While the general consensus has been that data is the new oil, the current question, for businesses at least, is how to harness this new oil to improve their customer-facing services.
During AIM’s Data Engineering Summit (DES) 2024, NoBroker Data Sciences and Engineering director Zaher Abdul Azeez highlighted why GenAI is the perfect solution to this, especially when it comes to gaining meaningful data out of unstructured human conversations.
“These (customer conversations) are very subjective, very conversational where standard variables are not readily available for analytics. This data is a goldmine, especially for businesses that rely on customer experiences,” he said, during his talk on ‘Navigating Data Chaos: Using Gen AI to Extract Structured Insights from Unstructured Customer Data’.
As mentioned by Azeez, customer conversations are incredibly valuable to C2C businesses like NoBroker in gaining valuable feedback. While the familiar note at the start of most customer care calls – “this call is being recorded for quality and training purposes” – has been a common occurrence for nearly a decade and a half, GenAI, Azeez says, can help parse these conversations for more than just quality and training.
“Generative AI lets us do a bunch of things. You have a broad spectrum of NLP capabilities, where LLMs let you do a variety of language tasks. So unlike conventional NLP applications where you have to build specific models for specific tasks, LLMs let you do a broad spectrum of stuff. And most importantly, it understands unstructured human conversations,” said Azeez.
This ability to understand unstructured human conversations is particularly important. While recordings are currently being reviewed manually, specifically to understand the customer experience, as well as to understand whether ratings given are accurate, the entire process is incredibly labour intensive.
“I focus on customer conversation because GenAI is very good with customer conversations. GenAI gives a very natural interface on top of being able to understand human languages and conversations in your business are a lot,” he said.
What About For the Customer?
On the other hand, while GenAI can help parsing unstructured data, Azeez also pushed the thought of using GenAI to have more interactive and natural customer service chatbots, especially when it comes to understanding context, which was not previously possible.
In a demo, Azeez showcased a chatbot that NoBroker was working on, which allowed the customer to verbally converse with the chatbot in order to parse their needs, budget and contact details. In addition, the bot was also able to advertise certain NoBroker products based on contextual hints.
“Human-like context aware response generation is something which generative AI can do. Especially when you talk about bots. Bots have been conventionally built with flows, diagrams, duties, etc. With LLMs and GenAI, you can do varying contextual conversations with your customers,” Azeez said.
Voice-based GenAI customer service reps are increasingly becoming a reality, with several larger companies already working on voice capabilities for their LLMs.
Recently, OpenAI came out with GPT-4o, which showcased voice capabilities, and Google did the same this month with Project Astra.
With this seemingly being the path taken for businesses, AI-based customer reps could become a thing of the near future.
