Automating common tasks and enabling self-service issue resolution for customers is an essential part of any online customer service experience. These automated flows directly address a specific well-scoped problem for the customer, getting them to resolution quicker and freeing up agents to handle more complex issues. But, automation doesn’t have to be an all or nothing proposition. At ASAPP, we automate flows before, during, and after agent interactions, increasingly reducing agent workload and growing the opportunity for self service over time.
Discovering and prioritizing new flows and understanding what’s needed for successful automation, however, can be challenging. It is often a time consuming and labor intensive process. ASAPP has developed AI Native® approaches to surface these workflows to humans, and we’ve been awarded a patent, “Identifying Representative Conversations Using a State Model” for a powerful solution we developed to perform flow induction.
It’s difficult for a human to imagine all the possible conversation patterns that could be automated, and which ones are most important to automate. It’s important to consider things like how many users it would affect, how much agent time is being spent on the intent, whether the flow has a few well-defined paths or patterns, what value the intent brings to the business, and whether there are any overlaps between this intent and other conversations.
Rather than manually sifting through all the data, an analyst can leverage patterns identified by the model to more quickly deploy automated workflows and evaluate their potential with real usage data.
We call the process of automatically discovering and distilling the conversational patterns—“workflows”, or “flows” for short—flow induction. We can condense a large collection of possible flows to a much smaller number of representative flows. These induced flows best capture interactions between customers and agents, and flags where automation can lend a helping hand. This facilitates faster and more comprehensive creation of automated flows, saving time and money.
Our patented approach for flow induction begins by representing each part of a conversation mathematically, capturing its state at the time. As a simple example, we would want the start of each conversation—where agents say “hello” or “how are you” or “welcome to X company”—to be similar, with approximately the same state representation. We can then trace the path the conversation traces as it progresses from start to finish. If the state is two dimensional, you could draw the line that each conversation takes as its own “journey.” We then group similar paths and identify recurring patterns within and across conversations.
The process of identifying automation use cases is dramatically simplified with this representation. Instead of manually sifting through conversations, talking to experienced agents, or listening into calls to do journey mapping—the analyst can dive into a pattern the model has identified and review its suitability for automation. Even better, because ASAPP is analyzing every customer interaction, we know how many customers are affected by the flows and what the outcomes (callback, sales conversion, etc) are — making prioritization a breeze.
ASAPP deploys “flows” like this across our platform. By identifying the recurring work that agents are handling an analyst can construct integrated flows for agents to serve in any part of a conversation. And over time, more and more flows can be sent directly to the customer so they can self-serve. Once deployed every flow becomes part of a virtuous feedback loop, where usage informs how impactful the automation is for our customers and their customers. This process informs both new flow opportunities and refinements to existing flows.
Michael Griffiths is a data scientist at ASAPP. He works to identify opportunities to improve the customer and agent experience. Prior to ASAPP, Michael spent time in advertising, ecommerce, and management consulting.