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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.

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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.

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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.

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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.

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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.

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Predictive Evaluation

Turning Predictive Evaluation Data Into Better Training

Training data becomes valuable when it changes what an organization does next. Completion rates, assessment scores, manager observations, workplace performance, and business outcomes can reveal whether a learning initiative is being adopted and applied as intended.

Predictive Evaluation provides a practical way to use that evidence before investing in another program. Rather than waiting for a final report, learning leaders can forecast likely adoption, identify weak points, and refine the learner experience while there is still time to improve results.

The goal is a stronger connection between employee capability and business performance. This requires more than collecting feedback; it requires interpreting patterns and translating them into decisions about content, delivery, reinforcement, and measurement.

Start With The Business Outcome

Future training should be shaped by the result the organization needs, not by a preferred course format. Begin by reviewing the original business objective: faster onboarding, fewer safety incidents, stronger sales conversion, improved compliance, or more effective leadership conversations.

Compare that objective with the available evaluation data. If participants enjoyed the course but supervisors see little behavior change, the problem may involve practice opportunities, manager support, workflow barriers, or unclear expectations. If knowledge scores are high but operational metrics remain flat, the training may be teaching information without building usable capability.

This distinction prevents teams from responding to every weak result by adding more content. Sometimes the right intervention is a job aid, a manager briefing, a process change, or a targeted learning burst.

Separate Adoption From Impact

Adoption measures whether people engage with and use the learning. Impact measures whether that use contributes to meaningful performance improvement. These are related, but they should not be treated as the same signal.

A program with low adoption needs a different response from one with strong adoption and weak business impact. Low adoption may point to poor scheduling, limited relevance, inaccessible resources, or a lack of leader reinforcement. Strong adoption with weak impact may indicate that the practice is not transferring into daily work.

Predictive Evaluation helps learning teams examine these conditions early. Forecasts can highlight which groups are likely to apply the training and which groups may need additional support, allowing resources to be directed where they can make the greatest difference.

Look For Patterns Across Learner Groups

Aggregate results can conceal important differences. Analyze the data by role, location, tenure, manager, delivery method, and level of prior experience. A program may perform well overall while producing weak outcomes for remote employees, frontline teams, or new supervisors.

Segmented analysis also helps identify the conditions associated with successful transfer. For example, employees who receive a manager check-in within two weeks may demonstrate stronger application than those who complete the course without follow-up. That pattern can inform the design of future reinforcement.

The onboarding redesign work illustrates why timing and workplace support matter. New-hire learning becomes more useful when it is connected to the moments when employees must demonstrate competence.

Convert Evidence Into Design Decisions

Evaluation data should lead to specific changes rather than broad statements such as “engagement needs improvement.” Map each finding to a design lever: objectives, examples, practice, assessment, facilitation, sequencing, accessibility, or reinforcement.

Evaluation signal Likely interpretation Useful refinement
High completion, low application Learning is accessible but transfer is weak Add realistic practice, job aids, and manager follow-up
Low completion in one group Access or relevance varies by audience Adjust scheduling, format, language, or examples
Strong test scores, flat performance Recall does not equal capability Use scenario-based assessment and workplace observation
Early drop-off in a learning burst Content may be too long or poorly sequenced Shorten modules and clarify immediate value
Positive learner ratings, limited ROI Experience is good but outcomes are misaligned Reconnect objectives to operational metrics

The most effective revisions are usually targeted. A single change to an assessment, manager toolkit, or post-course workflow may produce more value than rebuilding an entire curriculum.

Use Forecasts To Prioritize Investment

Training portfolios often contain more improvement opportunities than available budget or staff time. Predictive data can help rank initiatives according to expected adoption, potential business value, risk, and confidence in the available evidence.

A high-priority program may have a large performance opportunity and clear signs that a modest intervention could improve transfer. A low-priority program may have strong participation but little connection to strategic goals. This approach makes learning investment decisions more transparent for executives and operational leaders.

Organizations seeking support with enterprise learning strategy, custom instructional design, leadership development, or training delivery can explore Dave Basarab Consulting. Its Predictive Evaluation methodology supports a more disciplined link between learning activity, behavior change, and return on investment.

Build A Continuous Evaluation Cycle

Refining future training works best as a repeating cycle: establish a measurable outcome, collect early signals, forecast likely results, adjust the intervention, and monitor whether the change improves adoption or impact. This cycle should begin during design rather than after launch.

Set decision thresholds in advance. For example, define the adoption level that triggers additional manager communication, the application rate that requires more practice, or the business result that determines whether a program should scale. Predefined thresholds reduce subjective debate and make evaluation more actionable.

Practical Refinements To Make

  • Review leading indicators before relying on final completion or satisfaction reports.
  • Segment results to find differences between roles, locations, managers, and experience levels.
  • Connect every evaluation finding to a specific design or workplace intervention.
  • Include managers in reinforcement plans, observation, and performance discussions.
  • Recheck business outcomes after each significant training adjustment.

When Predictive Evaluation data is treated as a design resource, training becomes more responsive and commercially relevant. Learning teams can identify what is working, isolate the barriers to transfer, and make informed changes before weak results become expensive patterns.

Start by selecting one active program, clarifying its business outcome, and examining the evidence that predicts adoption and performance. Then use those findings to shape the next version of the learning experience—and measure whether the refinement moves the organization closer to its intended 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.

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