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Product Recommendation Engine - Should you buy or build?

Co-founder, CEO
product recommendation engine
Tags :
Product Recommendations
5 min

Whether you own an eCommerce store or an OTT platform, you need to engage your users with a set of solid recommendations. Users might get overwhelmed by the tons of choices available. It could take them hours to figure out which product to buy. A product recommendation engine makes users’ journeys hassle-free by providing them with the best suggestions. So, if you want to transform your users’ shopping or browsing experience, leveraging an AI-based recommendation engine is always a good idea. 

But the real question here is, “should you build or buy a recommendation engine?”. Answering this question is tricky. You need to understand the pros and cons of both alternatives to make a fair decision that works in your context. 

So we’ll discuss the considerations of building or buying a product recommendation engine and you can be the judge. Let’s dive in. 

What is a product recommendation engine? 

Before we move ahead with the build vs. buy discussion, it’s imperative to understand a product recommendation system . The concept of a recommendation system is pretty common these days. You probably already use it without knowing. 

On Amazon or Netflix, you see a set of recommendations, and most of these are pretty accurate in predicting what you might want next. So instead of spending hours searching, you can just explore Amazon or Netflix’s recommendations and make your choice. 

Netflix recommendation engine
Source: Medium 

That’s the power of a hyper-personalized recommendation engine. A recommendation system basically captures users’ data and analyzes it with top-notch machine learning systems to predict what the user might want to purchase or watch next. 

Does your business need a personalized product recommendation engine? 

To answer this question, look for these three signs: 

1. You have access to accurate datasets 

Personalized recommendation engines run on datasets. These could be any types of data starting from past purchase history, browsing history, customer profile, etc. But, before you buy or build a recommendation system, make sure you have access to these datasets.  

How should you plan your data strategy? 

  • Start with product attributes. Ensure your product inventory is tagged with appropriate attributes. Use a standard process to implement the tagging strategies for any new product. 
  • Keep track of your customers’ browsing histories and site interactions. Store customers’ data securely using a logical process. 

Once you have customer data and product data safely stored, you’re one step closer to making the buy vs. build decision for the recommendation engine. 

2. You know the costs involved 

Are you planning to build a recommendation system for your business? It is always a smart step to estimate the costs involved. To build a result-driven recommendation system, you need to hire: 

  • Skilled data scientists who can build the product recommendation algorithm 
  • Developers who can process the code 
  • Engineers who can keep the data infrastructure updated and secure 

You can also take the route of buying a product recommendation engine. In that case, you will not need to form a team. But at this point, you’ll need to know the market rate of a good product recommendation engine so that you can perform a cost-benefit analysis.. 

3. You’re interested in upscaling 

Recommendation engines improve customer engagement by simplifying the purchase decision-making process. That’s clearly a type of upscaling. Now, you need to decide whether or not you’re at a place to upscale your business. Your inventory will also grow as your business grows, making it harder for your customers to find what they like. That’s exactly when you need a personalized recommendation system to manage upscaling initiatives. 

Are you planning to build a product recommendation engine? Make a note of these points 

Once you know that you have proper data access, you can afford time and cost in building a recommendation engine, and upscaling is your priority, you can take the next step. 

If you’re planning to build a recommendation engine in-house, here’s a step-by-step guide. 

Personalized recommendation system
Source: Argoid

How to design a personalized recommendation system? 

There are three broad approaches to build a product recommendation engine. These are popularity-based filtering, classification-based filtering, and collaborative filtering. Let’s break down these approaches: 

Popularity-based Filtering 

  • It is the easiest approach to build a personalized recommendation engine. 
  • Here, the recommendation engine displays popular products to customers to assist them with their purchase decisions.  
  • The most popular products are generally identified based on the frequency of purchase. For example, the products purchased the highest number of times are considered the most popular products. 

Classification-based Filtering 

  • Another way to build a recommendation system is the classification-based approach. In this approach, you need to consider both users and products and match users to products. 
  • When a new user visits an eCommerce store or an OTT platform, the classification based recommendation system assigns binary values to the products. This is to identify if the users like the products or not and suggest accordingly.. 
  • Some of the common user attributes to consider in this engine include age, gender, etc.. The recommendation engine tries to match the user’s attributes with product features, including the cost, quality, color, size of products, and user’s purchase history, views etc.
  • On the basis of the above inputs, this product recommendation system offers binary values in the form of “User may like” and “User may not like”. Online stores and OTT platforms can use these booleans to make product recommendations. 

