Use a Product Recommendation Engine to Increase Your AOV

There's more than one way to achieving growth: it’s not always as simple as attracting leads and converting them into customers. Increasing your average order value (AOV) is an ingenious method of making use of your existing customers and existing database to drive profits. So, let’s see how we can make this happen.

The importance of ecommerce personalisation

Personalisation gives your store a voice and a personality. It makes your store memorable. If you’re looking to develop this, check out our tips on how to get your customers singing your praises and feeling like a valued member of your community.

Now, let’s narrow in on personalised recommendations.

73% of customers prefer retailers that use their personal information to make their shopping experience more relevant. The eCommerce marketplace gives customers a massive array of choice, so a company that works hard to simplify this process is likely to attract more customer attention.

I can’t emphasise enough how valuable your customer data is to your business. Data is what drives personalisation. It’s crucial in order to display products that fit in with your customer’s previous buying habits and preferences. To achieve this, it’s handy to know what product recommendation engines can do for your personalised approach.

What are product recommendation engines and how do they work?

Product recommendation engines are filtering systems that use algorithms to predict and display products that customers are likely to want to purchase. Over the last few years, there has been a massive surge in eCommerce sites using these engines, such as eBay and Alibaba. Amazon's product recommendation engine drives 35% of their revenue. A McKinsey report also reveals that companies experience a five to eight times ROI on their marketing spend by using a product recommendation engine.

It fills a service missing from online stores. With traditional stores, you have experts to advise customers on suitable products, as well as cross-sell to customers. Recommendation engines combine customer data and algorithms to create an automated version of that service.

You might be surprised by how much information you can garner about your clients, of which can be used to create enticing product recommendation lists. Here are some of the factors that these algorithms take into account:

Location/IP address
Search history
Previous purchases
Customer segmentation (purchases made by a similar demographic)
Shopping pattern

One way that you may be able to collect valuable customer information is by customers logging in to your store through Facebook or Google. This data can then help the recommendation engine cater to customer shopping habits.

Why you should consider using a product recommendation engine on your Shopify store

In this article, we are focusing on the AOV benefits of product recommendation engines. There are a few reasons why it's so effective:

The customer feels valued

A recommendation engine leads your customer through a path that is more likely to be stress-free and enjoyable, helping to foster brand loyalty. Customers appreciate when online retailers invest in creating a joyful shopping experience.

Option to bundle

Once recommendation engines become familiar with your buyers’ shopping habits, it can automate grouping (helping to increase the AOV!)

Attracts high-value, repeat customers

Once you see these statistics you’ll see how important it is to implement product personalisation. Although only 7% of customers find your website via product recommendations, they make up 26% of transactions! Not only that, but 37% of them will return, compared to the 19% average.

Surprise your customer

Encourage them to try out the newest trend your industry has to offer. Where a lot of online retailers go wrong is that they recommend items that the customer is already familiar with and recognises. With a product recommendation engine, you can use the behaviour trends of a customer’s peers as well as their preferences. This creates an impressive product discovery pathway.

Increase AOV

This tailored customer service aims to bring larger baskets to the checkout. When harnessing the benefits of a product recommendation engine, the average increase in AOV is 50%.

Revenue increase

All these advantages propel improvements to the bottom line. By how much depends a lot on which software package you choose, so be sure to check out our recommendations further below.

Where can product recommendations be displayed?

Below are some of the most popular spots to display product recommendations:

Homepage

Recommendations on the homepage instigate customer immersion and lead them right to where they need to go, even if they don’t know it yet. A top tip is to make sure that you don’t place purchased items in this area. Your customer may decide that they’ve seen all that your store can offer!

Product pages

Product pages are great place to mix and match items. Clothing is a great example: if someone is searching for a t-shirt and sees an entire recommended outfit, they may chuck it all into the shopping cart.

Feel free to get creative with the content. We’re all used to seeing the “you may also like” section on a website. This phrase blends into the background as we're used to zoning out obvious ad spaces on our screens. Try something unique like “rock these with…” or “we’re not saying you’ll like these, but you probably will”. This playful tone is an inbound marketing technique that makes the user feel part of your community.

Search pages

We’ve all become lazy typers when online shopping. I get a twinge of annoyance every time my phone doesn’t auto-predict my email address when I’m logging in. If your search bar can predict what the user is looking for within the first few characters, you’re making a great impression. Even better is to have a grammar correction system to smooth out any bumps like ‘0 results found’. Keep it streamlined people!

Category/collection pages

So, your lead has made it past the homepage and is now filtering their search. There are two options for your product recommendation position: either in the sidebar or at the end of the page. Shoppers who have yet to find a suitable item get further incentive to click into a product page.

Shopping cart page

Even as someone who works in digital marketing, I’m often susceptible to filling my cart during the final stages. It is a lucrative step if you play your cards right. An excellent incentive is to offer free shipping above a certain amount. Customers will often search through recommended products to snatch a last minute item.

Our top picks: product recommendation engines to use with your Shopify store

Limespot

What impressed us about Limespot was their transparency. Their impact study reveals significant average increases in revenue, AOV and conversion rates. A lot of this comes down to their intelligent product recommendations, which use real-time customer behaviour alongside product attributes to deliver results.

You can control the software’s features, including upselling and cross-selling tools, all within a single app. They waive the monthly fee if their tools drive less than five times your subscription fee, which makes it a risk-free investment.

Nosto

Nosto’s stand out feature is their patented, self-learning technology which enhances customer personalisation.

I’m also a fan of their checkout techniques. They capture sales from cross-selling to last minute checkout incentives. Nosto also use recommendations to prevent dead-end results pages, that can all too often disengage prospects. Given that their clients’ stores receive a 12% average increase in revenue, their personalisation campaign is certainly worth a look!