Role of Conservational Web in Effective Content Personalization


The term conversational web refers to a new approach that is altering the way how the customer interacts with an organization and it is creating the next generation of customer experience. The conversational web has started to provide significant opportunities, along with some challenges, to the industry. As per the reports published by IBM, there is a growing interest for people towards speech, language, and human-computer interaction (HCI) scientific communities in creating a conversational interface to the web. Conversational interfaces have gained momentum over the last few years. They do enact a conversation with a real human, wherein front end the users interact with a machine in the same way of how they communicate with a human.

Earlier, most businesses were operating based on the Mass Production model to ensure the quality of product and deliver it to the customer based on demand. However, today that model has no longer beneficial for gaining a competitive edge as consumers have more customized choices than ever in all product categories such as finance, retail, software, and others. The current business model has substantially turned towards a hyper-personalization approach with the help of recent technological advances. This makes the customer interaction easier for businesses to reach every customer uniquely as an individual and to provide a customized experience for each one.

With the conversational web model, any company can have access to their prospective customers based on their interaction history and their shopping interests they make and other preferences that help customer service associates as well as managers in effective decision making. As a rich and immersive place conversational web also creates positive and hyper-personalized experiences.

Here are some examples of hyper-personalization in companies.


World’s leading online video streaming company Netflix works successfully by delivering its users a unique customer experience. Netflix continuously reinforces its recommendation engine using machine learning algorithms and data algorithms to enrich its personalization mechanism. The number of landing cards or thumbnails that it creates for a specific movie or TV show is the best example to show how well it works.


When it comes to product personalization, Amazon leads the way by dominating its retail landscape. With its massive customer base, they have capitalized on how the shoppers interact with its website. Amazon also creates a personalized profile and homepage for the individual customers based on their historical shopping habits, shopping cart, and Wish list.


Starbucks uses a real-time customization engine that produces individual offers for their customers based on previous interactions and preferences. The company derives this data through the loyalty app that helps them to understand each of its customers. Their needs and habits are marked and with that data, the company sends customers customized emails with deals and new updates generated for them.

Further, the interest in creating a conversational interface to the web is on the rise, some of the web conversations are already driven by AI such as Slackbot and is expected that the conversational web will see an incredible uptake as businesses lean towards delivering enhanced and customized customer experience.


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