Collaborative Filtering 

The concept of collaborative filtering is associated with the idea of “social proof”. It assumes that people like things that are liked by other people with similar tastes and interests. Collaborative filtering is again of two types: nearest neighbor and matrix factorization. Let’s know those in detail: 

Nearest neighbor 
  • When you adopt this approach to the recommendation system, you find users with similar tastes and purchasing histories. It is a pretty broad concept and is further segmented into two perspectives namely user-based filtering and item-based filtering. 

Nearest neighbor filtering 
Source: Medium 

  • For user-based collaborative filtering, a recommendation system will search for users having similar purchase history, wishlist, etc. That way, it can recommend similar products to neighbor users and increase possibilities of conversion. 
  • Item-based filtering is just the opposite. Here the recommendation system focuses on the similarity between two or more items. The similarity calculation factor is users’ ratings for each item. Based on this calculation, the recommendation engine tries to figure out the user's intent and makes relevant recommendations. 
Matrix factorization 

Matrix factorization is the second type of collaborative filtering. Let’s look at an example: 

Say, you and I watched a great show on Netflix and gave our ratings. Now, Netflix’s recommendation system can represent our feedback in a matrix format. In this matrix, each row will belong to each user (like you and me) and each column will represent different shows. 

Matrix factorization 
Source: Medium


You can see that every user will not like every show. I may have given a 3-star rating for the same show while you’ve given five stars. Hence, matrix factorization focuses on finding out the latent features based on these ratings.

For example, you may like the action genre, and I like the comedy genre. But we found a movie that has our favorite actor in it. So, even though it belongs to the comedy genre, you may still end up watching it. 

Once you find out these features, it gets easier to predict ratings and make appropriate product recommendations. 

What resources will you need to build a product recommendation system? 

You cannot just start building a recommendation engine straight away. You’ll need some resources to ensure that you’re on the right track. Here’re a few things to start with: 

  • You need to generate high-quality data in large volume from different sources. 
  • You need a group of expert data scientists and engineers who can regularly create and maintain your recommendation system. 
  • You must have detailed information about your existing users and customers to decide which mode of filtering will work best for your in-house recommendation engine.  

If you’re keen on building an advanced recommendation engine, you must track each user’s interaction with each listed product/item. That’s the only way to develop insights into individual ratings and purchases, browsing histories, time spent on each product, etc. All this information will add new dynamics to your product recommendation engine. 

Benefits of building an in-house recommendation engine 

There are some benefits to building an in-house recommendation engine. To begin with, you have complete control and data ownership over the recommendation system. Since everything is happening internally, you can get regular updates and involve yourself in the development process. Also, with an in-house recommendation system, the scope of trial and error is much higher. However, there are some critical challenges too. 

Challenges of building an in-house recommendation engine

We’ll be honest here. Building an in-house product recommendation engine is not as easy as you read. So, if you decide to develop one, make sure you are  aware of the following challenges: 

  • One of the biggest challenges of building an in-house recommendation system is correlating the items and the users. It becomes your responsibility to determine which correlations to consider and which one to reject.  
  • The recommendation system you are building should support multiple recommendation types at once, and adapt to the scenario. Hence, the in-house team must emphasize factors like geolocation data, keywords, traffic source, and so on. 
  • The recommendation system must track every activity of online shoppers. These would include their brand preference, category preference, price range preference, and so on. Without tracking all these aspects, it is pointless to build a recommendation engine. However, tracking all these user activities requires a powerful data infrastructure that startups usually can’t afford. 

Are you planning to buy a recommendation engine? 

Are you overwhelmed by all the challenges associated with building a recommendation system? Don’t worry. You can always buy one for your eCommerce store/OTT platform. 

Benefits of buying a product recommendation engine 

Here’re why investing in a recommendation engine may be a good decision: 

1. High revenue earning potential 

Recommendation systems follow an advanced algorithm to offer tailor-made recommendations to customers. Hence, the likelihood of an increase in purchases. Personalized recommendation engines are built after years of research and experience. In most cases, they deliver accurate recommendations that match a customer’s/viewer’s intent. Thus, this indicates high sales potential and sky-high revenue for eCommerce stores and OTT platforms at a fraction of the cost of building it in-house. 

2. No time wasted in coding and configuration 

The biggest advantage of buying a recommendation engine? You don’t need to spend months coding and configuring the engine. Since it is taken care of. So, you can focus on more meaningful activities like improving customer experience, building strategy, etc.  

3. Unmatched customer satisfaction 

One personalized recommendation is always better for customers than tons of generic suggestions. 66% of customers prefer brands that understand their individual needs. So, if you can design your eCommerce store or OTT platform with recommendations that match directly with customer needs, they’re bound to select your website over others. That would mean a high customer satisfaction rate for your store. 

