Want to implement product recommendations in your online shop? Then this is your guide.
“A lot of times, people don’t know what they want until you show it to them.” This quote by Steve Jobs highlights why product recommendations are an essential element for any online shop, as they serve as one of the most powerful tools for boosting the value of the shopping basket.
Smart product recommendations can increase revenue by 10% The reason why product recommendations are so well established can be explained in two words: impulse buying. Just like in a physical store, the strategy is to put various products within the customer’s field of vision. The advantage of online shops is that you have a whole range of possible ways to spur customers into making a spontaneous purchase – from the homepage all the way to the checkout.
A much-cited report by McKinsey states that Amazon draws approximately 35% of its revenue from recommendations . Even though this figure may seem high, hardly anyone would deny that product recommendations have made a significant contribution to Amazon’s meteoric rise and can have a major impact on the overall customer journey.
What type of recommendations are there in an online shop? The most important types of product recommendations can be divided into the following categories:
user-based
product-based
expert-based
User-based recommendation (self-learning)
Whether you label this type of recommendation “Other customers also buy,” “Often bought together,” or a different heading, the foundation is still the same: learning from customer behavior and combined sales from the shop with the goal of automatically generating suitable recommendations. This type of recommendation is normally displayed on the product detail page or even better, in the shopping cart once customers have made their purchase decision and are ready to make further decisions.
Until a few years ago, retailers often had the problem of needing the largest possible database in order to generate relevant recommendations. However, new contextual approaches in AI have made relevant recommendations possible with smaller and smaller amounts of data. Even from individual combined purchases, it is possible to deduce which categories usually match. This way, changes in the product catalog have an immediate effect on the recommendations shown, and recently listed products sell effectively right from the very beginning.
Apart from the combined sales of all visitors to the shop, the affinities of the individual customers also play an important role. For example, someone’s click and purchase behavior in the shop can be used to deduce which brands, colors, price ranges, etc., a particular user prefers. This kind of personalization can provide the finishing touches to your recommendations and give your customers the feeling that you really understand them.
Best practice: Walbusch
“Other customers also buy“ – recommendations in a fashion shop: In this example, our customer Walbusch cross-sells additional products in the online shop
Product-based recommendation (rule-based or self-learning) This type of recommendation is perfect for product description pages – and your shop does not need to learn anything in order to implement it. Your recommendation engine only considers those products within your range that are similar to the product currently being viewed in terms of context.
The most exciting part about this method is that you can either let the AI define the similarities or you can define the rules according to which similar products are to be displayed. For example, to encourage customers to buy a higher-priced product (up-selling), you can specify that only similar products at higher prices should be shown.
Best practice: Kaiser+Kraft
Similar products as recommendations in B2B shops: Kaiser+Kraft in this example recommends higher-priced pallet trucks.
Expert-based recommendation (manual)
If you have the resources and don’t want to leave everything to AI, you can also ‘hardwire’ some of your products with the recommendations of your choosing, meaning that you determine precisely which recommendations should be displayed for which product. This is the most flexible approach and it can be implemented both statically (exact allocation) and dynamically (rule-based).
Two different modes are possible:
Do not recommend – blacklisting which products should be excluded from the recommendations. For example, in a sports shop, no FC Bayern jerseys for Manchester United pants, or in an online pharmacy, excluding products that interact with the medication that is being viewed.
Always recommend – whitelisting which products should definitely be shown. For example, product bundles, shop-the-look, etc.
Best practice: Walbusch
Bonus: best sellers as a multi-use tool
One category of recommendations that you can use almost everywhere is best-selling products. As the name suggests, your recommendation engine only needs to know which products you have sold the most so far. Best sellers work best on generic pages, such as the homepage or zero results pages. Unless there is active personalization that has been learning from previous transactions, these generic pages reveal little about the customer’s interests. So, it makes sense to recommend items that are most popular among your customer base. Novelty items are also easy to sell here.
Best Practice: OBI
On zero results pages, our customer OBI shows best sellers from varied categories, meaning customers don’t have to search again to view the product range – increasing the likelihood that they stay in the store.
Next-level predictive recommendations Today’s online shop systems are designed to assist with purchase decisions. However, in many industries, including online groceries, drugstores, pharmaceuticals, and B2B, on-demand purchases are customary, meaning that customers already know what they want to (re)order.
We developed the Predictive Basket for this exact purpose. It is a neural network that, like a recommendation engine, can make proactive purchase suggestions in order to simplify need-based purchases and reordering. In order to achieve this, AI takes many different factors into consideration. For example, it tracks the individual preferences of the customer, and the seasonal buying patterns of all customers (i.e., in online groceries, milk is reordered more often than jelly).
The AI also recognizes seasonal sales trends, such as charcoal for the grill. If a customer buys charcoal every two weeks during the summer, the Predictive Basket will show them charcoal products at the same intervals. Then, as soon as barbeque season is over, and few customers are buying these products, the Predictive Basket adapts to these changes in shopping behavior. The recommendation engine will no longer suggest related products to the customer who was previously buying barbecue charcoal regularly.
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Where can product recommendations be used?
Here is a helpful matrix that shows which types of recommendations are suitable during the different phases of the customer journey :
How do I integrate a product recommendation tool into my shop?
The process and timeframe of integration differ depending on technology, shop system, and requirements. In the case of FactFinder’s recommendation engine, full implementation, including AI training, usually takes two weeks.
Checklist for your recommendation engine1
To wrap up, here is a handy checklist that includes the aspects you should consider when selecting and using your recommendation engine. Your recommendation engine should:
take multiple data sources into consideration. This should include combination sales as well as attribute and feature-related user affinities. For example, it generally pays to have various types of recommendations on the product description page. draw general conclusions from individual combination sales. This ensures that you don’t have to collect data for months before logical recommendations are generated. This way, your recommendation engine will achieve a much faster ROI. learn from customer behavior, in a GDPR-conformant way. This happens on the basis of session ID, without third-party cookies. This way, your recommendation engine can work independently of cookie topics. enable authentic intelligence. In practice, it has been observed that the best results are obtained from a mixture of artificial intelligence and human knowledge.
Do you need support in defining and implementing your recommendation strategy? Our FactFinder team would be happy to help you. We have been supporting shop owners with the optimization of their search and recommendation strategies for over 20 years. Depending on the structure and volume of your product range, customer behavior, and product categories, we can give you a demonstration of our recommendation engine that includes all functionalities mentioned in this article.
Written by:
Jasmin Bleich Jasmin Bleich is Inbound Marketing Specialist in the FACT-Finder marketing team. She’s specialized in Social Media Marketing, Content Marketing, Search Engine Optimization / Search Engine Advertising, Conversion Optimization and Public Relations. During her work at FACT-Finder, she developed a deep understanding on e-commerce, product data quality, retail strategy, conversion optimization and the latest trends in retail. In 2011, Jasmin graduated from Pforzheim University and holds a Bachelor of Science in Marketing and Communication Science.