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Turning conversations – from customer service requests to user feedback – into tangible business value isn’t an easy task. It is also an ideal use case for AI-based automation.
One of the vendors helping organizations use AI to derive value from customer conversations is San Francisco-based Lang, which today announced it has raised $10.5 million in a Series A funding round. Lang’s platform integrates with help desk, customer relationship management, and user-centric operations for feedback and requests. The system uses an unsupervised learning model to adapt to the ever-changing flow of information by categorizing data and then helping determine what to do with the data to improve user experience and business outcomes.
“There has been an increase in the number of conversations business teams are dealing with, particularly things like customer support, which has been accentuated during the pandemic,” Lang’s CEO Jorge Peñalva told VentureBeat. “Of course there are a lot of AI technologies, but in general they are built by engineers for engineers, so they are very complex. We believe there must be a better way for business users to use AI.”
Lang is certainly not the only one in his corner of the market. For example, Zendesk has been building out its AI capabilities in recent years to help with its customer service platform. A core element of his capabilities came from the company’s 2021 acquisition of Cleverly.ai.
CRM giant Salesforce is also very active in the AI space with its Einstein platform. Contact center technology provider Genesys continues to actively expand its AI capabilities with its Google partnership.
A recent report from Fortune Business Insights estimated the size of the global customer experience management market at $11.3 billion by 2022. The report forecasts that the market will grow at a compound annual growth rate (CAGR) of 16.2% over the next seven years. to $35.5 billion by 2029.
How Lang is using AI to extract value from conversations
Peñalva is well aware of the market potential and competition. According to him, Lang provides a differentiated approach thanks to the use of an unsupervised AI model.
A common approach to enable AI is to use a supervised model that trains based on a particular set of data. The challenge with the supervised model is that AI is often trained on static data. Peñalva noted that data changes quickly and for organizations to really respond to users, training on static data is not good enough. That’s why his company has developed a specially developed unsupervised learning model that constantly looks at data that is constantly changing.
How it works: Lang connects to the customer data, and the unsupervised model analyzes the data and converts it into simple “drafts” — which Peñalva says is a business term for an item or operation a company needs to track. For example, a concept can be a delivery date, a product or a credit rating. The AI model automatically extracts key concepts in a conversation so that they can be grouped into categories that make sense for a particular business.
The interface to the categories is presented to users in a no-code model, allowing an organization to group things as desired. The no-code interface also helps provide some form of explainable AI so that users can easily see how the unsupervised model extracted concepts and what categories the concepts were placed in.
Using AI to extract business value from conversations can also help organizations scale their operations.
An example is with Lang customer Ramp, which offers online tracking services for expenses. According to Peñalva, Ramp’s challenge was to scale up operationally quickly. With Lang, Ramp was able to categorize customer requests faster and then provide automated workflows to accelerate resolution. For example, Disaster can direct a query about a credit problem to an agent who can respond quickly to those types of requests.
Ramp also uses Lang to understand customer feedback. As Ramp develops new products, Lang analyzes feedback and requests to better understand how the new product is received and whether changes need to be made to optimize the user experience.
“We are really operationalizing their support data for automation and also for internal insights that other teams can leverage,” he said.
With new Series A funding in hand, Peñalva aims to continue to help organizations extract business value from data more easily and help them automate repetitive tasks.
“We think a lot of companies today will think about how to become more efficient,” he said. “There are a lot of inefficiencies when you think about the repetitive tasks that people do in their day-to-day work when they really should be focusing on more high-level tasks,” Peñalva said.
The new funding round was led by Nava Ventures and included the participation of Oceans Ventures, Forum and Flexport Fund.
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