Applications of Artificial Intelligence for Efficient Customer Relationship Management

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Prity Birla

Abstract

Machine learning and artificial intelligence (AI) make it possible for organizations to make more efficient use of the data they already possess and to find answers to challenging challenges. Both of these capabilities enable businesses to grow their operations in a timely manner while minimizing the associated costs. Within the realm of customer relationship management (CRM), artificial intelligence (AI) and process automation have emerged as two of the most sought-after breakthroughs in recent years. This is due to the fact that companies put in a much greater amount of effort to ensure that their consumers are completely and utterly satisfied (CRM). The purpose of artificial intelligence in customer relationship management (CRM) is to reduce the amount of monotonous and repetitive work that employees are required to perform. This is accomplished by analyzing typical operations and releasing staff to concentrate on tasks that are more significant and demanding. Using artificial intelligence (AI), customer relationship management software has the potential to streamline and automate a wide range of labor-intensive activities. These responsibilities include following up with customers, sending thank-you notes, arranging appointments, and maintaining calendars. When artificial intelligence (AI) is integrated with conventional customer relationship management (CRM) systems, it is believed that AI will improve customer experience, procedures, and team productivity. Additionally, AI will boost the number of leads that are created and the visibility of those leads. When it comes to customer relationship management, the objective of artificial intelligence is to accomplish more than simply collect data; this has the ability to boost sales and ensure that existing customers continue to be delighted.

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How to Cite
Prity Birla. (2023). Applications of Artificial Intelligence for Efficient Customer Relationship Management. European Economic Letters (EEL), 13(5), 2007–2017. https://doi.org/10.52783/eel.v13i5.1187
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