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 Use Predictive Evaluation to Forecast Training Attrition

Training attrition is more than a registration problem. When employees enrol but fail to attend, leave before completion or never apply a new skill, the organisation loses time, budget and a valuable opportunity to improve performance. For Australian employers managing dispersed teams, compliance obligations and tight talent markets, these losses can accumulate quickly.

Predictive Evaluation gives learning leaders a structured way to forecast where participation and adoption may weaken. Rather than waiting for completion reports, the approach combines early evidence, business context and learner behaviour to estimate likely attrition and identify practical interventions before the investment is wasted.

Define attrition across the learning journey

Start by agreeing what “attrition” means for the programme. It may include cancelling after enrolment, missing a live workshop, abandoning an online module, failing an assessment or completing the course without using the capability at work. Each point reflects a different risk and requires a different response.

For example, a Sydney-based leadership programme may have strong attendance but weak workplace application because managers cannot protect time for practice. A blended programme supporting FIFO employees in Western Australia may show lower live-session attendance while maintaining strong results through recordings and mobile activities. Forecasting must distinguish access issues from motivation, relevance and manager support.

The delivery choices available to learners can therefore affect attrition before the programme begins. Map the full journey, from nomination and scheduling through to reinforcement, and assign a measurable outcome to every stage.

Build the forecast from observable signals

A useful model combines historical training data with operational information. Relevant variables may include previous attendance, time between enrolment and commencement, manager sponsorship, workload, travel requirements, digital access, assessment results and the learner’s confidence in applying the skill. Avoid treating demographic data as a shortcut for individual motivation; use it to identify design or access barriers that need investigation.

Segment learners by risk rather than labelling them permanently. A participant with low platform activity but a strong manager relationship may need a simpler digital pathway. Someone who attends consistently but misses practice tasks may need job aids or coaching. The objective is to forecast the probability of attrition and understand the reason behind it.

Organisations can begin with a simple scoring method, then improve it as evidence grows. Compare predicted risk with actual attendance, completion and application data. This creates a feedback loop that makes future forecasts more accurate and keeps the model connected to business outcomes.

Read the signals before the programme starts

Predictive Evaluation is strongest when it uses evidence before delivery, not just post-course surveys. Short readiness checks can reveal whether learners understand the purpose of the training, have the required prerequisite knowledge and expect to use the capability. Discussions with supervisors can expose competing priorities, rostering constraints or weak reinforcement plans.

Signal	Possible attrition risk	Useful response
Long gap between enrolment and start date	Loss of priority or competing work	Send targeted reminders and reconfirm relevance
Low manager involvement	Limited time or workplace application	Brief managers and schedule follow-up
Poor access to devices or bandwidth	Missed online activities	Provide offline, mobile or alternative access
Low confidence before training	Withdrawal or avoidance	Add preparation, coaching or peer support
High workload during delivery	Non-attendance or incomplete work	Offer flexible timing and shorter learning bursts

Local conditions matter. A national organisation may need different forecasts for Melbourne office staff, regional healthcare teams and employees working across Queensland sites. Australian public holidays, school holiday periods, seasonal retail peaks and end-of-financial-year demands can all change availability without indicating low commitment.

Use the forecast to improve learning design

Forecasting attrition is valuable only when it changes the experience. High-risk learners might receive a manager conversation, a pre-course briefing, an alternative session time or a short diagnostic activity. If the risk is caused by complexity, simplify navigation and clarify the first practical action. If it reflects low relevance, connect the content to customer service, safety, productivity or another visible business priority.

A multigenerational workforce may prefer different levels of structure, feedback and technology support. Guidance on multigenerational design can help teams avoid assumptions while creating flexible pathways. This is particularly relevant in Australia, where experienced tradespeople, graduate employees and digital-first professionals may learn together in the same enterprise programme.

Learning bursts, supervisor prompts and workplace practice can reduce post-course attrition even when initial attendance is high. Track whether learners complete the next action, use the tool correctly or demonstrate the capability in their role. Adoption data is often more useful than satisfaction scores when deciding whether the intervention worked.

Connect forecasts with business decisions

A forecast should be presented in operational language. Instead of reporting that 22 per cent of learners are “high risk”, explain that a group is likely to miss the workshop because of roster patterns, or that managers have not scheduled opportunities to practise the skill. This allows business leaders to choose an intervention and estimate its likely value.

Review forecasts at agreed checkpoints: before enrolment closes, shortly before delivery, during the programme and after workplace application begins. Compare predicted and actual attrition, then record which interventions changed the result. Over time, this evidence supports stronger budget decisions and more credible return-on-investment discussions.

Resources such as reader praise for Predictive Evaluation illustrate why learning measurement should focus on adoption and impact, rather than completion alone. For an Australian organisation, the practical takeaway is to define attrition precisely, monitor early signals, act before disengagement becomes visible in final reports, and link every intervention to a workplace behaviour or business result.

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.