Evaluating Training Outcomes with Real Data

Measuring the ROI of Virtual Behavioral Training Investing in behavioral training is not just about cost—it’s about measurable impact. The real question organizations must ask is: Does the training deliver a return on investment (ROI) in terms of improved retention, productivity, and leadership effectiveness? In our previous analysis, the total cost of a two-day virtual behavioral training for 60 mid-level managers was ₹19,63,000. Now, let’s calculate the potential ROI based on key business outcomes. 1. ROI Formula The standard formula for training ROI: ROI (%) = {Monetary Benefits} - {Training Cost}/ {Training Cost} * 100 2. Business Impact Assumptions To estimate the monetary benefits, we consider three key areas: A) Reduction in Attrition Average attrition for mid-level managers: 15% annually Assumed reduction in attrition due to training: 3 percentage points Average cost of replacing a manager (hiring, onboarding, productivity loss): ₹15,00,000 per manager Retention improvement: 60 managers × 3% = 1.8 managers saved {Cost Savings from Reduced Attrition} = 1.8*15,00,000 = ₹27,00,000 B) Increased Promotions & Internal Mobility Assumed impact: 5% increase in internal promotions Cost of hiring an external manager: ₹20,00,000 (recruitment, ramp-up, lost productivity) Savings from internal promotion: 60 × 5% = 3 managers promoted {Cost Savings from Internal Promotions} = 3* 20,00,000 = ₹60,00,000 C) Productivity Gains from Behavioral Improvement Behavioral training enhances leadership, communication, and decision-making, leading to improved productivity. Assumed productivity increase: 2% per manager Average annual contribution per manager (₹30L salary, assuming 3× salary as productivity value): ₹90,00,000 Total productivity gain per manager: ₹90,00,000 × 2% = ₹1,80,000 Total impact: ₹1,80,000 × 60 managers = ₹1,08,00,000 3. Total Monetary Benefit Benefit Area and Financial Impact Reduction in Attrition 27,00,000 Increased Internal Promotions 60,00,000 Productivity Gains 1,08,00,000 Total Benefits 1,95,00,000 4. ROI Calculation ROI (%) = {1,95,00,000 - 19,63,000}/{19,63,000} * 100 ROI = {1,75,37,000}/{19,63,000} * 100 ROI = 892% 5. Strategic Takeaways: Why This Matters High ROI Justifies Investment: An 892% ROI confirms that investing in behavioral training yields substantial business value. Retention and Internal Mobility Drive Cost Savings: Avoiding attrition and promoting from within reduces hiring costs significantly. Productivity Gains Create Long-Term Impact: Even small behavioral shifts in leadership and decision-making lead to tangible business outcomes. By linking training costs to measurable business benefits, organizations can move beyond cost discussions to strategic impact measurement—ensuring learning investments drive organizational growth. Would love to hear from others.

One of the biggest frustrations I hear from L&D managers is this: “We know we’re making a difference but we can’t prove it in a way the business actually cares about.” Thing is, most L&D teams don’t have a measurement problem. They have a focus problem. Too many teams still spend their time reporting metrics that mean nothing to performance: completions, attendance, satisfaction scores. These are admin stats, not impact stats. If you want to show that learning drives performance, you need to measure what matters. Start with behaviour change.... If people aren’t doing anything differently after the training, nothing has improved. It’s that simple. You can see it through quick spot interviews, manager observations, or checking how people apply the skills on the job. Behaviour is the first real indicator of transfer. Next is manager validation... Managers see performance daily. If they can’t see a shift, it hasn’t happened. A short post-training check-in with them will tell you far more than an LMS ever will. Then look at business KPIs... Learning only has value when it moves an operational metric like fewer errors, better customer scores, reduced turnaround time, higher sales conversions. Link every programme to one KPI and report back in business terms, not learning terms. Don’t forget before-and-after performance... Baseline data is the difference between “we think it worked” and “here’s the proof it worked.” A 30- or 90-day comparison is often all you need. Two underrated areas: retention and internal mobility... People stay longer and progress more when they feel they’re developing. Yet most L&D teams never claim credit for this, even though it’s one of the most valuable outcomes they create. Then there’s skills data... The backbone of capability building. If the right skills are growing in the right parts of the business, your learning strategy is working. And finally, the most overlooked: cost avoidance. Sometimes the biggest ROI isn’t extra revenue but what you didn’t have to spend like fewer mistakes, less rework, reduced churn. These numbers often tell the strongest story in the boardroom. If you focus on these areas, you won’t just “deliver training.” You’ll demonstrate performance improvement, the only outcome that really matters! --------------- Follow me at Sean McPheat for more L&D content and and then hit the 🔔 button to stay updated on my future posts. ♻️ Repost to help others in your network.

