8 product recommendation strategies to skyrocket eCommerce conversions.

Upthrust

How many times have you bought products based on recommendations?

We’re not talking only about online experiences, but try to memorize your real-life decisions. Probably, you’ve already recalled the moments where your friends or family members changed your mind and led you to buy a different product. Or you recalled that you chose to buy a computer based on a recommendation from a tech expert in the store. Well, we all do that.

And we do that for multiple reasons.

A piece of advice from a close friend, expert, or even a stranger on the internet is social proof and gives you comfort.
Following someone else’s decision is easier than making your own.

That’s why eCommerce product recommendations are an area that needs attention. Because, in online shopping experiences, you’re alone with your customers. You can make recommendations and influence their decisions which would eventually increase your conversions and purchases.

We believe every eCommerce manager or business owner should create better ways to recommend their products. Therefore, in this article, we’ll share the most effective product recommendation strategies with you.

By the end of this article, you’ll be able to improve your product recommendations strategy and hopefully get more conversions and sales.

We’ll talk about

Types of eCommerce product recommendation engines.
Benefits of a product recommendation strategy.
Eight best product recommendation practices to increase your conversions.

Types of eCommerce product recommendation engines.

In this section, we’ll show you the “how” of product recommendation engines. How they’re so accurate in suggesting products and how they’re working in the back-end of your online shop. Let’s see.

#1 Content-based filtering

Analysis of prospects’ behavior based on cookies. As you know, cookies can track you over multiple visits and track your decisions on the website. Based on cookies, the algorithm can then recommend certain products or services. The idea is simple: if you’re interested in a specific item, you can be interested in similar products and get personalized recommendations.

Example: if you liked hoodies, the algorithm could show you more hoodies.

#2 Collaborative data-filtering

This time, the algorithm combines data from users who display similar behavior. Then it makes recommendations based on this pool of collective user data. This is a more advanced method for sure, and it can make surprisingly complicated recommendations.

Example: If prospect X and Y like similar products, collaborative filtering can predict that X could purchase products that Y purchased before. And recommend relevant products based on behavioral data.

#3 Combination of content-based and collaborative filtering.

This hybrid method analyzes both personal and group decisions to create a tailored recommendation for a specific visitor.

Example: The YouTube video recommendation algorithm is the perfect example of this. It’s almost too accurate because it tracks your content consumption habits as well as users’ that matches your profile. That’s why YouTube is a black hole once you start to watch.

4 benefits of having a product recommendation engine

It is obvious that product recommendation engines benefit from high-tech machine learning processes. And that raises some questions in mind. Of course, the most dominant one: how costly is it? Is it worth the investment?

Well, in short: yes.

That’s how pioneer companies such as Amazon created a huge gap between their competitors. For years and years, they trained their machine learning engines to make the most accurate personalized recommendations.

Let’s talk about some tangible benefits and see why it is a well-worth investment:

Increased revenue: a study by Barilliance showed product recommendations are a huge part, adding up to 31% of the eCommerce revenue. 12% of the total purchases were coming from the recommendations.
Reduced cart abandonment: visitors that interact with your recommendations are up to 4.35% less likely to abandon their carts If you want to increase your add-to-care rate, you can also check our article about eCommerce activation tips.
Time spent in store: Visitors engaged with a product recommendation spent an average of 12.9 minutes on your shop vs 2.9 minutes for those who didn’t engage. Of course, this could easily lead to more conversions and sales.
Increased retention rate: accurate recommendations make things easy for your customers. Therefore, 56% of online shoppers are more likely to return to a website that offers smart recommendations.

Convincing, right? Now, let’s talk about the best practices and strategies out there.

Onwards.

9 best practices for eCommerce product recommendations

#1 Show your best sellers

The simplest yet most effective form of product recommendation. You don’t even need to have an advanced machine learning tracking system for this one. All you have to do is know/track your most popular products and recommend them to your visitors.

Remember, most of the time, 80% of your profits come from 20% of your products. So nailing this right could bring you immense growth.

#2 Show ratings and reviews

Looking for ratings and reviews from other customers is now a part of the journey for many online shoppers. As expected, products with 5-star ratings or hundreds of reviews lead to higher conversion rates.

See how Amazon displays the ratings, comments, and answered questions in one place to send strong social-proof signals to the buyers.

#3 Show product recommendations based on buyers profile

I think we can agree that “You may also like” is now a default practice among eCommerce businesses. But of course, accurate recommendations require a lot of groundwork. As we mentioned earlier, this kind of recommendation uses content-based, collaborative filtering or hybrid filtering approaches.

By analyzing the audience’s preferences, the algorithm finds products in conjunctions with one another then recommends them to customers. If you want to experience it, you can choose an eCommerce website you have never visited before and start to click things you like. And soon, you’ll witness the machine learning magic.

P.S. We believe Zalando has one of the best product recommendations and UX. Definitely check their app.

#4 Show discount and sales

Well, we are all looking for our next deal, right? We rather find bargains than purchasing the most expensive product in the market. This is the reality.

So, if you want more conversions, you should consider showing discounts and sales campaigns to your customers. Remember, 80% of people are incentivized to buy from a new brand if they could find a good bargain.

#5 Customers who bought [this item] also bought [that item] recommendations.

This is based on the same machine learning principles. But the real difference is how you present it. We believe the wording here makes this recommendation strategy work well. As we mentioned, people are inclined to follow other people even though they’re complete strangers.

Amazon has used this for years and is still using it for a reason.

#6 Frequently bought together

Does the customer journey stops after they buy a product? No. Say the product is a gaming laptop, they might need

Gaming Mouse
Gaming Keyboard
Gaming headphones
Mouse pad
And so on.

That’s why smart eCommerce platforms make complementary product recommendations. They always think one step further and teach their algorithms to suggest supportive/ related products to their customers.

#7 Offer recommended product pairings on checkout

Like the point above, you try to convince them of related products on the checkout page. This is your last opportunity to increase the average order value. However, this could turn out to be a double-edged sword quickly. So make sure you don’t distract your buyers by recommending them unrelated products.

#8 Show products for upcoming holidays or special days.

Pretty straightforward yet effective. Special days and upcoming events are the rocket fuel for eCommerce businesses. If you could plan and change your recommendations properly, you’ll likely see a jump in your sales.

Bottom line

We hope you learned new things that you could use on your eCommerce journey.

Now it’s time for us to recommend something “you might like”. If you like what you read and think your eCommerce store can benefit from these strategies, we can help you. You can contact us to discuss your needs and wants. And hopefully, we can build your recommendation engine together to increase your conversions!

If you like to do further reading, we advise you to read the articles below: