With bottom lines at stake, companies must walk the walk, roll up their sleeves and get their hands dirty finding practical, in-business value areas for AI. Beyond viewing the technology as some sort of holy grail, companies must look through the weeds to pair individual AI capabilities with specific business operations. Teradata Corp.’s Vantage CX aims to enhance one such operational area: customer experience.
Teradata’s Vedat Akgun and Pranav Dave talk with theCUBE about applying AI to CX use cases.
“I think AI is impacting quite a lot in this customer experience kind of scenario,” said Pranay Dave (pictured, left), global director of product marketing at Teradata. “We have been developing a customer complaint analyzer, and we have also found a lot of insights into it. You as a customer may be interacting with your banks, with your retailers, but for you, the most important is, ‘Okay, is my problem getting solved or not?'”
Dave and Vedat Akgun (right), vice president of data science and AI at Teradata, spoke with theCUBE Research’s Rob Strechay and co-host Savannah Peterson at Teradata Possible during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the complexities and opportunities for enterprise AI adoption, citing Vantage CX as a practical example. (* Disclosure below.)
Vantage CX: a data-driven approach to customer complaint resolution
In sensitive industries such as e-commerce and finance, there’s a pressing need for effective complaint-resolution strategies — particularly in large markets such as the US. Teradata’s customer complaint analyzer uses AI to analyze complaints in various formats, including text and voice. By categorizing and summarizing these complaints, companies can respond more swiftly and effectively, according to Dave.
“It’s very important to understand what are these complaints, what are these topics and how to quickly react and give a resolution back to the customer, because this is where you are adding value and you are reducing churn,” he said. “Our customer complaint analyzer does that exactly. It tries to analyze, with the power of intuitive AI, different types of complaints and make sure that in the end, the customer is happy and we have the perfect resolution for that particular customer.”
The crux of complaint resolution lies in honing data for predictive analysis. Predictive models play the dual role of analyzing past complaints and informing future resolutions. By learning from historical data, predictive and generative AI combine to identify which teams are best suited to address specific complaints, thereby increasing efficiency and enhancing customer relationships, according to Akgun.
“Those complaints come in different shapes and forms,” he said. “If you go to predictive AI only, you need structured data, so you would spend time to convert or find a way to convert a script of audio to structured data. But gen AI is a perfect tool and technology to bring all those different complaints together, and then analyze it and give you a perspective for resolution.”
On the other hand, large-scale AI systems come with considerable environmental impact. As they deploy AI at operational scales, businesses should strive to find solutions that are not only effective but also environmentally responsible, both guests agree.
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of Teradata Possible:
(* Disclosure: TheCUBE is a paid media partner for Teradata Possible. Neither Teradata Corp., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)