Most customers look forward to a personalized shopping experience on Shopify. Here are some powerful techniques that change the way businesses can interact with their customers and strengthens the relationship.
A study by Retail Customer Experience suggests that around 63% of consumers want a personalized shopping experience on Shopify and other e-commerce stores. They expect stores to understand their shopping behaviors and interests, preferences, and engage them in an interconnected way. And this doesn’t hold true only for marketing.
Consumers expect the same level of contextualisation and personalization when they’re navigating through your Shopify store as well. This is where product recommendations for Shopify stores come into the picture.
A study by invesp suggests personalization significantly improves shopping experience by making it easier to find enticing products, therefore, making it more likely for consumers to return to a site, improving brand affinity.
Brands make it easy for the visitors to explore the website, and drive conversions for their customers by setting accurate product recommendations. What can often be neglected, however, is ‘which’ products appear in the recommended options.
According to a study by Accenture, approximately 91% of customers are more likely to purchase from brands that recognize, remember, and offer them relevant rewards and recommendations. Nowadays, applying recommendation techniques has become a mandate of doing business online effectively. For example, consider Amazon’s much-cited statistic, which suggests that around 35% of the overall revenue comes from product purchases that customers found through recommendations. This is one of the most important metrics when analyzing a recommender system.
Given below are some of the most important metrics that describe recommender performance.
Now that we’ve discussed the metrics responsible for evaluating recommender systems during POCs, let’s move on to the different types of recommendations on e-commerce websites.
The landing page is the first thing that potential customers coming from direct traffic will see when they visit an e-commerce website. As these individuals don’t necessarily come looking for a product, the landing page recommendations serve the purpose of educating potential customers about your latest offers and discounts and displaying your product portfolio.
Given below are different types of product recommendations that can be added to e-commerce websites, including Shopify
This is one of the most common yet powerful recommendation techniques that works remarkably in almost all e-commerce websites. A product’s popularity is decided by the number of times customers have brought it.
However, more advanced recommendation systems include other event data into their code so as to serve more accurate recommendations. Getting the logic behind the ‘popular products’ recommendation system right is very important, given that Pareto’s rule in marketing says that almost 80% of sales come from 20% of products.
Personalized recommendation widget helps generate real-time product recommendations for your Shopify store based on each consumer’s shopping behavior — similar to Amazon. Bear & Bear features personalized product recommendations on its online store. It showcases different products to each user based on their purchase and browsing histories. Algorithms used in this case can vary in terms of their implementation and the factors considered. In any case, as popular product recommendations are great in catering to the mainstream, personalized recommendations increase the sales of long-tail products.
It is important to note that personalization in product recommendations for Shopify requires a lot of behavioral data on users, which the system doesn’t see in new visitors. This is known as the cold start problem for recommender systems. To provide a personalized and seamless user experience for existing customers — but also considering the needs of new visitors — the industry best practice is to establish a “fallback scenario” or a chain of such scenarios.
This means that the recommendation system recognizes whether it has enough data about a particular customer to serve him personalized product recommendations. If it doesn’t have enough data, the system will “fall back” to a more generalized logic (say, popular products) or category-filtered logic. It is crucial to plan the fallback scenario with utmost care and utilize the data as effectively as possible to provide relevant, accurate suggestions for new visitors.
A product page is where visitors find everything related to a product, including its features and functions, and can choose to add products to their shopping cart or order directly. The major goal of this page is to display the most relevant products and thereby provide a “next step” in users’ search and keep them hooked to your site.
In maximum cases, the more time they spend on the website, the higher the probability of actually making a purchase. Given below are some of the most common types of product page recommendations on Shopify and e-commerce stores.
Similar products recommendation gives your customers more options based on the product they were browsing. Here, there is no requirement for mutual selection. For example, lifestyle brand Nicobar Store uses a ‘similar products’ recommendation widget to boost user engagement.
This type of recommendation is based on a different logic. The least complex is the simple category-based filtering that can be executed even without a recommendation engine. If combined with meta-data-based similarity, it can boost the performance of the website. For this, your website will need to have an advanced recommender functionality feature.
