If you’re involved in ecommerce, this article is for you! Imagine yourself browsing through platforms like Amazon, Netflix, or Spotify. You often come across recommendations for products that catch your interest, movies or series you might enjoy, or music that matches your taste. Well, these recommendations aren’t random. They are part of what is known as data science recommendation systems, which many companies implement for numerous benefits.
This article will delve into this fascinating world and guide you through the step-by-step process of creating your own recommendation system.
Recommendation systems are algorithms designed to predict the products or services in an online store that a user is most likely to purchase. These predictions are then displayed on the website while the user is browsing.
Before the development of machine learning, ecommerce platforms relied on showcasing "most purchased" or "top-rated" lists to attract consumers. However, these sections displayed the same items and services to all users. While these lists are still in use, recommender systems have proven to be more effective by providing personalized suggestions tailored to each individual customer.
Recommendation systems analyze data collected from users’ browsing activities, such as the products they have viewed or purchased and their interaction with the platform. These systems use advanced algorithms to make detailed comparisons between user profiles to identify common patterns. Consequently, they can recommend products or servicing that become increasingly relevant to each consumer.
When it comes to creating recommendation systems, experts typically employ two main strategies:
Foster customer loyalty: When customers feel understood and are provided with valuable recommendations, they are likelier to remain loyal to your business.
While recommendation systems offer numerous benefits, it may not be the best time to implement them in your business if your customer base is small or your product or service catalog is limited. These factors can limit the algorithm’s effectiveness. Investing in data science becomes more profitable as your customer base grows and your offerings expand.
Python is widely favored for creating data science and machine learning tools and web applications due to its robust code and optimized syntax. It is recommended for programmers entering this field due to its reliability and extensive software development support.
However, alternative languages such as Java, Golang, Node.js, PHP, or Ruby can also be considered.
Java is the best alternative to Python and its main competitor.
If you want to implement a web recommendation system or improve the one you already have, our data science team can help you. Contact us if you like us to analyze your situation.
The placement of recommendations within your ecommerce matters. Take into account where and when the recommendations appear to optimize both the functionality of the system and the user experience.
The ideal location may vary depending on your website and the type of products or services you offer. However, standard practices in ecommerce include displaying recommendations at the bottom of the article or at the end of the purchase process.
If you need more clarification, we recommend doing A/B tests to make the best decision.
What is a good recommendation? Well, the truth is that not every recommendation for the customer is good for your company.
While offering practical recommendations is crucial, some may be too obvious to be valuable to the customer. Therefore, consider introducing risky recommendations that expose customers to unfamiliar products and services.
From a business perspective, it is crucial to base recommendations on product profitability. The trick is to strike a balance between what benefits your business and what is valuable to the customer is key.
If you want to implement your web recommendation system or improve the one you already have, our data science team can help you. We hope we have helped you learn to implement web recommendation systems or improve the ones you already have with the tips and tricks!