Product recommendation system for e-commerce
Amazon gets 30% of its revenue from recommendation systems only. That perfectly explains why product recommendations are so important in e-commerce. What do you need to know about them? And why in modern online trade product recommendation engines are indispensable? That’s what we’re going to discuss today.
When you think about growing an e-commerce business, it all revolves around customer satisfaction. The satisfied customers buy more products (which, in turn, increases average order value), and eagerly return to place more orders. Customers that are taken care of usually become regular ones and constitute a vital part of your business performance.
In the Monetate Ecommerce Quarterly Report (Q2 2018), we can read that “in sessions in which customers either buy recommended products or add them to their carts, average order value jumps 33% (compared to sessions in which customers only see recommended products, but don’t engage with them)”1. Amazon’s results are similar.
What’s the lesson here? You have to make sure your product recommendations are as effective as possible. It’s the only way to encourage customers to view them and buy suggested products. It all starts with adopting a proper strategic mindset.
The way of thinking of product recommendations in e-commerce
The key to boosting e-commerce business always lies in understanding user preferences and providing them with accurate product suggestions. In fact, these suggestions should welcome your customers the second they enter the store.
However, you have to understand that trust is always the necessary foundation. And what can cause your customers to lose trust in you? According to a study conducted by Adobe, there are three main behaviors that you have to avoid2. Here’s what Adobe respondents said about the stores they don’t trust:
- They are creepy (49%): “They track me online without my permission, send emails, but I do not remember giving them my information.”
- They are annoying (39%): “They send me too many communications, they are not clear about their privacy policies or what they do with my data.”
- They do not listen (39%): “They keep sending me ads or communications, even after I have opted out.”
If your product recommendations deliver a poor experience and focus on forcing customers to buy and not to make them happy and come back next time – you’re in dire straits. We advise you to choose a different approach. Think about making your customers happy and turning your product recommendation engine into an actual shopping assistant that’s helpful, not annoying.
Moreover, recommendations should always be helpful and adjusted to your best knowledge about customers. We often see e-commerce websites presenting their bestselling products wherever they can. That’s not the best strategy. In fact, in many situations, presenting irrelevant recommendations can ruin customers’ trust. The homepage is a good place for presenting bestsellers if you do not know anything about the customer, but as soon as the customer searches for something, their activity should be taken into consideration.
What is a product recommendation system for an e-commerce store?
In e-commerce, product recommendation algorithms gather customer data (e.g., history of purchases, products added to the cart, viewed product categories, etc.) and analyze it to provide them with product recommendations tailored to a given person’s needs. The more advanced the recommendation system you have, the more effective and accurate its suggestions are.
In e-commerce, recommendations can be displayed in different places and at different stages of the purchasing process. Here are a few examples of where you can put product suggestions:
- In the product tab (a perfect place to make the most of upselling and cross-selling)
- In the cart (before the customers finish their orders, show them some complementary products that they can find useful)
- In the thank-you email/message (even after the order is placed, you can offer other relevant products based on customer data)
- In the subsequent email campaigns (some customers can decide to buy more products even weeks after placing the first order)
In general, there are three recommendation engines that you can use. Usually, they all rely on machine learning. This AI-related technology allows your recommendation algorithms to improve over time as they have more and more data to process, but it’s all about the approach. Let’s see what your options are.
3 most important recommendation engines
This form of product filtering uses data about the items to suggest similar or complementary products to goods that a given user purchased or viewed in the past. There are some limitations to this model, though. The algorithm will only provide suggestions related to products that are similar to those already purchased or interacted with.
Here, the algorithm uses similarities between both products and other users to provide product suggestions. As a result, they can also offer products purchased by other customers with similar profiles or interests.
HYBRID RECOMMENDATION SYSTEM
Lastly, we have a combination of content-based filtering and collaborative filtering methods. It’s the way our RecoAI system works. In most cases, hybrid recommendation systems are based on adding the capabilities of the collaborative filtering method to content-based filtering. The goal, of course, is to provide users with the most accurate suggestions.
The benefits of a product recommendations engine
What are the benefits of a tailor-made product recommendation engine? Let’s have a look at them:
- Increased loyalty: Customers that get useful tips from you will be more open to your suggestions and will eagerly return to buy more products.
- Higher CTR: Well-thought-out product suggestions are highly clickable, which helps your business and SEO.
- Ability to use advanced selling techniques: Product recommendations are just perfect for upselling and cross-selling!
- A future-proof solution: The e-commerce sector grows dynamically (retail e-commerce sales are predicted to reach 7.4 trillion dollars by 20253). There will be more and more online stores and more customers buying there.
- Better UX: Personalized product recommendations make people feel important, and that positively affects their UX.
image source: Gladly.com CX report 2020
Build your new recommendation engine with RecoAI!
What makes our solution worth your attention? We’ve made sure that our recommendation engine uses the best machine-learning-based algorithms and a modern and quick codebase built with the Rust programming language. As a result, our systems are up to four times faster than competitive systems.
Moreover, our systems are not limited just to products. We analyze and adapt every single element on your website to make sure your store works like a well-oiled machine. With RecoAI, recommendations can be very different (articles to products, products to videos, influencer posts to products, etc.). We rely heavily on historical data, but when it comes to new customers with no purchasing history, we use real-time analysis of the shopper’s behavior so that our algorithms can provide instant suggestions based on each user’s actions and preferences.
Do you want to know more? Grow user engagement with RecoAI! Drop us a line, and we’ll take it from there.