The inspiring blog 0224

Turnover is expensive in ways most HR teams can feel even when they do not fully quantify it. There is the obvious cost of recruiting, onboarding, and ramp time. There is also the quiet drain: team members who absorb gaps, leaders who spend their weeks resetting instead of improving, and a culture that slowly learns that leaving is normal. The temptation is to treat turnover like a hygiene problem with hygiene fixes. Pay bands, engagement surveys, nicer perks, more training, a refreshed onboarding packet. Those can help, but when turnover persists, HR usually runs into the same wall: the initiatives get implemented, yet the churn keeps moving through the organization like a leak behind the wall. Root cause analysis is how you stop guessing. Not by running a clever workshop once, but by building a disciplined view of what is actually driving separations, where it shows up, and why it keeps recurring. Start with the real question, not the headline number “Reduce turnover” sounds straightforward until you watch two different types of exits happen in the same quarter. One team might lose people because the work is physically hard, the schedule is unstable, and the supervisor changes expectations week to week. Another team might lose people because the role is good, but the performance standards are unclear and managers penalize ambiguity. A third might lose people because the role is niche, internal mobility is rare, and the talent market pulls employees toward better-defined career paths. If you only look at an overall turnover rate, those patterns blur. HR ends up chasing a single solution, when you actually have multiple causes operating at the same time. A better way to frame the work is to ask something like: Which groups leave, when do they leave, and what conditions were present in the months before they left? That phrasing forces the analysis to connect outcomes to time and context, not just to opinions. In my experience, this shift changes everything. Managers stop arguing about whether “the people” are the problem, and start talking about what happened in real projects and real conversations. The data becomes less abstract. Build a turnover map HR can act on Before you run any root cause analysis, you need a map of the problem. You are looking for patterns you can tie back to processes, expectations, workload, leadership behavior, and career systems. A simple but powerful approach is to break turnover into slices you can compare: tenure bands (for example, less than 6 months, 6-12 months, 1-3 years, 3+ years) role families or job levels location or team voluntary versus involuntary exits internal transfers that preceded separation (if you track them) You do not need fancy tools to do this, but you do need enough sample size to avoid making up stories. If a segment is too small, you can still learn, you just treat the findings as directional rather than decisive. Here is what you often find when the turnover has a structural driver: High turnover concentrated in the first 3 to 6 months suggests onboarding, expectation setting, or role fit problems. High turnover in a specific function or product line suggests process friction, unclear priorities, or leadership style mismatches. Peaks right after policy changes or organizational restructures can point to broken internal communication or sudden workload changes. Voluntary exits clustered around performance cycles can suggest assessment, feedback, or career calibration issues. Turnover that is consistent across cohorts but worse at certain locations can point to management capability gaps or local constraints, like staffing ratios. The point https://www.remotelytalents.com/blog/hibob-review-features-pricing-competitors is not to label people as “unhappy.” The point is to identify recurring conditions that reliably precede exits. What root cause analysis should look like in HR Root cause analysis is often misunderstood as an exercise in blaming. In HR, blame is a dead end. You are not trying to find a culprit, you are trying to find a system failure that produces the outcome repeatedly. A workable HR root cause process has three phases: diagnose, test, and fix. Diagnose means you gather evidence and build hypotheses about drivers. Those hypotheses must connect to observable patterns. If the data suggests “lack of development,” your hypothesis should specify what development failure means in your organization, such as missing career conversations, unclear promotion criteria, or lack of project variety. Test means you look for supporting and contradicting evidence. If a hypothesis is right, you should see it show up across multiple data sources and in multiple time periods. Fix means you change something that causally influences the hypothesized driver, not just something that is easy to change. Then you measure whether the turnover pattern shifts after the intervention. A common mistake is treating “employee feedback” as the diagnosis and skipping the testing step. Surveys can be useful, but they are often broad, retrospective, and sometimes influenced by the employee’s relationship with a manager at the end of their employment. The most credible root cause findings triangulate: exit interview themes, performance data, manager actions, workforce planning indicators, workload proxies, and onboarding outcomes. The data that actually helps (and what to avoid) You will gather a lot of HR data, but not all of it is equally actionable. The goal is to collect information that can be connected to processes and decisions, not just demographics. Here is a focused set of data points that tend to produce the highest clarity when you are trying to explain turnover: separation records with dates, voluntary or involuntary status, and reason codes if available tenure at exit, plus role level and job family for every separation manager at separation, including manager tenure and prior performance feedback trends onboarding timeline metrics such as training completion, ramp milestones, and early performance checks exit