4. High conversion through personalization  

Did you know that 71% of customers are frustrated with generic shopping experiences? They need the utmost level of personalization that makes their purchase decision-making process simpler. Largely because, too many options can confuse the buyers and they may buy nothing. So, it is a smarter option to offer a few personalized options that they find useful. A personalized product recommendation engine helps you to achieve that. Hence, the increase in the conversion rate is high on the cards.

Challenges of buying a product recommendation engine 

Of course, a few challenges are involved in buying a product recommendation engine. You may need to make a significant investment in the beginning. As time goes by, you’ll need to update the engine and there may be a cost involved. Privacy concerns are also there since you are sharing your confidential datasets with an external entity. 

Two Brands that Recently Used a Personalized Recommendation Engine to Boost Conversion  

Now that we’ve discussed the considerations of both building and buying a product recommendation engine, allow us to tell you how two brands witnessed significant results with Argoid’s hyper-personalized product recommendation engine: 

#Case Study 1: Rare Rabbit

Rare Rabbit
Source: The House of Rare

Rare Rabbit is a popular eCommerce store, offering fashion apparel. This premium fashion brand is creating a lot of buzz lately for its top-quality and extremely fashionable apparel. Rare Rabbit’s journey started back in 2015 as a concept store. 


Rare Rabbit was looking for a way to personalize its customer experience. The brand was keen on adding a personalized product recommendation engine that helps customers during their purchase decision-making process. 


Argoid’s powerful AI-driven recommendation engine simplified personalization for Rare Rabbit. We helped to enable our hyper-personalized recommendation system in some of Rare Rabbit’s key pages to create a better online shopping experience for their customers. 


The results were staggering. Rare Rabbit a 20% increase in conversion for the pages where personalized recommendations were enabled, within one month of deployment. 

#Case Study 2: Mitron TV 

Source: Business Standard

Mitron TV is an Indian short-video platform with a 50 million+ user base.


Providing customized recommendations to users from millions of short videos was a huge challenge. The brand wanted to attract more and more audiences toward their platform and reduce churn rate. Mitron was keen to add personalized video recommendations on their portal to re-engage their 50 million+ users. 


Argoid’s AI-driven recommendation engine focused on delivering recommendations to users at the most opportune time. Armed with each user’s time preference, Argoid’s AI-driven engine engaged both content curators and customers by offering relevant notifications. 


There were some unbelievable results post-implementation of Argoid’s recommendation system. Mitron’s video completion rate increased to 90%. Furthermore, their video bounce rate reduced by 20% whereas over 50% of inactive users became active on the platform

Should you Build or Buy a Product Recommendation System - the Choice is yours! 

We hope you have all the information to compare build vs. buy. Obviously, you’ll make the final decision. But make sure that you consider the following factors: 

  • Be mindful about the cost vs. benefit factor. In-house recommendation engines can involve a lot of costs. In fact, the risk of perfecting your recommendation engine is still there. On the contrary, if you buy a recommendation engine, you’ll be in the hands of professionals. In fact, in most cases, the cost is also way lower than building one in-house. 
  • “Time” is one of the crucial factors to consider. Buying a Recommendation engine will enable you to go-live within a month and stay ahead of the competition, that means an instant boost in sales. Whereas if you decide to build, go-live with a recommendation engine might be a year or beyond. Largely because the entire life-cycle of building starts with forming a team, brainstorm, design, development, testing and golive.
  • If you’re only starting out and have a tight budget, you can also start with a basic recommendation engine. It doesn’t have to be fancy. Once you’re familiar, you can go for an advanced recommendation system. 

Final Words 

So, before parting ways, we just want to say that buying or building a product recommendation engine is purely dependent on your business context. 

If you want an efficient, reliable, secured, early time-to-go, affordable, hyper-personalized, AI-driven recommendation engine that’ll do all the hard work, then best to go with Argoid. 

Argoid will help you with: 

  • High conversion rate 
  • Increased revenue potential 
  • No headache of coding and reconfiguration 
  • Go-live with AI Driven 1:1 personalization within a month and stay ahead.
  • Advanced, real-time reports 

Want to see it in action? Book a free demo


What is a product recommendation engine?

Product recommendation engines can analyze user data to identify what type of product recommendations will work the best for which users. Accordingly it displays recommendations in the home page, product pages, cart page of eCommerce stores to increase conversion rate and build a repeat customer base.

Why do you need a product recommendation engine?

eCommerce stores need a product recommendation engine to create product recommendations for different customer segments and plan and acquire more customers with the power of personalization.

Should you buy or build a product recommendation engine?

There is no right or wrong answer to this question. If you have the required resources, building a recommendation engine can be good idea. On the contrary, if you are starting out and don't want to allocate a lot of budget in building a recommendation engine, it is better to buy a recommendation engine.

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