Shopping is a necessity of every human being, and when we do shop it’s definitely either the product we like or our friends like. We tend to buy products recommended by people because we trust the person. And nowadays in the digital age, any online shop you visit utilizes some sort of recommendation engine.
Recommendation engines basically are data filtering tools that make use of algorithms and data to recommend the most relevant items to a particular user. Or in simple terms, they are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross-selling and upselling.
With the growing amount of information on the internet and with a significant rise in the number of users, it is becoming important for companies to search, map and provide them with the relevant chunk of information according to their preferences and tastes.
You do not need a market research to find out whether a customer is willing to purchase at a shop where they’re getting maximum help in scouting the right product. They’re also much more likely to return to such a shop in the future. To get an idea about the business value of recommender systems: A few months ago, Netflix estimated, that its recommendation engine is worth a yearly $1billion.
Here are some of the major benefits of implementing a recommendation engine:
Revenue – With years of research, experiments and execution primarily driven by Amazon, not only is there less of a learning curve for online customers today. Many different algorithms have also been explored, executed, and proven to drive high conversion rate vs. non-personalized product recommendations.
Customer Satisfaction – Many a time customers tend to look at their product recommendation from their last browsing. Mainly because they think they will find better opportunities for good products. When they leave the site and come back later; it would help if their browsing data from the previous session was available. This could further help and guide their e-Commerce activities, similar to experienced assistants at Brick and Mortar stores. This type of customer satisfaction leads to customer retention.
Personalization – We often take recommendations from friends and family because we trust their opinion. They know what we like better than anyone else. This is the sole reason they are good at recommending things and is what recommendation systems try to model. You can use the data accumulated indirectly to improve your website’s overall services and ensure that they are suitable according to a user’s preferences. In return, the user will be placed in a better mood to purchase your products or services.
Discovery – For example, the “Genius Recommendations” feature of iTunes, “Frequently Bought Together” of Amazon.com makes surprising recommendations which are similar to what we already like. People like to be recommended things which they would love, and when they use a site which can relate to their choices perfectly then they are bound to visit that site again.
Provide Reports – Is an integral part of a personalization system. Giving the client accurate and up to the minute, reporting allows him to make solid decisions about this site and the direction of a campaign. Based on these reports clients can generate offers for slow-moving products in order to create a drive in sales.
In conclusion, there are various more benefits that directly or indirectly benefit the E-commerce with the use of recommendation engines. And with the increase in online shopping, we are to use even more smarter product recommendations done to us by our favourite Ecommerce.