April 3, 2023
It's not surprising that AI-powered product recommendations are the backbone of the biggest mobile apps, e-commerce businesses, and D2C brands. No matter your web marketing efforts, if your customers are not impressed, your business will suffer. This means that your online visitors will get frustrated and take their money elsewhere if you don’t quickly show them products that will likely match their interests.
If your business is struggling with customer retention, abandoned carts, and poor average order value, you are at the right place. In this article, we dissected 10 actionable tips for personalizing mobile user experience using product recommendations.
Product recommendation, in simple terms, involves recommending relevant products to your app or website users to improve their shopping experience.
With product recommendations, it's a win-win for you and your customers. You can trigger impulsive shopping, influence customer behavior, reduce cart abandonment, and provide great customer service.
Now, let's dive into more details of how exactly will this AI-powered tool benefit your business.
Here are more reasons why personalized product recommendation is best for your business:
With the help of analyzing tools, you can determine customer browsing behavior and other data to increase the appeal and the chances of making a sale.
Still don’t understand how personalized product recommendation drives sales? Here’s a basic explanation.
Ai-powered recommendations are practically an advanced version of the ‘in-your-face’ marketing strategy. Throw in psychology, buying patterns, and customer motivation into the mix, and you have your increased sale rates.
When done right, personalized product recommendations can increase the average amount your existing customers spend on a single order. This is especially true for cart pages. You can recommend related products, discounted products, complementary products, etc.
Take a quick look at how Beauty Bay implemented theirs:
Recommending personalized products based on customer interests provides them with a good shopping experience. This increases brand loyalty and makes it easy to stand out among competitors. It’s important to note that to achieve this, the recommendations must be data-driven and relevant.
Many tactics have been tried and tested. For instance, ASOS uses the sense of urgency tactic where customers have only one hour to complete a purchase or lose the cart.
Here are 10 best techniques for AI product recommendations we’ve effectively implemented for our clients.
To make user-specific recommendations, first, you must define your audience. There is no room for assumptions, and having a standard buyer persona alone is not helpful, as buyer intent differs from one customer to another. So, you’ll need to build a preference profile for every visitor to your site based on the following data:
This data helps the recommender system segment and predict what the customer intends to purchase.
There is no static product recommendation tool. To get maximum results, you need to keep analyzing what works and what doesn’t and make necessary tweaks. This makes sense considering that customers’ preferences are dynamic.
Here’s a practical way to go about it. First, ask yourself what recommendation is working and for which customers. Is it best left alone, or does it need to be amplified? Does it work across devices, or is it specific to a browsing device? A/B testing your ecommerce recommendation strategy trains your engine and increases its efficiency.
Most shoppers are indecisive, and displaying a timely and personalized list of suggested products can be what they need to make a purchase. Even if the customer doesn’t buy, it can inspire them to check a category page they ordinarily wouldn’t have bothered with.
This footwear brand recommends shoes based on the type of shoe the customer is interested in.
The “bought together” tag is a great recommendation strategy that shows a customer buying a product the complementary products other customers also purchased. This is similar to the beer and diapers strategy used by brick-and-mortar stores, where beers are stocked next to diapers because of past customer behavior. When it comes to recommending “frequently bought together” products, Amazon takes the cake. With Reteno’s help, your business can get its share, too. Recommending products that are often bought together ensures customer satisfaction and also increases the average order value.
If the art of human psychology is anything to go by, people are influenced by trends. Nobody wants to be left out on what other shoppers are purchasing in large numbers. This explains why recommending “hot picks” or “popular picks” to customers work.
Improve your omnichannel marketing efforts by sending customizable product recommendations directly to customers’ emails based on their recent browsing or purchase history. Sending reminders for abandoned carts and targeting impulsive shoppers are also great ways of making sales via AI-powered email campaigns. For inspiration, see how Tarte uses past buyer history to recommend relevant products the customer may like.
You can utilize push notifications as well as Android recommendations and iOS recommendations to point out new arrivals and products to customers. Again, you need to ensure that the customer’s purchase history shows that the customer is very likely to be interested in the product.
Most items on a visitor’s wish list reflect what they want but can’t afford. So, if you have related products, it pays to have a “products related to what’s on your wishlist” recommendation list. These similar products may be cheaper alternatives or the same product in different colors.
