Implementing Product Recommendation Engines in e-Commerce

Posted by Monis Khan on Oct 23, 2019 10:55:09 PM

Personalization improves the overall sales by 5% and delivers 5X to 8X the ROI on marketing spends.

What's the number one priority for any business owner today? Is it to create a strong differentiation for the brand? Absolutely! Let's face it - be it pricing, features, or delivery - competition potentially replaces all aspects of a business. However, if there's one thing that can't be imitated is how a company chooses to treat its customers.

Ask any e-Commerce founder what their goals are, and one of the first things they will say is to deliver a personalized experience to the customers. Given that 70% of shopping experiences depend on how the customers feel they are treated is a testament to the fact that personalization is the key ingredient of the secret sauce to business success.

Exceed customer expectations with product recommendations

A Forbes survey states that 40% of executives believe customer personalization efforts have a direct impact on the sales and profiles in e-Commerce. The classic example of this is product recommendations, pioneered by Amazon several years ago.  

When 74% of consumers get frustrated on landing on a website that shows content that has nothing to do with their interests, it’s a given that will adversely affect conversion rates. Therefore, people were left in awe when Amazon started making recommendations.

Amazon's recommendation algorithm.

source: Amazon.com

Speaking of Netflix, users expect the media services provider to recommend to them another binge-worthy series based on their tastes. No wonder, 35% of sales on Amazon.com and 75% of Netflix sales are directly related to product recommendations, found McKinsey.

Think of your experience on Amazon or Netflix. Have you ever bought or viewed something that was recommended for you? We are sure you have.

Some examples of brands using the recommendation engine are as follows:

  • Facebook — "People You May Know"
  • LinkedIn — "Jobs You May Be Interested In"
  • Waze — "Best Route"
  • YouTube — "Recommended Videos"

Click here – to read more about “How deep learning is revolutionizing e-Commerce today”.

Product recommendation engines based on algorithms that “learn” from past data

The key elements inherent to recommendation engines are algorithms that take into account massive amounts of customer data, including preferences, buying history, and feedback. After predicting the intent of the customer, the algorithms provide a single recommendation based on what has been observed.

Simply put, with an effective recommendation system, you can analyze customer data to create individual client profiles that highlight the kind of content and products your potential customers might be interested in.

Below is a diagram that highlights the logic flow for deciding what sort of product recommendations to make to a website visitor.

The logic flow for deciding what sort of product recommendations to make to a website visitor

Source: SAS

Three types of product recommendation systems

E-commerce stores carry an extensive product listing. That means if customers want to buy a product on Amazon, they will find they have to skim through thousands of product pages and listings to pick one. That’s where product recommendations can come in handy.

There are three types of recommendation systems:

1. Collaborative filtering

Here, the filtering is based on the assumption that website visitors who showed interest in one product in the past are likely to agree on it in the future as well. For example: if a consumer X likes items A, B, C, and consumer Y wants “B, C, D,” it is assumed that X will like item D, and Y will like item A.

In this approach, the algorithms are capable of recommending multiple items without understanding the item itself. Collaborative filtering is further dissected into two types:

2. User-to-user

The intent here is to search for similar customers and offer products on what their lookalike has chosen. The algorithm deployed for such type of filtering is very effective. On the flip side, it takes a lot of time as it requires to compute every customer pair information.

3. Item-to-item

Instead of finding a user lookalike, the algorithm searches for similar items. This is very much similar to the example on consumers X and Y we discussed earlier. This approach takes less time because it doesn’t need to calculate the similarity scores between all customers.

Amazon makes use of item-to-item recommendation technique, to show relevant products to all visitors, new and old, on the website.

  1. Content-based filtering

This method is based on the profile of the user’s preferred choices and the description of an item. One way to go about the process is to consider keywords as they are used to describe the items. In other words, algorithms try to recommend products that are similar to the ones that a consumer has liked in the past.

Have you ever noticed how Amazon Prime recommends the same type of movie or TV show? That’s because it has its roots in information filtering research and retrieval. The approach isn’t free from flaws.

Whether the algorithms can learn the preferences of consumers from their actions and replicate the same across different content types is still doubtful. When the system is limited to making recommendations of the same content type, its value degrades significantly less.

For example: recommending news articles based on the browsing of news on a website is useful. Yet, the same data can’t be applied to suggest music or podcasts on the same site to the same user.

  1. Hybrid filtering

What happens when you decide to combine collaborative and content-based filters? You can provide a more accurate recommendation. Netflix uses hybrid recommendation system. The platform suggests movies/TV shows by comparing user habits (collaborative filtering) and making recommendations that are similar to what the user has rated highly (content-based filtering).

Hybrid recommendation.

source: dataconomy.com

These are the 5 use cases of using ML for e-Commerce.

Over to you

A Harvard Business Review report states that personalization can help improve the overall sales by 5% and deliver 5X to 8X the ROI on marketing spends. That's why deploying specific AI-driven processes to refine customer data into accurate recommendations automatically is a must for businesses across industries.

This being said, building product recommendations is complex. While large companies have teams of data scientists and engineers working on this, SMEs do not have the same luxury.

Datoin is an automated machine learning platform that helps business owners to quickly derive product recommendations.

Try the power of processing your own data with Datoin, today.

Tags: AI, Machine Learning, ai for ecommerce, recommendation engine, Artificial Intelligence, machine learning algorithms, personalized product recommendation

About Datoin

For online businesses, E-Commerce startups Datoin is the best AI platform. Datoin is no code AI platform for business users who understand the domain and are not from tech or data science background. Datoin is designed with a vision that the online businesses of any size need not invest in building data science expertise to begin monetizing the insights from their own data to drive more sales.

Datoin looks at building a community of ECommerce startups, online subscription businesses  using advanced AI signals to sell productively.

ROI Centric Use cases:

  • Product recommendations, Engine 
  • Customer churn prediction
  • Predictive pricing
  • Dynamics discounting
  • Demand forecasting
  • Predicting List of Upsell/ Cross-sell ready customers 

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