On Machine Learning Powered Recommendation Engines

by Manpreet | Jul 22, 2018
On Machine Learning Powered Recommendation Engines

When the attention span of internet users is diminishing day by day, what should the companies do to retain the customers? What strategies would help them boost sales when they have to struggle even to keep the customer on-site?

Recommendations engines, currently being used by the major players in the industry, use machine learning and automate several processes that add up to improve customer retention.

Guess what! You’re already using them.

So, what are you watching on Netflix at the moment? How do you pick new shows? On the back end, a recommendation engine is working to make you binge on Netflix shows.

Amazon too has several recommendation engines working towards different objectives but ultimately, adding to over 35% of their revenue.

So, how do they work?

But first let’s understand what we mean by a recommendation engine

What is a recommendation engine?


A recommendation engine is a data filtering tool, but it uses powerful machine learning techniques to function.

It takes over humans at several processes in the sales funnel.

Task 1: Imagine, a customer enters your shop and he tells you he’s looking for something. The salesperson picks up available products and shows it to the customer. (Personalised recommendations)

Task 2: The customer decides to buy one product. The salesperson tells him that people also buy another product with the one they’ve chosen. (Cross-selling)

Task 3: The customer has placed his order. The salesperson shows him more recommendations that he can buy for the next order. (Up-selling)

When online, all of these tasks can be performed by a recommendation engine. With machine learning, the suggestions are made by data and are highly accurate, depending on the algorithm used.

There are three types of recommendation engines:

  • Collaborative
  • Content-Based
  • Hybrid


1. Content-based Recommendation Engine


The algorithm relies on the description of the items and user profiles. Depending on the user profiles, it recommends items that a user has liked in the past.

Various news websites use such recommendation engines. However, their scope is very limited.

It’s because they can only recommend on the basis of what you have liked previously and highly rely on keywords. It makes them limited to one form of content, and thus ineffective.

2. Collaborative Filtering Recommendation Engine


The algorithm compares the different set of user behaviors to make predictions.

It could be a user-user collaboration where products are recommended to a lookalike audience based on the theory that if user A likes products p, q, r, s and user B likes q, r, s, t products, there are high chances that user A will also like t and user B will also like p.

Lookalike audiences need a lot of resources and data, which is why one of the most successful uses of lookalike audience is being made by Google AdWords (now, Google Ads) and Facebook for dynamic remarketing.

It could also be an item-item collaboration where recommendations are made on the basis of similar items. The items are compared to each other and the similar items are recommended to the customer.

For example, Amazon uses this in their ‘frequently bought together’ recommendations to sell package deals instead of single products to customers. This increases their order value.

3. Hybrid Recommendation Engine


As evident from the name, it combines collaborative and content-based filtering.

TDepending on your earlier browsing history, Netflix recommends similar shows to you, which you ultimately end up binge watching. That’s collaborative filtering.

At the same time, it also takes into account the shows similar to the one you’re currently watching. That’s content-based filtering.

How do recommendation engines work?


They follow a  three-step process -

1.Gathering Data


Any recommendation engine needs a large set of user data, click-stream data and user profiling, depending on the type of recommendation engine.

It can either depend on user input data - reviews, filling forms, doing surveys etc or on implicit data that it gathers just by analyzing user behavior.

2. Processing Data


The gathered data must be converted in a machine processable form. Done through a combination of manual and algorithmic sorting, it normalizes the data to form specific information in a structured form.

3. Analyzing Data


When do you want the data to be analyzed and filtered? Real-time? In clusters?

After following these three processes, the system would be finally ready to deliver recommendations.

Why opt for a recommendation engine?


Considering this is a fairly new technology, why would you, as a business owner, invest in it?

We have challenges at all stages - collection all that data while we’re already seeing the trouble giants like Facebook and Google are in due to privacy concerns, storing all that data which certainly needs gigantic storage space, analyzing it and filtering it which would need a specialised recommendation engine for your unique business needs…

Phew...isn’t it too much of an overhead?

1.Data Set Is To Big To Be Processed By Humans


It’s only through use of machine learning that the available data can be used to drive decisions and take actions.

2. Increase Your Revenue


You’ll have a sure shot chance at increasing your revenues.
Having a recommendation engine is like hiring hundreds of extremely talented salesmen, so many that all your customers can be attended to at the same time.

Moreover, the recommendations are based on user behavior and data never lies. Your conversion rate will definitely shoot up.

3. Customer Satisfaction


Imagine the disappointment a customer faces if they cannot find the product they are looking for on your website! The worst part is you had the project. You could have sold it and made the profit. But you didn’t and lost a potential customer.

It wouldn’t have happened if you had a recommendation engine catering to your customer.

Through genius ‘discovery’ recommendations, like Amazon does in ‘frequently bought together’ recommendations, you’ll never let your customer get disappointed.

When a recommendation engine is solving the biggest problems of businesses - sales and customer satisfaction, you need to get it onboard.

 

Rounding up, you know of the three major types of recommendation engines - content based, collaborative and hybrid along with the three-step process involved in building a recommendation engine, concluded with three specific reasons why deploying such machine learning solutions could be a game changer for your business.

But maybe, you’re still wondering if this is relevant for your business? If that’s so, call us for a free consultation and let us help you find ML solutions tailored to solve your business problems.