This educational module outlines strategies for organizations to quantify and improve their workplace culture and human capital management. It details how to implement an Engagement Pulse Survey for continuous sentiment measurement, establish a Turnover Risk Model to identify employees likely to leave, and construct a DEI Scorecard to track diversity, equity, and inclusion metrics across various stages of the employee lifecycle. The module culminates in the creation of a People Health Dashboard that consolidates these insights with traffic-light alerts, enabling data-driven decision-making for a healthier, more productive workforce. The material also addresses common pitfalls and provides practical homework assignments to facilitate immediate application of the concepts.

The main goal of People & Culture Analytics is to transform abstract concepts like "culture" and "engagement" into measurable data. This allows organizations to proactively identify and address critical issues such as employee disengagement, turnover risk, and diversity, equity, and inclusion (DEI) gaps, much like they track product metrics. The ultimate aim is to improve organizational health, reduce "regretted attrition" (avoidable employee departures), and foster a more inclusive and productive work environment.

An Engagement Pulse Survey is a brief, frequent survey designed to continuously measure employee sentiment without causing "survey fatigue." It consists of typically five core questions, such as "I understand how my work contributes to company goals" or "I receive recognition for good work," and one open-ended question like "What could we improve next quarter?" Responses are typically on a 1-5 Likert scale, which are then averaged and multiplied by 20 to yield a 0-100 Engagement Score. These surveys are anonymous, mobile-friendly, have a short 3-day response window, and are administered frequently, usually every 60 days, to provide actionable data.

A Turnover Risk Model is a predictive tool that identifies employees who are at a higher risk of leaving the organization. It uses various "input features" such as an employee's tenure, engagement trend, manager eNPS (Employee Net Promoter Score), promotion wait-time, compensation percentile, commute/remote status, and PTO (Paid Time Off) balance. Using statistical models like logistic regression, it assigns a probability of an employee being "at-risk," "monitor," or "low risk." This enables HR Business Partners to proactively schedule "stay interviews" and develop mitigation plans for high-risk individuals, significantly reducing unwanted employee attrition.

A DEI Scorecard provides a structured view of an organization's diversity across different departments and role levels. It tracks key metrics like Gender %, Under-represented Ethnicity %, and Disability %, and critically, the "90-day Hire-to-Promotion ratio." Data for the scorecard is sourced from the Applicant Tracking System (ATS), Human Resources Information System (HRIS), and self-identification surveys. The scorecard aims to compare actual representation against local labor-market benchmarks (within ±10 percentage points) and visualizes the "funnel" from applicant to promotion to identify stages where diversity "bleeds" most. Red cells in the scorecard trigger corrective actions such as inclusive-hiring training or developing pipeline partnerships.

A People Health Dashboard consolidates all vital People & Culture Analytics into a single, live interface, providing a holistic view of organizational health. Key "widgets" or components typically include:

Ethics and privacy are paramount when dealing with sensitive employee data. Key considerations include:

Several challenges can arise during implementation:

To start, an organization can undertake several practical homework assignments:

This briefing outlines key strategies for leveraging data to gain actionable insights into an organization's people and culture. It emphasizes a shift from subjective "culture talk" to objective, measurable metrics, allowing organizations to track, trend, and improve critical aspects like engagement, diversity, and talent retention.

The overarching theme is to treat organizational culture and human capital with the same rigor and data-driven approach typically applied to financial metrics. As the instructor poses, "Would you rather learn about a key employee’s resignation from your dashboard—or from their goodbye email?" The goal is to move from reactive responses to proactive, data-informed interventions. The mantra encapsulates this: "Healthy companies measure culture like cash—frequently and objectively."

The framework proposes four core components for building robust people analytics:

The briefing acknowledges common challenges and offers solutions:

The briefing concludes with practical homework assignments, encouraging immediate application of the learned concepts: drafting pulse surveys, running quick turnover models, building DEI scorecard skeletons, and mocking dashboards. This reinforces the actionable nature of the program, aiming to "transform fuzzy ‘culture talk’ into hard numbers you can track, trend, and improve."

Transcript:

00:00 Would you rather learn about a key employee's resignation from your dashboard or from their goodbye email? Yeah. It sounds like a simple choice, doesn't it? It really does. But it's amazing how many organizations, I mean, there's been millions on tracking product metrics, yet they're basically flying blind when it comes to really crucial internal stuff. Exactly. Like engagement, DEI, turnover risk.

