Nirmal Mukhi leads the Machine Learning Infrastructure team at ASAPP and is the technical leader for our AutoSummary product. Prior to joining ASAPP, Nirmal held leadership positions in engineering and research at IBM, where he was R&D lead for Watson Education, and served as CTO at fast-growing startup. He has over 30 publications (with 4500+ citations), 15 patents, and has appeared on a Discovery channel documentary about AI.
Taking notes after a customer call is essential for ensuring that key details are recorded and ready for the next agent, yet it can be difficult to prioritize when agents have other tasks competing for their time. Could automated systems help bridge this gap while still delivering high-quality information? How should the data from customer interactions be organized so that it is useful and easily accessed in the future?
As we were developing AutoSummary, the ASAPP AI Service for automating call dispositioning, we asked our customers for input. ASAPP conducted customer surveys and discovered that agent notes needed to include Reason, Resolution, and Result for every conversation. This 3R Framework was key to success. Here’s a more detailed explanation:
Reason – Agent notes need to focus on the reason for the customer interaction. This crucial bit of data, if accurately noted, immediately helps the next agent assisting the same customer with their issue. They’re able to dig into earlier details and resolve issues more quickly and efficiently while also impressing customers with their empathy and understanding of the situation.
Resolution – It is essential to document the steps taken toward resolution if an agent needs to continue where another left off. When an agent clearly understands the problem and its context, it becomes much easier to follow a series of steps or flowcharts to resolve.
Result – All interactions have a result that should be documented. This allows future customer service agents to see whether the problem was solved effectively, as well as any other important details.
ASAPP designed AutoSummary to automate dispositioning using the 3R framework as a foundation. And, depending on the needs of the customer, AutoSummary can also provide additional information, like an analytics-ready structured representation of the steps taken during a call. We created AutoSummary with two goals in mind:
Maintain a high bar for what’s included: A summary is, essentially, a brief explanation of the main points discussed during an interaction. Although summaries lengthen as conversations continue, we maintain a limit so that agents can read the note and become caught up in 10-20 seconds. We also eliminate any data that could be superfluous or inaccurate. Our strict standards guarantee a quality output while still being concise.
Engineer for model improvement: While AutoSummary creates excellent summaries, a fundamental component of all ASAPP’s AI services is the power to rapidly learn from continuous usage. We designed a feedback system and work with our customers so that any changes agents make to the generated notes are fed back into our models. Thus, we’re constantly learning from what the agents do – and over time, as the model improves, we receive fewer modifications.
We’re always learning what our customers want and translating that into effective product design. For us, it’s been great to see how successful these summaries are in terms of business metrics such as customer satisfaction, brand loyalty, and agent retention. We strongly believe that good disposition notes for all customer interactions improve every metric mentioned above–and more!
On average, our customers who use Autosummary save over a minute of call handling time per interaction, which saves them millions of dollars a year. Who wouldn’t want those kinds of results?