Accurately analyzing large scale qualitative data

The shift to the cloud has led to a surge in data collection, but businesses are grappling to extract valuable insights, largely due to the unstructured nature of that data. 

Extracting meaningful insights from feedback is a time-consuming and tedious process that typically requires human reasoning. And while many tools exist to summarize large datasets, Viable⁠(opens in a new window) stands out as one of the first companies that unlocked the power of GPT‑3, and now GPT‑4, to go beyond simple summarization and conduct in-depth analysis with exceptional accuracy on a large scale.

True comprehension requires context

Summarization and analysis are distinct ML tasks with different training data and models: summarization compresses information, while analysis adds context for better comprehension. When converting vast data into accurate reports, summarization overlooks crucial nuances essential for grasping true customer sentiment and can distort data, leading to flawed business decisions. Text like online reviews and support tickets are often rife with ambiguity, sarcasm, and negation, requiring additional context for real comprehension. 

Viable has tackled this challenge by fine-tuning OpenAI’s LLMs⁠(opens in a new window) to deliver fast and accurate insights from customer support interactions to recorded transcripts and everything in between, using GPT‑4 to analyze qualitative data on a scale that exceeds current techniques and performance. Viable’s platform provides companies with actionable insights to improve their Net Promoter Score (NPS), reduce support ticket volumes, and better inform their product roadmaps, all while saving on operating costs.

Analyzing data manually just isn’t viable

Viable was founded in 2020 with the initial aim of helping businesses achieve product-market fit. They quickly realized that even the most data-driven organizations were unable to make full use of their qualitative data in decision-making. 

Dan Erickson, CEO of Viable