Whether you’re shopping online or watching your favorite series on a streaming service, the content that’s tailored to your tastes keeps you coming back. And what if some day, your content recommendations, libraries and Netflix’s section of suggestions show something your best friend dearly loves – but not you!
It’s not about the customer – it’s about the individual customer!
And this is exactly how product recommendation systems work – and why they exist, altogether!
These systems analyze user behavior to suggest products or content that you’re likely going to enjoy, ultimately enhancing your experience and boosting business growth.
Recommendation engines have revolutionized how businesses interact with their customers. They don’t just help in personalizing the user experience but also play a crucial role in increasing sales and building customer loyalty.
In this article, we’re taking you down the road to understanding machine learning-based product recommendation systems with:
- the different types of recommender engines
- benefits of implementing recommendation systems for businesses
- how to build a product recommendation system using machine learning
- common challenges associated with building and implementing recommendation engines
So, if you’re a founder or a key decision-maker currently planning to build or integration a recommendation system, here is the complete guide to help you take your fist foot forward.
In a nutshell, a product recommendation system is an advanced tool that uses different algorithms to suggest products to users based on data collected from their interactions.
Think of it as an assistant that reads & understands your preferences and makes suggestions accordingly. These product recommendation systems are especially useful to e-commerce platforms, streaming platforms, and online marketplaces, where personalization is essential in order to keep the users engaged.
But recently, product recommendation systems have found their applications in numerous industries, including education, gig and service-related mobile apps, real estate and property listing applications, ad programs with retargeting campaigns on social media and online dating.
Here’s the quickest explanation of how product recommendation engines work. The primary job of a product recommendation system is to analyze user behavior, preferences based on their past interactions on the platform to predict what products they might like. This can be achieved by
- having a record of a customer’s past purchases and browsing history
- and even what other users with similar tastes have enjoyed.
By leveraging vast amounts of data, these systems can offer personalized suggestions that enhance the overall user experience.
Also read: Product Recommendation Systems in E-Commerce Apps
Now that we’ve discussed what a Product Recommendation System is, let’s talk about the different types and why implementing one can benefit businesses.
While the business ecosystem is experiencing latest benefits of recommendation systems in literally all sectors and more advanced applications every day, here are the most notable advantages:
Personalized recommendations can significantly boost conversion rates by suggesting relevant products, leading to higher sales and improved revenue for the platform.
Users receive tailored content, enhancing their overall experience and satisfaction with the platform. This personalization makes users feel valued and understood, increasing their loyalty to the service.
By consistently providing valuable suggestions, businesses can foster customer loyalty and encourage repeated visits. Happy customers are more likely to return and make additional purchases.
Product Recommendation systems can help businesses manage their inventory by promoting products that need to be sold, ultimately reducing overstock and understock situations. This helps understand a better balance of supply and demand & promote products that are more likely to be sold.
Product Recommendation systems make effective use of data collected from user interactions, turning it into actionable insights for marketing and product development. This data-driven approach helps in making informed business decisions.
Now we know how a Product Recommendation System can help your business in effective ways – This leaves us with how to create a Product Recommendation System.
Starting from the data collection, there are multiple steps that go into creating a product recommendation system before you can actually deploy it.
In this section, we’ll discuss a complete overview of how to build a Product Recommendation System.
In order to create Product Recommendation System, you need the user data. Now this data is generally raw and most of it is irrelevant to your domain, but we’ll come down to that later.
For starters, you need to gather data on user interactions, product details, and transaction history. This can include:
- user ratings
- purchase history
- clickstream data
- search history
The quality and quantity of data significantly impact the effectiveness of the recommendation system.
As we discussed earlier, the data that you’ll collect will likely be the raw interaction of users with internet & irrelevant to your domain. You’ll have to evaluate & clean the data to ensure quality.
This step involves handling missing values, normalizing data, and encoding categorical variables. Proper data preprocessing is crucial for building an accurate model.
If you already have a software, or let’s say a mobile app and your customers actively use it, you’ve got your first thread to pull data.
Also read: 16 Applications of AI-Powered Object Detection in Businesses
Select an appropriate algorithm based on the type of recommendation system you want to build.
In order for effective utilization, you need to train the chosen algorithm using the preprocessed data. This process generally involves dividing the data into training and test sets, then using the training set to build the model. Note that the model training is a very crucial step that requires careful tuning of parameters to achieve optimal performance.
Once you’re done with the training process, it’s time to evaluate the model’s performance using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), precision, and recall.
Note that this is an ever-going process that you need to conduct in order to adjust the model parameters & boost accuracy to its maximum potential. Regular evaluation helps in maintaining the effectiveness of the system in the longer run.
