Customer Stories
Discover how forward-thinking teams use our intelligence engine to transform everyday data into clear strategic advantages. From global pioneers like GN Hearing consolidating messy, multi-channel user text streams across 46 languages to high-growth retailers automating trend detection, explore the exact operational blueprints engineering real market impact.
Consolidating global app feedback across 46 languages
GN Hearing collected user feedback across 10 apps, two global app stores, 154 countries, and 46 languages. With feedback scattered across markets and languages, it was time-consuming to translate, analyze, and identify issues before they escalated.
Deepdots helped GN consolidate app-store feedback into one automated workflow, making it easier to monitor sentiment, identify recurring issues, and proactively flag bugs for product teams.
Read the GN case study →
Accelerating digital conviction loops through automated text analysis
NREP required an agile, friction-free loop to capture qualitative user context from outbound channels. Sifting through inbound records manually created heavy operational friction, slowing down their iterative digital enhancement roadmaps.
Integrating Deepdots language intelligence modules allowed NREP to process qualitative queries instantly. Incoming texts are grouped by system intent, enabling product groups to prioritize high-value optimization milestones with absolute clarity.
“Deepdots is helping us prioritize and triangulate data points, giving us a stronger conviction about what we're doing and whether it's actually providing value to the customers.”
Turning scattered legal-tech feedback into actionable product insights
TestaViva collected feedback across support tickets, NPS, online reviews, sales notes, and CSAT, but the data was spread across teams and systems. This made it difficult to get a full overview, spot recurring issues, and act consistently on customer needs.
Deepdots helped TestaViva centralize and analyze feedback in one platform, making it easier to track sentiment, identify patterns, share insights across teams, and prioritize product and support improvements faster.
Read Full Case Story →
