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Contact centers have never been a walk in the park for employees, but they have become much tougher work environments during the pandemic. According to a survey, only 10% of contact center agents achieve proficiency in less than two months. Meanwhile, the volume of difficult calls to contact centers increasedwhile the turnover remains at an exorbitant rate between 30% and 45%.

It is in this context that automation products are attracting the interest of call center operators and investors. At the more sophisticated end of the spectrum, call center automation promises to solve customer service problems to free up agents for more complex tasks. Replicant, a leading provider of contact center automation, today announced it has raised $78 million in Series B funding led by Stripes with participation from Salesforce Ventures, Omega Venture Partners, IronGrey, Norwest and Atomic. Sources tell TechCrunch the post-money valuation stands at $550 million.

“[With the new capital,] we plan to increase investment in our customer success team to onboard new customers,” co-founder and CEO Gadi Shamia told TechCrunch via email. “We also plan to double our R&D team this year to make our conversations even more efficient and launch new automated channels. We will increase our investments in sales and marketing to capture the significant demand we are seeing. And finally, we will invest in our employees by launching additional professional development programs.

Shamia co-founded Replicant in 2017 alongside Andrew Abraham, Benjamin Gleitzman and Jack Abraham. Shamia previously served as general manager of products for SAP’s small business solutions group before becoming interim chief operating officer at EchoSign after its acquisition by Adobe. He also helped launch Magneto, a calendar system, and was COO at Talkdesk for almost four years.

Prior to Replicant, Abraham – who joined eBay in 2011 through the company’s acquisition of Milo.com – worked as a software engineer at Atomic and smart device company Leeo. Gleitzman was a senior software engineer at Hunch and eBay before co-founding several startups, including a “virtual reality therapy platform” called Mona.

“By [my] job, I’ve realized that the best way to increase agent efficiency and reduce customer and agent frustration is to automate many common tasks and let agents focus on more complex and nuanced calls. said Shamia. “Gleitzman was one of eBay’s AI pioneers and worked with Abraham and the team at Atomic to build a machine that could have an entire phone conversation with a human.”

Replicant aims to automate call flows by integrating with existing systems, including customer relationship management software, to recognize customers based on their order histories (if applicable) and calls made. . The product can capture, transcribe and make customer conversations searchable and, like some competing service automation systems, Replicant can interact with customers via SMS and the web in addition to voice.

Replicant provides agents with call summaries and measures trends such as overall customer satisfaction, average handle time, competitor mentions, defective products, and upsell opportunities. Customers can leverage a library of pre-built components to design conversation flows using a visual editor. Over the past few months, Replicant has added support for new languages ​​and conversational capabilities that Shamia calls “powers,” such as holding the line, repeating information “conversationally,” and matching a customer’s response to a base. of data.

“One of the key competitive advantages we have at Replicant is the rich and varied data we have accumulated from handling more than 30 million customer service calls across all industries and use cases. Our [product has] covered everything from hardware troubleshooting for small business owners, to relaying food orders to restaurant workers, to handling subscription issues for elderly callers, to high-emergency scenarios where callers need roadside assistance,” Shamia said. “[W]We turn scenarios that are typically frustrating (think about every time you had to go back and forth to spell your name or read a 15-digit police number to an officer on the phone) into a task that can be done efficiently in seconds with a specially designed model.

Asked about how Replicant manages, stores and retains customer data, Shamia said the company offers enterprise customers the option to choose a data retention window that “works for them”, typically ranging from six months to two. years. For use cases involving payments or protected electronic health information, Replicant offers a service called Highly Confidential Tower, which the company claims removes sensitive data during the conversation tower from the database and logs. Replicating.

Replicant also engages in sentiment analysis, a controversial process that involves the use of algorithms to determine whether a piece of audio or transcribed text has a positive, negative, or neutral tone. Sentiment analysis systems – both academic and commercial — showed that they had a bias according to race, agecultural, ethnic and sex lines. Some algorithms associated Blacks with more negative emotions like anger, fear and sadness. Others discriminate against non-native English speakers, who tend to use cognates – that is, English words that sound like words in their native language – more often than native speakers.

Replicant claims that it only measures customer satisfaction by asking specific questions (e.g., “Are you satisfied?”) and takes steps to mitigate bias in its systems – including its customer analytics systems. feelings – as well as the data used to develop these algorithms. Unfortunately, without independent audits or studies, it’s company word against general academic findings. This reporter hopes to see more transparency from Replicant in the future.

“Our models are thus trained on a variety of industry-specific accents, emotions and lingo, allowing us to achieve [high] inference accuracy even in the most complex service use cases,” said Shamia. “We are seeing an 85% call success rate (measured by expected business outcome) across all customers and use cases.”

Automation of customer interactions

Anecdotal evidence suggests that customer service organizations are embracing automation. A 2020 study According to the Harris Poll, commissioned by artificial intelligence provider Interactions, estimates that 46% of customer interactions are automated – a percentage that the co-authors say will rise to 59% over the next two to three years. Early adopters interviewed for the study cite “soft benefits” such as reduced wait times, faster resolution of customer complaints, and technical support and personalization.

In response to growing industry interest, countless call center automation products have hit the market in recent years, both from startups like Replicant and incumbents like Google, Amazon and Salesforce. Replicant competes with RedRoute, Skit, and Voximplant in addition to Ultimate.ai, a customer service tool designed to automatically respond to simple service requests.

Expert market research predicted that the global call center AI market will grow from $967 million in 2020 to $3.54 billion by 2026.

“Over the past two years, customer service has been under constant pressure as ‘The Great Resignation’ has created persistent agent shortages. And changes in consumer behavior due to [the pandemic] and supply chain issues have resulted in massive spikes in call volume,” Shamia said. “Executives now understand that the problem cannot be ignored or outsourced, as customers are unwilling to wait for hours on hold.”

But do customers like, or even love, automated call centers? After all, automation lacks a human touch — it can’t necessarily defuse a frustrated caller. Worse, automation can deter customers from engaging with a brand in a way they might trust. A survey conducted by PointSource found that 80% of customers would prefer to speak to a human when resolving issues. Adding fuel to the fire, 59% of consumers in a recent PwC survey believe companies have lost touch with the human element of the customer experience.

And what about call center workers? The measures could be held against them, and simple customer problems – while arguably not the best use of their time – can be satisfying to solve. Then there is the fear that automation will one day end take away their jobs.

Shamia recognizes that some forms of automation, like poorly designed chatbots, can be a hindrance for customers and agents rather than a solution. But he says Replicant has learned from past mistakes, allowing companies to automate call flows while freeing agents to focus on more difficult issues.

“The pandemic has accelerated a trend — contact center automation — that had already begun and exacerbated many existing challenges in the customer service space,” Shamia added. “Automation is now part of
more and more companies’ strategic plans – something that will not change after the pandemic.

To that end, Replicant, which has 100 employees, claims to have “dozens” of corporate clients who have used its tools to serve more than 8 million customers. Customer transaction sizes range from hundreds of thousands to millions in annual recurring revenue.

“In most of our transactions, we compete with the disbelief that the technology can actually achieve the resolution rates that our customers are seeing. However, we are also part of the replacement cycles of older technologies,” Shamia added. “We’re also seeing do-it-yourself solutions…in some offerings or legacy players like IPSoft’s Amelia.”

To date, Replicant has raised $110 million in venture capital. The San Francisco, Calif.-based company plans to expand its workforce to around 200 people by the end of 2022.