Today's shoppers are often spoiled with choices when it comes to product selection. Ironically, time constraints and the level of effort that goes into researching products prevent customers from going through all available items and making informed decisions. As a result, they may end up purchasing a product they're not entirely satisfied with or end up not purchasing at all.
Product recommendations provide a solution to this problem and an effective one at that. Although recommendations are hardly a novel concept - after all, store clerks have been recommending products since the dawn of retail - the ecommerce world didn’t pay too much attention until Amazon made a fortune off of them. According to McKinsey, product recommendations account for 35% of Amazon's sales. If you're not already leveraging product recommendations to boost your revenue, you should know you're leaving a lot of money on the table.
Product recommendations help guide your customers throughout their online shopping journey and ensure they have a seamless experience. When executed effectively, a good product recommendations strategy is the equivalent of a server at a restaurant suggesting a perfect wine pairing for your entree, or a clothing store clerk pulling a style of jeans that will pair perfectly with the sweater you're interested in trying on. The end result is a customer that feels more confident in their purchase, and ultimately, happier with the overall experience.
But there are some other benefits to be had beyond this:
91% of shoppers say they prefer to buy from a brand that provides product recommendations and offers. However, consistently recommending products your customers are not interested in or do not find useful can be counterproductive.
So how do you know what products belong with what? Manual curation makes sense in select situations, like helping customers find the exact pieces of an outfit that are styled together in a product shot. But it's also incredibly time-consuming, particularly as products go out of stock or you have seasonal turnover.
Enter artificial intelligence for product recommendations. Recommendation engines leverage algorithms to suggest different categories of products to your visitors. For every product a customer shows interest in, they'll have access to the categories' best-selling, new, and popular items. You can also run upsells and cross-sells by suggesting more expensive or complementary products to your customers based on items already in their cart or their browsing history. Importantly, these recommendations will be personalized to suit different customers or customer segments, further increasing the chances of conversion.
Ultimately the right product recommendation engine is one that suggests the products your customers need or want and guarantees high conversion rates.
There are different avenues or channels you can leverage to recommend products to customers. Some of the most popular ones include:
We'll get into how to leverage each of these product recommendation channels below.
Home page and collection page recommendations
Your home page and collection pages are similar to the experience of drawing a customer into a brick and mortar store, and then to a specific display - but with a twist. You can use recommendations to personalize this simulated experience, creating more value for the customer in a shorter amount of time. For example, instead of hoping to catch every customer's eye with the same 'new arrivals', AI-based recommendation engines can assess who the customer is and provide the most relevant new arrivals instead.
Product page cross-sells
One of the most common places to see personalized recommendations are on product pages. Specifically, you'll often see cross-sells on these pages, which are defined as products that go well with the item a customer is looking at, or products that are often purchased in tandem with a product. However, you can use just about any recommendation type on product pages, including upsells, bundles, and bestsellers.
In keeping with the Pareto principle, most ecommerce companies generate 80% of their profit from 20% of their products. In that sense, you can hardly go wrong with recommending the products you sell the most to your customers. Thankfully, you don't need any advanced tracking to know your bestselling products. AI-based recommendations engines can parse what your bestsellers are based on sales volumes, and then tailor which bestsellers a customer sees based on their buying or browsing behavior.
You'll most often see bestsellers on the home page or collection pages, but you also have the option to share them via bestseller emails. You can also include the bestseller label on your most popular products so that visitors can identify them as they scroll through your website.
Post-purchase cross-sells have the potential to boost your revenue by as much as 30%
Post-purchase cross-sells introduce customers to products that complement the item they just bought. For example, a customer that just bought a phone may be interested in accessories like screen protectors and phone cases. These recommendations have the potential to boost your revenue by as much as 30%. They typically come as emails or pop-ups on the site. Since the recommendation comes after the customer has completed a transaction, the focus is more on teeing up the next sale, as opposed to closing the current one.
Abandoned cart upsells
Cart abandonment is a challenge all ecommerce store owners have to battle with, whether you're a retail giant or small-scale retailer. While there's no silver bullet for getting your shoppers to come back to complete their purchase, abandoned cart upsells can prove pretty useful. Every abandoned cart should trigger a targeted email that reminds the customer about the item they were interested in and reminds them to make a purchase. You can even use these emails as an opportunity to generate more revenue than if you'd closed the sale in the first place, by upselling customers on a bigger volume of the item they were looking at, or a subscription. Consider adding in an incentive like free shipping or a discount code to increase the chances of conversion. Not seeing results with email? Create a seamless experience for returning customers by reminding them of what they didn't check out with last time they were on your site.
Google Shopping ads
Google Shopping ads have become increasingly popular in recent times. And it comes as no surprise since Google controls over 90% of the search market, with the reach of Shopping ads steadily increasing in recent years. Using the customer's search term, merchants can leverage shopping ads to recommend the products that the prospective customer wants. The chances of conversion are also relatively high since most people who click on shopping ads are primed to purchase.
