Lms
Upscend Team
-December 24, 2025
9 min read
This article explains how HR onboarding analytics turns onboarding into a measurable investment: define an event schema, join HRIS/LMS/IT data using a canonical employee ID, and build cohort and funnel dashboards. It includes sample SQL queries, A/B test ideas, a maturity roadmap, and practical fixes for data silos, skills gaps, and attribution.
HR onboarding analytics is the discipline of collecting, analyzing, and acting on onboarding data to improve time-to-productivity, retention, and new-hire experience. In our experience, teams that treat onboarding as an iterative, measurable process achieve higher early retention and faster ramp than teams that rely on checklists or anecdote alone. This article explains how HR onboarding analytics should be implemented, what to measure, sample queries to find friction, an analytics maturity roadmap, and concrete A/B tests you can run tomorrow.
We’ll focus on practical steps to move from siloed spreadsheets to repeatable dashboards and cohort insights, and we’ll address common obstacles like limited analytics skills and attribution so you can get reliable, actionable results.
HR onboarding analytics makes onboarding a measurable investment instead of a black box. Measuring onboarding outcomes allows you to prioritize interventions that move key metrics — first-week engagement, time to first contribution, 90-day retention — rather than guessing which initiatives matter.
Key benefits include clearer attribution of improvements, data-driven prioritization of content and workflows, and the ability to identify high- and low-performing cohorts for targeted support. Studies show structured onboarding can improve retention by up to 25% and time-to-productivity significantly; analytics tells you where the gains actually come from.
Start with a short list of high-impact, measurable outcomes. We recommend focusing on:
Tracking these consistently across cohorts makes comparisons meaningful and actionable.
Instrumenting onboarding with data requires standardizing sources, assigning unique identifiers, and defining event-level tracking across tools. Onboarding data should be captured at the event level (e.g., training completed, policy signed, first login) and joined to HRIS and LMS records for the individual employee.
We recommend creating an onboarding event schema and using a staging area (data warehouse or analytics layer) to normalize events before building dashboards and cohort views.
Combine these data sources to create a comprehensive view. Use a single employee ID to join records and maintain data lineage for attribution.
Design dashboards for three audiences: executives (high-level KPIs), people managers (team ramp & blockers), and program owners (content completion & drop-offs). Include cohort filters by hire date, role, location, and source.
Suggested A/B tests to run:
Sample queries help locate where new hires get stuck. Below are concise SQL-style examples you can adapt to your schema. They assume common tables: employees, lms_events, it_provisioning, contributions, and surveys.
Use these queries to create alerts and dashboards that flag cohorts needing intervention.
SELECT hire_week, COUNT(DISTINCT employee_id) AS hires, SUM(case when training_completed = false then 1 else 0 end) AS incomplete FROM employees JOIN lms_events USING(employee_id) GROUP BY hire_week;
SELECT role, AVG(DATEDIFF(day, hire_date, first_contribution_date)) FROM employees JOIN contributions USING(employee_id) GROUP BY role;
SELECT employee_id, DATEDIFF(hour, hire_date, provisioned_at) AS hours_to_provision FROM it_provisioning WHERE provisioned_at IS NOT NULL AND DATEDIFF(hour, hire_date, provisioned_at) > 48;
SELECT day_offset, COUNT(*) FROM lms_events WHERE event_type = 'module_started' GROUP BY day_offset HAVING COUNT(*) < (SELECT AVG(count) FROM lms_events WHERE day_offset < 7);
SELECT nps_score, AVG(time_to_first_contribution) FROM surveys JOIN contributions USING(employee_id) GROUP BY nps_score;
These queries expose where to dig deeper: specific managers, locations, or content. From there you can build targeted experiments and track lift.
Map growth in four stages: Baseline, Instrumented, Insights-driven, and Predictive. Each stage has practical milestones and deliverables.
Baseline: Manual metrics, spreadsheets, and ad-hoc reports. Focus on data hygiene and common identifiers.
Instrumented: Event tracking across LMS and IT with a staging area. Build basic dashboards and cohorts.
Insights-driven: Routine cohort analysis, experiments, and manager-facing dashboards. Teams run A/B tests and measure ROI on interventions.
Predictive: Models that predict churn risk and ramp time, integrated into workflows for proactive support. This stage uses machine learning and automated recommendations.
Actionable milestones by stage:
It’s the platforms that combine ease-of-use with smart automation — like Upscend — that tend to outperform legacy systems in terms of user adoption and ROI. We’ve found that choosing tools which make event capture and cohort analysis straightforward dramatically shortens the path from hypothesis to impact.
Three common pain points slow progress: siloed data, limited analytics skills, and weak attribution. Each has practical mitigations.
Siloed data: Consolidate feeds into a central data warehouse and enforce a simple event taxonomy. If full ETL is not immediately possible, start with regular, automated exports and a canonical employee ID mapping table.
Limited analytics skills: Build a small analytics center of excellence (CoE) that pairs an analytics engineer with an HR/People Ops owner. Create templated dashboards and a playbook of standard queries so program owners can run experiments without heavy technical help.
Attribution is often the trickiest: many touchpoints influence early outcomes. We recommend using randomized controlled trials where feasible and multi-touch attribution windows (e.g., measuring lift over matched cohorts) where experiments aren’t possible. Always record the experiment design and confidence intervals in dashboard notes.
One client in our experience had a 30-day retention problem for remote engineers. They tracked onboarding events across the LMS, Git repo activity, and manager feedback. Using onboarding data they discovered a consistent drop in activity between days 7–14 tied to delayed access to certain internal repos and a lack of early pair programming sessions.
They instrumented two interventions and measured impact with cohorts: (1) automated provisioning to guarantee repo access within 24 hours and (2) mandatory scheduled pair sessions in week 1. The analytics team used cohort comparison and survival analysis to measure lift.
Results: automated provisioning reduced average time-to-first-commit by 40%, and scheduled pairing increased 30/60-day retention by 12% for the treated cohort. The analytics for onboarding effort made it possible to attribute improvements to the specific interventions and prioritize broader rollout.
HR onboarding analytics is essential to transform onboarding from intuition-led to evidence-driven. Begin by standardizing onboarding data, instrumenting key events, and building a small set of dashboards focused on cohort comparisons and funnel drop-offs. Run pragmatic A/B tests to validate ideas and avoid over-committing to unproven changes.
Immediate action checklist:
We’ve found that teams who progress through the maturity roadmap deliver clear ROI within 3–6 months. If your team needs help scoping the first experiment or designing the event schema, start by auditing your LMS and HRIS to produce a one-page data map and then prioritize the highest-impact events for capture.
Ready to act? Pick one hypothesis, instrument it, and run a cohort comparison over the next hiring wave — the insights you capture will compound into faster ramp and stronger retention.