One of the best performing similarity-based recommendation logics is a technique called item-to-item collaborative filtering that Amazon majorly uses. Additionally, global clothing store GAP also uses the item-to-item collaborative filtering technique. A report by McKinsey & Company suggests that almost 35% of Amazon’s revenue is generated through its recommendation engine.
At its foundation, collaborative filtering works by gathering preferences or data about choices from many users. This technique can drive item-to-item and personalized recommendations.
Item-to-item collaborative filtering logic determines the similarity of two items by evaluating how often the items are placed together in users’ purchase histories. Recommendation widgets using this kind of logic are named “Customers who viewed/bought this also bought this…” which sums up the basic concept behind this.
This technique emphasizes the customers’ product purchase histories and compares them with other customers who purchased the same products through a machine learning algorithm.
By adding a header such as “People like you buy”, you encourage the customer’s desire to take their fellow shoppers’ path and put their purchase decision into the hands of previous buyers.
This recommendation technique can be implemented on the landing page, product pages, and search pages, as well as in your marketing emails. For example, leading holiday cottage provider Cottages.com implements this recommendation logic to increase engagement.
Recommending items similar or associated with the content of the customer’s shopping cart on cart pages can be a successful way of increasing average order quantities and values. Shopping cart page recommendations reach the customer in an exquisite psychological state, where they have most likely already decided to make a purchase. Hence, they will be more willing to say yes to further offers.
Recommending accessories for products can increase the overall value of the website, along with its average order size. Besides, implementing such widgets can be very effective and simple in terms of their technicality. But based on the size of your catalog and the category structure, it can be relatively admin-heavy. This is because it is arduous to automate the process of recommending compatible accessories for each product. Hence, it is driven by manually assigned product relations.
With a significant amount of data and creativity, a person can abstract rules on studying behavioral data to get genuine and compatible accessories recommendation results.
This is where volume comes into the picture; as with a few purchases, results yielded by this algorithm would be unsaid. However, by generating a significant amount of data, all statistics seem to correlate with reality more and more.
For example, Sarah Raven displays how customers can naturally continue their shopping journeys through recommendations once they’ve added products to the shopping cart.
Showcasing the items that are frequently bought together on shopping cart pages can be very effective. But for any cart page recommendations to provide these results, the checkout process must direct customers through the cart page, where the offers are featured.
The presentation of this page is vital as it is a relatively data-intensive recommendation technique. If you have the time and the right resources, A/B testing various layouts and designs can provide great results, and comprehension.
For example, Netflix uses A/B tests frequently for home screen layouts on various platforms, movie artworks, and featured shows. On the other hand, e-commerce store Amazon showcases frequently brought products on its product pages.
When potential customers look at a category page, they already have the relevant information they’re looking for. The main focus here should be to provide assistance, so they don’t need to go through multiple pages before landing on the desired product or item.
While the best option is to opt for advanced filtering and faceted search, recommendations help to a certain level in such cases.
One of the most obvious ways to help customers with what they’re looking for is to direct them to the most popular products on the page. It is important to teach your system your category structure so that it provides more diverse recommendations.
Another trick is to reorder products from a category on the basis of popularity by default.
This technique suggests the latest products to be made available in the browsed category or the shopper’s favorite category. This is one of the primary reasons why people rush to buy the latest iPhone model every few years.
Recommending the latest arrivals boosts user engagement and excitement in terms of the shopping experience. This factor holds especially true in the fashion and beauty segment. Latest product recommendations can be implemented on the homepage, category pages, and bulk marketing emails. For example, Surfdome implemented this technique and gave their latest products top billing on their homepage banner.
This is a limited version of a product deal that lowers the price for the total when you offer two associated products together. It’s an effective solution for boosting sales, but it is slightly expensive in terms of profit. Almost all e-commerce websites apply this recommendation technique. Amazon has this recommendation widget embedded into its product pages to increase its sale volume.