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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

The Role of Predictive Analytics in Justifying Learning Budget Increases

Learning leaders are often asked to defend budgets with evidence that reaches beyond attendance, completion rates, and satisfaction scores. Those measures describe activity, but they rarely show whether employees will apply new skills or whether the organization will gain measurable value from the investment.

Predictive analytics gives corporate learning teams a stronger basis for financial planning. By combining historical training data, business performance indicators, employee behavior, and implementation conditions, organizations can estimate adoption, impact, and return before requesting additional funding.

This approach changes the budget conversation. Instead of presenting learning as a fixed cost, leaders can show how specific investments are expected to improve capability, reduce performance gaps, and support strategic priorities.

Why Learning Budgets Need Evidence

Annual budget reviews tend to favor initiatives with a visible connection to revenue growth, productivity, risk reduction, customer experience, or workforce stability. Training may support all of these outcomes, yet the connection is often described in broad terms rather than demonstrated through a credible measurement framework.

Predictive analysis helps close that gap by identifying the conditions required for learning to produce results. It can reveal whether employees have enough time to practice, whether managers will reinforce new behaviors, and whether the work environment enables application. These factors make a funding request more precise and defensible.

From Activity Metrics to Business Forecasts

Traditional learning metrics are useful starting points. Enrollment, participation, assessment scores, and completion data show whether a program operated as planned. They do not necessarily indicate whether employees changed their behavior or whether performance improved afterward.

A predictive model extends measurement across the full chain of value: learning experience, knowledge transfer, workplace adoption, operational impact, and financial return. It can estimate likely outcomes under different investment scenarios, such as expanding coaching, improving manager support, or redesigning practice activities.

This lets executives compare proposed spending with expected business effects rather than comparing course costs alone. A budget increase becomes an investment hypothesis that can be tested and refined.

Predicting Adoption Before Spend

Training adoption depends on more than the quality of the content. Employees must understand why a new capability matters, see leaders using it, receive relevant practice, and have access to tools that make the behavior practical. Predictive analytics can identify weak points before a large rollout makes them expensive to fix.

A structured adoption evaluation examines the likelihood that learners will use what they have learned in their roles. Forecasts may draw on manager readiness, workflow alignment, reinforcement plans, participation patterns, and employee confidence.

These findings improve budget allocation. Funding might be directed toward manager enablement, job aids, follow-up coaching, or targeted learning bursts instead of being concentrated entirely on initial course delivery.

Connecting Learning to Operational Impact

The strongest business case connects capability development with indicators leaders already monitor. Depending on the program, these may include sales conversion, service resolution time, quality defects, safety incidents, employee retention, project delivery, or compliance performance.

Predictive impact analysis establishes a baseline and identifies the expected movement associated with improved employee behavior. The analysis should distinguish learning effects from other influences, including technology changes, staffing levels, market conditions, and process redesign. This strengthens credibility when results are reviewed.

Organizations can use impact evaluation methods to forecast likely business consequences before launch and measure actual effects afterward. The comparison between forecast and observed results also improves future funding requests.

Evidence Leaders Can Use in Budget Reviews

A clear budget case should translate learning data into decisions. The following view shows how predictive measures can support that translation.

Budget question Predictive evidence Funding decision it supports
Will employees use the capability? Adoption probability by role, team, or location Target reinforcement where readiness is low
What business result is likely? Forecasted changes in operational indicators Prioritize programs with stronger impact potential
What could weaken the result? Risk factors such as manager support or workflow barriers Fund implementation conditions, not just content
How much value may be created? Estimated benefit, cost, and return range Compare learning investment scenarios
When should results appear? Time-to-adoption and time-to-impact forecasts Set realistic milestones and review points

Ranges are often more credible than a single promised return figure. Leaders can see the assumptions behind a forecast, understand the risks, and decide whether additional evidence or a phased investment is appropriate.

Build A Funding Case That Survives Scrutiny

A persuasive request combines analytical rigor with operational detail. It explains what will change, who must adopt the change, how adoption will be supported, and which business measures will indicate progress.

Useful recommendations include:

  • Define the performance problem before selecting a learning solution.
  • Establish baseline business and workforce measures before deployment.
  • Forecast adoption by audience instead of treating all learners as equally ready.
  • Model several investment scenarios, including reinforcement and manager support.
  • Report forecast-versus-actual results at agreed review points.

The analysis should also make uncertainty visible. A conservative forecast with explicit assumptions can earn more trust than an optimistic estimate based on weak evidence. Scenario modeling allows executives to see the likely consequences of underfunding, sufficient funding, and enhanced funding.

Make Predictive Measurement Part Of Planning

Predictive evaluation works best when it begins during learning strategy and instructional design, not after delivery. Early analysis can shape the audience definition, learning experience, reinforcement plan, and success measures before resources are committed.

It also creates a feedback loop for the enterprise learning function. Actual adoption and business outcomes can be compared with forecasts, revealing which interventions consistently produce value and which require redesign. Over time, this evidence supports more accurate portfolio decisions and stronger alignment between learning investments and organizational priorities.

A learning budget justified through predictive analytics is easier to govern because it has clear assumptions, measurable milestones, and a direct connection to business performance. Apply this approach to the next funding proposal, quantify the outcomes that matter, and turn learning investment into a decision leaders can confidently support.

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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