AI-powered customer service analytics platform SupportLogic nabs $50M


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AI-powered customer 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 over $62 million, will be put toward supporting the company’s growth and ongoing platform development.

Santa Clara, California-based SupportLogic was founded in 2016 by Krishna Raj Raja, an early support engineer at VMware and the first employee at the company’s India office. Raja says he observed firsthand that customer intent signals were getting lost amid organizational silos and customer relationship management and support 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 via email. “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 customer service

The pandemic brought into sharp relief the value of AI in customer service operations. Gartner predicts that 15% of all customer service interactions globally will be fully powered by AI in 2021. And according to Deloitte, 56% of companies are investing in conversational AI technology to improve cross-channel experiences.

SupportLogic specializes in extracting customer signals from business communications with case evaluation and agent coaching tools. Using natural language processing, the platform provides recommendations to managers to get ahead of escalations and helps to identify the best cases in a backlog to review. SupportLogic also provides intelligent case routing, using its AI engine to determine the best available agent to handle a case based on factors like sentiment and churn risk. Moreover, the company’s product supports non-support functions, including product management, offering visibility to customer challenges that clients can act on.


Above: SupportLogic’s customer service analytics platform.

Image Credit: SupportLogic

“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 said. “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 designed its platform using an ensemble method — a machine learning technique that combines several base models in order to produce one optimal predictive model — running on Google’s BERT. Trained from millions of customer interactions, the model and its predictions are personalized for each customer, leveraging a core signal extraction engine built on a common framework.

SupportLogic claims that it has several thousand users across “many large enterprise accounts.” In 2021, the startup’s customer base grew 300%, while the number of interactions analyzed by its AI grew from 15 million in 2020 to over 60 million in 2021, the company says.

“When the pandemic hit, like in every other industry, we thought we’d be negatively affected. But surprisingly, we weren’t,” Raja said. “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.”


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