Consumer Convenience

Learning How to Keep Customers Happy

Machine learning can evaluate customer-service interactions and predict how satisfied a customer is, prompting an intervention if a customer is at risk of leaving a business. The data such as customer-support ticket texts, waiting time and the number of replies it takes to resolve a ticket is analyzed to estimate customer happiness. The system adjusts its predictive models in real time as it learns from more tickets and customer ratings.

Helping People save money

The AI algorithms can learn spending patterns and activities to offer personalized contextually relevant financial advice. People can specify financial goals, such as “saving enough money for rent,” and use AI assistance to achieve it.

Helping Consumers Buy What They Like

Machine learning algorithms identify consumers’ preferences using image-based social media to advise certain styles of clothes and online shops. For example, if a user favorites a picture with a particular pair of shoes, the algorithms can automatically send that user a link to buy the shoes online. Ecommerce becomes more and more personalized. Machine learning algorithms are creating personalized looks based on the user preferences history or even individual web design for their online shop page.

AI interventions types in use

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