Shawn Henry, PhD is Director of Intellectual Property and Research Scientist at ASAPP, leading patent, open-source software, collaboration, and data privacy efforts and driving innovation in non-standard areas of machining learning. Shawn received his PhD in mathematics from the University of Michigan in 2013. He previously held the position of Lecturer in Mathematics at the University of Colorado where he did research in Topos Theory, and was a founding member of the team at ASAPP.
When should a customer experience agent upgrade or upsell a customer? Even for a talented sales agent, it can be difficult to answer this question—choosing the right timing and the right offer in the flow of the conversation whether over messaging or a phone call. Add a steady stream of interactions, this year’s unprecedented demand for support, and the imperative to minimize handle time and it becomes next to impossible.
Since inception we’ve been helping businesses solve crucial customer experience challenges like this one by developing cutting edge AI. Most recently, we were granted a patent for “Automated Upsells in Customer Conversations.” The promise of our technology hinges on those challenges that every CX agent is facing along with the massive scale at which our customers operate.
Using AI to predict when to upsell and what to offer can help consumer companies increase sales and grow revenue.
Shawn Henry, PhD
When agents are rushing from issue to issue, there’s often not enough time to access and contemplate the history and preferences of an individual customer. Yet we know that everything from a customer’s next billing date to their list of purchased products could be valuable predictors of their interest in a new product offering or upgrade. In fact, because our models can learn from millions or billions of related customer data points, we can both extract novel correlations and effectively leverage those insights in real time.
While many solutions in Customer Experience involve generic one-size-fits-all automations, our machine learning models consume a variety of customer specific data points to augment an expert human agent. Our model begins by breaking down the conversation (whether in text or voice) into the content so far, the topic(s) of conversation, and sentiment of the customer. This context is complemented by customer data, including metadata on their account, preferences, products etc. When deployed at contact centers that employ thousands, or tens of thousands of people, speech recognition, natural language processing and machine learning make it practical to determine what conversations are going to deliver higher likelihood of upsell success, even when customers call in for many different reasons and in different emotional states. Sometimes, for example, a customer may be very agitated, and an upsell attempt could not only degrade their experience but harm their perception of the brand, so our model may actually discourage an upsell in favor of maintaining and strengthening that relationship.
In addition to a customer’s own data, the product selection can be determined by interpolating that data with the product preferences of similar customers, as is done with collaborative filtering. All of these factors inform better predictions. Being able to help customers knowledgeably with new products, upgrades or offerings, regardless of department, can be done without friction as the ASAPP platform can automatically surface the right upsell opportunities at the right time to agents across a CX workforce to help grow revenues.
Regardless of industry and agnostic to the channels customers use to communicate, businesses can benefit by surfacing upsell opportunities automatically to agents. ASAPP’s patented technology can dramatically transform this process and accelerate revenue growth as well as a company’s journey to providing unparalleled customer experience.