So far we've talked a fair bit about how recommendations serve a customer more relevant content and products based on the products they've shown an interest in. But what do you do when a customer is net new to your brand, with zero browsing or purchase history? Advanced recommender systems can still leverage the following touchpoints to give new customers a personalized experience.
Pay attention to your customer's traffic source and curate specific landing page experiences
One of the easiest ways to personalize the new customer experience is to pay attention to where they're coming from, by generating unique landing pages and recommendations based on the traffic source. For example, if the customer lands on the homepage from search, social, or a generic ad, you probably know very little about them at that point. You may simply recommend trending products or display social proof that shows customers are happy with your brand. On the other hand, if the person follows a clickable ad spotlighting a specific product, then you've already got a key piece of information about what they're interested in. You could create a tailored landing page for that experience, or simply embed cross-sells that auto-generate based on the product page the customer is in and historical cross-sell data.
Leverage social proof with rating-based product recommendations
Social proof is a solid strategy to driving conversions, and an excellent choice for driving customers to spend more time on your site by clicking on your recommendations. One of the most common forms of social proof are simple star ratings, which can be appended to your recommendation boxes to give customers confidence that a product that catches their eye works as well as it looks.
Shorten the time to checkout by recommending products frequently bought together
This technique relies on collaborative filtering to suggest useful products to repeat customers. For example, if Customer X got a pair of sneakers then comes back to get a pair of socks, your recommendation engine can save Customer Y the time it would take to go on that same exploratory journey by suggesting they buy a pair of socks on the product page for the sneakers.
Product recommendation algorithms work better for returning customers since you already have some information about them. Below are some suggestions on how to use recommendations for returning customers.
Take what you know about a customer and shorten the time to value through remarketing
Remarketing provides an avenue to automate your upselling while opening the channel for continuous customer engagement. A competent recommendation engine will help filter your customers based on their purchase history and introduce them to products they might be interested in through remarketing channels such as email or advertising. However, you can also place remarketing offers right on your website, from a 'Recommended for You' box on the home page to 'New Arrivals for John/Jane' in the customer account center. For example, a customer who recently purchased outdoor furniture may get recommendations about potted plants that can further beautify their outdoor space.
Drive up AOV with cart and checkout recommendations
Cart and checkout recommendations are useful tactics for increasing the AOV of your repeat customers. And since adding to the cart already suggests a strong affinity for the product, you can't do much harm at this stage. As usual, you want to introduce them to complementary products in a way that doesn't affect the user experience. A simple recommendation widget that allows them to add the suggested product to their shopping cart without leaving the page should do the trick.
Offer post-purchase cross-sell banners for first-time customers
Cross-sell banners help you introduce complementary products to first-time customers who have just made their first purchase. For example, a first-time customer that just bought pet food may be interested in a leash or other relevant pet accessories. Your recommendation engine can suggest these products to them via banners that pop up after they've checked out.
Again, to understand the importance of recommendation engines, you may want to look back on your experience while shopping at a brick-and-mortar store. You're more likely to prefer stores that always have a sales clerk on hand to give you useful advice or recommendations, as opposed to stores that leave you to figure things out alone. The same logic applies to your ecommerce site.
Without product recommendation engines and advanced machine learning algorithms, your customers will be in the dark while browsing your store. Consequently, the customer experience will be subpar, and they're less likely to make a purchase. Even when they do, you would not expect them to come back to buy again. It's why investing in artificial intelligence has become such a hot topic among retail marketers and digital leaders.
If you wish to make the most out of your first-time and repeat shoppers, you need a recommendation engine that can deliver intuitive, personalized, and high-converting product recommendations in real-time. LimeSpot is easily one of the best around, and it promises to treat your customers to a whole new level of personalized shopping.
Below are some of the benefits you'll enjoy from choosing LimeSpot as your personalization partner:
LimeSpot uses AI and machine learning to understand your customers' intent and purchasing behavior. It converts the data into actionable insights by providing personalized product recommendations for your customers just when they need them.
LimeSpot uses a variety of segmentation criteria to separate your customers into different groups. Each group is then targeted with personalized recommendations via email, SMS messages, in-store pop-ups, and other relevant marketing channels.
Personalized shopping experiences
With LimeSpot, every one of your customers will enjoy individualized product recommendations and email content. Your marketing channels will only show products unique to each customer's needs to guarantee a higher average order value and repeat purchase rate.
Even if you already have a product recommendation partner, it's vital to know that there's always room for improvement.
LimeSpot is a best-in-class leader in offering personalized product recommendations and improving the overall customer experience. LimeSpot guarantees 2-5x boost in conversion rates, and you start getting results almost immediately.
Get started with LimeSpot today.