00:25 All that critical people data. So what's the payoff for listening today? What are we going to help people figure out? Well, the goal here is to show you how to move past that sometimes fuzzy culture talk and turn it into actual hard numbers. Numbers you can track, you can see trends in, and most importantly, you can actually improve. Okay. This deep dive is about getting practical tools to measure and act on your organization's, let's call it, people health.

00:50 Love that. Okay. So our mission today to get actionable insights into that people health, we're hitting four key areas, right? That's a plan. First, the engagement pulse survey, how to do it without driving everyone crazy. Second, the turnover risk model, predicting who might leave and why. A big one. Third, building a clear DEI scorecard, making progress you can actually see. Yep. Measurable progress.

01:15 And finally, pulling it all together into a live people health dashboard. That unified view. Exactly. The whole picture. All right. Let's dive into the first one. Taking the pulse. How do we measure how employees are feeling without causing total survey fatigue?

01:32 That seems like the first hurdle. It really is. And that's precisely why the Engagement Pulse Survey is designed to be, well, different. It's not about exhaustive annual surveys. Okay. Its core purpose is to measure sentiment continuously but lightly. We're talking about a really short, like, five-question survey. Just five? Just five. Quick enough to get regular feedback without it feeling like a chore. Okay. The source material gives us five specific questions. Walk us through those. What makes these five so effective?

02:00 Sure. These aren't random. Each one probes a key aspect of organizational health. First, I understand how my work contributes to company goals. Okay, that's alignment. Exactly. It's about feeling connected to the mission. Second, I have the tools and resources to do my job well. That's enablement.

02:18 Makes sense. Roblox kill motivation. Totally. Third, I receive recognition for good work. Appreciation, right? Huge driver. Absolutely. Fourth, I see a path for growth here.

02:30 This taps into future prospects, career visibility, a big reason people look elsewhere. And the fifth is crucial. It's open ended. What could we improve next quarter? This gives employees a direct voice for suggestions. OK, got the questions. How do you actually run this thing effectively? Logistics matter here.

02:49 They really do. Key things. Make it anonymous. That builds trust. Essential. Make it mobile friendly. People need to do it easily wherever they are. Keep the response window tight, maybe three days. Okay. And the cadence is key. Run it consistently, say every 60 days. That creates a rhythm, allows you to track trends reliably. And scoring. How does that work? Super straightforward. The first four questions use a standard one to five Likert scale. Like strongly agree to strongly disagree.

03:16 Exactly. You average the scores for those four, multiply by 20, and boom, you get a 0 to 100 engagement score. Simple, clear. And that score translates into action thresholds. Precisely. We use benchmark categories. 80 or above, you're green, healthy engagement. Good place to be. Definitely. 70 to 79, that's amber. Means, you know, keep an eye on things. Maybe some errors need attention. Below 70, that's red. That's your signal for immediate serious focus. It tells you where action is needed now.

03:44 So a clear signal. Okay, listener, quick pause for thought. What was your last measured engagement score, if you even have one? Something to think about. All right, so we've got a pulse on engagement. Now let's get into something really fascinating. Predicting the future, almost. Using data to spot employees who might be flight risks before they resign. Yeah, this is where things get really proactive, moving beyond just reacting to turnover. How does a model even begin to predict something like that? What signals does it look for?

04:13 It learns from historical data, looking for patterns. Key input features often include things like the employee's tenure. How long they've been there. Right. Their engagement trend are their scores going up or down, their manager's ENPS. That's the employee net promoter score. Yeah. Basically, would you recommend your manager? Also, how long since their last promotion, their compensation percentile compared to market. Okay. Even things like commute time versus remote status and their PTO balance, are they taking enough time off?

04:43 Wow. Lots of factors. Now, the sources mentioned tech like logistic regression or gradient boost models. Can you demystify that a bit? What are these models actually doing?

04:53 Think of them as sophisticated pattern matching machines. They weigh all those different inputs, tenure, pay, engagement trend based on past employees who left. They calculate a probability score for each current employee based on those learned patterns. And you don't need like massive big data necessarily. A typical HR system export, maybe 5,000 rows or so, can often be enough to train a decent starting model.

