Recommendation System for Ecommerce: How to Automatically Recommend Products in an Online Store
Personalized solutions in the online store are based on adapting the content displayed on the store's website to the needs and expectations of customers to improve their experience with the store.
There are many ways to personalize online stores: starting from newsletters to special mailing campaigns and automated customer service. Today we will look at one of these ways - personalized product recommendations.
In today's article on our blog, we will answer questions such as:
- What is a recommendation system in an online store?
- How to display recommendations in an online store?
- How do product recommendation engines work?
- What can be done to generate more conversions with personalized recommendations?
What is a product recommendation system for eCommerce?
In today's digital world, a recommendation engine is one of the most powerful personalization tools.
Despite appearances, the product recommendation engine is a simple solution. As the name suggests, it recommends products that users may be interested in.
The key to success of the recommendation system is the use of artificial intelligence and machine learning technologies.
Thanks to the algorithm, the engine is able to effectively select recommendations in such a way as to increase the likelihood that the users will find them attractive.
Recommendation systems in the online store use 3 types of data for their operation:
- Product features - name, price, specification, product description, image, category etc.
- User traffic - pages visited, user behavior, product pages, products added to the cart, etc.
- User interaction with recommendations - clicks on product recommendations.
This information is obtained both via cookies (data on user behavior) and directly from the store's website (data on product features).
The system is machine learning, which means the propositions will improve as data is collected. In other words, the longer the engine runs and collects data, the more accurate the recommendations are.
Recommendation engines are used on various websites, from streaming services, through social media channels, i.e. TikTok, to websites of popular magazines.
However, the eCommerce market is the most popular place where these systems are used.
How to automatically suggest products in the online store?
Install the appropriate recommendation tool that meets your expectations
There are several main ways to introduce and install the recommendation engine on your online store page.
There are 4 most popular ways to implement the recommendation system:
1. Tailored solutions
The first of the above-mentioned ways is by far the most ambitious.
Tailor-made solutions mean engines built specifically for the needs of a given online store.
The largest stores, such as Amazon or Alibaba, decide on such solutions.
The development costs of such a tool can quickly eat up tens of thousands of dollars from the company's budget.
There are many guides on building a recommendation engine on the web and we have already dealt with the topic of the recommendation engine and generating recommendations on our blog.
2. Ready-made add-ons for specific eCommerce platforms
Another type of recommendation tools are add-ons on the largest eCommerce platforms.
Many large platforms such as Shopify, Woocommerce or Idosell offer recommendation systems in the form of an additional plug in their Market Place. By far the biggest advantage of this type of solution is the simple installation process. Simply pay and add the selected application to your account, and product recommendations will begin to appear on the store's website.
3. Marketing Automation
Another approach to integrating personalized product recommendations with your online store is to use various solutions for marketing automation.
Solutions of this type do not boil down only to an extensive product recommendation engine, but also to many other solutions aimed at improving marketing automation activities in an online store.
These platforms offer comprehensive solutions to improve stores’ sales results.
4. Specialized tools compatible with each eCommerce platform
The last solution includes specialized tools offering personalized product recommendations.
Engines such as Recostream combine the simplicity of installing add-ons for large eCommerce platforms with extensive customization options for the appearance and recommendation models known from tailor-made recommendation systems.
Installation of a recommendation engine of this type on the store's website is quick and does not require technical IT knowledge.
Choose the best recommendation models from several recommendation types
There are many types of recommendations that differ in the type of data used to identify recommended products.
We can distinguish several of the most important and popular:
1. Most Viewed in Category
One of the most popular models on sites such as x-kom or Allegro.
It displays the most popular products to the users in the category they are browsing.
The biggest advantage of this type of recommendation is that it makes it easier for customers to navigate the store and reach the most popular products faster.
2. Bestsellers in Category
This model presents the products most often added to the cart from the currently viewed category.
The most important difference as compared to the Most Viewed in Category model is that in this case, it shows potential customers the products that were actually purchased by other users.
Undoubtedly, this has a positive impact on the trust of customers in relation to the brand.
3. Bestsellers in Store
This model displays the most popular products across the entire store.
It works great on the home page of the store, informing visitors about the most popular products on offer.
Following the principle of social proof, potential customers will be more likely to be interested in products that other users have also chosen.
