Product recommendations in Digital Age

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By 1994 the web has
come to our doors bringing the power of online world at our doorsteps. Suddenly
there was a way to buy things directly and efficiently online.

Then came eBay and Amazon in 1995....... Amazon started as bookstore and eBay as marketplace for sale of goods.

Since then, as Digital tsunami flooded, there are tons of websites selling everything on web but these two are still going great because of their product recommendations.

We as customers, love that personal touch and feeling special, whether it’s being greeted by name when we walk into the store, a shop owner remembering our birthday, helping us personally to bays where products are kept, or being able to customize a website to our needs. It can make us feel like we are single most important customer. But in an online world, there is no Bob or Sandra to guide you through the product you may like. This is where recommendation engines do a fantastic job.

With personalized product recommendations, you can suggest highly relevant products to your customers at multiple touch points of the shopping process. Intuitive recommendations will make every customer feel like your shop was created just for them.

Product recommendation engines can be implemented by collaborative filtering, content-

based filtering, or with the use of hybrid recommender systems.

There are various types of product recommendations:

           ·       Customers who bought this also bought - like Amazon

           ·       Best sellers in store – like HomeDepot

           ·       Latest products or arriving soon – like GAP

           ·       Items usually bought together – like Amazon

           ·       Recently views based on history – like Asos

           ·       Also buy at checkout – like Lego

There are many benefits that a product recommendation engine can do for digital marketing and it can go a long way in making your customers love your website and making it their favorite eCommerce site to shop for.

Advantages of product recommendations:

·       Increased conversion rate

·       Increased order value due to cross-sell

·       Better customer loyalty

·       Increased customer retention rates

·       Improved customer experience

Application of Data Science to analyze the behavior of customers to make predictions about what future customers will like. Big Data along with machine learning and artificial intelligence are the key to product recommendations.

Understanding the shopper’s behavior on different channels is also a must in personalizing the experience. Physical retail, mobile, desktop and e-mails are the main sources of information for the personalization engines

Amazon was the first player in eCommerce to invest heavily on product recommendations. Its recommendation system is based on a number of simple elements: what a user has bought in the past, which items they have in their virtual shopping cart, items they’ve rated
and liked, and what other customers have viewed and purchased. Amazon has used this
algorithm to customize the browsing experience & pull returning customers. This has increased their sale by over 30%.

Yahoo, Netflix, Yahoo, YouTube, Tripadvisor, and Spotify are other famous sites taking advantage of the recommender systems. Netflix ran a famous 1 million dollars competition from 2006 till 2009 to improve their recommendation engine.

Many commercial product recommendation engines are available today such as Monetate, SoftCube, Barilliance, Strands etc.

Ultimately most important goal for any eCommerce platform is to convert visitors into paying customers. Today the customer segmentation era as gone and its hyper- personalization. 

Product recommendations are extremely important in digital age !!