Remember what we said about trends? “Best-selling” product recommendations allow brands to show off their most popular products while indirectly influencing customers to make a purchase.
The following are good examples of AI product recommendations at its finest.
Umico is an all-in-one e-commerce platform with a marketplace, loyalty program, and mobile bank. It boasts over 1.5 million customers, and its app version generates over 5 million organic visits monthly. Thanks to Reteno, Umico’s customer data is segmented in one place and also includes different communication channels in their marketing strategy. These include personalized product recommendation widgets, push notifications, emails, and app inbox notifications.
Prom.ua is one of the leading m-commerce companies in Europe, having over 100 million products, 60 thousand merchants, and 140 million monthly visitors. Even then, they were struggling with customer retention. We solved this problem by utilizing AI product recommendations, customer segmentation, customer recommendation, and implementing event-triggered campaigns.
For instance, if a visitor viewed a category page without proceeding to the product page, recommendations, including new arrivals and bestsellers, pop up. Visitors, seeing product listings based on search queries, receive reminders with search results. Otherwise, they’d get recommendations from past purchases.
If you go over to Prom.ua and search for Apple Watch, something similar notification to the image below would pop up:
Time is ticking! Contact us to deploy Reteno’s personalized product recommendation engine and watch your sales skyrocket.
Are you aware that 80% of movies people watch on Netflix are from AI-powered recommendations? Here’s a pictorial illustration.
Netflix uses a recommender system known as collaborative filtering, where the algorithm analyzes content association from user ratings while keeping the user’s movie taste in mind. YouTube, Spotify, and TikTok use a similar recommender system, and this explains why most recommendations you get in these apps revolve around your recent searches.
For instance, after playing, say, Rihanna’s music for one hour on YouTube, your music playlist should look similar to the image below. The reason is simple. The algorithm works in favor of the customer’s selections.
Amazon was one of the first ecommerce businesses to take AI personalization seriously. Without it, a customer can be easily overwhelmed by the numerous options available on the app. For instance, if you want to buy a book from Amazon, the first thing you see is:
But the second you start selecting your preferred book, personalized product recommendation comes into play. Let’s say you select a Colleen Hoover book, then the recommendation page changes to the image below:
This is because other customers that bought a Colleen Hoover book also bought other titles by the same author or similar books in the same genre.
E-commerce and m-commerce businesses can utilize Reteno’s recommendation engine to stimulate demand, enhance user engagement, and improve user experience. At Reteno, our AI-powered personalization tool dynamically provides omnichannel recommendations for mobile apps, websites, and emails.
To personalize the mobile user experience with app product recommendations, you should pay quality attention to the following:
If your mobile app or website is not easy to navigate or use, the bounce rate will definitely be high. Bounce rate is a vanity metric in the grand scheme of things, but how can you convert a visitor when they’ve already left?
At Reteno, we have a track record of individualizing products/services to meet every customer’s specific needs at different stages of the buyer’s journey. The power of our personalization engine lies in its ability to accurately predict users’ needs and provide real-time tweaks to applicable product listings to fit them.
You may already know that AI is nothing without data. Your personalization efforts will backfire if you don’t do a deep analysis of your customer’s interests, attributes, geolocation, past purchase history, and current buying patterns.
With Reteno’s product recommendations engine for e-commerce, rest assured that no stone (data) will be left unturned.
During the implementation of your intelligence-based product recommendations, we recommend addressing your customer by their names. It enhances a 1-1 customer experience and shows empathy towards users.
The Reteno product recommendations engine can help you tailor user profiles appropriately while showcasing content based on their specific interests.
Your personalized app marketing effort is incomplete if you don’t cover other channels of communication between you and your app visitors. These can include pop-ups, time-limited offers, discounts, free shipping, and other factors we’ve already dissected in the best techniques for AI product recommendations. Contact us now!
In today’s world, AI product recommendation is almost indispensable to the success of e-commerce. However, analyzing and copying the recommendation patterns of big competitors in your niche may likely keep you afloat, but it won’t give your brand the voice it needs to leave their shadows.
AI recommendation costs both time and money, but when done right, it massively pays off. To get started, all you require is a professional graphic designer and a good marketing platform like Reteno to support all your mobile UX personalization activities.
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