There are 1.1M credentials but our latest research finds that only 12% offer significant wage gain earners wouldn’t have otherwise gotten. The Burning Glass Institute is launching the Credential Value Index to show which ones work, evaluating the outcomes from 23,000 non-degree credentials from over 2,000 providers, including every certification in America—from Coursera digital marketing certificates to OSHA certifications. To see whether they actually deliver for workers, we analyzed how each changed the course of the careers of 7 million people who had earned them. While only 1 in 3 credentials meet a minimum threshold vs. counterfactual peers for either boosting wages, facilitating career changes, or moving people up within their field, we still found 8,000 credentials that really move the needle for workers—often in ways that are transformative. The top decile of credentials yields annual wage gains of nearly $5,000 vs. counterfactual peers, increases by 7x vs. bottom credentials the chances of switching jobs into an aligned career, and boosts by 17x the probability of an earner’s getting promoted within their current field. We found wide variances in outcomes even for the same credential across named providers–and across the portfolio of credential offerings of even high-reputation providers. That says that learners can’t just trust brands and they can’t just trust that a credential will help just because it’s in a high-paying field. Instead, they need real data to help them make informed decisions. Our goal in this work is practical: to put these evaluations in the hands of workers and learners, employers, education institutions & training providers, and policymakers. The Credential Value Index–available through our Navigator site available on https://lnkd.in/e_BTX9bs –makes all 23,000 evaluations accessible to the public, with easy-to-understand metrics of performance, comparisons with other credentials, and helpful context, like which roles earners find themselves working in, which employers they’re working for, and which skills they master along the way. Our research is summarized in an American Enterprise Institute working paper which I coauthored with AEI senior fellow Mark Schneider and Burning Glass Institute colleagues Shrinidhi Rao, Scott Spitze, and Debbie Wasden. You can find it on https://lnkd.in/ezynMA-v. I want to express my deep thanks to Ellie Bertani, Matt Zieger, and the GitLab Foundation for all they have done to support this initiative. I am grateful for your partnership. And a big thank you to Patti Constantakis and Sean Murphy at Walmart for the opportunity to test this framework in a real-world laboratory. Finally, the Credential Value Index builds on a close partnership with Jobs for the Future (JFF). Many thanks to Maria Flynn, Stephen Yadzinski, and their terrific team. #education #careers #highereducation #learning #skills

📈 Unlocking the True Impact of L&D: Beyond Engagement Metrics 🚀 I am honored to once again be asked by the LinkedIn Talent Blog to weigh in on this important question. To truly measure the impact of learning and development (L&D), we need to go beyond traditional engagement metrics and look at tangible business outcomes. 🌟 Internal Mobility: Track how many employees advance to new roles or get promoted after participating in L&D programs. This shows that our initiatives are effectively preparing talent for future leadership. 📚 Upskilling in Action: Evaluate performance reviews, project outcomes, and the speed at which employees integrate their new knowledge into their work. Practical application is a strong indicator of training’s effectiveness. 🔄 Retention Rates: Compare retention between employees who engage in L&D and those who don’t. A higher retention rate among L&D participants suggests our programs are enhancing job satisfaction and loyalty. 💼 Business Performance: Link L&D to specific business performance indicators like sales growth, customer satisfaction, and innovation rates. Demonstrating a connection between employee development and these outcomes shows the direct value L&D brings to the organization. By focusing on these metrics, we can provide a comprehensive view of how L&D drives business success beyond just engagement. 🌟 🔗 Link to the blog along with insights from other incredible L&D thought leaders (list of thought leaders below): https://lnkd.in/efne_USa What other innovative ways have you found effective in measuring the impact of L&D in your organization? Share your thoughts below! 👇 Laura Hilgers Naphtali Bryant, M.A. Lori Niles-Hofmann Terri Horton, EdD, MBA, MA, SHRM-CP, PHR Christopher Lind

