19 April 2018
Give your customers exactly what they want and at the time they’re most engaged using product recommendations.
Product recommendation algorithms present online shoppers with a personalised selection of the most relevant items for them in real time. They have become incredibly popular on eCommerce websites and email campaigns, most notably on Amazon which has integrated recommendations so well across their website that an estimated 35% of all sales are driven by their recommendation engine.
eCommerce personalisation tools like our partner Barilliance provide informed product recommendations based on both individual and aggregate browsing behaviour and purchase history. These tools help to shape unique experiences across the buying process for each of your customers, providing undeniable benefits.
Read on and find out how product recommendations can improve your website’s user experience and ultimately boost your profits.
First thing’s first, we know that your bottom line is the most important to you and the effect of product recommendations on conversion rates is nothing to be sniffed at. In fact, Barilliance has found that customers who click on product recommendations have a conversion rate 5.5 times higher than those who have not.
By showing a customer the right content at the right time, you can reach them at the moment they’re most open to making a purchase.
This rate is so high because it optimises product discovery where customers are exposed to items deeper in your product line that suit their tastes that they would otherwise not have found by themselves.
While 20% of products may account for 80% of your sales, this type of personalisation enables you to boost the ROI on long-tail items in your product line that may currently have a low turnover rate.
Crafting this personalised experience for shoppers where they feel valued and seen by your site is also instrumental in building loyalty and repeat purchases.
By showing customers what they want to see and saving them the effort of performing multiple searches, you are creating a good user experience and improving customer satisfaction for both new and returning visitors alike. Data from Salesforce has proven that shoppers who clicked a recommendation were nearly twice as likely to return to a website. Over the long-term, this has the effect of boosting the holy grail of customer lifetime value.
Recommendation engines also have the effect of increasing your average order value by up to 50% as well as raising the average number of items per order.
This happens as a result of creating product bundles, showing highly relevant products when a customer is in the mood to buy, triggering impulse purchases and more that we will cover below.
Looking for ways to make your campaigns stand out? Try adding some dynamic product recommendations into your emails.
Email marketing is renowned as the channel which generates the most ROI for a site and a large part of that is because they provide personalised content directly to a customer.
With Barilliance, you can integrate their product recommendation blocks directly into your existing templates no matter which email provider you use.
By enticing customers with such relevant messaging, you can boost your email clickthrough rates by 35%, driving more and more traffic to your website.
Phew! Those are some enticing benefits.
So how can you capitalise on the data that your customers are giving you?
When implemented correctly, product recommendations work nearly everywhere an eCommerce site, for example on the home page, category pages, product pages, cart page, 404 pages and more, and is also very successful when used in email campaigns.
Below we have summarised the best product recommendation blocks to use and where to place them, covering those that use personalised data, aggregated data and product data.
In all these strategies, you can customise the underlying rules of the recommendation engine to include and exclude specific products and categories from the recommendations based on your individual company needs.
Customers want to feel seen and understood when shopping on your website, which is why capturing individual data and using that to provide recommendations that are unique to them is always the best way to go.
With the clickthrough rate of personalised ‘Top Sellers’ recommendations 200% higher than recommendations that aren’t personalised, it’s easy to see just how effective these are.
Recommendation engines like Barilliance take into account the browsing patterns in the current session to determine a customer’s intent and combine this with their historical habits and purchase history.
For example, if a customer sorts a category by low to high price, the algorithm can infer that they are price sensitive and recommend them cheaper products or those that are on sale, or if they have bought medium sized clothing in the past, they will only be shown products that have stock available in that size.
This is also combined with data from other visitors who have made similar choices in order to provide the best recommendations.
As such, personalised recommendations work anywhere on your website, even on 404 pages which encourages a customer to continue browsing instead of leaving your site altogether. While a mix of the most relevant products for an individual could be shown on the homepage, for category pages, consider segmenting the recommended products so they match the category the visitor is currently browsing.
Even after a purchase has been made, you should still be showing personalised recommendations. On the checkout success page, thank the customer for their order while suggesting even more products that they may be interested in to encourage repeat sales.
