Every day in the United States about 5,000 shoppers walk into grocery stores to buy a carton of milk and end up coming out with a basket full of things they hadn't planned on buying.
Okay... We made that stat up.
But the fact that it sounds so plausible means you have to admit that grocery stores do a pretty bang-up job of using product placements to tempt you into buying something you never knew you wanted in the first place.
Who among us hasn’t been waiting in line behind an octogenarian paying with loose change and gazed over at the display of tantalizing confections beside them and thought, “I should add two packs of gum, some batteries, and this diligently researched tabloid about mole people to my cart”?
In the ecommerce world, tempting customers with products they can’t pass up is called personalized product recommendations. The benefit of personalized product recommendations in a digital environment over shopping in-store is you can show customers product recommendations based on their past behavior and preferences.
You’ve probably already seen these kinds of recommendations in the wild when you’re shopping online. It’s so common that 52% of customers now expect offers to be personalized.
Personalized product recommendations aren’t just placed in front of a customer hoping they’ll buy it, they help customers find something they'll actually be excited to purchase. It’s a win-win for both customers and brands. Shoppers enjoy a more personalized customer experience and merchants see boosts in average order values (AOV) and customer retention.
For example, Taste Salud uses personalized product recommendations in their Smart Cart to tempt customers with some of their most loved products (and all the while gain free perks through their tiered progress bar).
Here are four surefire ways to use personalized product recommendations to help you increase conversions, retain customers and boost the average order value on your ecommerce store.
User data helps you understand your customer’s needs and expectations so you can provide them with more tailored recommendations. As an Accenture study shows, 83% of shoppers are willing to share their data to enable a more personalized experience. One of the most powerful, effective (and lucrative) ways to collect user data for making hyper-targeted recommendations is through collaborative filtering.
With this method, AI-powered product recommendation engines analyze data from multiple customers to recommend products for each customer. This data “collaborate” to predict a customer's preferences based on other similar customers.
This increases the likelihood of recommending a product customers might not have otherwise discovered from their purchase history alone. And this ultimately creates more upselling and cross-selling opportunities for your ecommerce store.
Amazon, an absolute beast in the ecommerce market, was a pioneer of collaborative-based filtering. In 2003, they published Recommendations: Item-to-Item Collaborative Filtering, which later won an award from the Institute of Electrical and Electronics Engineers (IEEE).
Amazon's algorithm selects recommended products for each user based on their purchase history, interactions, and ratings of other items. It then combines that data to curate similar products viewed by users with similar preferences and interests.
For example, upon visiting the product page for a popular drone, Amazon displays recommendations after the product information that includes an additional battery and a drone landing pad.
Because similar users who previously purchased the drone also bought those accessories, Amazon's recommendation system presents the same options based on the assumption that you will find them useful too.
The company amassed $514.0 billion in net sales in 2022, and nearly 35% of its sales came from personalized product recommendations. Not too shabby, eh?
In the world of sales, the law of the land is "ABC" or "Always Be Closing." In the ecommerce world, it should be "ABT," "Always Be Testing."
That’s because, if you're not running A/B tests, you're leaving money on the table. It's an essential practice for ensuring that you're constantly recommending the right products and not missing opportunities for improved user experience and conversions. The more you tweak and measure your product recommendation strategy, the better your results will be.
A/B testing helps you identify how combinations of personalization impact your product recommendation strategy. For example, two elements that impact sales are product photos and product titles. A/B testing reveals your most successful elements.
A photo can make all the difference when it comes to conversion rates. 75% of shoppers say product photos influence their buying decisions. So it makes sense to test your product photos and use the results to optimize them for more sales.
For example, you can try A/B testing lifestyle images against product-only photos. Or try incorporating copy into your images such as product benefits or facts.
A captivating title can attract attention and drive action. Try A/B testing different catchy titles that drive urgency or experiment with various lengths to discover which drives more clicks. Also, try testing popular search terms to increase the chances of the right shoppers clicking on your product.
Netflix understands "ABT" very well. They run about 250 A/B tests a year with around 100,000 users.
Netflix tests versions of its landing cards —the images or video teasers shown to users browsing through Netflix's category of recommendations — to find the most popular ones.
For its hit show Stranger Things, the platform A/B tested different versions featuring different characters. The landing cards with the most clicks were adopted more widely.
Product upselling and cross-selling are one of the most tried and tested strategies in retail. Let's look at grocery stores again as an example. On an average grocery run, you might be handed free cheese samples in the produce section, find buy-one-get-one-free offers in the cereal aisle, and encounter shelves stacked with magazines and candy by the checkout. All of these touchpoints are set up to get you to purchase more products throughout your shopping experience.
When it comes to your ecommerce site, pull a page out of the retail playbook and use upselling and cross-selling techniques to drive sales and create an enjoyable customer experience.
You'll need a different type of recommendation strategy depending on where your customers are in the sales funnel. You can strategically place product recommendations on your online store’s home page, product page, checkout page, and shopping cart.
Your online store's home page is prime real estate for converting first-time visitors into loyal customers. Well-crafted recommendations on a home page grab users' attention. But at the same time, you don't want it to feel as if you're bombarding customers like an overzealous salesperson.
Use social proof to hook your user's attention as soon as they reach your site. Social proof highlights the real experience of consumers, something customers look for. In fact, reviews displayed alongside higher-priced products increased the conversion rate by 380%.
Fill your home page with items with the highest customer ratings or include photos or video reviews of relevant products.
Customers who land on a product page are still shopping, so while they browse, offer relevant alternatives and complementary products by adding a "Related Products" widget. This allows you to keep shoppers engaged and increases the likelihood of converting them into paying customers.
Also, consider adding a "Recently Viewed" widget to your product pages to allow shoppers to return to products they showed an interest in.
When a customer adds an item to their shopping cart, it's one of the strongest indicators of purchase intent. Therefore, if you want to increase AOV, placing targeted recommendations on your cart pages is a must.
For example, you can place a “Frequently Bought Together” widget on the cart page when a shopper has added a product that matches the criteria.
At checkout, the customer already has committed to a purchase so consider cross-selling accessories and other complementary items.
For example, if a customer has a mobile phone in their cart, suggest a new case or an extra charger. Another tactic is recommending products based on the customer's browsing or purchasing history.
Licorice.com is a provider of artisanal licorice from around the world. One of the first things customers see when they land on the homepage are customer reviews and “best-selling flavors” at the top of the page.
And to entice customers to make an impulse candy purchase before checking out, they also include highly rated product suggestions with a one-click way to add items to their cart.
Email marketing is the most cost-effective marketing tool. When paired with personalized product recommendations, you’ll engage and convert customers to increase your average visit value.
Marketers report a 760% increase in email revenue from a personalization strategy that includes segmented campaigns.
Integrate personalized product recommendations into your own email segmentation campaigns to gently nudge users to make purchases depending on where they are in their customer journey:
A well-crafted and executed personalized product recommendation strategy gives you magic powers to figure out what your customers want even before they do. Rebuy lets you take things even further.
Using proprietary AI/ML technology with modern (no-code) software, our Intelligence Engine analyzes billions of data points to provide real-time, intelligent insights that enable you to take complete control of the buying journey and guide shoppers down your preferred path.
Book a demo to learn more.
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