Should you buy or build a Product Recommendation Engine?

If you own a streaming 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

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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.

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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 a streaming 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.

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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.

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 streaming 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 a streaming platform 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 streaming 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.

A 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 a brand witnessed significant results with Argoid’s hyper-personalized product recommendation engine:

#Case Study : Mitron TV

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

Problems

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.

Solutions

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.

Results

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: