Measuring Employee Training Effectiveness

I want to show you a clever trick you didn't know before. Imagine you have six months' worth of data. You want to build a model, so you take the first five months to train it. Then, you use the last month to test it. This is a common approach for building machine learning models. Unfortunately, you may find out your model works well with the train data but sucks on the test data. Overfitting is not weird. We've all been there. But often, the worst you can do is try and fix it before understanding why it’s happening. Ask anyone about this, and they will give you their favorite step-by-step guide on regularizing a model. They will jump right in and try to fix overfitting. Don’t do this. There's a different way. A better way. Here is the question I want you to answer before you start racking your brain trying to fix a model: Do your test and training data come from the same distribution? When building a model, we assume the train and test come from the same place. Unfortunately, this is not always the case. Here is where the trick I promised comes in: 1. Put your train and test set together. 2. Get rid of the target column. 3. Create a new binary feature, and set every sample from your train set to 0 and every sample from the test set to 1. This feature will be the new target. Now, train a simple binary classification model on this new dataset. The goal of this model is to predict whether a sample comes from the train or the test split. The intuition behind this idea is simple: If all your data comes from the same distribution, this model won't work. But if the data comes from different distributions, the model will learn to separate it. After you build a model, you can use the ROC-AUC to evaluate it. If the AUC is close to 0.5, your model can't separate the samples. This means your training and test data come from the same distribution. If the AUC is closer to 1.0, your model learned to differentiate the samples. Your training and test data come from different distributions. This technique is called Adversarial Validation. It's a clever, fast way to determine whether two datasets come from the same source. If your splits come from different distributions, you won't get anywhere. You can't out-train bad data. But there's more! You can also use Adversarial Validation to identify where the problem is coming from: 1. Compute the importance of each feature. 2. Remove the most important one from the data. 3. Rebuild the adversarial model. 4. Recompute the ROC-AUC again. You can repeat this process until the ROC-AUC is close to 0.5 and the model can’t differentiate between training and test samples. Adversarial Validation is especially useful in production applications to identify distribution shifts. Low investment with a high return.

Decoding the True Cost of Virtual Behavioral Training: A Strategic Cost Analysis A strategic cost analysis helps in making informed investment decisions and optimizing training effectiveness. Let’s analyze the true cost of a two-day virtual behavioral training for 60 mid-level managers, facilitated by two in-house trainers, with an annual salary of ₹30 LPA each. 1. Direct Costs: Explicit Expenditure a) Trainer Cost (Internal Facilitators) Since the trainers are full-time employees, we calculate their cost per day: • Annual salary per trainer = ₹30,00,000 • Annual working days = 250 • Daily cost per trainer = ₹30,00,000 ÷ 250 = ₹12,000 • Cost for two trainers over two days = ₹12,000 × 2 × 2 = ₹48,000 Trainer Cost: ₹48,000 b) Technology & Platform Costs Assuming the organization uses an internal virtual learning platform (e.g., Microsoft Teams, Zoom, or an LMS), the marginal cost per session is low. However, factoring in licensing, tech support, and bandwidth usage for 60 participants, we estimate: Technology Cost: ₹30,000 c) Learning Materials Digital workbooks, assessments, and post-training resources could cost around ₹750 per participant: Materials Cost: ₹750 × 60 = ₹45,000 d) Administrative and Support Costs Includes training coordination, pre-session readiness, IT support, and evaluation setup: Admin & Miscellaneous: ₹40,000 2. Opportunity Cost: The Hidden Economic Impact a) Participant Salary Cost Each participant earns ₹30 LPA, so their daily salary cost is: • Daily salary per participant = ₹30,00,000 ÷ 250 = ₹12,000 • Cost for 60 managers over two days = ₹12,000 × 60 × 2 = ₹14,40,000 Participant Salary Cost: ₹14,40,000 b) Productivity Loss (Opportunity Cost) While training enhances long-term performance, it results in a temporary dip in operational output. Assuming a 25% productivity loss multiplier (lower than in-person training since managers can still manage urgent tasks), the opportunity cost is: ₹14,40,000 × 25% = ₹3,60,000 3. Total Cost of Virtual Training Trainer Cost ₹48,000 Technology & Platform ₹ 30,000 Learning Materials ₹45,000 Admin & Miscellaneous ₹40,000 Participant Salary Cost ₹14,40,000 Productivity Loss ₹3,60,000 Total Training Cost ₹19,63,000 4. Strategic Insights: Ensuring ROI on Training Investment While a virtual format reduces logistics costs, the largest cost driver remains participant salaries and lost productivity. To optimize ROI: Ensure training relevance: Align content with business objectives to maximize post-training impact. Incorporate blended learning: Spread learning over multiple short sessions to reduce productivity loss. Implement pre- and post-training interventions: Reinforce learning through coaching, peer discussions, and real-world application. Ultimately, the real return on training isn’t just cost efficiency—it’s behavioral transformation that drives business results. Would love to hear how your organization measures training ROI. Let’s discuss in the comments!

