Ecommerce has come a long way since its inception; it is no longer just the game for big players like Amazon, Target, Walmart, eBay, etc. As of 2023, virtually every industry, from apparel and fashion to appliances and electronics, has embraced online retail.
And with the increase of ecommerce stores, the competition multiplied. But an interesting aspect is that the success of ecommerce depends on one simple factor: customer satisfaction.
71% of customers say that they will purchase from an ecommerce store that provides personalized recommendations. To meet this demand, businesses are leveraging first-party data with AI algorithms, enabling them to deliver tailored shopping experiences.
The integration of first-party data and AI has leveled the playing field. AI-driven recommendation engines enable even small businesses to curate a personalized customer shopping experience. (Not anymore, the game of big players.)
Experts predict that AI recommendation engines will generate $30 billion in revenue for the ecommerce industry in 2025. In this article, we’ll discuss how this engine transforms ecommerce from browsing to buying.
AI recommendation engines are intelligent systems built using AI and machine learning algorithms. These engines analyze readily available first-party data like customer’s website behavior, browsing history, purchase data, location, demographics, interests, and preferences to provide a personalized shopping experience to customers.
Why? What do ecommerce brands get in return?
It benefits both businesses in various ways:
To answer ‘why,’ ecommerce businesses aim to build customer trust and loyalty, encouraging repeat purchases while minimizing customer churn. This is precisely why AI recommendation engines are used—to foster returning customers and reduce attrition.
There are two types of recommendation systems businesses are actively using:
1. Collaborative Filtering
2. Content-Based Recommendations
This recommendation system analyzes purchasing patterns and preferences across multiple users. For instance, if Jack has similar preferences and purchase behavior as John, then the system will recommend products liked by John to Jack and vice versa.
This recommendation system studies a particular customer’s purchase behavior and interests and suggests similar products or accessories relevant to them. For example, if you have purchased a particular brand of shoes, the system will recommend similar styles or related accessories.
And we have the hybrid models:
It unifies collaborative and content-based filtering to provide more accurate and diverse recommendations. By leveraging user behavior and content similarities, hybrid models can offer more personalized suggestions.
Now that we know how AI recommendation engines work, let’s address the elephant in the room: how is it transforming the ecommerce landscape?
By analyzing customer’s preferences, purchase behaviors, and website activities, AI recommendation engines deliver personalized product recommendations, ensuring a smooth purchase experience. It makes shopping easier and more exciting without much navigation, and when done right, it also stimulates impulse purchasing.
By strategically placing enticing recommendations throughout the user journey, these engines enable users to explore and purchase items they might not have considered. This impulse buying behavior significantly contributes to increased sales and revenue for ecommerce retailers.
Since all these recommendations rely on first-party data, they are accurate and relevant. This ensures a seamless and non-frustrating experience for users.
Recommendations are not limited to purchases and website activity; AI recommendations can analyze a customer’s demographics, location, and browsing history to personalize the customer’s journey further.
Personalization doesn’t stop with the homepage; just like sales, recommendations follow even to the cart pages. Upselling and cross-selling tactics are used to increase the average order value. In the early days, complementary and upgraded products were recommended as an upselling and cross-selling tactic.
However, with AI recommendation engines, these capabilities have evolved. They now transcend traditional upselling and cross-selling by automatically analyzing relevant products aligned with customers’ browsing histories, offering a comparative view of potential purchases.
Have you ever noticed that your dwell time on a few ecommerce sites is higher than that of other ecommerce retailers?
We have all experienced it. Over the past few years, platforms like Myntra, Zalora or Showpo have effectively prolonged website visits, and AI recommendation engines are the driving force behind this engagement.
Previously, the recommendations were static based on what one searched for, but now, recommendations adapt themselves as we browse the websites, taking us closer and closer to the purchase.
Myntra’s features like “Shop the Look” and “View Similar” are adaptations of these engines. View Similar’ carousel curates a collection of similar items based on visual attributes like the style or color of the item end-users are looking at. This also supports better conversion when certain products are out of stock hence increasing revenue per user.
Myntra’s ‘Shop the Look’ showcases more products from the model images supporting AOV growth.
Customer engagement and retention are paramount in this new-age ecommerce game, and AI recommendation engines are vital in delivering those. And online retailers that leverage these engines are becoming more popular daily.
Customers look for outfit ideas for special occasions and then visit ecommerce stores to purchase similar outfits. What was once an isolated activity, seeking products inspired by particular wardrobes, has now become mainstream, thanks to the impact of these engines.
Visual search, powered by AI, has made browsing and buying easier. Customers no longer need to navigate countless product listings to find their desired outfit; instead, they can simply upload an image of their inspired attire.
AI recommendation engines swiftly process this visual data and present exact or similar outfit options, reducing navigation hassles and increasing conversion rates.
But the engine doesn’t stop there; it recommends accessories or complementary ornaments relevant to the outfit’s aesthetic. This new-age approach is not just about fetching products based on the image; it has started curating various recommendations and outfit ideas.
These algorithms significantly enhance the likelihood of conversions by providing a comprehensive stack of suggestions. The integration of AI recommendation engines with visual search not only simplifies the buying process but also improves user satisfaction.