As we scale GenAI from demos to real-world deployment, one thing becomes clear: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 𝗰𝗮𝗻 𝗺𝗮𝗸𝗲 𝗼𝗿 𝗯𝗿𝗲𝗮𝗸 𝗮 𝗚𝗲𝗻𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺. A model can be trained on massive amounts of data, but that doesn’t guarantee it understands context, nuance, or intent at inference time. You can teach a student all the textbook theory in the world. But unless you ask the right questions, in the right setting, under realistic pressure, you’ll never know what they truly grasp. This snapshot outlines the 6 dataset types that AI teams use to rigorously evaluate systems at every stage of maturity: The Evaluation Spectrum 1. 𝐐𝐮𝐚𝐥𝐢𝐟𝐢𝐞𝐝 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 Meaning: Expert-reviewed responses Use: Measure answer quality (groundedness, coherence, etc.) Goal: High-quality, human-like responses 2. 𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 Meaning: AI-generated questions and answers Use: Test scale and performance Goal: Maximize response accuracy, retrieval quality, and tool use precision 3. 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 Meaning: Malicious or risky prompts (e.g., jailbreaks) Use: Ensure safety and resilience Goal: Avoid unsafe outputs 4. 𝐎𝐎𝐃 (𝐎𝐮𝐭 𝐨𝐟 𝐃𝐨𝐦𝐚𝐢𝐧) Meaning: Unusual or irrelevant topics Use: See how well the model handles unfamiliar territory Goal: Avoid giving irrelevant or misleading answers 5. 𝐓𝐡𝐮𝐦𝐛𝐬 𝐝𝐨𝐰𝐧 Meaning: Real examples where users rated answers poorly Use: Identify failure modes Goal: Internal review, error analysis 6. 𝐏𝐑𝐎𝐃 Meaning: Cleaned, real user queries from deployed systems Use: Evaluate live performance Goal: Ensure production response quality This layered approach is essential for building: • Trustworthy AI • Measurable safety • Meaningful user experience Most organizations still rely on "accuracy-only" testing. But GenAI in production demands multi-dimensional evaluation — spanning risk, relevance, and realism. If you’re deploying GenAI at scale, ask: Are you testing the right things with the right datasets? Let’s sharpen the tools we use to measure intelligence. Because better testing = better AI. 👇 Would love to hear how you’re designing your eval pipelines. #genai #evaluation #llmops #promptengineering #aiarchitecture #openai

𝐓𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭 𝐭𝐨 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐖𝐨𝐫𝐤𝐬? 𝐒𝐭𝐚𝐫𝐭 𝐚𝐭 𝐭𝐡𝐞 𝐄𝐧𝐝. 🏁 I used to think my job as an L&D professional started with a syllabus. I was wrong. Recently, I was tasked with building a learning solution for our Talent Acquisition (TA) team. The goal wasn’t just to "train recruiters"—it was to solve a business problem. Instead of looking at what they needed to know (Level 2), I started with what the business needed to achieve (Kirkpatrick Level 4). The "Reverse" Approach I didn’t start with slides. I started by analyzing Voice of the Customer (VOC) survey results, focusing on various metrics from both Hiring Managers and Candidates. Working Backwards: ✅ Level 4 (Results): I defined the business KPI. ✅ Level 3 (Behavior): Based on the VOC metrics, I identified the specific actions recruiters needed to change—specifically around "Precision Intake" and "Candidate Experience Management." ✅ Level 2 & 1 (Learning & Reaction): Only then did I design the actual training content that addressed those specific behavior gaps. The Result? The training didn't feel like a chore; it felt like a solution. Because I built it based on the actual metrics revealed in the VOC surveys, the TA team saw immediate value, and the business saw a measurable shift in hiring efficiency. The Lesson: If you want your learning solutions to be more than just "check-the-box" exercises, stop asking "What should we teach?" and start asking "What does the data say I need to solve?" How do you use VOC data to shape your enablement programs? 👇 #LearningAndDevelopment #InstructionalDesign #TalentAcquisition #KirkpatrickModel #Enablement #DataDrivenLD #BusinessImpact

Inspired by the latest FSI Training conference and Fabio Nakamura's lecture, I wanted to share the methodology for data analysis that I have implemented within my team. Previously, I analyzed drill intensity compared to full match data. My approach involved looking at intensity metrics over the full duration of a match and comparing them with training data. This method proved inadequate for capturing individual training performance and match intensity. In the last few days, I have focused on a more precise approach by segmenting match data into individual cuts. This allowed me to establish thresholds for each player, enhancing the accuracy of the analysis. To streamline the process before importing the data into Power BI, I divided each player's match data into segments of 3, 4, 5, 6, 8, and 10 minutes. For each segment, I calculated the average of the three best values for the variables: Total Distance, High-Speed Running (HSR) Distance, Sprint Distance, Acceleration Efforts (Zones 2+3), Acceleration Distance, Deceleration Efforts (Zones 2+3), Deceleration Distance, and Player Load. The rationale behind averaging the three best values, rather than using a single best value, is that outliers can create unrealistic thresholds. For example, during a short two-minute period, an athlete may be highly motivated, resulting in an intensity peak that does not represent sustainable performance. Averaging the three best values provides a more reliable and representative benchmark. By dividing drill values by these thresholds, I calculated the percentage of match intensity. This adjustment revealed that the previously analyzed drill intensities were often overestimated by up to 50% in some cases. An example in the images involves two players with different game thresholds. When examining absolute data, it appears that Player 1 experiences a higher mechanical load (in terms of accelerations and decelerations). However, both players exhibit similar intensity levels when compared to their match thresholds. This discrepancy becomes even more apparent when analyzing a full 90-minute match, as was done in my earlier approach. This finding underscores that absolute values alone cannot provide meaningful insights into individual player intensity during training. Each player and each drill must be carefully analyzed to draw accurate conclusions, such as determining whether a player was exerting sufficient effort. The next step in this process is automation. Ideally, the program should recognize drill duration and automatically adjust it to the match intensity thresholds. I'm looking forward to chatting about this approach to data analysis. Any ideas or suggestions on how to enhance this method further are welcome! #sportsscience #dataanalysis #strengthandconditioning #soccer