Training and coaching programmes in many workplaces are often seen as one-size-fits-all solutions. Its time for that to change, especially when it comes to leadership development. Too often, learning and development initiatives are decided without involving the people who are not actually taking part in them. Organizations make huge investment into programmes, without effective research into people's needs. They don't ask people what they want or need. They presume everyone's needs are the same. There are times where this might be ok....specific technical skills for example or simple standard work practices. But leadership development requires a different approach. To be honest, I used to deliver one-day trainings on leadership skills here and there. But I never felt good about it. I felt like I wasn't adding real value to anyone. I knew most people were likely to forget everything they learned. It seems like such a waste of time and money. Now, I largely provide a blend of training and coaching programmes. They include an assessment of participant needs. They have a measure of individual development over time. Each person's coaching programme is tailored to what they need. I communicate with my programme participant's managers, to support the continuation of coaching long after their initial coaching programme ends. I always think I can do better so I gather feedback from every participant and improve my programmes all the time. These are the best practices guidelines I follow and teach: 1️⃣ Assess participant needs and customize programmes 2️⃣ Clarify the measures of effectiveness that will be used. 3️⃣ Personalize learning paths- this is possible through blending training with 1:1 coaching programmes 4️⃣ Foster a culture of continuous learning where coaching and training is part of what people regularly give and receive. Ensure all managers have effective coaching skills 5️⃣ Evaluate and adjust all training and coaching programmes. Make improvements based on feedback and measures. ❓What else would you add to ensure training and coaching programmes are highly effective? #learninganddevelopment #employeedevelopment #leadershipdevelopment #traininganddevelopment #training #learning #coaching

𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 I've been asked this at least 3 times in the last two months. "How do I know that my leaders are improving?" This is where we distinguish knowing from application. 10% of capability comes from learning from formal sources. 20% comes from networks and interactions. 70% comes from application to portfolios and projects. One thing that sets this all apart are data points. Even if I apply skills to my projects, how do I know I did it well? Most large companies have a 360-degree or leadership assessment process in place. So, I'll share my thought process for this in case you are attempting to develop this for your own organization. Step 1: Determine organizational strategy and business outcomes. This is necessary to align expectations of desired behaviors. This is where a Balanced Scorecard can come in handy. Step 2: Assess expectations of leaders. You'll then assess them across leadership behaviors for new, mid and even senior managers. Granularity of differences supports focus and clarity. Often, a list of pre-existing behaviors/competencies are used to make the exercise easier. Validated psychometric tools such as the 16PF help to anchor it to scientific rigor. Organizational psychologists like me conduct surveys to gather insights. Then, focus groups are used to drill down to details information. After that, we'll create categories basedon the information and produce working behavior-based definitions. Step 3: Prioritize the list Now, the leadership team decides which behaviors are more important by way of ratings. Step 4: Build the 360 We then build a 360-degree feedback survey questions. These questions are reviewed for validity. Step 5: Allocate the survey A system specializing in the 360 (there are many) can be used. Feedback Recipient selects 6 to 12 people to rate them. In organizations, to avoid selection bias, leaders of the feedback recipient can review and veto the people doing the rating. Then, the participant does the survey too (self-rating) Step 6: Debrief of survey Usually, participants need guidance from a trained coach who understands feedback requirements. This is to provide grounding and objective input. Often, 360 surveys tend to be met with resistance unless the coach is skilled in facilitating the reflection conversation. Step 7: Action Planning The participant then produces a set of actions for improvement. This plan and the priority of focus should be made known to the feedback givers. Step 8: Pulse Surveys After a designated time (within 6 to 12 month period) a validated pulse survey is set up for the observers to rate improvement in specific behaviors. Step 9: Continued Leadership Coaching, Mentoring and Peer Support A combination of these can be used to enhance development. Step 10: Final Comparison Survey Toward the end of the year, a comparison survey is done to see how the key areas have improved or not. ---

