At the company’s Xperience ’22 conference today, Genesys announced its new integration with Pointillist’s contact center optimization technology. Genesys acquired Pointillist in December 2021 in a bid to help bring new AI capabilities for managing customer journeys into the Genesys platform. The integrated platform will now enable Genesys users to be able to better understand the direction of a customer experience, with AI-driven insight that can help an organization determine when there is a need to take action to remediate any potential issues.
Olivier Jouve, EVP and GM of Genesys Cloud CX explained that the company’s customers have been moving from just managing contact centers to actually managing customer experiences, which comes from all user touchpoints and includes various sources of data that need to be collected and analyzed.
“In the customer journey we’re trying to deliver personalized recommendations and personalized experiences to customers and AI is being very central to that,” Jouve told VentureBeat.
The need for AI in the contact center market has grown in recent years.
Omdia Analyst Mila D’Antonio commented that since the start of the pandemic, business leaders have realized that omnichannel engagement, personalization and automation are essential for survival. In her view, that realization has sparked a reimagining of the contact center that establishes AI as a foundational layer aimed to propel the contact center from a cost center to a value driver.
AI is entering the contact center in a number of ways. There are AI-augmented agents that can quickly and proactively service the needs of the customers; AI-powered assisted self-service via chatbots and adaptive knowledge bases and intelligent routing that not only aligns customers with agent skill sets, but also with other subject matter experts across the enterprise.
“Significant advances in AI/ML over the past five years have enabled companies to increasingly provide the means to gather deep insight around customer actions and behavior with an accuracy and at a scale simply not possible a few years ago,” D’Antonio told VentureBeat.
According to Omdia’s “2022 Digital-First Customer Experience” survey, which surveyed business leaders responsible for CX in their enterprises, 38% of respondents said they had minor investments planned for AI this year, and 29% have strategic investments planned. D’Antonio commented that key areas of AI investments include social media monitoring and ticketing, caller intent, intelligent call routing, chatbots, and AI-enabled speech analytics.
“The majority of companies that have made investments in these AI-powered technologies are reporting that they are realizing significant or moderate value,” she said. “Only a small percentage report seeing limited value and point to incorrect implementations, siloed AI, and lack of training as the top reasons.”
In D’Antonio’s view, one of the most important aspects of proactively engaging with customers is so enterprises can link customer interactions across channels. Going a step further, it’s important to use the interactions as indicators of where a customer or prospect is in their journey and then trigger the most appropriate response in terms of next-best action. She noted that Genesys is working to achieve that by infusing AI throughout the Genesys Cloud CX solution, making it pervasive throughout the platform.
The integration of the Pointillist capabilities will enable strong journey visualizations, with a centralized view of a customer’s active and past interactions, she said, adding that Pointillist also brings AI-supported predictive engagement, predictive routing, and embedded AI and analytics to identify potential issues.
How Pointillist AI works to improve CX
Large organizations can have tens of millions of customers. Tracking all the potential customer experiences in an effort to find ways to improve the experience can be a daunting task.
“Deploying AI across that data in a way that’s able to discover the things that matter was central to our thesis,” Ron Rubbico, CEO and cofounder of Pointillist, and now GM of Pointillist at Genesys, told VentureBeat. “Being able to find those opportunities manually is not possible.”
Rubbico explained that the Pointillist AI system included a custom-built data architecture that integrates time series and relational data. On top of the data, Pointillist developed its own machine learning technology that makes use of the gradient-boosted trees technique to help build a prediction model. It’s an approach that Rubbico said provides a supervised machine learning model that is driven by a user’s goals.
For example, if the goal is to improve an outcome in the call center, and multiple people have called in about a specific issue, then the issue will become an output label for the gradient boosted trees model to work on.
“Usually within two to five minutes the platform starts feeding back up insights for you to consider,” he said.
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