Lucca

Lucca is a modular HRIS covering time tracking, absence management, expense reporting, payroll, and employee development. The full data model exposes entities across four distinct modules: timesheets and time entries (Timmi Timesheet), leave periods and leave accounts (Timmi Absences), expense claims and expense claim items (Cleemy Expenses), and training demands (Poplee Training). This integration covers the three modules most directly linked to individual performance management: time tracking, absences, and expenses, with one KPI drawn from the training module. Two modules were deliberately excluded: Timmi Project, which tracks billable hours against clients, was excluded because it is an optional module not universally deployed across Lucca accounts, and Poplee Payroll, which does not expose individual performance indicators. Seven KPIs were retained, selected against three criteria: ability to attribute to an owner, resistance to gaming, and balance between leading and lagging indicators.

Time tracking: declared activity as a process compliance signal

Three KPIs cover the Timmi Timesheet module. Approved Hours per Week measures the volume of hours that have completed the full validation cycle: submitted by the employee, reviewed, and approved by a manager. Timesheet Approval Rate measures the proportion of submitted timesheets that receive approval, as opposed to those rejected or left pending. Submission Delay measures the average number of days elapsed between the end of a period and the moment the employee submits the corresponding timesheet.

These three indicators address separate but interdependent dimensions of time management compliance. Approved Hours per Week is a lagging indicator: it measures what was completed and validated. It does not distinguish between an employee who works at full capacity and one who submits incomplete timesheets that are nevertheless approved. Timesheet Approval Rate corrects this reading: a low approval rate reveals recurring errors in declaration, whether due to inattention, misunderstanding of time categories, or incomplete records. An employee with a high Approved Hours count but a low Approval Rate has likely benefited from permissive managerial review rather than disciplined time reporting.

Submission Delay operates as the only leading indicator in this group. A widening delay between period end and submission is an early signal of process breakdown: it reveals either organizational friction, insufficient manager enforcement, or active disengagement from the time-reporting process. Because it precedes approval failures, an increase in Submission Delay typically anticipates a degradation in Timesheet Approval Rate over subsequent periods. Reading the two leading and lagging indicators together produces a more reliable picture of compliance than either one in isolation.

Absence management: volume as a diagnostic tool

Absence Days per Month measures the total number of absence days per employee per month, across all leave types recorded in Timmi Absences. The Lucca data model distinguishes between leave accounts by category: paid leave, RTT, sick leave, and other statutory types are stored separately and can be filtered independently. This KPI aggregates across categories by default, but the category filter is available for HR teams that want to isolate a specific absence type.

The strategic value of this indicator is not in the individual data point but in the trend. A single month with elevated absence is uninformative. A multi-month increase, concentrated in a specific team or individual, is a leading signal for HR intervention: it may indicate burnout, disengagement, or a workload imbalance that will eventually affect retention. Conversely, an unusual absence rate of near zero over an extended period may signal pressure that discourages employees from taking legitimate leave, which produces its own downstream risk. The KPI is most useful when read against historical baseline and in comparison with peer cohorts, not as an absolute threshold.

Several absence-related metrics were evaluated and excluded. Leave balance remaining was excluded because it reflects an administrative state rather than a performance dynamic: it does not change as a function of the employee's work and offers no actionable trend. Total department absences were excluded because they cannot be attributed to a single individual owner and therefore cannot feed an individual OKR.

Expense management: activity volume and budget discipline

Two KPIs cover Cleemy Expenses. Expense Claims Submitted measures the number of expense claims filed per employee per month. Expense Amount per Month measures the total monetary value of expenses declared over the same period. The two indicators are structurally related but serve distinct management purposes.

Expense Claims Submitted is a leading activity indicator: it reflects the employee's level of field engagement and their adherence to the expense reporting process. For sales teams or consultants, a higher claim volume is typically associated with more client-facing activity. For roles with limited travel, a sustained volume of claims may signal process misuse and warrants review. Expense Amount per Month provides the complementary dimension: it captures the financial magnitude of declared expenses per capita and supports budget control decisions.

The tension between the two indicators is particularly useful in anomaly detection. A high Expense Claims Submitted count paired with a low Expense Amount per Month suggests frequent, low-value claims, which may indicate administrative inefficiency in expense capture. A low claim count with a high total amount suggests large, infrequent expenses, which may require closer scrutiny at the approval stage. Neither pattern is inherently problematic, but both warrant contextual review.

Employee development: Training Completions as a non-gameable output

Training Completions measures the number of training demands that reached a completed status per employee over the measurement period. It is the only KPI drawn from the Poplee Training module. Several other training metrics were considered and rejected: training demands submitted was excluded because submission is not validated completion and carries a clear gaming risk, and catalogue size was excluded as a vanity metric with no individual ownership.

Training Completions was retained specifically because its status is set by HR upon validation, not self-declared by the employee. This makes it one of the more reliable KPIs in the selection: it cannot be inflated through declaration alone. Its limitation is directional rather than methodological. A high Training Completions count indicates that the employee has passed through validated training programs; it does not measure knowledge acquisition, behavioral change, or skill transfer. The indicator measures HR process output, not learning outcome. This distinction matters when the KPI is used to set development OKRs: the target should reflect meaningful program completion, not training volume optimization.

Scope and limits of the integration

Lucca records what employees declare and what managers approve. It does not measure work quality, actual output, or the effectiveness of time spent. An employee with high Approved Hours per Week and low Absence Days per Month may be highly productive or simply present without delivering meaningful work; the HRIS data does not distinguish between the two. Similarly, a high Timesheet Approval Rate indicates compliance with the declaration process, not accuracy of the underlying activity classification. Expense data captures financial flows but does not capture the business context or return on investment of each expense.

The reliability of all seven KPIs depends entirely on team discipline in using the platform. Timesheets submitted late and approved without review, absences logged under incorrect categories, and expense claims submitted in batches rather than in real time all degrade the interpretive value of the data. Organizations with rigorous process enforcement will derive accurate, actionable indicators from this integration; organizations with loose platform adoption will produce data that reflects administrative behavior more than operational reality.