5 Best Practices for AI- and Data-Driven Call Centers

Call centers have been revolutionized in the past decade. While some static call scripts and one-size-fits-all strategies still remain, technology has drastically changed the way call centers are capable of functioning. Today, call centers have the unique ability to leverage all available data to drive each customer interaction. These data sources include which digital marketing campaign the customer is viewing, what transactions the customer completed in the last hour, what was the customer’s previous bill or the next bill, and what the customer just asked about on the call.

Layering in AI can also include lifetime value, propensity to churn, customer satisfaction, sentiment, likelihood to buy, credit risk, etc. This type of call center can leverage successful online tactics and use technology to generate the most business value out of each call even though the actions taken on each call could be very different.

Call centers must provide a seamless and easy experience for customers or risk losing out to a competitor. American Express found that 78% of consumers have bailed on a transaction or not made an intended purchase because of poor service experience. To ensure that doesn’t happen, many call centers are turning to technologies such as AI and machine learning to provide them with details on the next best action, customer churn and retention rates, product purchase propensity, and more.

Here are five best practices for data-driven call centers that can improve selling and the customer experience using AI and big data.

1. Implement intelligent call routing

Call routing optimizes human resource costs and helps get the right customer to the right representative. AI can determine the best available service group for a given customer taking into account not only the reason for the call but also the lifetime value and call complexity.

Alternatively, many opt to use skills-based call routing based upon the likelihood to respond to a promotion. If one call center team is working on promotion X and another promotion Y, calls can be routed to the appropriate team based on likely caller behavior. In order to minimize bias, a subset of the calls should take a round-robin routing method, which can help ensure a representative sample when using AI to model customer and agent behavior. Layering in AI to skills-based call routing ensures the customer arrives to the right agent who can help them.

2. Leverage the context of the conversation

Context is crucial in a call center because it enables agents to provide a personalized conversation. Using data, agents can have a complete view of the customer. Historical data shows how the customer already interacted with the company, while real-time data provides a continuous experience (from mobile to the website to the physical store) for the consumer.

Context makes the process easier for the customer as well, as they don’t need to re-explain their problem or question. Plus, context is a key element for call routing -- it gets the right customer to the right agent quickly. Predictive analytics can determine what possible questions or concerns that a customer may have.

Context is also vital for chatbots. Poorly built, static chatbots are becoming useless for companies and frustrating for customers. Chatbots that leverage conversational context, however, can stand out in an extremely crowded market. Chatbots are also less expensive than agents, but if left unoptimized, they become irrelevant. AI-powered assistants are now leveraging the context of the conversation to respond better to prompts. Many note the increasing abilities of Siri, Alexa, and other assistants because advancements in machine learning have improved natural language processing.

3. Use agent or personality profiling

While most call centers focus solely on the customer, it’s also important to focus on the agents. Each agent communicates differently and appeals to a different segment of customers. Some agents cannot handle negative callers, and some excel in that setting. Companies should look at the personality profiles of the agents and view experience level, sales experience, sales numbers, call handle time, etc. to match customers to agents better.

AI can assist in that. For example, maybe an agent thrives at selling to customers who have just downloaded the company app. Companies can route those calls to that agent to increase sales. While much of the data surrounds customer profiling, businesses should focus on agent profiling to create selling matches.

4. Focus on CRM Integration

CRMs give agents quick access to data needed on customers, promotions, activity history, and more. Personalized service, which many customers have come to expect now, is only possible with a robust CRM coupled with intelligence-based call routing. CRMs can track information on how a customer interacts with a brand and what products they already own, which means providing answers to questions and suggesting another purchase easier.

With this information, agents can use customized prompts and promote products or add-ons that the customer is more likely to purchase.

5. Close the loop

When an agent extends an offer to the customer, all responses should be tracked. Responses such as accepted or declined the offer, not offered, a quote, purchase, etc. with a reason and code for each is crucial information for businesses. Looking at all of the information together, AI can help discover patterns. For example, perhaps agents continuously make sales using a specific marketing prompt. Companies should leverage this data for informative insights and edit prompts and offers accordingly. Other metrics such as response time, handling time, abandonment rate, etc. are important as well to discovering what works and what doesn’t work in the call center.  

All of this response data is critical to begin leveraging machine learning and AI. Having a good historical indication of what good or bad response looks like within the call conversation data is a key stepping point. By gathering the agent’s information, actions taken, and the responses, companies can leverage AI and machine learning to create more insightful models to use in the call center.

Improving the customer experience

Ultimately, businesses want to create a personalized, positive experience for the consumer. American Express found that people tell an average of nine people about good experiences and 16 about poor experiences. This statistic shows that while marketing call centers may not get as much credit for providing a good experience, a bad experience can have lasting effects. Companies can increase selling rates and provide a high-quality experience for their customers by leveraging data and using these five best practices.

If your call center needs a more data-driven approach, Quickpath can help. Contact us to get started.