In a digital competitive landscape, it’s more important than ever to provide visitors with a personalized experience that caters to their unique preferences and needs.

This is why e-commerce websites often use special algorithms to display complementary or similar items to visitors to encourage them to make more purchases. You’ve most likely seen this before: “you might also like” and “bundle these items” are two algorithms that are very popular among e-commerce brands. It could also be in the form of emails sent to customers suggesting similar items to capture their interest and get them back on the website to complete another transaction. 

They also provide opportunities for up- and cross-selling so as a user views one product these algorithms automatically offer suggestions for other similar items. Seeing items in the context of a complete set can entice customers to add more items to their cart, thereby boosting sales for the company. 

This is the idea behind a product recommendation engine. 

What is a product recommendation engine? 

A product recommendation engine is a filtering system which aims to predict and display additional products to visitors that may be of interest to them. 

A product recommendation engine tracks your visitors’ behavior on a website then, through a combination of machine learning and artificial intelligence (AI), it generates a list of product suggestions and other relevant offers or discounts most relevant to them according to their browsing and purchase history.

In other words, it’s a combination of information filtering and matching algorithms to find the right match between the user and content.

Therefore, the engine gathers large volumes of user data such as user behavior and to find patterns behind the behavior and actions of a user in order to generate valuable insights. These insights are key to displaying the most relevant content to each user.

The idea behind a product recommendation engine is nothing new. All the major brands from Netflix, LinkedIn to Amazon and Spotify implement this strategy to deliver suggestions based on data tracked and stored in their system. 

With millions of products on sale on its website, Amazon relies on a number of recommendation strategies on its product pages to make it easier for customers to find what they need quickly and drive purchases as seen in the image below:

Based on what a customer is viewing and their browser history, Amazon will recommend similar items to encourage them to make another purchase as a “package deal” through cross- and up-selling to ultimately increase average order value. 

Types of recommendation engine algorithms

Product recommendation engines typically rely on certain advanced algorithms drawn from customer data that include purchase history, preferences and search and browse behavior. 

With that information, the recommendation engine will automatically generate a set of personalized recommendations for each customer based on this data. The system will continuously update as new information comes in regarding the customer to provide fresh, new recommendations.

Based on your business goals, you can utilize a variety of algorithms to influence website visitors with different suggestions. Let’s take a look at a few different algorithms that can be used based on the information collected by the engine.

Content-based filtering

In a content-based recommendation system, a user profile is built according to the items the user previously liked, purchased, searched for or clicked on. The keywords used during the search process are the foundation for the product suggestions.

Through machine learning, the recommendation engine establishes similarities between products and will then recommend similar products recorded in their history. This type of filter relies on the assumption that if the user liked a certain item in the past, they are more likely to have a similar reaction to a similar item.

Collaborative filtering

Collaborative filtering involves collecting large amounts of data from multiple users about their preferences and behavior and then predicting what an individual user will like based on similarity.

A big advantage with this type of filtering is that it doesn’t require information about the actual user or item per se; it only needs data about past user behavior. 

In particular, as seen in the image above, Amazon relies on Item-Item collaborative filtering to boost sales. The Item-Item algorithm follows the logic “if you like this, you will also like….”. In other words, items are recommended to a user based on their previously-liked items by finding a “lookalike” item.

Meanwhile, the User-User collaborative filtering approach seeks to find “lookalike” users and suggest products to a user based on what their “lookalike” has chosen. 

Hybrid recommendation system

As the name suggests, this type of recommendation system combines both content and collaborative filtering approaches. This system is highly effective as it brings together a complex mix of multiple algorithms while overcoming the downsides of each individual filtering model on its own.

Netflix is a great example of the implementation of the hybrid model as it seeks to base its recommendations both on how a user interacts with the platform (i.e their viewing history and highly-rated titles), content-based filtering, and what similar users have liked or watched in the past, collaborative filtering.

Transform your e-commerce website with an AI product recommendation engine

A product recommendation engine is a highly effective tool to deliver highly personalized user experiences by providing customers with relevant content that caters to their individual needs and interests. 

