activity, and demographic information. This data is then used to create individual user profiles that are constantly updated and refined over time. The AI algorithms analyze and use the data to personalize the user experience, e.g., by recommending relevant products or services, optimizing the user interface, or predicting user behavior.
While we’re only scratching the surface of what AI can do in ecommerce, dynamic personalization has already been proven to increase customer engagement, improve conversion rates, increase average order values, and drive revenue growth, while also reducing costs and increasing efficiency by automating the personalization process.
Omnichannel E-commerce Personalization
Omnichannel e-commerce personalization refers to the practice of providing personalized experiences to users across multiple channels and touchpoints, such as online and offline stores, mobile apps, social media, and email.
Users often switch from one device to another when shopping or browsing the Internet, which can make it difficult to form a complete picture of who they are and what their preferences and needs are.
By taking an omnichannel approach, user data is collected and analyzed from multiple sources and channels, including browsing and purchase history, social media activity, email interactions, and more, to create a unified customer profile. This profile can be used to greatly boost your marketing efforts and conversion rates.
A company that sells clothing and accessories may use AI algorithms to analyze a returning user’s previous purchases and behavioral data to display personalized recommendations on the homepage. They can then follow up with personalized social media retargeting ads, personalized emails with special discount codes, or even use a mobile app to provide recommendations when the customer visits the physical store, using Bluetooth beacons or other location-based technologies to send personalized notifications to the user’s phone when they are near a relevant product.
Businesses can speak to their customers wherever they are, through whatever channels they are using, creating truly personalized shopping experiences across different touchpoints.
Customers are becoming increasingly concerned about the way their personal information is being collected and used by ecommerce companies. On the other hand, there can’t be personalization without personal information. Businesses have to strike a balance between collecting the information they need to deliver unique and personal experiences and respecting their customers’ right to privacy.
Privacy-First Personalization does exactly that. By using privacy-preserving technologies and techniques to collect and process user data, ecommerce businesses can minimize the risk of unauthorized access or the accidental disclosure of sensitive information.
Companies can use techniques such as differential privacy, federated learning, and homomorphic encryption to protect user data while still gaining insights into user behavior and preferences. They can also provide users with more control over their data by allowing them to opt in or opt out of data collection and by giving them the ability to delete or edit their data. The only data that is collected is the data provided by the shoppers themselves.
Privacy-First Personalization helps companies build trust with their users and demonstrate their commitment to protecting their personal information.
Anonymous Visitor Personalization
How do you personalize content when you don’t know who the visitor is? Anonymous Visitor Personalization is a type of personalization that is aimed at website visitors who have not yet identified themselves or provided any personal information, or created an account on the website. A website might use anonymous visitor personalization to recommend related products based on the visitor’s browsing history or display content that is relevant to the visitor’s location.
Anonymous Visitor Personalization is the best way to deliver a personalized recommendation to first-time or unknown visitors without compromising their need for privacy.
Sometimes a customer simply doesn’t know what they want until they see it. Other times, they know exactly what they want, but the ecommerce site simply can’t match their specific search criteria. If a customer is looking for a burgundy-colored dress with balloon sleeves, a scoop neck, and silver embellishments and the site can only deliver results for a red dress, low neckline, and long sleeves, their experience will be far from ideal.
Companies have wisened up to this mismatch and have started using machine learning algorithms trained on a large dataset of labeled images to automatically tag or label images of products in an ecommerce store. The tags or labels describe the content of the image, such as the color, style, material, brand, or product category. Once the algorithms are trained, they can automatically tag new images of products in real time based on the visual characteristics of the products. This process can be automated and integrated into the ecommerce platform, allowing for a more efficient and scalable way to tag large numbers of products.
The tags enhance the search and discovery capabilities of an ecommerce store by allowing customers to find products that match their preferences and needs more easily. When customers search for a specific product or browse a category, they can use filters based on the tags to narrow down their options.
While this is an efficient way to keep the product information up-to-date and searchable, it also offers numerous opportunities for ecommerce companies to create personalized experiences.
ML image tagging can help ecommerce platforms provide more accurate and relevant product recommendations to individual customers. By analyzing the images that customers have interacted with and their purchase history, ML algorithms can identify patterns and make personalized recommendations based on the customer’s preferences.
ML image tagging can also be used to power visual search, where customers can upload an image of a product they are interested in, and the platform will return similar products. Interactions with images, including purchases, can be used to adjust pricing in real-time so that ecommerce companies can offer personalized pricing and promotions that match customers’ willingness to pay.
The Use of Facial Recognition
Facial recognition is an important area of interest for ecommerce and brick-and-mortar stores alike. By simply recognizing the face of a shopper, e-commerce retailers can use artificial intelligence algorithms to analyze the shopper’s demographics, facial expressions, and body language to make personalized product recommendations, tailored messaging, and promotions. Advanced facial recognition can even identify emotions such as happiness, surprise, confusion, or frustration and tailor the shopping experience according to the shopper’s moods.
Facial recognition can also enhance the omnichannel experience in physical retail stores by identifying customers as they walk in and providing personalized recommendations based on their online purchases.
This technology isn’t widely used yet, as many consumers are hesitant to have their faces scanned. Companies must be transparent about the data that will be collected and used and obtain consent before going down this route.
One thing is clear: the future of e-commerce is in personalization. By adopting personalization strategies and leveraging emerging technology, businesses can create more seamless and relevant customer journeys, leading to improved customer loyalty, increased revenue, and greater customer satisfaction.
In 2024, we’ll see savvy ecommerce companies leverage personalization tools to reduce friction and improve customer experiences. The ones that don’t might just fall behind the curve for good.
Book a free consultation