Ecommerce stores lose around $18 billion in revenue annually due to cart abandonment. Various factors lead to customer exiting the website without checking out their cart, some reasons which are high shipping costs, lack of product-related content, poor payment flexibility, security concerns, etc.
Online retailers send cart recovery emails to customers, including special discounts or next-order coupons, to entice them to return to the store and complete their purchases.
Once the abandoned customer returns to the ecommerce store, recommendation engines deliver personalized content like tutorials, UGC, enticing product videos, video reviews, etc., relevant to the customer and the products left in the cart.
AI also analyzes user behavior and adjusts prices or offers time-sensitive deals to entice customers to complete their purchases, thereby converting lost sales into revenue.
While the traditional method of sending abandoned cart emails remains effective, the infusion of AI recommendation engines has revitalized these strategies, resulting in heightened success rates and improved conversions.
Visual search and suggesting outfit recommendations are just the tip of the iceberg; the real deal comes with its predictive analysis. Recommending similar products based on customer’s purchase behavior and activity is evolving rapidly.
With advancements in ML algorithms, these AI engines analyze vast customer data, going beyond reactive recommendations and moving toward a proactive approach by predicting a customer’s potential purchases even before they actively start browsing (collaborative filtering).
It analyzes various customers’ purchase behaviors and histories, categorizing them into similar groups or segments. This enables the engines to anticipate a customer’s next potential purchase.
This forward-thinking approach is a game-changer in personalization, transcending traditional recommendation systems. Recommendations provided by predictive analysis not only streamline the shopping experience but also foster unparalleled customer loyalty.
The adoption of predictive recommendation systems like ViSenze marks a change in ecommerce. Brands utilizing such technologies are positioned to establish loyal customer relationships, offering an experience beyond mere transactions.
ViSenze is a leading AI-powered visual search and recommendation platform that enables the world’s leading retailers to help their customers simply ‘See. Style. Shop’.
ViSenze’s cutting-edge products are used by Rakuten, Zalora, John Lewis, Myntra, Ajio, Meesho, Mango, Target, and others to power their product discovery for shoppers.
ViSenze processes over a billion queries a month from retailers, helping them increase conversions and amplify revenue growth.
ViSenze’s smart product recommendation engine is a game-changer for retailers aiming to increase their conversion rates and average order value. By offering tailored suggestions, including outfit recommendations and visually similar product recommendations, retailers can uplift their revenue per session by 20% and their conversion by 35%.
ViSenze is committed to personalization without relying on personal data. This innovative technology drives engaging experiences for all visitors, whether logged-in or first-time users, creating an inclusive and personalized shopping journey.
ViSenze’s Visual AI Search marks a significant shift in customer shopping behavior. With an industry-leading 99% accuracy in image search, high-intent customers can quickly locate and purchase products, resulting in a 3X decrease in cart abandonment and a 6X increase in conversions.
ViSenze knows that customers might not always find the right keywords to express their needs. The intuitive visual search tool enables shoppers to find products through uploaded or saved images, social media posts, or screenshots.
This enhances search accuracy and provides retailers with valuable insights into customer preferences and trends, enabling them to fine-tune their offerings accordingly.
Explore this success story from Meesho showcasing how they utilize Visenze’s AI image search to enhance the precision of product discovery.
Let’s take a quick look at the types of AI recommendations Visenze offers:
This feature leverages ViSenze’s advanced visual recognition to recommend products similar in appearance to the one a customer is viewing by analyzing visual attributes like shape, color, pattern, and style.
This feature uses AI and image recognition technology to identify and suggest products worn or showcased in a particular model or influencer image. It lets customers shop directly from the image (for example, Shop the Look).
This feature goes beyond suggesting similar items; it recommends products that complement the primary item a customer is considering. For instance, if a shopper looks at a dress, this feature suggests suitable shoes, ornaments, accessories, or other items that enhance the outfit.
ViSenze’s Inspire feature provides shoppers with multiple outfit ideas or provides suggestions based on a single item. Showcasing various ways to style or wear a specific product sparks creativity and encourages customers to explore different looks. This ultimately drives increased engagement.
This feature capitalizes on social media and UGC content, transforming inspiration into actionable purchases. By enabling users to shop directly from social media images or posts, it bridges the gap between inspiration and purchase. Additionally, it provides a seamless shopping experience from browsing to buying.
ViSenze’s session feature ensures continuous and dynamic personalization throughout a shopper’s browsing. By analyzing real-time behavior, preferences, and interactions, it adapts recommendations and content. The latter then caters to the individual’s evolving interests, fostering a highly personalized and engaging shopping journey.
AI recommendation engines are transforming ecommerce as we speak. Particularly, platforms like ViSenze are bringing this transformation to this digital era.
Thus, with stores around every corner of the digital space, online retailers who adopt modern innovations like AI recommendation engines are witnessing increased sales and revenue.
This article delves into how AI recommendation engines are reshaping the ecommerce landscape, covering both personalized recommendations and predictive analysis.
So, where do you stand in this evolving landscape? Schedule a demo and discover why the world’s leading retailers trust ViSenze!