✋Before rushing into training models, do not skip the part that actually determines whether the model is useful: Measuring performance. Without the right metrics you are not evaluating a model, you are just validating your assumptions. Check out theses nine metrics every ML practitioner should understand and use with intention 👇 1. Accuracy Good for balanced datasets. Misleading when classes are skewed. 2. Precision Of the samples you predicted as positive, how many were correct. Important when false positives are costly. 3. Recall Of the samples that were actually positive, how many you caught. Critical when false negatives are dangerous. 4. F1 Score Balances precision and recall. Reliable when you need a single metric that reflects both types of error. 5. ROC AUC Measures how well a model separates classes across thresholds. Useful for model comparison independent of cutoffs. 6. Confusion Matrix Exposes the exact distribution of true positives, false positives, true negatives, and false negatives. Great for diagnosing failure modes. 7. Log Loss Penalizes confident wrong predictions. Important for probabilistic models where calibration matters. 8. MAE (Mean Absolute Error) Average of absolute errors. Simple, interpretable, and robust for many regression problems. 9. RMSE (Root Mean Squared Error) Heavily penalizes large errors. Best when you care about avoiding big misses. Strong ML systems are built by measuring the right things. These metrics show you how your model behaves, where it fails, and whether it is ready for production. What else would you add? #AI #ML

Everyone’s excited to launch AI agents. Almost no one knows how to measure if they’re actually working. Over the last year, we’ve seen brands launch everything from GenAI assistants to support bots to creative copilots but the post-launch metrics often look like this: • Number of chats • Average latency • Session duration • Daily active users Useful? Yes. But sufficient? Not even close. At ALTRD, we’ve worked on AI agents for enterprises and if there’s one lesson it’s this: Speed and usage mean nothing if the agent isn’t solving the actual problem. The real performance indicators are far more nuanced. Here’s what we’ve learned to track instead: 🔹 Task Completion Rate — Can the AI go beyond answering a question and actually complete a workflow? 🔹 User Trust — Do people come back? Do they feel confident relying on the agent again? 🔹 Conversation Depth — Is the agent handling complex, multi-turn exchanges with consistency? 🔹 Context Retention — Can it remember prior interactions and respond accordingly? 🔹 Cost per Successful Interaction — Not just cost per query, but cost per outcome. Massive difference. One of our clients initially celebrated their bot’s 1 million+ sessions - until we uncovered that less than 8% of users actually got what they came for. That 8% wasn’t a usage issue. It was a design and evaluation issue. They had optimized for traffic. Not trust. Not success. Not satisfaction. So we rebuilt the evaluation framework - adding feedback loops, success markers, and goal-completion metrics. The results? CSAT up by 34% Drop-off down by 40% Same infra cost, 3x more value delivered The takeaway: Don’t just measure what’s easy. Measure what matters. AI agents aren’t just tools - they’re touchpoints. They represent your brand, shape user experience, and influence business outcomes. P.S. What’s one underrated metric you’ve used to evaluate AI performance? Curious to learn what others are tracking.

𝐓𝐡𝐞 𝐜𝐨𝐬𝐭𝐥𝐲 𝐦𝐢𝐬𝐭𝐚𝐤𝐞 𝐦𝐨𝐬𝐭 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 𝐦𝐚𝐤𝐞 𝐰𝐢𝐭𝐡 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 They treat it like a one-time event. A workshop. A box ticked. An expense. The result? Underwhelming impact and wasted budgets. The truth is: training only works when it is designed like a leadership journey, not a classroom session. That’s how executive presence gets built - through repeated practice, reflection, and reinforcement. Here are 3 ways to make training stick and deliver business results: 𝟏. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐰𝐢𝐭𝐡 𝐩𝐮𝐫𝐩𝐨𝐬𝐞 Build structured journeys. Pre-work, dynamic sessions, post-work application. Like a mission, not a meeting. 𝟐. 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞 𝐟𝐨𝐫 𝐫𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧  Group Coaching, virtual peer huddles, and daily quick-hit refreshers so new skills don’t fade. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐰𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 Track the business impact. Not just attendance sheets and smiley-face feedback. One of our clients discovered this the hard way. For years, they invested in sending leaders to The Ivy League MBA schools, skills workshops, communication templates, even role-play drills. Each worked in rehearsals. But in real CXO and board conversations, the impact never stuck. That’s when they shifted to our 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐏𝐫𝐞𝐬𝐞𝐧𝐜𝐞 𝐈𝐧𝐭𝐞𝐫𝐯𝐞𝐧𝐭𝐢𝐨𝐧 that included an 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐏𝐫𝐞𝐬𝐞𝐧𝐜𝐞 𝐈𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐞 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 and 100-day journey. The difference? Senior leaders didn’t just learn, they practiced, measured progress, and reinforced behaviours until they became second nature. Within 4 months, senior leaders reported: ✅ 𝟔𝟑% 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 𝐢𝐧 𝐡𝐢𝐠𝐡-𝐬𝐭𝐚𝐤𝐞𝐬 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞  ✅ 𝟓𝟕% 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐜𝐥𝐚𝐫𝐢𝐭𝐲 𝐨𝐟 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧  ✅ 𝟓𝟓% 𝐮𝐩𝐥𝐢𝐟𝐭 𝐢𝐧 𝐨𝐯𝐞𝐫𝐚𝐥𝐥 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐩𝐫𝐞𝐬𝐞𝐧𝐜𝐞 CEO noticed the shift immediately in boardroom decision-making and stakeholder engagement. When you do this, training shifts from being an expense to becoming a strategic asset that fuels collaboration, loyalty, and decision-making. That’s how organizations grow leaders with true presence. 👉 What’s one reinforcement practice you’ve seen work well in your company’s L&D programs? #ExecutivePresence #CoachVikram #Impact #Leadership

