Indy
Indy is a SaaS company that helps over 100,000 independent professionals manage their accounting, taxes, and finances with ease.
Location:
Lyon, France
Industry:
Fintech
Size:
300
Integrations used:
Context
Indy, a fast-growing SaaS company helping independent professionals manage their finances, has always placed customer feedback at the heart of its product strategy. But as their user base grew past 100,000, so did the volume of feedback from NPS surveys, support tickets via Intercom, and internal suggestions. The product team struggled to process this growing data set at scale, with many feedback points left uncategorized or siloed in spreadsheets and BI tools. Valuable insights were slipping through the cracks, and it became increasingly difficult to detect trends, prioritize the right topics, or connect feedback to business outcomes. Manual processing was too time-consuming and unsustainable as Indy kept scaling.
Strategy
To solve this, Indy turned to Harvestr to centralize and analyze all customer feedback in one place. Harvestr became their single source of truth for categorizing, understanding, and acting on voice-of-the-customer data across NPS and Intercom. Over time, Indy created a collaborative ritual called “Harvestr Fever”: a monthly team session where PMs work together to explore customer pain points and align on product priorities. This ritual helps teams stay close to user needs while promoting alignment and cross-squad knowledge sharing.
As feedback volumes surged, reaching over 10,000 NPS entries a year, Indy adopted Harvestr’s AI-powered auto-categorization to scale their analysis. Harvestr was selected over other tools because it offered the best categorization accuracy on the market, ensuring that high volumes of feedback could be processed without sacrificing quality. Intercom conversations are now automatically analyzed and categorized as well, helping product squads tap into support insights without needing to read through every thread.
This structured and scalable approach to feedback analysis has led to tangible product decisions. For example, feedback collected through Harvestr revealed a recurring need to upload receipts on the go, leading to the launch of Indy’s mobile app. The app quickly became a major success and is now used by nearly half of Indy’s 100,000+ customers.
“Harvestr's AI categorizes 94% of our customer feedback, with customization and accuracy that outperform all the other solutions we've tried. By processing Intercom conversations and NPS responses 3.5x faster, it saves our product team hours each week, helping us focus on what matters.”
Damien Colin
Product Ops, Indy
Impact
With Harvestr, Indy transformed feedback management from a manual chore into a strategic engine for product growth. Feedback is now processed continuously, categorized at scale, and used to guide product discovery, align teams, and reduce user friction. Harvestr’s AI has saved countless hours of manual work, enabling the product team to focus on what matters most: building the right solutions, faster.
Looking ahead, Indy plans to integrate new feedback sources, like sales call summaries, to continue strengthening its voice-of-the-customer system and deliver impactful products at scale.
Results
