Your Chief Virtual Learning Architect

Learning to Performance

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Custom Design & Delivery

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Predictive Evaluation

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Enterprise Learning Strategy

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Dave Basarab Consulting has elevated the training experience, offering an end-to-end comprehensive approach that includes training strategy, instructional design, development, delivery , post program training transfer, and evaluation (via the unique Predictive Evaluation methodology). Virtual Chief Architect Dave Basarab has combined all of these individual training elements with his user-friendly, comprehensive Learning to Performance approach, which significantly increases companies' training ROI.

Why we are different

Innovation: Our primary focus is creating learning programs using a Learning to Performance approach.
Expertise: Dave offers the depth and breadth of his experience, including working internally at prestigious companies (Motorola, Ingersoll Rand and Pitney Bowes) as well as his knowledge and expertise as a highly-respected, sought-after consultant.
Partnership: Basarab collaborates with companies, serving as their own Chief Learning Officer whenever they need to plan, strategize, or implement training initiatives.

Training Services

Enterprise Learning Strategy

Enterprise Learning Strategy

At Dave Basarab Consulting, we're experts in creating Enterprise Learning Plans. We work with you to develop your company's learning strategy and direction.

Custom Design, Development, & Delivery

Custom Design, Development, & Delivery

Custom training is an effective way of developing the capability required to execute your strategy. We are a custom design house that creates programs specific to your business.

Learning Burst Development

Learning Burst Development

We can create and deliver your courses via our unique Learning Burst Method - keeping your employees at their jobs while receiving world-class training.

Predictive Evaluation

Predictive Evaluation

We predict the ROI for your courses and establish success gates. We then evaluate the course against the success gates to show value realized and continually improve results.

Leadership Development

Leadership Development

Turn-key virtual custom leadership development program that combines world-class leadership speakers/educators with post-event personalized coaching to provide you with a cadre of highly skilled leaders.

Predictive Evaluation

How to forecast training adoption from pre-work data

Training adoption is rarely a mystery that begins when a course launches. Useful clues often appear earlier, in registration behaviour, manager involvement, learner confidence and the practical conditions surrounding the programme. Pre-work data gives learning teams a way to estimate participation before committing to a full rollout.

For Australian organisations, this matters across dispersed workforces in Sydney, Melbourne, Brisbane and regional locations. A mandatory programme may compete with operational rosters, customer demand, school-holiday schedules and hybrid-work routines. Forecasting helps distinguish genuine learner demand from attendance that exists only because a calendar invitation was sent.

A reliable forecast also connects learning activity with business outcomes. Rather than reporting that 800 people enrolled, teams can estimate how many will start, complete, apply and sustain the intended behaviour. That creates a stronger basis for instructional design, leadership development and investment decisions.

Reading the signals before launch

Pre-work data includes anything collected before the formal learning experience begins. Registration timing, diagnostic scores, manager nominations, survey responses, calendar acceptance and access to preparation materials can all indicate likely adoption. No single measure is decisive; the value comes from patterns across several signals.

For example, a high registration rate with low completion of pre-reading may indicate compliance rather than commitment. Conversely, a smaller group that completes preparation, attends optional briefings and reports a clear workplace need may adopt the training at a higher rate. The retiring an old course perspective is useful here because declining pre-work engagement can reveal that content no longer fits current work.

Selecting useful pre-work variables

Start with variables that have a plausible relationship to adoption. These might include time between enrolment and launch, prior course completion, perceived relevance, manager support, confidence with the subject and the learner’s ability to practise on the job. Include business-unit, location and employment-pattern data where privacy and governance rules allow.

Avoid collecting information simply because it is available. A large dataset can still produce a weak forecast if the variables are disconnected from learner behaviour. Define adoption first, then select pre-work measures that could reasonably predict it.

