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Customers of the past could only address their general inquiries by phone. Customers of today have a whole range of possibilities to ask their questions. They can use phones or mails, chats, forms on the site, and social media. In addition to general requests, they can ask for financial information, leave feedback, challenge the quality of the service, and so on.
Thanks to the shift towards customer centricity, customers are now in the heart of many businesses’ operations. And retaining and attracting them is one of companies’ core priorities. Now customers receive quicker answers and better solutions. They get personalized experience and individual approaches. That’s all to improve customer service and create a special customer experience. But how do we actually make it happen?
Transformative Power of Generative AI in Customer Experience
The biggest benefit of using generative artificial intelligence is to receive plenty of useful insights in no time. The algorithms of machine learning work as absorbers of historical data. Whether you want to work on customers’ interactions with agents, their most frequent requests, or the purchase history, you can feed that data to a ML model.
The model quickly collects the data, analyzes it with natural language processing (NLP) algorithms, and comes back with feedback. The feedback will usually depend on the task you want a model to accomplish. And when we say “analyze”, in reality it happens within seconds, if the model is trained accurately and correctly.
That’s why customer service AI brings numerous opportunities. When companies decide to implement these algorithms into their daily activities, their customers’ satisfaction always increases.
AI Assistant in Action
Let’s see how AI works, taking a virtual assistant as an example. An AI model can be integrated into customer support’s CRM system. Trained on big amounts of historical data, it then helps an agent to accomplish daily tasks. For example, such models are often used as copilots in email channels. Analyzing the previous communication with the customer and also their purchase history, the model can suggest a ready answer to a customer’s email.
What’s more, machine learning algorithms can identify the customer’s behavior and mood. By providing an answer in the same tone, addressing the customer by name, and offering personalized solutions based on the previous interactions, an AI assistant accomplishes the task like a human agent. The agent only needs to validate the answer and send it.
Improved Customer Experience
Forbes cites that while aiming to improve customer experience, 61% of businesses use AI for their emails, and 55% to provide personalized services. Since the investments into AI only continue to increase, there will be no surprise if by 2050, a customer will mostly talk with customer assistants.
The success of ML algorithms is not exaggerated. Here’s how generative AI already helps today:
- Reduced response rate. Validating responses takes much less time than reading previous interactions with the customer and typing an email in response. By receiving quick answers to their concerns, customers feel that they are important and cared for. There is no surprise that their satisfaction goes up.
- Increased efficiency. From the point of view of customer support agents, treating tickets with customers’ requests can be tedious. With AI assistants, they can process mails quicker. Processing a couple of mails to tens of mails per hour does make a difference.
- Personalized response. As mentioned above, AI algorithms have become really good in identifying a customer’s intention. Suggesting personalized offers, tailored solutions, and adding a note of understanding into communication help to reduce issues.
- Increased empathy. While we expect AI assistants to provide template answers, they have become capable of maintaining a personal touch. Personalized answers are signs that the companies hear customers and are ready to help.
However, as we get excited about the possibilities of AI algorithms, we should continue putting efforts into their training. To actually provide all these accurate email answers, a model not only needs to analyze the historical data, but also continuously train. Besides, the historical emails are only the beginning. You need to integrate the model with various customer support’s backend systems. Only a well trained and validated model will give you the expected results.
Final Thoughts
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Generative AI in customer support continues to amaze us with its endless possibilities. In the form of virtual assistants or chatbots, it saves us time on repetitive tasks and streamlines business operations. Putting a customer in the center, businesses can now optimize their processes, save costs, while at the same time increasing customer satisfaction.
By prioritizing customer centricity with generative AI at the backend, businesses can build stronger relationships with their customers. Additionally, they promote their brand and become competitive in the market. However, it’s important to remember that even though AI has a transformative power, the final decision is always on a human agent. It’s customer support agents who make the final validation and constantly train. It’s only in the synergy of both, a human and a machine, that every interaction will lead to success.
