One situation that really sticks out was when we noticed a few strong hires in our operations team were leaving within the first six months. At first, we couldn’t figure out why; on paper, everything looked fine, but something wasn’t clicking. So we started looking at the data we already had, not just gut feeling.

We pulled together things like how quickly people reached full productivity, how often they interacted with their managers, whether they completed onboarding tasks on time, early engagement survey scores, and even participation in optional learning programs.

When we mapped these out, patterns started to show. Employees who were slower to ramp up, had fewer check-ins, or weren’t connecting socially with the team were leaving more often.

Once we saw the trends, we acted. We added more structured onboarding, encouraged managers to check in proactively, and set up early mentoring for people flagged by the model. Within a few months, attrition went down noticeably.

The big takeaway for me was that data doesn’t have to be complex to be powerful; even simple, thoughtful points can uncover trends you’d never notice otherwise, and acting on them quickly makes a real difference.

Abhishek Shah, Founder, Testlify

One example: we used a simple attrition-risk model to catch early flight risk in a remote support team where resignations were showing up with almost no warning. The “action” wasn’t to label people as risky; it was to flag teams and roles where the conditions were drifting (workload spikes, manager bottlenecks, slow growth signals) so we could intervene with staffing, schedule fixes, and manager 1:1 resets.

The data points that ended up most valuable were the boring operational ones, not personal ones: tenure band, role/shift, manager span of control, schedule volatility (late swaps, overtime, consecutive days), ticket/work volume trend vs baseline, time-to-first-response / backlog, PTO balance + inability to take PTO, and manager 1:1 consistency (did it happen, not what was said). Those features worked because they’re directly tied to friction and burnout, they’re measurable across dispersed teams, and they avoid “creepy” signals like sentiment scraping, which tends to backfire on trust.

Upeka Bee, CEO, DianaHR

Predictive analytics helped us stop good candidates from dropping out late in the process.

We noticed that certain roles were losing strong candidates right before the offer stage. The turning point was when we started tracking two simple data points: response delay after an interview and how often candidates checked in proactively about next steps. When those signals dropped, it was usually a sign they were entertaining another offer. That prompted recruiters to accelerate decisions or strengthen communication at the right moment. It wasn’t complicated, but it helped us secure more preferred candidates before competitors stepped in.

Aamer Jarg, Director, Talent Shark

www.talentshark.ae

Aamer Jarg, Director, Talent Shark

We used data to figure out who was about to quit our mentoring program. By looking at engagement metrics, feedback, and how often they showed up, we could spot the warning signs early. My experience in talent development taught me that a quick, personal message right then was what kept people involved. If you’re building a model, focus on behavioral data. It tells you much more about who might leave than standard demographic info ever did.

Matthew Reeves, CEO & Co-founder, Together Software

We used Alkimii Insights to predict early turnover risk among new hires in their first 30 days. The strongest signals were repeated late arrivals, missed check-ins, and early leaver patterns, which we correlated with pay and absence data and reviewed by department. These inputs produced early warnings that enabled timely retention outreach.

Sinead Marron, Director of Growth UK, Alkimii

As part of our approach, predictive modeling was used to identify high-risk turnover within our new engineers based on their engagement data, feedback rates, workload distribution, and time to make their first contributions. Certain patterns related to poor async communication, slow feedback cycles, and role confusion were identified as having a high positive correlation to voluntary turnover in the first six months. By acting on these indicators, managers could receive guidance on reducing turnover among their new engineers.

George Fironov, Co-Founder & CEO, Talmatic

We helped a firm deal with puzzling surprise churn in their engineering department. Instead of running exit interviews–which are lagging indicators–we instance a predictive model sitting in their operational HR and project management systems, returning a real-time ‘flight risk’ score for every developer.

The most valuable data comes from operations, not the therapist’s couch. Time-since-last-promotion and manager-change frequency are useful signals, but the key learning was from combining HR data with performance data. A sudden drop-off in code commit frequency, mixed with an increase in how many projects that engineer is working on simultaneously, is the single best predictor of voluntary turnover in the following quarter. Managers have enough time to proactively have conversations with these engineers about problems with their workload and where they’re looking to go, not offer a counter-offer.

Girish Songirkar, Delivery Manager, Enterprise Software Engineering, Arionerp

Key teams were losing experienced staff within 12-18 months, even though overall turnover seemed stable. Exit interviews came too late to prevent departures.

We built a predictive model using tenure by role, internal mobility history, manager change frequency, workload variance, delayed promotion cycles, and time-off patterns. Surprisingly, performance scores were less predictive than stagnation: employees who stayed too long in a role, reported to multiple managers in a short period, or reduced time off were most likely to leave.

Managers acted on the insights by holding structured career conversations, prioritizing internal transfers, and rebalancing workloads before attrition occurred. This proactive approach meant no immediate changes to compensation were required.

Within nine months, voluntary attrition in flagged teams dropped 22%, internal mobility rose 31%, and hiring costs declined. Employees noticed the proactive engagement, and trust improved across teams.

Key takeaway: Focus predictive analytics on early signals, connect existing data, and tie insights to specific actions. Preventing problems before they happen is more effective than reacting after the fact.

Riken Shah, Founder & CEO, OSP Labs