Business Strategy&Lms Tech
Upscend Team
-January 2, 2026
9 min read
This article explains which learning analytics to prioritize when comparing LMS and LXP — engagement, completion, skills progression, and business metrics — and how implementation choices (SCORM vs xAPI) affect measurement. It outlines dashboards, a step-by-step 90‑day analytics plan, and governance tactics to link training metrics to business outcomes.
Learning analytics guide decisions about platform selection, content strategy, and measurement of impact. In our experience, teams that treat learning analytics as a business signal (not just an admin report) get better adoption and clearer ROI. This article prioritizes the metrics that matter, explains implementation options, and shows how to build an analytics plan that works across both LMS analytics and LXP analytics.
When you compare platforms, start by deciding which metrics will change decisions. Not all learning analytics are equally valuable. Focus on signals that connect learner behavior to business outcomes.
We recommend grouping analytics into four priority buckets: engagement, completion & progression, skills and competency, and business impact. Each bucket translates differently between traditional lms analytics and modern lxp analytics.
Answering "which learning analytics to track for lms and lxp" depends on your strategic goals. If compliance is primary, prioritize completion and audit logs. If capability-building is primary, prioritize skills progression and on-the-job transfer metrics.
A practical short list to start with:
Implementation choices shape what you can measure. Traditional lms analytics that rely on SCORM give you completion and score, but they miss many deeper learning interactions. For richer learning analytics, xAPI is the standard for capturing learning experiences across platforms and real work.
xAPI enables event-based statements (actor, verb, object) so you can track microlearning, social interactions, simulations, and offline activities. Exporting xAPI statements to a Learning Record Store (LRS) then into BI tools unlocks advanced analysis.
How to use xapi for lxp analytics effectively: ensure the LXP sends xAPI statements for actions you care about (viewed, completed, recommended, rated, shared). Map those statements to a canonical schema so you can join LXP data with HRIS and CRM exports in a BI tool.
Key implementation steps:
SCORM still has value for compliance; xAPI fills gaps for modern experience analytics.
Dashboards translate learning analytics into decisions. Build two core dashboards: an executive view for strategic leaders and an operational view for learning teams.
Executive dashboards require simplicity and business linkage; learning ops dashboards need granularity and troubleshooting tools.
Key elements for executives:
Design notes: use normalized metrics, confidence intervals, and short commentary explaining causation assumptions. Executives want to see the link from learning analytics to revenue or operational metrics, not raw course counts.
Learning ops needs diagnostic detail:
Operational dashboards combine xAPI streams with LMS logs and HR data to surface interventions. 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. This highlights an industry shift toward platforms that provide both data capture and action automation without heavy bespoke engineering.
Creating a repeatable analytics plan ensures your learning analytics deliver outcomes. Below is a pragmatic, stepwise approach our teams use.
Implementation checklist:
Three persistent challenges distort learning analytics: noisy engagement signals, difficulty attributing learning across systems, and governance/privacy risks. Each requires specific mitigations.
Noisy signals: page views and time-on-page can be misleading. We recommend triangulating engagement with active behaviors (quiz attempts, resource downloads, application evidence). Apply smoothing and cohort analysis to reduce false positives.
Cross-platform attribution: learners use multiple touchpoints — LMS modules, LXP recommendations, job aids, mentorship. Create a master user identifier and central event store so you can trace learning journeys end-to-end. Use xAPI statements with consistent actor IDs to connect interactions.
Data governance: establish roles for data stewardship, retention policies, and PII masking. Document consent flows and ensure the LRS and BI platforms comply with your security standards. Regular audits and sample checks protect trust.
Traditional lms analytics emphasizes compliance, enrollments, and completions. Modern lxp analytics focuses on discovery, recommendations, and informal learning signals. When comparing, align metric definitions so you measure equivalent behaviors (e.g., “active learning minute” across platforms).
Ultimately, learning analytics should link to business outcomes. That means measuring impact, not just activity. Use a mix of experimental and observational methods to claim causality when possible.
Practical methods we use:
Examples of business-level metrics tied to training:
| Business KPI | Learning metric to link |
|---|---|
| Sales conversion rate | Completion of product training + certification pass rate |
| Customer satisfaction (NPS) | Customer service role upskilling + performance assessment delta |
To compute ROI, convert performance deltas into monetary impact and compare to program costs, but always present confidence ranges and sensitivity to assumptions.
Comparing LMS analytics and LXP analytics is less about platform labels and more about the quality of events, the consistency of schemas, and the ability to tie learning to business outcomes. Prioritize engagement, completion, skills progression, and business metrics, instrument with xAPI where you need richer capture, and centralize data for cross-platform attribution.
Start with a concise analytics plan, implement robust governance, and build two dashboards—executive and learning ops—to translate data into action. We’ve found that teams that standardize event capture and align metrics to outcomes reduce noisy signals and speed decision-making.
If you want a practical next step: pick three business outcomes, agree on one primary metric per outcome, and instrument those metrics end-to-end for the next 90 days. Revisit results, refine your xAPI schema, and iterate.
Call to action: Choose one outcome, document the metric, and run a 90-day measurement sprint to validate whether your current LMS/LXP setup provides the reliable learning analytics you need.