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

“Is my manager going on this?” I’ve been asked when someone goes through a leadership programme. At the time the answer was no. It should’ve been yes. They might not have gone through this specific programme but a complementary programme that walks them through what should be, could be done differently. Creating culture change rather than individual interventions at a level or role type. Then you go back to day job, excited about leading differently and you receive the opposite. You now have a toolbox to find fault, or what “bad” leadership is and you disengage, get disheartened and retreat. With your feet or your heart. You stay and “leave” or you go and leave fully. How much is wasted on this? Who are becoming because of this? What is reinforced and never actually transforms? What if change was through the organisation rather than TO it? I know the value of interventions. I know the value of simple practices that create environments for people to thrive. If we’re not serious about change, we’re just spending money, measuring bums on seats and wondering what’s going on. At least it feels good to put on training? If you’re serious about intentional change for you and throughout your organisation, some questions you might ask: If our most senior leaders don’t model the behaviours we’re training, what’s the real message the organisation will hear? What percentage of what we teach will be reinforced - or contradicted - in our current reward, recognition & promotion systems? Are we trying to change leaders - or are we trying to change leadership culture? If we never ran a training session, but only changed how managers lead meetings, make decisions, and handle conflict - what cultural shifts would happen anyway? What part of our current culture would fight against the behaviours we want after training? Whose behaviour would have the biggest impact if it changed - & are they signed up for development themselves? What visible signals are we sending every day that will either make this training matter - or make it irrelevant? Are we building skills for individuals - or designing experiences that build collective leadership muscle? If leadership habits don’t change at the executive table, what happens to the credibility of this entire initiative? In 12 months, would employees say leadership feels more consistent, more human, and more accountable - or would they say ‘some managers are better but not much else has changed’? Happy to be wrong but with a focus on these, I wonder how differently training and organisations might evolve to be in the times we’re in.

"We brought in a trainer for two days and nothing changed." Of course it didn't. You treated training like a checkbox activity. Sales leaders constantly make this mistake: → Hire external trainer for 2-day workshop → Everyone gets excited during sessions → 30 days later, zero behavior change → "Training doesn't work" Wrong. Your approach to training doesn't work. Here's what actually happens: Day 1: Reps are pumped. Taking notes. Asking questions. Day 2: Still engaged. Ready to implement everything. Day 30: Back to old habits. Zero retention. Why? Because you treated symptoms, not the disease. You didn't change their daily habits. You didn't provide ongoing reinforcement. You didn't build systems for accountability. Real training that creates lasting change looks different: #1 It's diagnostic first. Before any training, you identify specific skill gaps through call reviews, deal analysis, and performance data. Not generic "they need better discovery" but specific "they ask surface level pain questions but never uncover business impact." #2 It's delivered in sprints. Six weeks of twice-weekly sessions beats a 2-day workshop every time. Reps can practice between sessions, get feedback, and build muscle memory. #3 It includes reinforcement systems. Weekly coaching calls, peer practice sessions, and manager check-ins. The learning doesn't stop when the trainer leaves. #4 It measures behavior change, not satisfaction scores. "Did you like the training?" is worthless. "Are you now asking better discovery questions?" matters. #5 It provides job aids and frameworks. Reps need cheat sheets, email templates, and conversation guides they can reference in real situations. Most importantly: It's customized to your specific challenges, not generic sales advice. The companies that see 40%+ improvement in performance don't do one-off training events. They build learning into their culture. They have weekly skill-building sessions. They do call reviews with specific feedback. They practice objection handling until it's automatic. Stop buying training like it's a magic pill. Start building capability like it's a muscle that needs consistent exercise. Your reps deserve better than motivational speeches that wear off in a week. — Tired of wasted training budgets? I'll design a performance improvement system that actually creates lasting behavior change. Book a diagnostic: https://lnkd.in/ghh8VCaf