interview notes or structured responses, coded into themes consistently across interviewers You should be cautious with data that looks precise but tells you almost nothing. For instance, using “engagement score” alone to explain turnover can trap you. Low engagement often correlates with churn, but it rarely tells you which operational decision to change. The analysis needs to translate sentiment into process, such as goal clarity, staffing levels, or how performance expectations were communicated. Also avoid over-weighting exit interview reasons. Some employees answer strategically, others answer from memory that has faded, and some focus on one moment at the end rather than the pattern across months. Exit interviews are valuable, but they should be triangulated. Turn exit interviews into evidence, not stories Exit interviews can be a goldmine when they are handled like qualitative data. If you treat them as a pile of anecdotes, you will end up with a “Top reasons” slide that leadership reads once and then forgets. The better method is human resources to code responses into structured themes and tie those themes to segments from your turnover map. That is where root cause analysis gains traction. For example, suppose you code reasons into categories like “compensation,” “manager,” “growth,” “workload,” “career path,” “role clarity,” “location,” and “culture.” You might then see patterns such as: employees who leave within 90 days overwhelmingly mention role clarity and onboarding support employees who leave after 12 to 24 months increasingly mention growth and internal mobility employees who leave from one location cite workload and scheduling constraints more frequently Once you see those links, you can move beyond “people are unhappy” to “our onboarding process is not setting real expectations” or “our career progression system is too slow for certain job families.” If your HR team has limited coding capacity, you can still do this. Assign a small group to code a subset of interviews and refine a shared coding framework. The first pass will be imperfect. The second pass improves because you create consistent definitions. In one organization I supported, the early coded themes were too broad, and managers kept saying, “Those themes are vague.” That feedback led to better definitions. After that, the themes stopped being arguments and became operational targets. Hypotheses that are specific enough to test Root cause analysis fails when hypotheses are too general. “Low engagement” is not a root cause. “Employees did not understand performance expectations” is closer to something you can test. A practical way to build testable hypotheses is to anchor them in specific HR processes: Hiring and selection: Are interviews screening for role fit, or for buzzwords? Onboarding and expectation setting: Are early goals realistic and aligned with the actual workload? Goal management: Are performance expectations communicated consistently? Manager capability: Are managers trained in coaching and feedback delivery? Workforce planning: Are staffing levels consistently below the workload required? Career systems: Are progression criteria transparent and accessible? Internal mobility: Are there mechanisms to move across projects and teams? When turnover is clustered, you can often pinpoint which process is failing by asking: what step changed in the months before the exits? For example, if turnover spikes shortly after a new performance management framework is introduced, you might hypothesize that the framework changed workload, increased documentation requirements, or made feedback less frequent. Testing could involve comparing manager behavior before and after the rollout, or reviewing if new expectations were communicated clearly to incumbents. If the spike is restricted to one manager, the hypothesis might shift toward leadership behaviors, coaching cadence, or decision-making clarity. Testing could involve comparing those behaviors across managers with similar teams and performance outcomes. Common root causes, and how they show up in data You asked for root cause analysis for HR, which usually means identifying the “usual suspects” while still respecting what your organization’s evidence says. Here are patterns that frequently drive turnover. Each one is tied to what you can observe in HR signals. 1) Role expectations do not match the job as performed This shows up as early exits, especially under 6 months. Exit interviews often mention “I thought it would be different,” “the role is unclear,” or “I was not set up to succeed.” In the data, you might see lower onboarding completion at the time of exit, inconsistent early performance feedback, or frequent goal revisions. Fixing this requires more than a nicer orientation. It requires a “truth-in-advertising” loop: hiring managers, recruiters, and HR must align job descriptions with what actually happens in the first 90 days. The trade-off is that being precise can scare off some applicants, but it reduces misfit hires. The organization gains stability, even if headcount fills more slowly. 2) Managers do not coach consistently Employees may stay for a while, then leave after a performance cycle or after they realize that feedback is sporadic or punitive. Exit interviews often highlight manager support, communication, or fairness. In data, this appears in manager-level patterns. One team can have higher churn than others with similar work. Sometimes performance ratings are inconsistent or arrive too late. Addressing this usually involves management capability and process. Coaching training alone is not enough. You also need cadence, tools, and accountability: weekly check-ins, clear goal templates, and calibration practices that prevent “surprise” outcomes. 