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Five Ways Artificial Intelligence Enhances Customer Engagement and Experience

Five Ways Artificial Intelligence Enhances Customer Engagement and Experience

It is impossible for any business to remain untouched by the possibility of losing customers. The challenge, however, is to ensure that a single instance does not lead to the loss of several customers. The consequences of such events not only affect the brand financially but also diminishes its position and competitiveness in the market.

 

SwissCognitiveCustomer acquisition and retention are fundamental focus areas for any brand looking to build stronger, longer, and more fruitful relationships with their consumers today. Apart from in-depth strategizing, brands are required to effectively engage their customers and provide them with a distinctive experience during all interactions, across all channels. Doing so can ultimately influence whether or not a consumer becomes a loyal customer. 

The Importance of Building Strong Customer Relationships

This holds particularly true in the present business ecosystem, where consumers have an abundance of choices and information. With the target audiences having the upper hand, customer retention becomes a substantial challenge for businesses in any industry. Conventional cookie-cutter methods of acquiring and retaining customers just do not work today.

However, most brands tend to overlook this change in market dynamics. This can be a costly oversight. Various industry studies highlight how acquiring new customers can cost five times more than it does to retain existing ones. This should be a reason big enough for brands to emphasize on customer engagement and relationship building.

Capitalizing on and Automation to Drive on Enhanced Customer Experiences 

In recent times, has emerged as a powerful tool for brands and marketers. By leveraging advanced business intelligence, -driven automated tools can turn data from multiple, disparate sources into valuable insights. More interestingly, the more data is fed into the system, the more accurate these insights become – and the higher is the business value driven.

Moreover, by integrating an -based solution with the organization’s customer relationship and data management interfaces, marketers can ensure more accurate end-user assessments and deploy the right strategic interventions. Such integrations can also offer the following benefits:

Predicting churn

Machine algorithms can be programmed to collect data on at-risk customers. -powered tools can accordingly augment customer engagement efforts. Such interventions can help brands define the ‘how’, ‘what’, and ‘where’ to create the maximum impact. This is true for specific time-based campaigns, as well as for a brand’s routine market communications.

A study by Forrester showed that -based customer management solutions can lower customer churn by 10-50%. Their computation power can assess vast amounts of customer data to predict the likelihood of a customer discontinuing his/her relationship with a brand. not only helps in swiftly cleaning large amounts of raw data but also extensively analyzing these data sets to develop predictive models for identifying customers who might pose the threat of churning.[…]

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