The Power of AI in E-commerce: Personalization Strategies That Drive Sales
Introduction To E-commerce Personalization
Google ads, Facebook ads, TikTok ads, emails, SMS, catalogs – whatever your marketing strategy is, you are likely employing significant resources and advertising spend to attract potential customers to your website.
But then what? You spent all that money and time to get visitors to your website, but what are you doing to keep their attention and convert them to customers? How are you helping the consumer discover relevant products? How are you ensuring that return visitors don’t see the same thing they previously saw and were uninterested in?
That’s where AI-powered e-commerce personalization comes in. Effective personalized e-commerce means understanding who your customers are and what they are interested in, and then personalizing the website and digital experience accordingly.
E-commerce personalization has emerged as a powerful ally in navigating the challenges of heightened online competition, rising digital marketing costs, and changes in data privacy.
In this article, we’ll explore how e-commerce brands and retailers that embrace AI-powered e-commerce personalization can combat these formidable challenges by creating unique, memorable and personal experiences for their customers, while simultaneously improving website conversions, increasing sales, and lowering costs.
What Is E-commerce Personalization?
In the not-so-distant past, all websites were static and updated manually. Every visitor to the Nike website would see same thing as any other visitor. These days are in our review mirror, but not by much. E-commerce personalization entails delivering a unique version of the website to each visitor, or a segment of visitors, designed specifically to cater to their individual or segment preferences and improve the shopping experience. For example – a returning customer who purchased running shoes in the past may see running-centric products on the Nike home page on their next visit, while a new visitor may see a variety of best sellers from different product categories. What’s the end goal? Simply stated, to create an extraordinary customer experience by treating customers as individuals and understanding and fulfilling their expressed and unexpressed needs.
How Does E-commerce Personalization Work?
Big data, cloud computing, and AI are not just buzzwords – they are what make e-commerce personalization possible. As shoppers browse websites, make purchases, leave product reviews, engage with social media posts, click ads, and/or communicate with companies, they leave behind a digital data trail. All of this data can be tracked, stored, and analyzed in real-time thanks to advancements in computer storage and processing.
With this data, AI and machine learning algorithms predict customer shopping preferences and behaviors such as what items they are most likely to purchase, enabling websites to offer each customer a tailored e-commerce experience.
What Are Examples Of E-commerce Personalization?
There are many ways to personalize the digital shopping experience, but here are the five we are going to highlight:
Product recommendations
Outfit recommendations
Collection Merchandising
Site Search
Surveys and Quizzes
Product Recommendations
If you’ve ever shopped on Amazon, you are familiar with product recommendations. Product recommendations are algorithmically generated suggestions for items that a customer might be interested in purchasing based on their browsing behavior, purchase history, and other relevant data (like location, weather, etc.).
Common product recommendation types include similar items (e.g., “You Might Also Like”) and complementary items (e.g., “Frequently Bought Together”). Similar items are useful for helping customers discover products they might not have otherwise found, while complementary items are commonly used to upsell or cross-sell product.
Description: Example of similar items recommendation featured on the PDP for a pair of women’s jeans from Bloomingdales (see Others You May Like displayed vertically along the right side of PDP).
Product recommendations can appear on different web pages throughout the shopping experience. Common website placements include the home page, product listing page (PLP), product detail page (PDP), cart preview, and checkout. Recommendations can also be utilized in email marketing and retargeting ads, expanding personalization beyond the website.
Description: Example of complementary items recommendation in the add-to-cart preview for a men’s blazer offered by Nordstrom.
Outfit Recommendations
In the apparel and fashion space, an increasingly common type of personalization is “Outfit Recommendations” which are generally featured on the PDP and used to suggest several products that can be combined with the featured product to form an outfit.
Outfit recommendations come in two types, “Shop the Look” or “Complete the Look,” which are often used interchangeably, but are different.
Shop the Look – Features a single outfit recommendation comprised of the exact products the model is wearing with the featured item from the PDP (sometimes called “As Seen on Model”).
Complete the Look – Includes multiple outfit recommendations featuring products that complement the featured item from the PDP, but are not limited to what the model is wearing.
Description: Example of shop the look outfit recommendation from J. Crew for men’s long-sleeve textured sweater-tee.
Description: Example of complete the look outfit recommendation for a women’s blouse from Eloquii.
Collection Merchandising
Collection merchandising involves arranging and displaying products on the collection page, also called the product listing pages (PLP), to influence customer shopping behavior and drive higher sales. The ranking and ordering of products displayed on the PLP are tailored to each user based on their individual preferences, behaviors, and interactions with the website and optimized to show products that maximize the likelihood of the user finding and purchasing the products they are most interested in.
Collection pages are excellent for personalization for several reasons. They draw a significant portion of website traffic, making them extremely valuable. Additionally, with only a brief window to capture a shopper's attention and make an impact once they land on your site, personalization becomes even more crucial. Furthermore, given the limited space to showcase products—usually only two to four above the fold on mobile and desktop screens—selecting the right products to display becomes especially critical.
Effective AI-powered collection pages can help determine which few products to show the customer to grab the shopper’s attention, leave a lasting impression, and maximize the likelihood of a sale.
