Mimir vs Sprig

Sprig specializes in catching users at the right moment — contextual surveys triggered by specific actions in your product. Just finished onboarding? Sprig can ask how it went. Used a feature for the first time? Sprig captures that reaction in context. It's a smart approach to getting timely, relevant feedback.

Mimir takes a broader view. Instead of collecting new feedback through surveys, Mimir synthesizes feedback you already have — interviews, support tickets, Slack threads, analytics — and generates ranked recommendations for what to build. Different starting points, different value propositions.

Sprig's contextual targeting is its superpower. Surveying a user immediately after they complete a task gets far more accurate responses than a generic email survey days later. The in-product placement means higher response rates and more specific feedback.

Sprig's AI analysis on open-ended responses is also well-executed. It can cluster themes from hundreds of survey responses and surface patterns. For teams that rely heavily on in-app feedback, Sprig provides a focused, high-quality signal that's hard to replicate with general-purpose survey tools.

The key difference is scope of input. Sprig analyzes what users tell you through its own surveys. Mimir analyzes everything — interviews, support tickets, survey results, Slack threads, analytics data — and cross-references patterns across all of them.

You could even pipe Sprig survey results into Mimir as a source alongside your other feedback. Mimir would synthesize the in-product sentiment with interview depth and support ticket frequency to give you a more complete picture than any single channel provides.