Personalized Product Recommendations (& How To Use Them)
Personalized product descriptions adjust the presentation of a product to complement the products a customer has already bought or viewed online. Personalized product recommendations improve user experience, increase conversion rates, decrease shopping cart abandonment, and augment average order values.
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What's an example of personalized product recommendations?
Suppose a customer buys new running shoes every three months. They always buy the same brand. If this customer comes back to your site and searches for running shoes, it's a good guess that they will buy their favorite brand again—but maybe they would like new features, a different color, or a shoe with new product endorsement. A personalized product recommendation for this customer keeps old choices available, but dangles the possibility of a "new and improved" customer experience at a different price point first.
How can e-commerce sites generate personalized product recommendations?
Back-end artificial intelligence creates a model of each customer's online personality. The personalization algorithm is not limited to shopping history. It can also crunch numbers from social media posts, buying decisions on other sites, and Google queries. These complicated mathematical models rely on metrics for openness, conscientiousness, extraversion, agreeableness, and neuroticism, all tied back with data generated in millions of customer transactions to attractive features of products. But the owner of an e-commerce site never sees these calculations, completed in milliseconds with personalization software added to the back end of your site.
Your job is to keep finding options that enable you to recommend products that deliver a good margin and that your customers will love. Your product personalization software crunches the numbers, so your site presents the products your customers will find irresistible. Your site will show your customers only the products they want to buy. Product personalization done right reduces your marketing expense and increases your customer retention.
What are the benefits of personalized product recommendations?
Product personalization generates a great return on investment. In fact, a recent study reported that 49% of consumers said they have purchased a product they did not originally intend to buy after receiving a personalized product recommendation.
Product personalization can operate at any point in your customer funnel. It can give you a 360° view of your customer that accumulates information for better product personalization every time a customer visits your site.
How do personalized product recommendations work?
Personalized product recommendations are generated with user data taken from your website. Ideally, you’ll only show your customers products that they actually would like to buy. The more personalized the experience, the better.
When done right, personalized product recommendations can save you money and increase your customer retention. A 2018 report shows that returning customers who engaged with a suggested product (by clicking on it and reading the description, for example) were 55% more likely to make a purchase during that shopping session. For new customers, that figure was a cool 70%.
Recommendation engines, which are programs that analyze data on both products and users, drive features like this. Not only do retailers employ these engines, video streaming websites use them to generate lists of recommended clips for their users to watch.
There are 3 main types of recommendation engines:
- Collaborative filtering systems
- Content-based filtering systems
- Hybrid recommendation systems
We’ll discuss these recommendation engines in more detail and provide examples of each later on in this post.
What do recommendation engines look at?
With so much user data at your fingertips, you can use a recommendation engine to offer very specific suggestions to your customers.
Recommendation engines usually look at data such as:
- A customer’s search queries
- Their purchase history
- What’s currently in their shopping cart
- Their social behavior (likes, shares, etc.)
- Their geographic location
- The customer’s audience segment (demographics)
Your engine might use their geographic location to recommend a good coat when fall rolls around in their part of the world. In the case of our photographer, it might recommend a high-capacity memory card to go with their expensive camera after they’ve had it for a while.
What are examples of personalized product recommendations?
The AI of product personalization primarily relies on three types of recommendation engines: collaborative filtering systems, content-based filtering systems, and hybrid recommendation systems.
Collaborative filtering analyzes data from multiple customers (the statistics work best if the number of customers is at least 1,500) to generate a panel of products the customer is probable to like the most. It uses the experience of all of the customers to recommend products for each of your customers.
What is an example of collaborative filtering?
For instance, if a site visitor viewed a DSLR camera, it might offer them lenses that other customers bought with that camera.
Collaborative filtering doesn't just rely on data amalgamation across customers. It filters product recommendations by search history, by geographic location, and by the customer's history with the model and with the site. Its main benefit is that it adds insights from multiple customers to personalized recommendations for one customer.
Content-based filtering generates "If you liked this, then you might also like that" recommendations. It looks at the individual customer's pattern of purchase decisions, rather than data from a group of customers. Content-based filtering looks at data from just one customer at a time.
What is an example of content-based filtering?
Suppose a customer bought almonds in March, walnuts in April, cashews in May, and is back to your site to place an order for nuts again in June. Content-based filtering might display macadamias and pistachios as additional products they may enjoy.
Hybrid recommendation systems
Hybrid recommendation systems combine collaborative and content-based filtering. They use data from similar customers as well as data from the customer's prior search and purchase history.
What is an example of a hybrid recommendation system?
Video streaming services typically run collaborative screening (to determine which new videos are likely to appeal to you) combined with content-based screening (that matches currently available videos to videos you gave high ratings to in the past). This kind of screening can also take into account geographic location and stated likes and dislikes.
