Most buyers want customized product suggestions when shopping online, making personalization a must-have for e-commerce businesses.
In fact, a Zendesk report shows that 62% of shoppers prefer personalized recommendations to general ones. And 60% can tell when suggestions are customized.
By recommending items customers might be interested in, you can significantly enhance engagement, boost conversions, and build long-term loyalty.
With that in mind, we’ll cover how personalized product recommendations work and how to include them in your ecommerce email campaigns to grow your business.
What are personalized recommendations?
Personalized recommendations are suggestions a system makes to an individual user based on their unique preferences, behaviors, and past interactions. They give customers a more tailored and relevant experience, making it much easier for them to find what they’re looking for.
For instance, if someone frequently buys basketball jerseys and shorts in an online store, they might get recommendations for top-rated basketball shoes.
How do personalized product recommendation systems work?
Most product recommendation engines use one of the following systems:
Collaborative filtering system
Collaborative filtering is where recommendation systems suggest products to new users based on the preferences and shopping behaviors of other consumers.
For instance, suppose someone viewed a particular mobile phone in your online store. In this case, the system can show them related items other shoppers bought with that smartphone, like screen protectors.
Amazon is an excellent example of a brand that uses collaborative filtering.
The system finds similar items for users when they show interest in a particular product. It also considers highly rated and trending products that other shoppers frequently purchase.
Content-based filtering system
This type of system doesn’t rely on the preferences of other users. Instead, it looks at a product’s details and suggests related products based on what the customer has liked before.
For example, the recommendation system can use data like product descriptions to learn more about an item and others similar to it. It then uses this information to suggest complementary products to a customer.
To see this in action, check out Kylie Cosmetics. The beauty website has a “You may also like” section that recommends items that complement or even boost the benefits of the product being considered. This helps shoppers quickly find the products they need.
Hybrid filtering system
In a hybrid approach, the system combines collaborative and content-based filtering, making it much more accurate. As a result, customers get superior suggestions that encourage them to buy.
Here’s how it works:
Some of the products the recommendation engine suggests are based on what other users have bought. But it will also show items similar to what the customer has already purchased, using information in the products’ descriptions to find these products.
An excellent example of a brand that uses a hybrid model is Netflix. The company’s recommendation engine suggests what a viewer should watch next based on both their interests and the movie or TV show’s description. It also looks at what similar users enjoy watching to make new and relevant suggestions.
Benefits of personalized product recommendations in ecommerce
Customizing your product suggestions to suit each shopper’s needs has many advantages. Let’s look at some of these benefits.
Higher sales and revenue
Today’s customers want to feel understood and valued. And personalized product recommendations are one of the best ways to achieve that. In fact, 80% of businesses say that when they personalize a user’s experience, the average order value (AOV) goes up by around 38%.
Suggesting relevant products shows customers that you care about their preferences and interests. And this can significantly enhance their overall experience with your brand, which can make them spend even more.
Increased customer loyalty and lifetime value
As shoppers buy your recommended products, you gain more data that you can use to suggest even more personalized items. This creates a flywheel effect that builds solid, long-term customer loyalty and boosts their lifetime value.
According to Mastercard’s 2023 Retail TouchPoints Report, 53% of businesses have increased customer loyalty and retention through personalization strategies. Moreover, a report by Twilio Segment shows that 56% of customers say they’ll buy from a company again if they receive a personalized experience.
More cross-selling and upselling opportunities
Once a client buys a recommended product, you can suggest other relevant items in your post-purchase emails. For instance, suppose they buy premium steak knives from your online store. In that case, you can recommend a gourmet steak seasoning set as a complementary product.
Or, if a consumer is interested in a standard product model, the recommendation engine might show them a premium model as an alternative.
For example, a customer might want a standard Model X laptop for $799. However, at the checkout stage, they see a popup encouraging them to upgrade to a premium Model Y notebook with much better specs for an additional $200.
If the customer is intrigued, they could go for the premium model, resulting in a higher sale than if they bought the standard model.
