AI recommendation engines were first introduced in the market by companies such as Amazon, Netflix, Spotify. These companies used the recommendation engine to create a personalized experience for their users which in turn transformed them into some of the biggest companies in the world.
Now how does a company track the satisfaction levels of its customers?
Net promoter score is a very important metric for the companies to check how satisfied the customers are with their services. This score determines how valuable their website or service is to their users and whether their users would recommend them to friends and family or not. This gives the company real-time feedback into understanding the aspects of the website experience which the users liked or found difficult to navigate.
Now that we have a better understanding of the two subjects, we can proceed to talk about and understand how they coincide with each other.
A better user experience can be the difference between a website that the users come back to and a website that they only visit once. After the success of Amazon, Netflix and Spotify for their use of AI-based recommendation engines, more and more companies are integrating this technology into their website functionality. Infact, 45 percent of the e-commerce and online service providing websites are now deploying their own recommendation engine on their websites for increased customer engagement.
AI-based recommendation engines help your website be more user friendly. That, in turn, increases your audience retention. A website that has dedicated users are likely to recommend it to others too. Thus, having something like an AI recommendation engine can directly affect your net promoter score which then positively influences your sales and business.
Recommendations to users can change depending on the context in which the user is browsing the website. Factors such as time, day of the week and platform used whether mobile or laptop can greatly impact the choices of the customer. By optimizing the recommendation algorithm to encompass these contextual parameters can help produce relevant results thereby improving NPS.
Since AI recommendations systems primarily focus on personalising the browsing experience for each user, they create a unique experience that is suited to the likes of the user which in turn results in loyal customers. Nowadays, people want to find their favorite products/services in the shortest time possible. By learning the user’s habits on the website and their browsing patterns, recommendation systems can save them time thus augmenting their search experience and making it more likely to revisit the website.
From the time when AI recommendation engines were first introduced on websites like Amazon, Netflix and Spotify till today where almost all major e-commerce websites have this feature, it is easy to see the impact of machine learning based recommendation engines in the market today.
It has become a bare necessity for almost all e-commerce and online service providers to have, which translates into a well functioning website that is user-friendly and brings in more traffic. Thus it is clear that there is a direct correlation between net promoter score and AI-based recommendation engines.