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

Predicting employee exits after training

Using predictive analytics to forecast which employees are likely to leave after training can give workforce leaders an earlier view of voluntary attrition. Instead of treating completion and satisfaction as the final measures of value, organisations can connect learning records with employment outcomes and identify where capability investment may be affecting retention.

A forecast estimates probability within a defined period, such as six or twelve months; it does not label a person as certain to leave. Its value comes from helping leaders identify where support, internal opportunities or management changes could keep capable people engaged.

Measure	Conventional programme evaluation	Predictive evaluation
Primary outcome	Reaction, learning and behaviour	Voluntary exit within a defined period
Timing	Immediately after learning	Three to twelve months afterwards
Evidence	Surveys, assessments and completion rates	HR, learning and workforce data
Typical output	Aggregate scores and comments	Calibrated exit-risk estimates and drivers
Business use	Improve content and delivery	Target retention support and test impact

From reaction scores to exit forecasts

Reaction surveys and end-of-course assessments remain valuable because they reveal whether participants found a programme useful. They rarely explain what happened after employees returned to work, however, or whether learning investment influenced retention, internal mobility and performance.

Predictive evaluation changes the unit of analysis from the programme to the employee’s post-training journey. It asks whether participants with similar characteristics subsequently left, transferred or remained, then estimates the factors associated with each outcome. The result is a decision-support signal rather than an automated verdict.

Building a reliable attrition signal

Start with a precise outcome. Separate voluntary resignation from retirement, redundancy, parental leave and internal transfer, then choose a forecast horizon that reflects the business. A six-month model may suit immediate workforce planning, while a twelve-month model can support capability and succession strategies.

Baseline predictors can include tenure, role, pay position, manager, prior internal applications, absence patterns and previous engagement with learning. Training records add signals such as completion, assessment performance, confidence, relevance and manager support. Logistic regression, survival analysis or gradient boosting can provide a baseline, provided the model is tested against simpler alternatives.

Training completion must not leak information about the outcome. A record created after resignation, for example, cannot be used as a pre-exit predictor. Models should be validated by time, business unit and location, with calibration showing whether a predicted 20% exit risk occurs in roughly one in five cases.

What training data can reveal

Training creates a subtle analytical problem: it may improve retention by increasing engagement, but it may also make employees more attractive to competitors. A positive association between participation and exit therefore does not prove that training caused the departure.

Useful training signals include manager follow-up, access to meaningful assignments, recognition, learning quality and confidence applying new skills. Managers also need practical guidance on training adoption barriers, because unsupported programmes can generate certificates without workplace change.

Strong signals can separate two distinct patterns. One cohort may leave because skills have opened external opportunities; another may leave because training repeatedly failed to change the work environment. Each pattern requires a different response, from career pathways to manager accountability.

Validating forecasts in Australian workplaces

Validation should reflect the Australian operating environment rather than relying on a generic global model. Large Sydney and Melbourne employers often combine corporate offices, distributed teams and hybrid work arrangements, making manager quality and access to opportunities relevant predictors. Segment testing should capture these differences without treating a postcode as a proxy for commitment.

Australian labour-market conditions also matter. Skills shortages, wage competition and sector changes can influence resignations independently of learning. Australian Bureau of Statistics workforce data can provide external context, while differences between professional roles and TAFE or RTO pathways require distinct definitions of successful training and career progression.

Regional and occupational validation helps prevent urban or head-office patterns from dominating the forecast. Models should be recalibrated when recruitment conditions, internal mobility or workforce composition shift, particularly in organisations expanding through apprenticeships, skilled trades or nationally recognised qualifications.

Turning predictions into fair action

People analytics requires strict governance. Australian employers should assess applicable obligations under the Privacy Act 1988, the Australian Privacy Principles, state and territory employment-privacy regimes and employment contracts. Access should be role-based, retention periods documented, and breach-response procedures aligned with OAIC guidance.

A risk score should prompt a supported response, never punitive treatment or withdrawal of training. Depending on the cause, that response could include a stay conversation, internal project access, mentoring, workload changes or a clearer career pathway. The wider methodology and service context are explained by Dave Basarab Consulting, including how learning signals can be connected to workforce outcomes.

Fairness checks must consider whether apparently neutral variables act as proxies for age, gender, ethnicity, disability or socioeconomic background. Protected characteristics may be used in controlled aggregate audits, but individual scoring and decisions should remain reviewable. Human review, documented thresholds and outcome monitoring help prevent a statistical score from becoming an employment barrier.

A practical operating model

A workable operating rhythm begins with a baseline forecast, followed by segmented validation, manager enablement and scheduled review. Each retention response should have an owner, expected mechanism and measurable result, while low-risk groups still receive the standard employee experience. This distinguishes predictive evaluation from a one-off modelling exercise.

The practical takeaway is straightforward: a dependable forecast becomes useful only when it is paired with timely, fair action and a measured retention outcome.

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.