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

Using Predictive Analytics to Spot Which Programs Will Succeed

Learning teams are under pressure to prove that training produces measurable business value. Attendance, satisfaction scores, and completion rates offer useful signals, but they rarely show whether employees will apply new skills or whether performance will improve afterward.

Predictive analytics gives organizations a more practical way to assess program potential before investing heavily. By combining workforce data, operational priorities, learner behavior, and past program results, companies can identify which initiatives are most likely to gain traction and deliver measurable impact.

For organizations building a stronger connection between learning and business performance, this approach shifts evaluation from a retrospective exercise to an active decision-making tool. It helps leaders refine program design, anticipate adoption barriers, and focus resources where the expected return is strongest.

Start With A Clear Business Outcome

A predictive model is only as useful as the business question behind it. “Improve leadership” or “increase engagement” may describe a worthwhile ambition, but they are too broad to guide analysis. A stronger objective might be reducing first-year turnover among new managers, shortening service resolution times, or improving safety compliance.

The desired outcome should be connected to observable performance indicators. These may include productivity, quality, sales conversion, employee retention, customer satisfaction, or the speed at which employees reach proficiency. Clear measures allow learning teams to compare forecasts with actual results and improve future predictions.

Combine Learning And Workforce Signals

Historical training data can reveal patterns in participation, completion, assessment scores, manager support, and post-training application. When combined with workforce information such as role, tenure, location, workload, and previous performance, these signals can show where a program is likely to succeed or struggle.

Operational context matters just as much. A technically strong course may perform poorly if employees lack time to practice, managers do not reinforce the content, or the program arrives during a major organizational change. To address resistance, predictive analysis should account for readiness, communication quality, and the conditions surrounding adoption.

Evaluate Program Potential Before Launch

Predictive analytics can be used during program planning, before large-scale delivery begins. Learning leaders can compare different formats, audiences, reinforcement methods, and delivery schedules to estimate likely participation and behavior change. This makes it easier to identify weak assumptions while there is still time to adjust them.

A methodology such as Predictive Evaluation can connect these forecasts to adoption, impact, and return on investment. Rather than waiting months to discover that a program missed its target, teams can establish success criteria in advance and monitor early indicators that suggest whether the initiative is on track.

Predictive Signal	What It Can Reveal	Possible Design Response
Manager participation	Whether employees will receive reinforcement	Equip managers with coaching guides
Time available for practice	Risk of low completion or weak application	Shorten modules and add workflow support
Prior performance data	Where capability gaps are concentrated	Target specific roles or teams
Learner confidence	Likelihood of attempting new behaviors	Add demonstrations and guided practice
Operational alignment	Connection to current business priorities	Tie activities to real work outcomes

Use Engagement Data Carefully

Engagement data should be interpreted as evidence, not treated as proof of success. A high completion rate may indicate that a course was easy to access, while a low rate may reflect scheduling problems rather than poor content. Predictive models become more accurate when they include multiple indicators instead of relying on a single metric.

Learning experience data can also guide timely intervention. If employees pause repeatedly at a difficult activity, skip practice, or perform poorly on an early assessment, the organization can provide targeted support. Short, focused interventions may be more effective than asking learners to repeat an entire course. Learning bursts can support this kind of responsive, data-informed reinforcement.

Improve The Design Before Scaling

Forecasting should inform instructional design, rather than operate as a separate analytics exercise. If the data suggests that employees need immediate job support, the program may require simulations, checklists, coaching prompts, or embedded resources. If learner confidence is low, realistic examples and low-risk practice can improve readiness.

Content relevance is another major predictor of adoption. Employees are more likely to use training when it reflects their decisions, tools, language, and daily constraints. Custom instructional materials can help translate broad concepts into situations that feel credible and useful to specific teams.

Build A Repeatable Measurement Cycle

Predictive analytics should continue after launch. Early adoption data can be compared with the original forecast, while business results can be tracked over an appropriate period. This creates a feedback loop that improves both the model and the learning strategy.

The measurement cycle should include leading indicators, such as participation, practice, manager coaching, and confidence, alongside lagging indicators, such as productivity or retention. Reviewing both types of evidence helps leaders identify problems early without losing sight of the outcomes that justify the investment.

Prioritize Programs With Stronger Signals

Organizations do not need perfect data to begin making better training decisions. They need consistent definitions, reliable baseline measures, and a disciplined process for linking learning activity to performance. The following practices can create a practical starting point:

Define the business problem before selecting a course or delivery method.
Combine learner, manager, workforce, and operational data.
Test program assumptions with a small audience before scaling.
Track behavior change and business outcomes beyond completion rates.
Reuse forecast results to improve future instructional design.

When predictive analytics becomes part of enterprise learning strategy, program selection becomes more evidence-based. Leaders can invest with greater confidence, adjust programs earlier, and demonstrate how capability building supports organizational priorities.

Dave Basarab Consulting helps organizations apply learning strategy, custom development, leadership development, delivery, and Predictive Evaluation to connect training with measurable results. Visit the consultancy to explore how your next learning initiative can be designed, forecast, and evaluated for stronger adoption and business impact.

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.