Case study
Revolut speeds up engagement reviews by an entire month
Industry: finance | Stage: enterprise | Headquarters: UK
- Reduced time spent on engagement data review by a month per quarter with AI automation
Turning data into action
We're a global financial app helping people and businesses manage all their finances, with over 70+ million customers worldwide. But as we scaled up, we found it difficult to keep our top performers engaged and maintain a high talent density.
We send out engagement surveys regularly — but with 50+ departments and 1,300+ teams, our managers had to review a massive amount of info just to make a priority list, let alone build action plans.
Our engagement team usually took 4 weeks to analyse the data and decide on our next steps. This delay made it difficult to make strategic, timely choices for our people.
Implementing intelligent AI
Introducing Revolut People's AI tools across our global workforce was key to solving our scaling challenges.
The Revolut People AI can cross-reference both engagement and performance data to provide a precise analysis of what motivates our top performers. It gives us the data we need to maintain talent density, without spending hours sorting through surveys and reviews.
The AI tool also suggests action plans for our managers, further reducing the review time from weeks to days and enabling rapid, targeted improvements.
Smarter decisions, backed by data
Using Revolut People for our engagement review freed up approximately 1 month per quarter that would otherwise be spent manually sorting through data.
However, the most valuable outcome was strategic. Without the lengthy review process, leadership could now base decisions on much more current data.
Our first new project was an overhaul of our compensation package — a crucial source of engagement for top performers. This timely, data-informed initiative resulted in a 13 percentage point increase in employee satisfaction (based on engagement survey results immediately following the change) in just 6 months, proving the link between analytical speed and business impact.
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