If such a system is set up right, it can provide a major boost to conversions and transactions among other important metrics. A recommendation engine can also have a major positive impact on the user experience, as each visitor is shown a set of unique items based on their history. In other words, one visitor’s product recommendations will differ from another visitor’s list of recommendations. Thus, it can significantly increase customer satisfaction and retention.  

Thus, product recommendation engines are a great way to boost your business and take your e-commerce experience to the next level with cutting-edge AI product recommendations.

Want to add a product recommendation engine to your tool stack? AB Tasty is the complete platform for AI-powered recommendations, experimentation and personalization. With embedded AI and automation, this platform can help you achieve a richer digital experience.

What is progressive delivery?

Progressive delivery, which can also be referred to as progressive rollout or phased rollout, is a software development practice that allows you to gradually roll out new features to users to limit impact on end-users and obtain feedback for improvement purposes.

Progressive delivery builds on the concept of continuous delivery and encompasses deployment strategies such as a/b testing, ring deployment and canary deployment.

How to do progressive delivery?

Progressive delivery involves releasing your features to a small group of users, who would usually be your most relevant users that would give you the most constructive feedback, through phased rollouts. 

This would allow your team to work according to this feedback to optimize the release by catching and fixing any bugs before releasing to the rest of your users.

By using feature flags, you would be able to control which users have access to your new features by toggling these features on or off to limit the impact on users. 

There are many different ways to implement gradual rollouts. All these methods can be considered to come under the umbrella of progressive delivery.

One such way is canary deployment. This is where a small percentage of users get access to your feature; these are users that share similar attributes and can give the most relevant feedback to improve your product. You may also want to consider a dark launch, which is another way to gradually release your feature but without pointing out this feature to users.

Another way is through ring deployments. This is where users are divided into rings with each ring representing a group that undergoes the feature rollout. Therefore, the process could start with your internal users and then once the new functionality is tested, the features are released to the next ring and so on.

You may also choose to do a blue-green deployment. This is where there are two identical production environments; one is active while the other is not. Once you have completed all your changes, these are deployed in the second environment. A router would then direct traffic to the environment where the changes are active and tested on a subset of users while the first environment is inactive.

An alternative to blue/green deployment would be feature flags, which allow for targeted rollouts without having to maintain two production environments.

Additionally, progressive delivery allows room for experimentation. For example, through A/B testing you can test how different variations of a feature perform to see which one has the best impact on your pre-selected users. This would then be chosen for a broader rollout once the experiment is done.

Benefits

Nowadays, there’s more pressure than ever to release software quickly. As a result of working under such pressure under tight time constraints, this could inevitably lead to bugs, which then could lead to disaster and a tarnished reputation amongst your users.

This is where the true value of progressive delivery lies. It allows developers to release much more frequently and in smaller batches so that users can get new higher quality features much quicker. Just as quick, you can roll back if anything goes wrong.

As it is impossible to detect all bugs before a release as some bugs can only be detected once they start affecting real users but with progressive delivery, this bug only affects a small percentage of users. 

It also gives an opportunity for teams to see how their new variation is performing, whether it does optimize the user experience and so they can choose which variation is working best before rolling out to everyone else.

Progressive delivery also provides a less risky way to practice continuous integration and continuous delivery as it mitigates the risk of continuously pushing code to production by allowing any unfinished changes to be toggled off.

Thus, with feature flags, progressive delivery vastly improves a CI/CD pipeline by allowing for safe rollouts to a small user base and quick rollbacks if anything goes wrong in production.

Conclusion

Progressive delivery unlocks rapid releases. The steps towards progressive delivery are straightforward; with careful planning and the right tools, you can ensure your future releases are quicker and risk-free. Most importantly, phased rollout gives you full control over your feature rollout process.

A feature management system, such as AB Tasty‘s server-side feature, allows you to segment your user base by assigning specific flag values to different user segments. This way, you can gradually release features to different target groups resulting in more stable, higher-quality releases.