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Personalized product recommendations best practices
Your product personalization engine automates the analysis of customer search queries, demographics, purchase history, social behavior (especially shares and likes), and geographic location, compared to what is already in their cart. But you can get the most out of your site's product recommendation engine by following five best practices.
1. Analyze the right behaviors.
No AI system operates entirely without human input. Make sure your product recommendation is analyzing relevant behaviors to figure out the reasons your customers make their purchase decisions.
For your personalized product recommendation engine to generate relevant results, it needs to focus on user behaviors that are, well, relevant. Not all of your customers buy based on the same criteria. Your engine should always be looking at what those criteria are and figuring out the “why” behind what people are buying.
If you sell T-shirts, some of your users will prefer particular colors or styles. Others will be loyal to their favorite brands. Others might even go out of their way to buy T-shirts that were made in the United States.
Does one of your customers prefer shirts sewn from eco-friendly fiber? That user probably cares about the environment and would be more interested in other “green” products than another shirt that’s a similar style. They might also be interested in things like non-toxic household cleaners and glass food-storage containers.
2. Test your strategies.
As with any marketing campaign, testing and benchmarking are paramount. If you don’t test out your strategies before using them in the real world, you’re missing a valuable opportunity.
Recommendation engines are powerful, but they still need direction from human beings like you. Always watch and make a note of what seems to be working. If you’re not sure, consider A/B testing.
A/B testing is a good idea for any marketing campaign. If you don't test your settings before scaling up your product personalization tool, you could be missing opportunities for improved user experience and increased sales.
Certain kinds of recommendations will perform better on some pages than others. "Trending Products" recommendations may work on your home page but not on the shopping cart page. But a "complete satisfaction" box on your shopping cart page—for instance, adding a belt and a bag to a purchase of shoes—could boost sales. The only way to know what will work is to test.
Pay attention to whether certain kinds of recommendations perform better on some pages than others. A “trending products” box might test well on your homepage but not on the shopping cart page. If somebody is on the shopping cart page and is about to buy a new pair of shoes, maybe a “complete the look” box suggesting complementary products like handbags and belts would be effective. The only way to know is to test and make changes according to what works and what doesn’t.
3. Add recommendations in unexpected places.
You don’t have to only place recommendations on pages that already feature products. Why not place them on your 404 page and give shoppers an easy way to click back to your main site? A recommendation box on your checkout page could inform the customer that they only need to spend a little more to have their entire order shipped for free.
You can also put recommendations in your emails. An abandoned cart email might suggest products based on what the customer has waiting in their shopping cart. Many companies send follow-up emails after someone has bought a product, suggesting things that might go with it. If someone has looked at the same product several times, you can send them emails asking if they’re still interested and show similar products around the same price point.
4. Feature the right number of recommendations.
How heavily you incorporate recommendations into your website is up to you. Some companies have personalized product recommendation boxes on every single page of their site.
Marketers and designers who follow this approach believe that maximizing the customer’s exposure to the recommendations makes them more likely to purchase the featured products. Detractors of the approach argue that featuring too many recommendations distracts from the purpose of the page and risks annoying users.
If you build personalized product recommendations in everywhere, be careful that you’re doing it right. Make extra sure your recommendation engine is performing correctly and displaying relevant results, or your customers might start to feel bombarded and tune them out.
Even if the recommendations are spot-on, some customers may be inclined to ignore them if you place them front and center on every page. It bears repeating: The purpose of a personalized product recommendation is to suggest something to your customers that they actually will find useful.
Some marketers advocate a tightly focused effort. You could choose a “quality over quantity” approach and only display a few curated recommendations on pages that your research suggests will be effective. Alternately, you can incorporate recommendations across your entire site, but do so subtly, placing them below the fold or in a sidebar.
5. Use social proof.
When you check a product’s reviews before buying something or ask someone you trust for their recommendation, you’re looking for social proof. You want to be sure that the thing you’re about to buy is worth your money.
As a business owner, you can use your personalized product recommendations to provide social proof. Add little badges beside each product that show how many people viewed or purchased it that day. If a customer can tell that hundreds of other people viewed a product, it might nudge them a little closer towards buying it. Labels like “best seller,” “top pick,” or “staff choice” also lend credibility to your products. This process is collectively known as “badging.”
Some websites notify their users in real time when someone buys a product they’re considering. For example, “Jenny in California just bought a carved hematite ring.” This creates a sense of urgency and can motivate people to either make a purchase or investigate other products.
How would you recommend a product to someone? If you encourage customers to review your products, or you incentivize your customers to recommend your products to others, you are using social proof. Your customers want social inputs that reassure them that they are getting value for their money. Tie your product personalization efforts to your management of social media.
Create a seamless customer experience
Personalized product recommendations make it easy for customers to find what they want to buy. The smoother you can make their experience, the more positively they’ll think of you.
When you use your sales data to tailor your recommendations and showcase relevant products, the more likely shoppers are to make a purchase—and come back again.