Signals worth collecting

Registration lead time and attendance history
Completion of diagnostic activities or preparation tasks
Learner-rated relevance and confidence
Manager endorsement and protected practice time
Access constraints such as shifts, travel or connectivity

These measures should be captured consistently across cohorts. A Melbourne office using one rating scale and a regional Queensland site using another will make comparisons unreliable. Clear definitions are especially important when programmes run across multiple time zones or business divisions.

Building an adoption forecast

Define the outcome in observable terms. “Adoption” could mean attending the programme, completing the learning burst, applying a target behaviour within 30 days or reaching a manager-verified performance standard. A forecast is only meaningful when its target is specific and measurable.

A simple model can begin with historical rates by cohort, then adjust for current pre-work signals. More advanced teams may use logistic regression or predictive analytics, but sophistication should follow data quality. Predictive Evaluation provides a practical structure for forecasting adoption, impact and return on investment without treating a statistical score as a business result.

Pre-work signal	Likely interpretation	Forecast use
Early registration	Immediate interest or policy pressure	Estimate initial participation
Completed preparation	Readiness and effort	Increase expected completion
Strong manager support	Protected time and reinforcement	Increase application likelihood
Low perceived relevance	Weak value proposition	Flag disengagement risk
Limited practice access	Workplace barrier	Reduce expected behaviour transfer

Testing the model against reality

Use previous cohorts to test whether the selected signals predicted what actually happened. Separate the data into a development period and a validation period, or use cross-validation when the sample is modest. Compare predicted adoption with observed adoption by role, location and delivery format.

Calibration matters as much as accuracy. If a model predicts 70% adoption for a group, roughly seven in ten similar learners should adopt the behaviour over time. Review false positives and false negatives, since both can carry costs: overestimating demand wastes capacity, while underestimating it can lead to insufficient facilitation or support.

Accounting for stakeholder confidence

Forecasts must be understandable to leaders who control funding, time and operational access. Explain which factors influenced the estimate, how certain the result is and what action could change it. A forecast should support a decision, not create an impressive but opaque score.

Stakeholder scepticism is common when training data appears disconnected from commercial measures. Address it with stakeholder ROI concerns, showing how pre-work indicators connect to attendance, behaviour change, productivity, safety or customer outcomes. For an Australian business, that may mean linking a supervisor programme to reduced rework in Perth operations or stronger service consistency across a national retail network.

Using the forecast to shape delivery

A low forecast does not automatically mean the programme should be cancelled. It may indicate that the timing, manager communication, format or practice environment needs attention. Learning teams can use the results to create targeted reminders, provide manager toolkits, adjust the sequence or offer a shorter learning burst before a deeper course.

A high forecast should receive equal scrutiny. Strong early interest can fade when workloads rise, especially in sectors affected by seasonal demand, field travel or shift coverage. Set monitoring points after launch and compare actual attendance, completion and application with the original estimate.

Connecting adoption with business impact

Adoption is an intermediate result, not the final measure of value. Learners can complete a course without changing their work, while a smaller group may produce significant operational improvement. Link adoption data with assessments, observation, performance metrics and manager feedback.

This is particularly important for leadership and coaching programmes, where transfer may develop gradually. Guidance on coaching impact shows why predictive measures can help identify whether participation is likely to become sustained workplace behaviour.

Before publishing a forecast, check that the target outcome is defined, the data is comparable and the assumptions are visible.

Confirm the sample includes relevant learner groups
Test predictions against at least one completed cohort
Report confidence ranges rather than false precision
Identify practical interventions for low-adoption segments

The final step is to turn the forecast into an operating decision. Set a launch threshold, assign an owner for improving weak signals and schedule a review after the first meaningful behaviour measure. Begin by exporting the last two comparable cohorts and matching their pre-work indicators to 30-day adoption results.

Learning to Performance

Learning to Performance, a complete training approach, gives companies world-class training to drive significant return. This approach includes upfront work (Impact Mapping, design), training (for staff and company executives), and post-training efforts to ensure training transfer. This unique recipe - the key for successful training and adoption - is changing the way companies implement training. This methodology could work with any content for organizations in any industry.