Mine actionable insights from every piece of customer feedback automatically

Sift analyses free-text customer feedback to extract sentiment, feature requests, pain points, and praise so product teams can prioritise what actually matters.

The problem

Feedback pours in from forms, surveys, and reviews, but it sits in a spreadsheet nobody analyses. Product teams make gut-feel decisions because reading thousands of free-text responses is impractical. Real patterns stay buried.

The solution

Sift processes every feedback submission in real time, extracting sentiment, topic, specific feature mentions, and actionable suggestions. Product teams get a live dashboard of what customers love, hate, and want next.

See the extraction

What users type

I love the reporting dashboard — it's the reason I upgraded to Pro. But the mobile app is painfully slow, especially loading the analytics tab. Also, would be great if I could schedule reports to send automatically every Monday morning.

What you get

Overall Sentiment

Mixed — positive on desktop, negative on mobile

94%
Praise

Reporting dashboard quality (upgrade driver)

96%
Pain Point

Mobile app performance — analytics tab loading

95%
Feature Request

Scheduled/automated report delivery

97%

Why teams switch to Sift

Real-time sentiment tracking

See how customer sentiment shifts over time, correlated with releases, outages, or pricing changes.

Feature request aggregation

Automatically group and rank feature requests by frequency, so product roadmaps reflect actual demand.

Pain point detection

Surface recurring complaints before they become churn drivers. Fix what matters, not what's loudest.

Actionable over anecdotal

Replace cherry-picked customer quotes with quantified trends across your entire feedback corpus.

The numbers

90% reduction

Feedback analysis time

AI processes thousands of submissions in seconds versus weeks of manual reading and tagging.

Data-driven

Feature prioritisation accuracy

Product decisions are backed by aggregated customer demand rather than gut feeling or recency bias.

Weeks earlier

Churn risk detection

Negative sentiment patterns surface before they appear in cancellation metrics.

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