3) Workload and staffing ratios create chronic burnout This shows up when turnover is voluntary and clustered around high-demand periods. Exit interviews mention stress, lack of time, constant interruptions, or “too much to do.” HR can detect proxies even when you do not have direct burnout metrics. For example, you might see overtime patterns, increased leave usage, higher internal issue volume, or role vacancies that never get backfilled quickly. The root cause is often workforce planning, approval processes, and leadership decisions about what to stop doing. If teams cannot reduce scope or increase staffing, engagement interventions feel like putting a bandage on a structural problem. 4) Career growth is invisible or inaccessible People may like their work but leave because progression is slow or unclear. This is common in professional roles and in companies that rely on a small number of “feeder” pathways. In data, you might see longer tenure among those who stay, and a concentration of exits among certain levels or job families. Exit interviews might mention that they “did not see a future” or “did not know what good looks like for promotion.” Fixing this means building transparent progression criteria and credible internal opportunities. It also means investing in assignments that build breadth, not just checking a box for “training completed.” The trade-off is time. Career systems take time to design and even more time to show results in turnover, especially if internal roles require coordination across functions. 5) Compensation is not always the culprit, but it becomes one when other issues exist Sometimes compensation is the stated reason for leaving, but it rarely operates alone. In practice, low pay might be tolerable if role clarity is high, managers coach well, and workload is reasonable. When multiple negatives stack, compensation becomes the tipping point. So the root cause analysis should treat compensation as part of a broader system. You might find, for example, that certain job levels are under-banded relative to market, but also that those same roles have unclear performance expectations and weak onboarding. The fix then becomes a package: pay adjustments, plus changes in expectation setting and manager support. A way to run the analysis without turning it into theater The biggest risk with root cause analysis is that it becomes a meeting series. Everyone brings opinions, no one agrees on definitions, and the final output is a generic plan that could apply to any company. To avoid that, constrain the scope and use evidence as the organizing principle. Start by selecting one or two priority segments. For example, you might focus on voluntary turnover in the first 6 months for a specific role family, or turnover within a particular location where patterns repeat. HR teams often want to boil the ocean. That leads to vague recommendations. Then build a small working set of data and themes, and keep the number of hypotheses manageable. If you have too many hypotheses, the team spends time defending ideas instead of testing them. You also want agreement on how you define the outcome. Are you targeting overall turnover, early turnover, or manager-specific turnover? Each needs a different lens. Outputs that keep leadership engaged Root cause analysis should end with decisions that leadership can act on, not just a narrative. Here is what to aim for: a ranked list of the most likely root causes, with evidence and confidence levels the turnover pattern each root cause explains, including timing and affected segments specific process changes tied to each root cause, with owners and timelines leading indicators to track before turnover changes, such as onboarding goal completion or manager check-in cadence an evaluation plan that defines what “success” looks like after implementation That last item matters. Without an evaluation plan, the organization will claim improvement from one good quarter, even if the root cause still exists. Turn findings into fixes that can actually shift behavior Once you identify root causes, you need interventions that match the failure mode. If your analysis says the problem is onboarding expectation mismatch, you cannot fix it only with a longer training calendar. You must change what is communicated, when it is communicated, and what success looks like in the first weeks. Here are examples of interventions that align with specific root causes. If onboarding mismatch is the culprit Create role-specific “day 1 to day 30” success criteria that hiring managers confirm in writing. Run structured job reality conversations during onboarding that cover trade-offs, constraints, and what “good performance” means early. Add a feedback checkpoint at week 4, not week 10, so problems surface while there is still time to adjust. The trade-off is workload for managers, but it reduces rework and turnover. If you are understaffed, you may need to scale the approach for roles with the highest early churn first. If manager coaching inconsistency is the culprit Standardize a minimum coaching cadence, including a clear format for one-on-ones. Provide managers with templates for feedback and goal setting that reduce ambiguity. Use calibration sessions to enforce fairness in performance expectations. The trade-off is that managers may experience this as additional bureaucracy. You reduce friction by focusing on a small number of high-impact behaviors, like consistent goals and timely feedback, rather than demanding excessive documentation. If workload and staffing are the culprit Conduct a capacity review for the affected teams, using staffing ratios and project intake data. Adjust intake controls, so work stops entering faster than it can be delivered. Create a short-term stabilization plan: pause low priority initiatives while you backfill critical roles. The trade-off is deferred work, which leaders can resist. This is where the root cause analysis helps. Instead of arguing philosophically, you show that the current workload pattern produces measurable churn and hidden costs. If career growth is invisible Define career paths and promotion criteria in plain language, including example evidence. Build internal mobility mechanisms, such as short-term project moves or skill-based pools. Ensure managers hold career conversations with their employees on