Case Study: Indy scales customer feedback analysis with Harvestr

Indy categorizes 94% of customer feedback with Harvestr

Indy, a fast-growing fintech SaaS company, faced the challenge of processing and analyzing a massive volume of customer feedback from sources like NPS surveys and Intercom support tickets. As their user base grew past 100,000, manual methods became unsustainable, causing valuable insights to be lost and making it difficult to prioritize product decisions. They turned to the vendor Harvestr to implement a centralized solution for this voice-of-the-customer data.

Harvestr provided Indy with an AI-powered platform to auto-categorize and analyze feedback at scale. The solution, which offered superior categorization accuracy, processed Intercom conversations and NPS responses 3.5 times faster. This saved the product team hours each week and enabled them to identify key trends, leading to impactful product decisions like the successful launch of a mobile app. With Harvestr, Indy now automatically categorizes 94% of its customer feedback, transforming it into a strategic engine for product growth.

INDY