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 Learner Dropout Before Training Starts

A course can lose learners before the first module opens. Employees may register with good intentions, then disengage because the content feels irrelevant, the schedule conflicts with operational demands, or their managers do not reinforce participation. Predicting this behavior early gives learning teams time to remove barriers rather than react to poor completion data.

Dropout forecasting is a practical use of learner analytics, audience research, and business context. The goal is not to label employees as likely failures. It is to identify conditions that make persistence difficult and design targeted support around those conditions.

A reliable forecast combines historical completion rates with information gathered during needs analysis, enrollment, and pre-course communication. It can also connect course participation with performance outcomes, creating a stronger case for investment in learning.

Define What Dropout Means

“Dropout” should have a precise operational definition before any prediction begins. Depending on the program, it may mean failing to launch a course, missing a required checkpoint, remaining inactive for a set number of days, or registering without completing the final assessment.

Different formats require different thresholds. A two-hour compliance course might define dropout as noncompletion within 30 days, while a six-week leadership program may track attendance, assignment submission, peer interaction, and post-session application. Consistent definitions make historical data useful and prevent teams from comparing incompatible programs.

Gather Signals Before Enrollment

Useful predictors often exist before learners enter the platform. Examine prior completion behavior, role tenure, workload, manager support, access to devices, preferred learning formats, and the distance between the course and the employee’s daily responsibilities. Short intake surveys can reveal confidence, motivation, perceived relevance, and scheduling constraints.

The quality of the forecast improves when behavioral data is combined with stakeholder evidence. Managers may know that a department is entering its busiest quarter, while instructional designers may identify unclear prerequisites or an overly demanding sequence. A Predictive Evaluation model can help organize these inputs around adoption, impact, and expected return rather than treating completion as the only result.

Score Risk Without Creating Stigma

A simple risk score can rank conditions that need attention. For example, a team might assign points for low manager involvement, limited time, weak perceived relevance, previous noncompletion, and poor technology access. The score should guide support decisions, not become a permanent judgment about an individual or group.

Use broad risk bands instead of false precision. A learner with several moderate barriers may need the same intervention as someone with one severe barrier. Review the model for bias, explain how data is used, restrict access to sensitive information, and allow learners or managers to correct inaccurate assumptions.

Early signal	What it may indicate	Preventive response
Low relevance rating	Learner cannot see a job connection	Add role-specific examples and outcome statements
Repeated prior noncompletion	Time, access, or motivation barrier	Offer coaching, reminders, or a smaller pathway
No manager acknowledgement	Weak local reinforcement	Give managers discussion prompts and progress alerts
Heavy workload period	Limited capacity to study	Adjust deadlines or provide flexible pacing
Low platform readiness	Friction before learning begins	Run a technology check and orientation
Missing prerequisite skills	Anxiety or early failure risk	Add diagnostic activities and preparatory resources

Test Relevance And Readiness

A pre-course diagnostic can ask learners to rate the usefulness of each objective, identify situations where they expect to apply the skill, and report their current confidence. Low relevance combined with low confidence is a strong warning sign: the learner may feel both unconvinced and unprepared.

Course communications should set expectations in concrete terms. State the time required, explain what learners will be able to do afterward, provide a calendar of milestones, and show how activities relate to performance. Aligning learning objectives and KPIs helps translate abstract course value into outcomes that employees and managers recognize.

Use Interventions Matched To Risk

Forecasting matters only when it changes the learner experience. A person facing schedule pressure may need flexible deadlines, while someone questioning relevance may need a role-based example from a manager. Sending the same reminder to every learner produces activity, but rarely addresses the reason for disengagement.

Create intervention rules before launch. High-risk learners might receive a manager check-in, a short orientation, or an alternate learning path. Medium-risk learners could receive milestone reminders and peer support. Low-risk learners may need only normal communications. After launch, compare predicted risk with actual attendance, activity, and completion to refine the rules.

Build A Repeatable Forecasting Process

Prediction should be part of course design, not an emergency report produced after completion rates fall. Include dropout assumptions in the project brief, collect baseline data during discovery, and assign ownership for reviewing risk indicators. Learning teams can then connect adoption measures with business outcomes and improve future programs.

Organizations evaluating enterprise learning programs may benefit from working with Dave Basarab Consulting, which supports learning strategy, instructional design, leadership development, training delivery, and predictive evaluation. An external perspective can help establish useful measures without overcomplicating the learning technology stack.

Practical Steps For Course Teams

Define dropout and successful participation for the specific learning format.
Add readiness, relevance, workload, and manager-support questions to enrollment.
Combine historical behavior with qualitative insight from learners and supervisors.
Create risk-based interventions before the course opens.
Review forecast accuracy and business impact after each delivery cycle.

A thoughtful forecast does not seek perfect certainty. It creates an early-warning system that makes learner support more timely, targeted, and measurable. Begin with one upcoming course, identify its strongest dropout signals, and build a small intervention plan around them. Then use the results to strengthen adoption and connect learning activity with business performance.

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.