Duration
Implementation and cadence agreed
Metrics & Continuous Improvement
I design the model connecting reaction, learning, behavior, and impact without treating correlation as causation. The work defines what to measure, where the data comes from, and how to read the signals; business outcomes and integrated dashboards depend on data quality and later implementation.
Service at a glance
Audience
Sponsors, HR/L&D, analytics, and area managers
Outcome
Baseline, instruments, indicator dictionary, and a reading prototype
Format
Measurement design
Who it's for
- Sponsors who need an evidence narrative compatible with the data that is actually available.
- HR, L&D, and managers who need to separate satisfaction, learning, and behavior change.
- Analytics, technology, and operations teams that will own sources, integrations, and recurring reviews.
How it works
- Map the decisions measurement needs to support and the sources that can be accessed.
- Build the reaction → learning → behavior → impact ladder, with explicit hypotheses between levels.
- Define the baseline, instruments, calculation rules, segments, and data owners.
- Create the indicator dictionary and the cadence for collection, review, and decisions.
- Prototype the reading as a non-integrated dashboard or report, based on data volume and maturity.
What you get
- Measurement model with questions, levels, and interpretation limits
- Baseline plan and collection instruments
- Indicator dictionary with formula, source, frequency, owner, and caveats
- Review cadence and a decision guide for improvements
- Non-integrated dashboard prototype or report model validated with available data
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Frequently asked questions
Which data does the company need to provide?
It depends on the selected decisions and indicators. Participation, learning, survey, authorized tool usage, quality, or process data may be needed. The company remains responsible for access, legal basis, privacy, quality, and an owner for each source.
Does the model prove that AI caused a business outcome?
Not automatically. Attribution requires a comparable design, a consistent baseline, and controls for other factors. When these do not exist, the model separates observed evidence, perception, and hypothesis instead of converting correlation into a proven gain.
Is the deliverable an integrated dashboard or a report?
The scope delivers the logic and a reading format: a non-integrated dashboard prototype or report model, depending on the data. Connectors, automation, a BI environment, and ongoing operations are technical dependencies or later scopes.
Contact
To talk about this service, write to oi@dorly.com.br.
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