How predictive personalisation smashes AOV - SwiftERM

How predictive personalisation smashes AOV. Picture the scene: you’re in your favourite clothing store, trying on a great new pair of jeans. Suddenly, the associate you’re with clicks their fingers and in a eureka moment, declares they know a top that would work perfectly with those jeans.

Or perhaps you’ve visited a tech store and bought your new tablet up to the checkout, but before scanning your item the clerk pauses and asks whether you’ve seen the brand new screen protector that’s just perfect for your tablet. 

If either of these scenarios sounds familiar to you, you’ve experienced personalised product recommendations. 

Of course, in the world of ecommerce, having real sales associates around to do this isn’t possible. Instead, online retailers need to use technology and data to understand their users and recommend products based on their interests and behavior from the very first time they visit their homepage.

Done right, these personalised product recommendations are extremely effective. Montetate, 75.5% of businesses are getting positive ROI from personalisation, with every industry responding in the affirmative at 70% or above. 

With ecommerce on the rise and 90% of shoppers attesting to being willing to share their behavioral data if their shopping experience is made cheaper or more convenient, there’s never been a better time to implement more personalisation into your online store.

Let’s explore why personalised product recommendations should be one of your first priorities.

What are Personalised Product Recommendations?

Personalized product recommendations are when a site shows a selection of product recommendations that’s unique to the individual visitor, based on their behaviours and profile. This is almost always based on a machine learning algorithm.

What’s important to remember is that not all forms of product recommendation are personalised. How do you know if they’re personalised or not? The question you should ask is whether you’d be seeing the same recommendation as the person next to you. 

Here are a few ideas of what constitutes ‘personalised’ product recommendations: 

  • Recommending products based on the user’s browsing or purchase history. Amazon is one of the gold standards for this, using historical data to serve unique related products  to each visitor: 
How predictive personalisation smashes AOV
  • Recommendations based on customer location or profile. A good example of this is using data on the customer’s location and making product recommendations based on the current weather conditions. One could just as easily use data on the visitor’s age or gender to change the recommendations.
  • Using product affinities to recommend products.  The best example of this is showing recommendations based on user behavior and what other, similar users have done. Again, Amazon is a great example of this, showing a set of recommendations on a product detail page (PDP) to take the user on the next step of their journey:

     

    So if these are ‘personalised’ product recommendations, then what are ‘unpersonalised’ ones? Here are a couple of examples. 

    • Showing social proof to indicate how popular a product is. This is an important tactic, but it’s generally the same, regardless of what the user has been doing.
    • Showing recommendations based on ‘business rules’. For example, indicating which items are low in stock or top sellers is a good tactic, but this is static and won’t change based on the visitor.

       

    Now we know the true meaning of personalised product recommendations, let’s unpack why they’re worthwhile.

    Why Use Personalised Product Recommendations In Ecommerce?

    Put simply, SmartHQ reports that they ‘found strong correlations between customers who see unique product suggestions and recommendations who not only stayed on brand’s websites longer, but they also compared prices on Amazon less, if at all.

    Around 84% of consumers rate being valued as a person and not as a number as very important to winning their business. 

    Relevant recommendations are also important to us because of their convenience. Rather than having to search for something else we might like around our original purchase, we automatically get signposted to something, saving us valuable time. Most ecommerce CMS systems should also allow you to automate most of this process fairly quickly.

    In case you’re not convinced, here are some more reasons to use personalised product recommendations: 

    1. Decrease shopping cart abandonment rate.

    In ecommerce, cart abandonment rate is one of the most important metrics. Showing personalised recommendations on the cart page can improve cart abandonment rates by 4.35%.

    Consumers abandon carts for a number of reasons. Sometimes they get distracted, sometimes they’re just browsing, but sometimes they feel they haven’t found what they’re looking for. 

    For example, a customer buying a scarf might have wanted to buy a winter bundle including gloves and a hat. Without slick product recommendations, the user will then have to go to subsequent categories to find these. This experience is inconvenient, with every extra step presenting an extra risk that they’ll abandon. 

    This is where personalised product recommendations can save the day. 

    Once the user has added their scarf, showcasing gloves and other relevant winter wear products could save them from abandoning their purchase. Lost revenue turns into extra revenue.

    2. Increase average order value (AOV).

    Personalised recommendations drive revenue by positively impacting a customer’s total cart amount. They offer relevant cross-sell and up-sell opportunities that pique a customer’s interest, resulting in them purchasing more than just the original item they came in for. 

    Statistics show that sessions that contain no engagement with product recommendations have on average, an AOV of $44.41. However, when prospects engage with just a single recommendation, this number multiplies by 369%.

    3. Increase session time. 

    Product recommendations create that rabbit hole-like feeling that all internet users are familiar with. Shoppers begin on one product, click through to another, get distracted by another, and before they know it, it’s been two hours.

    This pattern helps shoppers to stay on your site longer by capturing their attention and engaging them with recommendations for products they hadn’t considered or expected to find.

    4. Stand out among competitors.

    By the end of 2020, U.S. spending online smashed $861.12 billionTo serve this huge growth in online shopping, new ecommerce retailers launch their stores every day. But such a quick-growing, burgeoning market comes at a price: individuality.

    With such a huge quantity of options available to them, consumers now have the luxury of choosing which stores to visit based on what they want from their shopping experience. Personalization is high on the wish-list: 80% of consumers state they are more likely to make a purchase from a company when presented with a personalized experience. 

    Before we talk about how to get started with personalised product recommendations on your site, a quick word of warning – this might not be for everyone. Truly ‘personalised’ product recommendations rely on an algorithm, and algorithms need good volumes of data in order to be able to work effectively. 

    That means that if you’re a small site and don’t have much traffic yet, you might not have enough data to feed the machine, and you might need to hold off on this type of strategy for now. However, you can easily get started with some of the ‘non-personalised’ product recommendations we mentioned earlier in this article. 

    Wrapping Up

    As the number of online shoppers continues to grow and the industry continues to flourish, personalisation should become a top priority for all ecommerce stores.

    Whereas before personalisation used to be limited to email marketing sequences or chatbots, now it starts from the very moment your visitor arrives on your site. 

    We’ve seen how personalization can retain and convert customers, which is why personalised product recommendations are a vital addition to any store. 

    Start with your website data. Analyse and evaluate what the trends are telling you, and which groups of customers want to see what. Once you’ve got this information you can begin to deploy strategies across your site, making sure to test and evaluate them continually. 

    As marketers you’ll always be looking for ways to drive performance. Product personalisation, with a bit of automation or optimisation will help you do just that. If done successfully, your website will edge ahead of your competitors, increase average order values, and ultimately drive more revenue.

     

    We hope you enjoyed this article, intended to help improve our client’s profitability. It reflects the care SwiftERM offer, if you need help please ask, we can often help for free. If you haven’t already done so, then please enjoy a FREE month’s trial of our predictive personalisation software on your site, to see how powerful it is. Here

    Other articles of interest below:
    (Index to all articles here)

Creating an online store marketing strategy
How to use Google Analytics for ecommerce
How mobile-friendly is your ecommerce site?