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AI-powered client service analytics platform SupportLogic today announced that it raised $50 million in series B funding led by WestBridge Capital Partners and General Catalyst, with participation from Sierra Ventures and Emergent Ventures. CEO Krishna Raj Raja says that the funds, which bring SupportLogic’s total raised to more than $62 million, will be place toward supporting the company’s development and ongoing platform development.
Santa Clara, California-based SupportLogic was founded in 2016 by Krishna Raj Raja, an early assistance engineer at VMware and the initial employee at the company’s India workplace. Raja says he observed firsthand that client intent signals have been obtaining lost amid organizational silos and client relationship management and assistance ticketing systems.
“[I] founded SupportLogic with the mission to transform the role of customer support as a proactive change agent within companies by being able to capture and act on the true voice of the customer to grow and protect customer revenue,” Raja told VentureBeat by means of e-mail. “Our new funding will help SupportLogic to add more customer interaction channels to the solution set — for example, multiple data sources such as chat, voice, discussion forums, surveys, and emails. We will also expand our agent coaching and customer health management capabilities.”
AI-powered client service
The pandemic brought into sharp relief the worth of AI in client service operations. Gartner predicts that 15% of all client service interactions globally will be totally powered by AI in 2021. And according to Deloitte, 56% of corporations are investing in conversational AI technologies to enhance cross-channel experiences.
SupportLogic specializes in extracting client signals from business enterprise communications with case evaluation and agent coaching tools. Using organic language processing, the platform gives suggestions to managers to get ahead of escalations and assists to determine the greatest instances in a backlog to assessment. SupportLogic also gives intelligent case routing, applying its AI engine to figure out the greatest offered agent to deal with a case based on aspects like sentiment and churn threat. Moreover, the company’s item supports non-assistance functions, such as item management, providing visibility to client challenges that customers can act on.
“Off-the-shelf sentiment analysis and entity extraction machine learning models are trained on a completely different corpus and do not work on these datasets. Most of the tools in this space focus on case deflection use cases, such as chatbots, robotic process automation, and knowledge management,” Raja stated. “As such, there have not been any software-as-a-service solutions that do what SupportLogic does to date. In fact, many of our customers initially started down the path of building their own solutions and SupportLogic often displaces these homegrown projects.”
SupportLogic developed its platform applying an ensemble system — a machine finding out approach that combines numerous base models in order to generate one optimal predictive model — operating on Google’s BERT. Trained from millions of client interactions, the model and its predictions are customized for every client, leveraging a core signal extraction engine constructed on a typical framework.
SupportLogic claims that it has numerous thousand customers across “many large enterprise accounts.” In 2021, the startup’s client base grew 300%, whilst the quantity of interactions analyzed by its AI grew from 15 million in 2020 to more than 60 million in 2021, the corporation says.
“When the pandemic hit, like in every other industry, we thought we’d be negatively affected. But surprisingly, we weren’t,” Raja stated. “The support engineers of our customers all started to work more remotely and collaboratively. SupportLogic delivered an immediate benefit for these organizations — e.g., agent coaching became easier to do … We also evolved the product to help our customers to manage the impact of the pandemic within their own businesses. For example, a few customers asked us to help them track pandemic-related keywords like ‘COVID 19’ that we quickly turned on within our product.”