Does your website recommend products to its shoppers?
If not, then you must know this!
Hope all these stats have made it clear that you’ve been leaving tons of profit on the table without using a product recommendation in eCommerce.
eCommerce product recommendation is serving/suggesting relevant products or appealing offers to individual shoppers based on their previous search behavior, product preferences, and purchases.
With the recommendation engine market soaring to reach USD 15.13 billion by 2026 (with a CAGR of 37.46% from 2021-2026) without deploying eCommerce product recommendations on your website driving sales or even surviving in the industry can become tougher than you think.
With product recommendations becoming increasingly pivotal for all eCommerce businesses, below we shall check out 5 compelling eCommerce recommendation techniques and how to implement them for maximized engagement, conversions, and profits.
1. eCommerce Product recommendation popup
Popup of recommended products is an absolutely intelligent technique in eCommerce product recommendation as it helps shoppers find the right accessories for the product they have added to their cart.
In brick and mortar stores the salespeople recommend products based on what shoppers have in their shopping cart. Sometimes these recommendations may be relevant sometimes not.
But, these eCommerce product recommendation popups are purely based on algorithms and data. They help shoppers identify accessories relevant to the order, create tailored user experience and drive revenue to your eCommerce website by increasing the average cart value.
To instantly add personalized product recommendation in eCommerce website of yours, click here.
2. Recently viewed products
Online shoppers easily get lost in the middle of the shopping process. They leave without purchasing for a number of reasons. But whatever the reason may be it is always good to remind them about the last products they have checked and give them a second chance to complete the shopping process they’ve started.
By including “recently viewed products” in your store you remind your customers about the products they have recently viewed, searched, or checked on.
When customers easily find products they have already searched for, they are most likely to buy them.
Most shoppers appreciate recently viewed product listings as most of them tend to forget exactly how they found the product or where they located it. Recently viewed suggestions solve this problem making saving them time and effort.
3. Product flags / Product USP highlighters
You have only a few seconds to grab the shoppers’ attention, “WOW” them, and make them click “BUY NOW”. You need to communicate your highlight feature as quickly and clearly as possible in the fraction of seconds available and using product flags is the perfect technique to do that.
Labels like “New”, “X% OFF”, “Made in America”, “Hot Deal”, “Price Drop”, “Hot selling”, “Sale”, “Featured”, and more, right on the product image grab the attention of shoppers.
Product flags or USP highlighters are mainly used to recommend shoppers products of specific criteria or specialty. It allows you to highlight a product’s unique and salient features on the product images in a visually impressive way. Assigning labels to various products in the product images also helps shoppers choose easily and wisely.
To assign eye-catching labels to your products check out our homegrown Product USP Highlighter extension.
4. Star rating
Displaying a star rating for the product right next to the product image expresses your brand value and character. Products with higher star reviews and ratings sell in higher volumes.
It is an indirect recommendation for a product that has better reviews from other customers. A good start rating encourages people to buy the product and adds value to your brand.
Though shoppers may perform detailed research about the product by checking the customer reviews, a prominently displayed star rating makes a good first impression. When shoppers realize that people are praising a product, they reduce their research and focus more on buying the product.
5. Similar product recommendation
In this strategy products similar to what the customer is searching for is suggested. Personalized product recommendation in eCommerce makes customers feel their needs are recognized and valued.
You can recommend similar products to your customers matching their preferences and affinities based on what the customer is searching for at the moment, the product choices of similar shoppers, and the customer’s previous purchase history.
This way you offer recommendation for a product, help your customers find products they need, alternatives for their search and you can also eliminate the display of the “No results” page.
eCommerce product recommendation techniques are a must-have today. But to increase its effectiveness keep in mind the various factors like page context, target audience, and optimal eCommerce recommendation strategy before implementation.
On the whole,
Sometimes it’s the smallest things that impress customers to a greater extent. If you’re smart enough to meet customer needs and demands, you are sure to drive engagement, skyrocket conversions, boost sales and accelerate profits.
Hope these trending eCommerce product recommendation tips help your eCommerce store convert like crazy in 2022.
After reading these research-backed statistics and proven tips are you all ready to deploy some personalized product recommendation in eCommerce? Then get started by having a one-on-one discussion with our conversion rate optimization experts and get a free CRO audit for your site today.
An eCommerce product recommendation engine is a software that traces the customer’s behavior on your eCommerce site and recommends products based on that to the customers.
eCommerce product recommendation engines help you recommend products to your customers directly on your website, or through emails or advertising banners. The more advanced software you use the more personalized and precise the recommendations will be.
eCommerce product recommendation engines work on algorithms and data. It uses a filtering system to recommend the most relevant or similar product to a user and displays the products that the shopper might be interested in.
A typical product recommendation engine’s customer data processing has 4 phases:
You can broadly classify product recommendation engines into 3 types:
In the collaborative filtering method based on the users’ preferences, activities, and behavior, the collected information is analyzed and items are recommended.
There are two types of collaborative filtering techniques used.
Example: If customer A likes mobiles and laptops and customer B likes mobiles and tablets, as both customers have a similar interest (mobiles), there is a high probability that A may like tablets and B laptops. This is user-user collaborative filtering.
A similar approach is followed for items in Item-Item collaborative filtering. Thus, in collaborative filtering, the algorithm pair off customers or items based on their information and recommend accordingly.
2. Content-Based Filtering
In this method, cookies are used to track user’s over multiple visits. In this filtering method, the algorithm makes product recommendations based on what the customer has liked in the past.
The content-based filtering works on the premise that if a customer likes a specific product, he/she will also like a similar product of it.
3. Hybrid Recommendation Systems
As per its name, a hybrid recommendation system is a combination of collaborative and content-based filtering. It is the most effective type of product recommendation engine for eCommerce stores.