Lower cart abandonment rates
Cart abandonment is a big issue for many retailers, as it leads to lost sales. According to a SaleCycle survey, cart abandonment rates in the retail sector were 67.49% in 2022.
Personalizing your product recommendations can significantly address this issue. When customers get suggestions tailored to their preferences, there’s a high chance those items will resonate with them, reducing the likelihood of them abandoning their shopping carts.
Moreover, shopping online can sometimes be overwhelming due to the abundance of choices. That’s why 34% of customers who abandoned their carts in 2022 said they were “just looking” and not ready to buy, according to the same SaleCycle report.
Customized suggestions can narrow down the options to the most relevant. This allows shoppers to find what they need quickly, which reduces the chances of them changing their minds and abandoning their carts.
In addition, personalized recommendation systems that adjust in real-time to user behavior can provide alternatives or related products before a customer leaves their cart. And if they do abandon their cart, they automatically get a customized email that draws them back in to complete their purchase.
How to implement personalized recommendations in your email campaigns
Let’s now look at 5 ways you can implement customized recommendations in your email marketing campaigns to boost engagement and, ultimately, sales.
1. Use email marketing tools with AI product recommendations
According to a 2023 Coveo report, 24% of consumers become frustrated when shopping online due to too many irrelevant suggestions that aren’t customized to their preferences or shopping habits.
Because of that, most companies are using artificial intelligence to personalize their product suggestions. In fact, the number is as high as 92%, according to Twilio Segment’s report.
AI algorithms analyze a lot of customer data, including search queries and browsing history, to understand what a shopper might be interested in. They then suggest products that are tailored to the buyer’s interests and preferences.
Over time, the algorithms improve their recommendations based on what they learn about a customer, which enhances the user experience.
Some email marketing platforms integrate AI product recommendation features into their systems. They use artificial intelligence to find relevant products based on what shoppers like and what they usually buy. This means that every customer who reads your emails or visits your ecommerce site sees the type of products they’re most likely to buy.
An excellent example of a company that leveraged this feature is Pako Lorente, a men’s fashion brand. Before adding AI recommendations, the company had two problems. One, its customers often looked for similar products at competitors’ stores. And two, the brand had low sales of complementary products.
By implementing AI recommendations, Pako Lorente was able to add “Others also bought” and “Similar products” sections on its product pages. Within a month, 206,000 pln (around $49,000) worth of products were added to shopping carts because of these new features.
2. Segment your audience based on purchase behavior and preferences
Based on Zendesk’s report, 59% of customers think companies should use the data they obtain from users to personalize shopping experiences.
However, before you do that, you need to segment your customers. That way, you can offer relevant recommendations that are more suitable to their needs.
But how do you do this?
First, research the shopping patterns and preferences of your customers. Then, create buyer personas that describe the typical shoppers you intend to target.
For example, if you run a fashion brand, you can create a persona with the following characteristics:
- Shopping preferences: Prefers buying online and loves to stay up to date with the latest fashion trends.
- Device usage: Primarily uses her smartphone for browsing and shopping.
- Social media presence: Active on Instagram and TikTok. Follows various fashion influencers and often shares her outfits of the day.
- Brand loyalty: Loyal to brands that offer quality products and have a robust online presence.
Once you create your buyer personas, place customers into different segments based on their behavior. Then, target each group with customized product recommendation emails.
According to our 2024 Benchmarks report, personalized emails have higher opens and click-throughs than non-personalized emails.
3. Be transparent in how you use customer data
Shoppers are becoming more conscious of how businesses use their personal data and want more control over it. The report from Twilio Segment shows that 23% of consumers feel less comfortable with their information being used for customization purposes than they were a year ago.
The same report also shares that only 51% of shoppers trust businesses to keep their personal information safe and use it responsibly.
With such skepticism among consumers, it’s best to create a clear privacy policy that explains what data you collect, how you use it, who you share it with, and how long you retain it. That way, you can show you’re trustworthy and build a stronger relationship with your customers.
4. Provide value in your product recommendation emails
It’s not enough to send an email that recommends certain products to customers.
You also need to provide information that helps them understand why the recommended items go well with what they’ve already bought. You can even include reviews, ratings, or testimonials to show that other people got value from the suggested products.