Description: There is limited space on the collection page to feature products. For example, Nordstrom’s “Men’s Blazers & Sport Coats” collection page features 748 items, of which only 4 of these items are visible above the fold on desktop and 2 on mobile.
Site Search
E-commerce site search refers to the built-in search engine that allows shoppers to easily find the items they are looking for using keywords and search queries
Good site search entails understanding what the customer is searching for in real-time and then providing relevant and timely results based on the nature of the search and the individual preferences and past interactions of the visitor performing the search.
Modern AI-powered site search leverages large language models (like Bard and ChatGPT) to understand and process users’ search results. As with collection merchandising, AI-powered search engines also employ complex ranking algorithms to determine the order in which products or search results are displayed to users to maximize the likelihood of the user finding and purchasing the products they are most interested in.
Description: Example of site search results for “women’s tank top” on Lululemon. AI-powered personalized site search uses a ranking algorithm to determine which items to show shoppers and in what order to show them.
Surveys and Quizzes
Customer surveys and quizzes can be a valuable tool for personalizing the shopping experience and collecting first-party customer data, which is becoming increasingly important as privacy regulations evolve and third-party cookies are phased out. For example, Savage X Fenty by Rhianna offers a 7-step quiz to customers and uses the responses to gather valuable insights directly from their audience and personalize the shopping experience in several ways:
Product Recommendations - Based on the customer’s stated style preferences, body type, and size, the quiz recommends specific products that are likely to appeal to them, helping provide a more tailored shopping experience.
Email Marketing - The quiz data is also used to segment customers and send targeted email campaigns with products and promotions that are relevant to their stated style and preferences.
Description: Example of a style quiz by Savage X Fenty by Rhianna. Savage X Fenty uses shoppers’ answers to create rich customer profiles and personalize the digital shopping experience and email marketing.
What Are Some Of The Benefits Of E-commerce Personalization?
E-commerce personalization creates a win-win situation and significant value for both customers and website owners. There are many benefits to using personalization, but here are the five we are going to highlight:
Enhanced Customer Experience
Increased Sales and Conversions
Increased Customer Loyalty and CLV
Lower Customer Acquisition Costs
Lower Operational Costs
Enhanced Customer Experience
E-commerce personalization enhances the customer experience by helping shoppers discover relevant products more efficiently. For example, showing shoppers personalized home pages, product recommendations, and other tailored content makes the customer feel more understood and increases satisfaction. Customizing merchandise results and providing personalized search results also improves product discovery, saves time, and reduces decision fatigue.
Increased Sales and Conversions
Personalization allows websites to show the right products to the right people at the right time. Modern AI and machine learning algorithms can be applied to predict customer shopping preferences and behaviors such as what items they are most likely to purchase, which in turn allows websites to deliver each customer a unique e-commerce shopping experience. Not only does this enhance the customer experience, but it can significantly boost conversion rates and revenue.
Increased Customer Loyalty and CLV
Collecting customer data helps companies deliver personalized shopping experiences and predict customer preferences. Accurate predictions lead to enhanced customer experiences which leads to more purchases. This virtuous cycle then further enriches the data making for even better predictions, which in turn leads to further improvement in customer loyalty and customer lifetime value (CLV).
Lower Customer Acquisition Costs
One of the benefits that shakes out from a well-executed personalized digital shopping experience is lower customer acquisition costs. By harnessing what you know about visitors to your website, the more likely it is you will help them discover products they are interested in buying and the better chance you have of creating a lookalike audience to optimize your paid marketing efforts.
Lower Operational Costs
Another benefit is general operational efficiencies. With AI handling the bulk of data analysis and customer insights, there’s a reduced need for extensive human intervention in processes like customer segmentation, data interpretation, and content creation. This not only frees up valuable human resources for more strategic tasks but also significantly cuts down on labor-related expenses.
Conclusion:
In the rapidly evolving landscape of e-commerce, it's abundantly clear that personalization is not just a passing trend - it's the very future of this digital frontier. As the competition intensifies, digital marketing costs soar, and privacy concerns reshape the marketing landscape, e-commerce businesses must adapt or risk getting left behind. By harnessing the power of AI-driven personalization, brands and retailers can navigate these challenges with finesse. The benefits are manifold: enhanced customer experiences foster deeper connections and loyalty, improved sales and conversions fuel growth, and operational efficiency gains translate into cost savings. From personalized product recommendations that guide shoppers to their ideal purchases to AI-driven site search and dynamic product listings, e-commerce businesses have many choices for how to personalize the digital shopping experience. The future looks bright for online businesses (and their customers) that make personalization a cornerstone of their digital strategy in the years to come.
About
SabinoDB is a leading e-commerce and marketing technology company committed to helping companies grow their business by making data and technology their competitive advantage. Our solutions include Digital Advertising, E-Commerce Personalization, “Shop The Look” Outfit Recommendations, Direct Mail and Catalog Response Models, and Web Development. If you are interested in learning about how SabinoDB can help your company, please reach out directly to Ryan Hammon. Email - ryan.hammon@sabinodb.com Phone - (415) 847-8103.
