The Origins of Level AI: Improving Contact Center Call Categorization
When I was part of the Amazon devices (Kindle, Fire tablets, etc.) team, our contact center team was the hub of customer engagement and troubleshooting.
To scale a budding business, we wanted our customer service call center agents to disposition call reasons accurately for analytics and deep dives. This data is invaluable in improving the function of a contact center.
What Is Call Center Customer Categorization?
Call categorization is a system for placing customers into different segments within a contact system. Segments are based on a variety of criteria. Customers who need help with warranties or customers who are running into a specific issue with a product are examples of customer categories.
It is popular to use an AI that searches for keywords to do this, but semantic AI is quickly proving itself more effective in this space. Now, back to the story.
The Problems We Faced
We faced several call center categorization and agent training challenges. Amazon used a common approach to call categorization.
The agent selected the call category from a tiered tree at the end of every contact. This approach had two primary issues.
- Definition and management of call center categories
- Accuracy of contact categorization
Improving Call Categorization Definition and Management
The first issue was the definition and management of the categories. We went through a few iterations of expanding the number of categories to gain issue fidelity.
Later, we reduced the number to simplify. Every change required new guidance and training for our agents, and it never resulted in feeling more confident in the data.
Improving Call Categorization Accuracy
A study by our quality improvement team led by an experienced team leader found that many contacts weren’t being categorized well.
Poor categorization was not surprising because agents weren’t rated on the quality of their categorization.
Instead, they were rated by their average handle time (AHT), and properly categorizing customers takes time.
Additionally, the study also found that most contacts fell into multiple categories. Unfortunately, we were forced to assign a single category to each call and lost a lot of rich data in the process.
The Benefits of Improved Contact Center Call Categorization
These are the same problems we see teams of all sizes struggle with in the modern contact center, and they apply to every conversation that takes place there, whether it’s on headsets or keyboards.
What we needed was an analytics layer that could categorize calls appropriately. An analytics layer would have:
- Helped reduce after-call work leading to more calls handled
- Improved data quality and call center metrics
- Kept agents focused on helping customers and improving the customer experience
It would’ve been even better if the system could have helped identify categories and provided real-time assistance to agents in finding the correct information for customers. Call center performance and agent performance would have increased, and the service level could have been improved.
I am excited about Level AI because it is clear how our product addresses these challenges — Level AI gives superpowers to your support organization.
At the product’s core, our semantic intelligence engine helps identify detailed drivers of interactions with your customers, not just the words they use. It enables your agents, quality assurance teams, and product teams to operate more efficiently and with greater insight into customer needs.
While our competitors are still using keywords to analyze the customer experience, we are going beyond that to deliver customer insights that can guide business strategy more effectively. Our system goes a level deeper, searching for customer intent instead of stopping at word matching to produce better customer categorization.
What to Do Next: Contact Center Customer Categorization with Level AI
Let’s talk about how we can help your business get to the next level by making better use of customer data in your contact center spanning calls, chats, emails, and more.
Here’s what you can do:
- Talk to a contact center professional to see if Level AI is right for you.
- Explore the Contact Center Content Hub to get the latest info for agents and QA teams.
- Watch the Level AI overview video to see how we’re disrupting the Contact Center as a Service (CCaaS) market.
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