a schedule. The trade-off is coordination effort. You will need alignment across HR, leadership, and functional heads. But if employees leave because they cannot see a path, you can only solve it by creating one you can point to, not one you promise. Measure the right things, early enough to steer Turnover changes slowly. By the time you see improvement in quarterly turnover rates, you might be evaluating work that has already lost momentum. A better approach is to track leading indicators tied to the root cause. Leading indicators are not perfect substitutes for turnover, but they tell you whether the intervention is changing the system. Examples of leading indicators that map to common causes include: onboarding checkpoint completion rates, especially for roles with early churn time-to-first-quality-feedback after hire internal mobility activity, like job shadowing or short project transfers manager one-on-one cadence compliance early performance goal clarity measures, if you can gather them The evaluation plan should also include guardrails. Sometimes an intervention reduces turnover in one segment but increases it in another. For example, being more selective in hiring might reduce early exits but also reduce diversity of applicant pools if not designed carefully. Or a stricter performance framework might lower churn among high performers but raise churn among people who need coaching and time to adapt. Good root cause analysis anticipates those trade-offs and monitors them. A short lived example: how “engagement” became a process fix A few years ago, I worked with an HR team that had one headline problem: turnover was up “companywide,” and the engagement survey showed lower scores in the same period. The leadership team pushed for broader engagement initiatives, recognition programs, and a “culture refresh.” When we mapped turnover by tenure, the story changed. The biggest increase was among hires between 3 and 9 months in a specific role family. When we coded exit interviews, a theme dominated: people felt they were “working hard but not sure how to win.” They also mentioned that their goals changed without explanation. Performance reviews later backed that up. Goals were being updated frequently, but feedback on what “right” looked like was not coming early enough. Managers were trying to respond to shifting priorities, but the process did not include a communication mechanism that employees understood. The fix was not another survey. It was a goal management process with a clear cadence and decision ownership. Managers got a lightweight template for goal changes and a requirement for a documented conversation when priorities shifted. HR added an onboarding expectation conversation that made it explicit how priorities might shift and how employees would be evaluated. Turnover did not drop instantly, but early churn stabilized within a quarter, and exit interviews changed from “not sure how to win” to “work was clear even when priorities changed.” The engagement scores did not magically soar, but the churn pattern shifted in the segment that mattered. That is the difference between treating turnover as a feeling and treating it as an outcome created by processes. What to do when data is messy or incomplete Not every organization has clean reason codes, consistent exit interviews, or perfect workforce tracking. Even then, root cause analysis can work if you treat it as an evidence-building effort. Start with whatever you have, but be honest about limitations. If your exit interview reasons are inconsistent, you can still code themes manually. If you do not have manager-level coaching data, you can sample one-on-one frequency from manager calendars or ask employees directly about feedback cadence during exit conversations. If sample sizes are too small, focus on patterns that appear repeatedly in multiple segments, or prioritize the highest-risk groups. Root cause analysis is not about proving one hypothesis with certainty. It is about reducing decision noise and moving toward fixes that are more likely to change outcomes. Avoid the HR traps that make turnover worse Even solid root cause work can fail if HR actions trigger unintended consequences. One trap is implementing solutions that do not reach the frontline. Policies get updated, but managers keep running meetings the way they always did. That leads to employee cynicism and, ironically, higher turnover. Another trap is focusing only on “fixing employees.” If you conclude the issue is role fit or attitude, the organization might tighten recruiting and expect onboarding to carry the rest. When workload or expectation ambiguity remains, those hires leave too, just later. A third trap is rolling out interventions too broadly at once. If you try to fix onboarding, manager coaching, and career paths simultaneously without sequencing, you will not learn what worked. Root cause analysis helps you prioritize. You change one major driver first in the segments where evidence is strongest. Make turnover reduction a system, not a project Reducing turnover requires persistence, because the forces that drive it do not disappear after a training rollout. Employees still join and leave. Managers rotate. Priorities shift. That means your root cause process needs a rhythm. If you can, build an ongoing “turnover review” cadence where HR and key leaders revisit a small set of segments and leading indicators monthly or quarterly. The first iteration will feel uncomfortable, because teams are not used to looking at the same evidence repeatedly. Over time, you learn which questions produce real answers and which ones lead to debate. The long-term benefit is that turnover becomes less of a crisis and more of a managed risk. When exits happen, you can respond with clarity: this pattern points to this process failure, and here is the fix we tested last time. Turnover is not just an HR metric. It is a signal about how work gets defined, resourced, coached, and valued. Root cause analysis turns that signal into action that holds up under scrutiny.