Product Recommendations In The Digital Age

Hyper-personalization is now the name of the game


By 1994, the web had arrived, bringing the power of the online world to our doorsteps. Suddenly there was a way to buy things directly and efficiently online. Then came eBay and Amazon in 1995. Amazon started as a bookstore and eBay as a marketplace for the general sale of goods.

Since then, as the digital tsunami flooded the world, up sprung tons of websites selling everything on the web - but these two, in particular, are still going strong 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 the 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 store 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 good things that a product recommendation engine can do for digital marketing, and it can go a long way to making your customers love your website and making it their favorite e-commerce site to shop with.

Advantages of product recommendations:

- Increased conversion rate

- Increased order value due to cross-sell

- Better customer loyalty

- Increased customer retention rates

- Improved customer experience

The application of data science allows companies to analyze the behavior of customers, and then 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 e-commerce to invest heavily into 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 and pull returning customers for years. This has increased their sales by over 30%.

Yahoo, Netflix, Yahoo, YouTube, Tripadvisor, and Spotify are other famous sites taking advantage of similar systems. Netflix ran a famous million dollar competition from 2006 to 2009, to improve their recommendation engine with a new algorithm - though they never actually used the winning model. 

Many commercial product recommendation engines are available today, such as Monetate, SoftCube, Barilliance, Strands etc. Ultimately, the most important goal for any e-commerce platform is to convert visitors into paying customers. Today, the customer segmentation era as gone and hyper-personalization is the name of the game. 

Product recommendations are extremely important in digital age. 


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