5 Best Practises for Data Driven AI Call Centers


The call center industry has seen a rapid revolution in the past decade. New and evolving technologies like AI (Artificial Intelligence) have significantly impacted how call centers function and operate. To drive customer interaction and personalization, call centers take advantage of all the available data and information. This data includes customer’s online transactions, their previous or next bill, the digital marketing campaigns they are viewing, customer queries, and more.

Call centers are required to provide an easy and seamless experience to customers, and failure can lead to the loss of customers to other competitors. Poor customer service is one of the primary reasons for customers bailing out on transactions and purchases from a particular center. To avoid this situation from happening, many call centers are now incorporating machine learning and switching to AI call centers.

Adopting AI call center technology enhances operational efficiency, customer experience, product purchase tendency, increases retention rate, and more.

To leverage AI to increase customer experience and drive sales, follow the following best practices for your data-driven AI call centers.

1. Incorporate an intelligent call routing

Call routing or call center automation optimizes human resource costs and helps the customer reach the right representative. AI determines the best available customer service group for a particular customer, considering the reason behind the call, the call complexity, and the lifetime value.

Alternatively, many call centers also use Skills-Based Call-routing (SBR), which is a call-assignment strategy used to reduce biases. Instead of assigning the incoming call to the next available agent, it ensures that the customer is assigned to the most appropriate and suitable agent. Therefore, incorporating AI into skill-based call routing makes sure that the customer is directed to the right agent, who can solve their problems accurately.

2. Use personality profiling or an agent

While call centers completely focus on their customers and their issues, it is also necessary to focus on the agents. Agents are humans at the end of the day, and each of their personalities differs naturally. Some agents excel in managing negative callers or customers, while others cannot handle them with ease. Hence, to match customers with relevant agents, call centers must go through their agent’s personality profiles and check their total experience level, call handle times, sales numbers, etc.

AI helps businesses achieve this perfect matching of customers and agents to help increase sales. Thus, focusing on agent profiling is of significant benefit to call centers.

3. Leverage the conversation context

The context in call centers helps agents deliver a personalized conversation by viewing and getting a complete picture of customer’s data. Context also helps and saves customer’s energy as they need not explain their problems or repeat the question again. In addition, for call routing, context is a significant factor and a key element, getting the right customer quickly to the right agent. Through predictive analytics, agents can determine the kind of questions or issues the customer might face.

But most importantly, context is essential for chatbots. Chatbots that are poorly built, unresponsive, and static annoys customers as well as businesses. AI chatbot call centers leverage customer’s conversational context and stand out from their competitors. Chatbots are less expensive than normal agents, which can respond to the customers in a much better and improved prompt through conversational context.

4. Focusing on CRM integration

Through CRMs (Customer Relationship Management), agents can get quick access to information and data required on promotions, customers, activity history, and more. A robust CRM coupled with intelligence-based call routing helps customers get a personalized service experience. CRMs track customer’s data on their interactions with brands, products they own, services/ products they are looking for to provide relevant suggestions and recommendations to make future purchases much easier.

5. Close the loop

The responses received from the customers when an agent offers a service to the customer, such as accepting the offer, declined the offer, a quote, purchase, no offer, must be all tracked. These responses with a proper code and reason are vital information for businesses. AI call centers online look at this tracked information and discover customer patterns. Businesses can leverage this information to get an insight and offer personalized and edited prompts to customers. This data includes marketing prompts, abandonment rate, handling time, response time, etc. This data also provides business feedback, allowing them to understand what works in the call centers and what does not.

This tracked data enhances the possibilities with AI and machine learning. Getting an insight into what a bad or a good response looks like in the call conversion data helps businesses track where they can improve and what is working for them. Thus, businesses can create a much enhanced and insightful model useful for call centers through AI and machine learning through the agent’s information, data, responses, and actions.

Summing Up

Every business wants to provide an enhanced and personalized customer experience. This is why call center AI online is emerging in the market at a rapid speed. Using the five best practices mentioned above, organizations can drive sales and increase user engagement and quality experience.

interface.ai provides an intelligent AI call center automation service to credit unions and banks. Check out our services at interface.ai and get increased revenue, call center productivity, and engagement for your business.