Once you’re done with the steps mentioned above, it’s now time to deploy your product recommendation system to a production environment. Here, your system can start making real-time recommendations. Deployment involves integrating the model with the existing system and ensuring it can handle the expected load, both in terms of the user’s and their data.
In order to ensure the seamless running & longevity of the system, you need to continuously monitor the system’s performance and retrain the model as new data comes in to ensure it remains effective. Regular updates and maintenance are essential to keep the system relevant and up to date. There are many ways to go about what you need to update; for example, you can conduct surveys in order to utilize customer feedback and upgrade/update your system accordingly. Working with an experienced IOS app developer in Dubai can also help ensure that your app integrates these updates smoothly.
Using the steps mentioned above, you can effectively create and deploy your Product Recommendation model. Therefore, before we conclude, it is important to discuss some of the most common challenges that you’ll come across when creating such a system.
Many companies complain that their Product Recommendation systems are not as much accurate as they’re supposed to be. While in some cases this is true, but generally such issue arises due to insufficient data for new users or items, making it difficult to generate accurate recommendations.
Using techniques like demographic data, employing hybrid models, or incorporating content-based methods can help resolve this issue.
Another common challenge for an accurate recommendation system is the sparsity of data from users. This means that most users have interacted with only a small subset of items.
Matrix factorization and other advanced techniques can help fix data sparsity problem.
While ensuring the recommendations are accurate and relevant is crucial, inaccuracy of recommendation systems is one of the most common problems for businesses.
In order to fix this problem, selecting the right algorithms, tuning parameters, and continually refining the model based on feedback is essential. High accuracy in recommendations leads to better user satisfaction and engagement.
Scalability ensures the recommendation system can handle large datasets and provide real-time recommendations, yet it is one of the major problems of such systems when not planned from the beginning.
As the number of users and items grows, the system must be able to scale effectively & in order to achieve this, efficient data structures, distributed computing, and parallel processing are essential.
Providing diverse recommendations ensures users are exposed to a broader range of products, enhancing their experience. This can be achieved by incorporating diversity-promoting techniques in the recommendation algorithm.
Diversity prevents the recommendation system from becoming too narrow and repetitive. On the good side, it also helps maximizing sales and overall revenue.
Emerging brands already using product recommendation systems for a while are now experimenting with an interesting strategy – they are providing suboptimal recommendations to users through the product recommendation systems: suboptimal, meaning that they are not 100% relevant but streamlined and scheduled. Then the product recommendation engine observes and learns from the reaction of users on specific recommendations – for positive reactions like clicks, wishlist, or add to cart, the recommendation engine gets a new product line. For negative reactions, well, the machine learning algorithms now are smart enough! Working with an experienced android app development in Dubai company can help integrate such advanced recommendation systems seamlessly into your mobile applications, ensuring better user engagement and ROI.
Here are the top two business-level benefits they tend to achieve:
- By controlling similar content recommendations, these brands are balancing their marketing and sales efforts so to not annoy and bore users with redundant products
- By sending in not-so-on-point recommendations, they are attempting to broaden the horizon for users – so they can explore and discover new product lines, and make better purchases, with higher customer lifetime value.
With applications of suboptimal recommendations and reinforcement learning, machine learning engineers at Techlancers Middle East are observing newer trends in business requirements for developing such systems.
Businesses now want to build more ethical, private and transparent recommendation engines. With emphasis on use of explainable AI and privacy best practices, they tend to ensure fairness, information security and avoid biases – as long-term business objectives.
Beyond basic demographics, recommendation engines now go deeper into real-time content, real-time location, weather and time of day, and in fact, emotional state to delivery highly tailored suggestions – call it hyperpersonalization or contextual understanding, it’s definitely helping the end users.
Brands more interested in visual storytelling are utilizing AR and VR technologies for marketing and sales and customer experiences. The business world is up for integrating these immersive technologies with recommendation systems to further improve the buying experience.
The revenue advantages of implementing product recommendation can be forecasted and analyzed using Return on Investment (ROI of Development) – but the long-term business advantages and the competitive edge go beyond calculation. The more robust your recommendation system is, the better chances are for you to scale and diversify your business step-wise. By continuously improving and adapting your system to meet user needs, you can ensure sustained engagement and convert the same into sales and revenue.
Put your right foot forward here! Techlancers Middle East has a dedicated team of top 3% of global ML developers proficient in building, testing and deploying recommendation systems. We recently built and trained one for our project, Kitchenara, an AI-powered food discovery and delivery app that utilizes a recommendation system to provide personalized food and cuisine video-based suggestions from in-app influencers. The app has achieved $380,000 in its Series A funding. Read the full case study here.
