Profiling your agents and customers, CLV optimization… AI-powered analytics can make you and your bottom line very, very happy. Without spending much time.
More and more call centers begin to use basic data and analytics tools, McKinsey reports. However, only 37% of companies believe that they are using advanced analytics to create value. Most organizations are not taking advantage of the AI analytics of the future – they only evaluate the past and present results – which means they are missing out on generating actionable insights about the future.
On the other hand, companies that have already applied advanced call analytics have seen a 40% drop in average handle time, a 5% to 20% increase in self-service containment rates, as well as reduced employee costs by up to $5 million — not to mention customer satisfaction and employee engagement constantly improving.
Let’s take a closer look at how your contact center can benefit from AI-powered call analytics.
Identifying customer and agent profiles
Experts say that AI and machine learning can help your workforce develop a holistic customer profile: use external data to find out otherwise unknown facts about customers and internal data to identify their similar or different traits.
Artificial intelligence can determine call patterns that let operators predict which customer types are likely to call with which specific issues or requests. This information lets operators proactively help clients, leading to lower call volume.
"By using a call center AI software, you can look at 16 months worth of data to see what the most common call-in topics were and why customers are calling. This helps you to better prepare your agents for the calls that they are most likely to receive," notes Jafar Adibi, head of AI at Talkdesk.
AI can be used for agent profiling as well. Each agent communicates differently and gets on well with different customer profiles (some can handle aggressive callers while others can’t). Insurance call centers should employ analytics tools to assess experience level, sales numbers, average handle time, etc. to match clients to agents better.
This article is a part of a detailed report on how insurance companies are improving their call centers with voice AI. Download the full report here!
Improving customer satisfaction
Researchers state that several scholars have been using data mining to analyze customer behavior and improve customer satisfaction. eTalk and GartnerGroup built data mining tools into their monitoring systems. These tools are intended mostly for non-experts, such as supervisors and managers. This technology helped find out that call transfers frustrate customers.
Textual data mining has also been applied in analyzing call center performance. Busemann et al. classified email requests from clients based on shallow text processing and machine learning techniques. Their system boasted a 73% correct accuracy when responding to customer emails.
Finally, there have been experiments with audio data mining. ScanSoft employed context-free-grammar to parse the speech and then apply Sequence Package Analysis to caption the text to which data mining is applied. This allowed them to spot early signs of caller frustration.
CLV optimization
Customer Lifetime Value (CLV) is a metric tracking the value of a customer to a company throughout the customer experience. The premise here is that retaining existing customers is much better for ROI than gaining new ones. While the likelihood of selling to a first-time customer is 5-20%, an existing customer will buy again with a 60-70% probability.
Using AI-powered data analysis to understand CLV gives organizations the data they need to consistently improve or to identify areas of excellence; every agent should keep CLV in mind and use analytics insights to optimize this metric.
Cost vs Value
It’s important to monitor how much time, effort and money your workforce spends on calls and measure the returns on these resources to improve your contact strategy. You also want to measure metrics such as average handle time, average wait time and first call resolution.
You can employ an AI analytics tool to get detailed and up-to-date information on the amount of the call center’s time spent helping clients, the most and least frequent types of requests etc. Leveraging this knowledge will enable you to identify underlying trends in customer service and be in the vanguard of customer experience.
These are just a few ways you can use AI and data science to better understand your clients and help them every step of the way.