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AI Customer engagement

The top 10 uses of AI for customer engagement

Michelle Mitchell,
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The initial focus of this blog was originally to explore the impact of AI on the traditional call center. My world consists of a lot of call centers, and I am both curious and apprehensive about what could happen to call centers in a world where AI seems to be lauded as the answer to everything – from being sold a set of pans to driving my car for me.

Having started on this journey of exploration, however, it quickly became evident that I needed to expand the scope of this discovery to include all types of customer interaction. While the call center has primarily been focused on handling telephone communications with customers, their role has already changed as they increasingly support additional channels such as email and chat.

Within these customer interactions, there are several common types of AI applications that are already being used to enhance customer service and engagement. These AI technologies can already be found in many different industries, from e-commerce and retail to finance, telcos, healthcare, and more. Some of the most common types of AI in customer interactions include:

  1. Chatbots and virtual assistants: Chatbots and virtual assistants use natural language processing (NLP) to interact with customers in real time, answer frequently asked questions, provide product recommendations, and assist with various customer queries. They are often integrated into websites, messaging apps, and call centers.
  2. Natural language processing: NLP technology is used to analyze and understand customer text and speech inputs. It enables sentiment analysis, chatbot interactions, language translation, and can help identify customer preferences and sentiment.
  3. Speech recognition: Speech recognition technology allows AI systems to convert spoken language into text. This is used in automated phone systems, voice assistants, and transcribing customer service calls.
  4. Personalization engines: AI-driven personalization engines analyze customer data and behavior to provide personalized product recommendations, content, and offers. They enhance the customer experience by tailoring interactions to individual preferences.
  5. Predictive analytics: Predictive analytics uses AI to forecast customer behavior and preferences. This is valuable for customer segmentation, targeted marketing, and anticipating customer needs.
  6. Sentiment analysis: AI can analyze customer feedback, reviews, and social media posts to gauge customer sentiment and satisfaction. This information can be used to improve products and services and respond to customer concerns.
  7. Recommendation systems: Recommendation algorithms use AI to suggest products or content to customers based on their past behavior, preferences, and the behavior of similar customers.
  8. Virtual reality and augmented reality: In some industries, virtual reality (VR) and augmented reality (AR) are used to provide immersive and interactive customer experiences, such as virtual showrooms, try-before-you-buy simulations, and virtual tours.
  9. Automated email responses: AI can be used to automatically generate responses to customer emails, reducing response times and providing consistent support.
  10. AI-powered analytics: AI-driven analytics tools help companies gain insights into customer behavior, trends, and patterns, allowing data-driven decision-making to improve customer interactions.

These AI technologies serve multiple different purposes, all of which ultimately aim to improve the customer experience, delivering a more personalized and frictionless experience, driving loyalty, efficiencies, and, ultimately, revenue. The modern consumer expects nothing less.

The world’s most innovative organizations trust AI to optimize their core business activities, like Amazon with logistics optimization and Netflix with content personalization. Service-based organizations can look to their core activities of providing an exceptional customer experience and exploit AI to be more focused, effective, and efficient. While I believe the history of the call center with its legacy of rooms full of people answering the phone will persist, it is an undeniable truth that AI is here to stay and should be embraced as a way to improve the modern customers’ experience, not as a replacement. Our interactions with our customers matter, a human experience matters, but AI can help organizations match the customers need with the cost to serve. The only way to have no costs is to have no customers. But by being smart, we can align operational costs with the need of the customer at that point in time, and that could mean a totally AI based experience or a real human interaction.

In the next blog, I will examine some of the challenges to implementing AI in contact centers and engagement strategies and discuss options for how to overcome those challenges. 

Go to to learn more about how AI is revolutionizing the customer service experience.


Défi: Engagement client Groupe de produits: Customer Decision Hub Industry: Tous secteurs Thème: Expérience client personnalisée Thème: IA et prise de décision Thème: Transformation numérique

À propos de l'auteur

Michelle Mitchell is a collections Fellow at Pega, with more than 35 years in the collections market, focusing on operational and strategic excellence and Best Practice.

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