As Instructional Designers, we often track training completion in spreadsheets. But rows and columns rarely show us the real shape of a learning culture. So I used Gephi to model a sample organizational training network. 🔵 Blue nodes: Training topics 🟣 Purple nodes: Employees Each connection represents actual participation, not just assignment. When the data turned into a network, the story became much clearer: 🔹 Hidden silos appeared immediately. A group of employees clustered only around Health & Safety, completely disconnected from core digital topics like Data Security. They are compliant — but isolated. 🔹 “Super Learners” stood out naturally. Employees like Emp #7 emerged as bridges between technical and soft skills. These are not just learners — they are potential mentors, knowledge carriers, and internal champions. 🔹 Core vs. Edge became visible. While Data Security sits at the heart of the learning culture, Leadership training appears at the fringe, signaling a possible disconnect between strategic development and daily learning behavior. This reminded me of something important: Instructional Design is not only about creating content. It is about revealing gaps, breaking silos, and intentionally designing connections. Spreadsheets show who completed what. Networks show who is truly connected to learning. How do you currently look at your training data: as a list — or as a living system?

You've just launched a reskilling program aimed at boosting digital literacy across your organization. Now, the big question is: how do you measure its success? To answer that, a combination of hard data and real-world feedback is key. Take the example of AT&T, which famously invested $1 billion in reskilling its workforce for the digital age. They tracked success through KPIs like training completion rates and skill acquisition. Post-training, they saw a marked increase in employees' ability to handle new technologies, evidenced by improved performance metrics. But metrics only tell part of the story. Gathering qualitative feedback is equally important. IBM, for instance, uses surveys and pulse checks to gauge how employees feel about their upskilling efforts. This feedback allows them to tweak programs in real-time, ensuring that learning remains relevant and engaging. Lastly, consider long-term evaluation. Adobe ties reskilling outcomes to annual performance reviews, allowing them to see if the new skills are leading to sustained improvements. This holistic approach—combining KPIs, feedback, and long-term tracking—ensures that reskilling initiatives not only deliver immediate results but also contribute to lasting change. Are you ready to measure the true impact of your reskilling efforts? #hr #chro #reskilling #datainsights #employeedevelopment #employeeskilling

“What’s the ROI of this training?”, asked the organization that: • Didn’t brief the manager on what the program actually covers • Didn’t align learning to real, on-the-job challenges • Didn’t follow up meaningfully beyond Day 1 • Didn’t change supporting systems, KPIs, or everyday behaviors • Relied on generic 30-60-90 journeys with limited ownership or reinforcement • Still expects transformation in 2 days Let’s get something straight. Training is not a vending machine. You don’t insert a trainer and expect “Productivity +15%” to pop out. Training is an enabler. A catalyst. A spark. Not the fire. Not the fuel. Not the oxygen. 70% of learning happens on the job. And yet, most managers: • Don’t know what was taught • Don’t reinforce it • Don’t coach for application • Don’t ask reflective questions Then we ask: “Why didn’t behavior change?” Because you sent people to the gym… and expected muscles without lifting weights. Here’s the uncomfortable part. Most post-training follow-ups rely on: • Happy sheets • LMS completion ticks • TMS attendance reports Which raises a simple question: If your Level-1 feedback is superficial, how are you expecting Level-3 results to be meaningful? Smiles, stars, and “great session” comments don’t measure: • Behavior shifts • Manager reinforcement • Real workplace application • Obstacles participants are facing You can’t build business impact on feel-good feedback. Real ROI happens when: • Learning captures real challenges, not just reactions • Reflection continues beyond the classroom • Managers see, coach, and reinforce micro-behaviors • Follow-up is designed, not assumed Otherwise, don’t ask for ROI. Ask instead: “Did we measure learning deeply enough to deserve results?” #SaHRcasm #LearningAndDevelopment #TrainingROI #BehaviorChange #ManagersMatter #BeyondHappySheets