Training analytics that answer the questions the board asks.
Training analytics tools mostly count what’s easy: enrollments, completions, hours. Future Proof’s analytics were built backwards from three hard questions — what did people retain, who needs intervention now, and what did the spend return?
Activity analytics measure the wrong thing precisely
Hours logged, courses completed, learners active — these numbers are accurate, available, and answer no question a decision-maker actually has. They describe the training program’s activity, not its effect. And when effect goes unmeasured, budgets get set by anecdote.
Effect means memory and capability over time. Because Future Proof’s practice engine measures both continuously, its analytics can draw what activity tools can’t: the retention curve per cohort, the risk forecast per learner, and the value line per program.
Retention curves you can act on
Per concept, per cohort, per site: see where knowledge holds and where it drains, and let the scheduler close the leaks it finds. Analytics and intervention are the same system here.
Early warning that’s actually early
Risk flags fire on leading indicators — missed schedules, calibration drift, decaying accuracy — not on the lagging fact of a failed assessment. Each flag ships with its recommended next step.
The honest ROI line
Investment in, retained capability out, by quarter. Assumptions are printed on the chart, and the underlying model is the one documented on our measurement pages — no black-box ROI theatre.
Analytics that measure memory
Retention curves, mastery by Bloom level, confusion pairs — per team, per site, per person, exportable to the board pack.
Interface shown as an illustration with representative numbers, not a screenshot — the layout is the product’s.
See your data in these dashboards.
We’ll run a sample of your training history through the analytics and walk the three questions with your numbers.
Reporting that survives a hostile reading
The counterfactual is the whole argument
A retention number alone is uninterpretable — 62% retained after ninety days is good or bad depending entirely on what would have happened otherwise. Every serious retention claim needs two lines: what the cohort held, and what decay would have taken without the intervention.
Producing the second line honestly is the hard part. Where staggered rollouts exist, later cohorts serve as natural comparisons. Where they do not, the projection is built from your own early decay data and labelled as a projection. What should never happen is a single confident line with the comparison implied.
Leading indicators beat lagging ones, and cost less
Completion is lagging. A failed assessment is lagging. By the time either fires, the recoverable moment has passed. Adherence, accuracy trend and calibration drift all move weeks earlier and are cheap to observe because practice generates them continuously.
This changes what a weekly review is for: not reporting what happened, but acting on what is about to. The flags that matter carry a cause and a recommended intervention, because a red dot without a diagnosis just moves the work to the manager.
Where these analytics cannot help
Attribution to business outcomes remains genuinely hard, and we will not pretend otherwise. Measured capability is a leading indicator of performance, not a substitute for measuring performance, and the confounders between the two are real.
What the platform can do is make the knowledge layer unambiguous and export it cleanly, so your analysts can join it to operational data on their own terms. Anyone offering you a clean causal line from training to revenue is selling something the evidence does not support.
The numbers, and where each one comes from
Questions buyers ask
Can this analyze training that runs on other platforms?
Partially — activity data can be imported for context, but retention analytics require practice data, which means the measurement layer needs to touch learners. Many customers start exactly that way: keep delivery where it is, add Future Proof as measurement.
What does “at risk” mean concretely?
A learner whose leading indicators — schedule adherence, accuracy trend, calibration — forecast a lapse in required knowledge. The flag includes which concepts, how urgent, and the intervention the engine recommends.
How defensible are the ROI figures?
Every figure traces to a printed assumption: loaded hourly cost, measured retention delta, time saved. You can change any assumption and watch the line move — it’s a model you interrogate, not a verdict you accept.
Does it integrate with our BI stack?
Yes — dashboards are built in, and the same aggregates export via API to your warehouse and BI tools.
Who uses which dashboard?
Managers get team views with names and actions; executives get trend and risk rollups; compliance gets evidence coverage. Same data, scoped by role.
See it on your own content.
Bring one course. We’ll show you the retention curve your current training leaves behind — and what scheduled review does to it.
- 30 minutes, on your calendar — pick a slot here
- Run on your own content wherever possible, not a canned deck
- You see the dashboards, the learner surface and the evidence exports
- No commitment — and pilot data stays yours either way