Personalised product recommendations also work really well in email campaigns. Embed a personalisation block into the template of your regular newsletters, or even better, recommend more relevant products in the last email of your abandoned cart series when it is clear that the customer is no longer interested in the product they left behind.
As Barilliance dynamically generates the recommended products at the time the email is opened, this means they are always up to date with the data you have on that visitor.
Have you ever had a product catch your eye as you’re browsing through a store but when you finally make up your mind to buy it, you’re unable to find it again?
If a customer can’t find a product, they can’t buy it. It’s as simple as that.
Help your customers avoid this problem by providing them with access to their browsing history.
This is an especially suitable feature for stores where customers regularly purchase the same items, like in supermarkets.
Data compiled from the actions of previous visitors to your site highlights trends and relationships that humans may overlook.
Much like how customers trust reviews from their peers rather than messages from the company itself, these recommendations indicate the endorsement of a product by other shoppers and make a compelling argument for someone to also purchase them.
Highlighting your best sellers and most popular products on your homepage is a highly effective tactic for giving new visitors to your site a better understanding of your product offerings.
It also helps to capture the interest of those who are just browsing and aren’t looking for anything specific as it lends social proof to certain items and makes a customer feel confident about purchasing it.
When you haven’t gathered enough data on a particular customer to show them personalised products, these types of recommendations also work as a good fall back option both on your website and in emails.
Optimise this feature by segmenting the items displayed to be more specific to the category a visitor is currently browsing.
Through combining information from a large number of visitors about which products are viewed or purchased in the same session, recommendation engines can be used to automatically bundle products together.
Algorithms can predict product preferences in a way that you cannot, and you can use this to recommend relevant items to visitors even if you don’t have personal data about their tastes.
Much like how displaying ‘best sellers’ is effective, customers perceive these products to be approved by other shoppers similar to them, and they are influenced to view more and more items on your website.
The result is that your average order value receives a boost as customers are easily made aware of items that they might want or need without them having to do additional research.
This type of recommendation is especially effective when shown on the cart page as it reaches the customer when they have committed to a purchase and triggers further impulse buying.
For those who want more control over what products are recommended to a potential customer, tools like Barilliance allow you to manually select and filter the products shown.
Much like how the “Frequently Bought Together” strategy makes use of automatically created product bundles that encourage customers to spend more on each order, you can also manually control which related products are displayed on product pages.
If a customer is still researching and deciding between products, promoting alternatives and substitutes encourages them to keep browsing on your site instead of abandoning you for a competitor’s site to find items that better meet their criteria.
You can also use this opportunity to upsell similar products that have more features and are sold at a higher price point.
By showing products that are related on the features and functions level, you improve the user experience as visitors who have an aim in mind don’t have to take a step back and begin their search from the category page again, they can flow from product to product until they find the right one.
Using recommendation engines to cross-sell on the cart page can also increase your average order value and number of items bought per order. This involves matching your product range with their corresponding accessories, for example a dress with coordinating shoes and a bag, so customers can shop a whole outfit in one go.
Keep in mind that this may require some manual effort to set up, especially for electronics and the like where you must ensure that the add-ons you’re suggesting are actually compatible. You don’t want to recommend a bluetooth mouse that doesn’t actually work with the tablet the customer is looking to buy.
This type of recommendation is also very effective to use in an automated follow up email that you send after a customer has made a purchase. For example, 6 months after a customer has purchased a printer from you, check in with them and see if they need to purchase more of the related toner.
The earlier you set up product recommendations on your site, the earlier the algorithm can start gathering data to provide more accurate suggestions.
Not sure which of the strategies are best for your business? Contact us today and we will analyse your site and devise the best plan for you.
acidgreen is an award winning eCommerce agency specialising in Magento, Shopify Plus and digital marketing with over 15 years of industry experience. Our certified developers and digital marketers are highly qualified to create aesthetically pleasing Magento and Shopify Plus websites that generate the highest amount of conversions and provide the best ROI. Be sure to follow us on LinkedIn, Facebook and Twitter, or give us a call to learn more about our services.
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