Moreover, explain why you’re recommending a specific product and how it relates to the customer’s preferences or previous purchases to add a layer of context and a personal touch.
For instance, suppose a customer buys a high-end digital camera from your ecommerce store. In that case, you could send them a thank-you email that recommends camera lenses and other photography accessories as complementary products.
The email could read:
“Thank you for your recent purchase. We believe the XYZ Pro Lens Kit would make a great addition to your digital camera. The kit includes 5 lenses designed to further boost the quality of your photos.”
5. A/B test and optimize your email campaigns
A/B testing allows you to fine-tune the level of personalization in your emails. This way, you can be sure you’re always suggesting the right products to the appropriate people, which increases customer engagement and conversion rates.
Testing also allows you to figure out the best time and frequency to send your email campaign, which is when customers will most likely engage with it.
On top of that, you can use an automation platform like GetResponse MAX that offers Time Travel and Perfect Timing features to personalize your send times (and has personalized product recommendations in its toolset too).
Time Travel ensures your emails are delivered at a specific local time, no matter where your customers are located geographically. For example, if you schedule your emails to be sent to each recipient at exactly 10 PM, they’ll arrive in every inbox at that time.
Perfect Timing, on the other hand, uses historical data for each customer to establish when they typically open and click your emails. The system then sends your message at that “perfect” time.
Product recommendation examples
Here are some examples of brands that use the recommendation systems we discussed earlier to personalize their product suggestions and, as a result, increase their chances of making a sale.
Crate & Barrel — “Based on your recent purchase, you may also like”
Crate & Barrel is a retail company that sells modern furniture, small kitchen appliances, and dinnerware. In this thank-you email, the brand includes a section below the primary image that promotes other items the customer may like.
The email uses a content-based filtering approach, where it suggests similar items based on what the recipient has bought before. These handpicked products show that the company pays attention to what its customers prefer, which may encourage shoppers to buy more.
Lush — “We’ll think you’ll love”
Lush is a British cosmetics retailer that produces creams, soaps, shampoos, and other cosmetics.
The above email welcomes new subscribers with a playful tone — “your inbox smells better already” — that aligns with the brand’s style, making the email feel personal and entertaining.
After welcoming the recipient, it goes to the “We’ll think you’ll love…” section that introduces the customer to some of the brand’s products, nudging them to make their first purchase.
The email likely uses a collaborative filtering system, which recommends products based on what other customers like. The brand could also be suggesting popular products it wants to promote.
As the subscriber interacts more with the company, the recommendations may become more tailored based on their preferences and purchase history.
Warby Parker — “Playing favorites”
Warby Parker is an American retailer that sells prescription glasses, sunglasses, and contact lenses.
This email may use a collaborative filtering system to promote frames based on their popularity with other glasses wearers. And by highlighting what pairs the Warby Parker team loves, the brand offers social proof, which can persuade shoppers to buy the frames.
Forfolk — “Best-seller”
Forfolk is an American alternative medicine retailer that sells adaptogenic capsules that aim to boost immunity and decrease stress.
Like Warby Parker, the brand probably uses a collaborative filtering system to showcase its most popular product for the month. By highlighting that it’s a best-selling item, Forfolk offers proof that many customers find value in it, which can motivate shoppers to buy.
Thread — “New arrivals”
This email from Thread, a personalized clothing marketplace purchased by Marks & Spencer, uses words like “We wanted you to be the first to know about the latest arrivals from brands you like” to give the recipient a sense of exclusivity and personalization.
The statement “from brands you like” shows that the company uses a content-based recommendation system, which suggests products based on what a customer liked before. This tactic boosts the brand’s chances of making a sale.
Boost your conversions with personalized product recommendations
We’ve seen how customized product recommendations can boost your revenue and build customer loyalty, among other benefits. By using this strategy, you can differentiate yourself from competitors and scale your business much faster.
To get a head start, invest in a marketing automation platform like GetResponse MAX that offers AI product recommendations, which suggest the items your customers are most likely to buy and can increase your conversions.