A 40% Lift in Self-Resolution: Using FAQ Data to Make the Product Better

ROLLCAKE Co., Ltd.

D2C / Consumer Subscription App·1-500 employees·Customer Support

ROLLCAKE runs three consumer subscription services: ALBUS (a monthly photo-print album app), Letter, and FLOWER. A pre-implementation analysis found that ~40% of all inquiries were self-resolvable if the FAQ were easier to search. But small phrasing changes broke the old search entirely, and a trial AI chatbot was abandoned for surfacing irrelevant articles. Response times hit ~2 business days, and adding headcount wasn't a scalable answer.

Why Helpfeel

Search quality was decisive: after reviewing Helpfeel on other companies' sites, the team trusted it to match colloquial queries ("can't do X," "Y hasn't arrived") to the right article. Guaranteed Customer Success support was the other factor: they'd been running the FAQ on intuition, not data. Migration was essentially a link swap.

What They Did

Distinguished "things Helpfeel should resolve" from "things that need a human," running a deliberate dual-track model.

Tuned keywords from Customer Success analysis, dropping the no-hit rate from ~6% to 2-3%.

Fed CS insights into the product: a post-order address-change feature and in-app notifications that keep users from getting lost mid-flow.

Results

Self-resolution rose ~40% and human-handled contacts now arrive with context (the user already tried the FAQ), making each exchange more efficient. Helpfeel's quantitative monthly reports ("this many users hit this problem") are shared at full-team meetings, making it easier for engineers to prioritize fixes.

“The reports they create are so thorough, and the improvement support goes beyond what we expected. We've started treating them as an unofficial fourth member of our support team.”

— Endo, Customer Support / Customer Success