4. AI-Driven Maximized Conversion
It is a model that combines various elements of other models.
Using artificial intelligence algorithms, it is able to select a list of recommended products that are likely to generate conversions.
This model gets automatically optimized based on actual traffic and customer behavior.
It is one of the most advanced recommendation models and is the best way to optimize offers in terms of generating additional income.
Its downside, however, is the fact that it can only be launched after collecting the first data range, while other models are ready to return recommendations faster.
Place your product recommendations in the most profitable place
Customer journeys across eCommerce sites are becoming increasingly complex.
You cannot just focus on individual points of interaction, but on the customer experience as a whole, particularly when it comes to presenting product recommendations.
When implementing the recommendation engine, you should consider where to place product recommendations in the store, so that they bring the most profit.
Below you can see a list of places on an eCommerce site where statistics show that these locations have the greatest potential to increase a store's revenue (in order of the most effective):
- Product page
- Category page
- Pop-up after adding to cart
- Store home page
- Shopping cart
- Blog article
Depending on the place of display, different recommendation models will work best.
For example, on the home page of the store, the best choice would be the "Bestsellers In Store" model, while on the product page, "Popular in Category" would be a better choice.
It is estimated that limiting recommendations to the product page in many cases would mean losing up to 50% of the potential benefits.
Matching recommendation models and their correct placement on the store's website is a key to maximizing engine efficiency.
Measure system effectiveness
After selecting the recommendation engine, the best models and the appropriate locations, it is time to measure the effectiveness of the system.
This is a step that will help you validate your results and fine-tune your models to maximize your revenue.
A / B tests are recommended to measure the effectiveness of the recommendation system.
A / B testing (also known as split testing) is the process of showing two variants of the same website to different segments of website visitors at the same time and comparing which variant is generating more conversions.
We end the test when we collect enough data.
Let's say that while the test is running, 6,000 users visit the home page and each version of the page will be viewed 2,000 times.
After the test is complete, you will be able to see which version of the displayed recommendations converts best.
What's more, some recommendation engines also allow for direct tracking of conversions obtained thanks to the tool thanks to integration with Google Analytics.
Thanks to this, it is possible to independently monitor and analyze the profitability of the system on an ongoing basis and check whether the obtained return on investment is at a satisfactory level.
Tips for better results from your recommendation
In order to optimize the operation of the recommendation engine, the store owner, in addition to the correct configuration, may introduce several improvements to the website itself.
First of all, the store owner should ensure good optimization of the website in terms of SEO.
Modern product recommendation engines for generating lists of recommended products use 2 main methods of data filtering:
- Collaborative filtering - relies on analyzing the behavior and preferences of users
- Content-based filtering - analysis of the products themselves
Both methods must work closely together to deliver the most relevant recommendations.
Appropriate SEO optimization, i.e. taking care of extensive descriptions and professional photos of products, can significantly help in filtering based on content.
Thanks to it, the recommendation engine will have a much larger amount of data to use when generating product recommendations.
When deciding to implement a personalized recommendation system, it is also worth estimating the number of available products and the average number of visitors.
For the recommendation engine to work optimally, the store should stock at least 100 items and around 30,000 monthly visits.
Thanks to this, the system will have a sufficient amount of customer data needed to generate offers and the return on investment will be at a satisfactory level.
SEO, proper placement, enough page views - undoubtedly, all these factors have a significant impact on the effectiveness of product recommendations.
Michał Głomba, CEO of Recostream, when asked about the secret of the effectiveness of the implementation of product recommendations, says:
In order for the recommendations in the online store to start bringing significant value, their implementation must be carefully considered. Recommendations should be placed at various stages of the shopping path in a way that does not disturb the store's UX, especially where we can recover lost conversions. Their appearance should be consistent with the look & feel of the store so that they become an additional, natural element of navigation around the store's offer. However, the most important thing is to present recommendations that are really interesting for a given client. This can only be ensured by the use of an advanced engine of personalized recommendations based on AI / ML algorithms, whose ability to analyze traffic and customer behavior in our store definitely exceeds our human capabilities.
Recommend and sell more
Smart recommendation engines can be extremely helpful in growing your online business, but simply placing them on your store page is not enough.
Their location should be carefully considered and the appropriate model should be selected depending on your goals and expectations. This will allow you to maximize the benefits and increase the sales of the store.