05:19 So the model spits out a probability. How do you translate that into, is this person actually a risk? You set clear thresholds based on the probability score. For instance, a probability of 0.45 or higher might flag someone as at risk. High likelihood. Exactly. Maybe 0.25 to 0.44 is monitor, keep a closer eye, maybe check in. And below 0.25 is low risk. It gives HR managers clear tiers to work with.

05:45 So you have the risk tiers. How does that turn into action? What's the alert workflow look like? It needs to be timely. Often it's a weekly automated script. It writes that risk tier at risk monitor right into the main HR system. So it's visible. Right. And then crucially, it triggers an alert, maybe a direct Slack message to the relevant HR business partner, giving them the heads up and the context. So HR knows immediately who needs attention.

06:09 That's the idea of proactive intervention. OK, but let's talk ethics and privacy here. Predicting who might leave.

06:16 That feels like it could get tricky fast. How do you build these models fairly? Absolutely critical point. You never use protect characteristics like gender or ethnicity as direct input features for the model. That's rule number one. And you have to rigorously test for bias, like check if the model performs equally well across different demographic subgroups. You don't want it unfairly flagging certain groups. Makes sense. And what happens when someone is flagged? What's the process?

06:41 There should be a clear intervention SOP, a standard operating procedure. It usually involves the HR business partner scheduling a stay interview promptly, say within seven days. A stay interview, not an exit interview. Exactly. The goal is to understand why they might be at risk and what can be done to improve their situation before they decide to leave.

07:01 OK. The HRBP logs a mitigation plan based on that conversation. Maybe it's about growth, maybe resources, maybe recognition. And then they follow up, say, 30 days later to see how things are going. That sounds much more supportive. And the sources mentioned a story, right? A manufacturing plant that saw a big drop in attrition. Yeah, a great example. They implemented this kind of data driven stay interview rhythm and cut regretted attrition, losing people they wanted to keep by 35 percent. That's huge.

07:29 Wow, 35%. That really shows the potential impact. But getting managers comfortable with state interviews, is that a challenge? It can be initially. It requires a shift in mindset. It's about training managers on how to have these conversations constructively. It's not an interrogation. It's about listening, understanding, and genuinely trying to help.

07:49 Building that trust is key. Absolutely. Trust and good training. Okay. So we've covered engagement and turnover risk. Let's pivot to another really crucial area. Diversity, equity, and inclusion. DEI. How do we move beyond just talking about it and make it measurable? Yeah. Good intentions aren't enough here. Data brings clarity. That's where a DEI scorecard comes in. It provides a structured way to track progress.

08:13 What does that structure typically look like? What's on the scorecard? It usually breaks things down by, say, department and role level. Then you track key metrics like gender percentage, underrepresented ethnicity percentage, maybe disability percentage. So representation numbers. Exactly. But also crucial is tracking movement. A really insightful metric is the 90 day hire to promotion ratio.

08:34 Ah, so are diverse hires actually moving up? Precisely. It shows if your inclusion efforts translate into equitable growth opportunities. Where does all this data actually come from? It's pulled from a few key systems. Your ATS applicant tracking system. For hiring data. Right. Your main HRIS human resources information system for current employee demographics and promotions. And often, voluntary self-identification surveys play a big role too, especially for things like disability status. Got it.

09:03 And is there a target? What does good look like for representation? A common approach is to set a practical goal. Aim to be within, say, plus or minus 10 percentage points of the relevant local labor market benchmark for different groups. So comparing yourself to the available talent pool in your area. Exactly. It gives you an objective benchmark, not just an arbitrary internal target.

09:26 How do you make this data easy to understand and act on? Visualization must be key. Definitely. Stacked bar charts are great for showing representation across different departments or levels at a glance. But the real power often comes from a funnel visualization. This tracks diversity percentages at each stage. Applicant interview, hire, promotion. So you can see where the drop-offs happen.

09:47 Exactly. It immediately highlights where in your talent pipeline you might be losing diverse candidates or where barriers might exist. It pinpoints the leaks. And if you see a problem area, a red cell on the scorecard or a big drop in the funnel, what happens then?

10:03 Those red flags trigger specific actions. It's not just data for data's sake. A red cell might trigger, say, rolling out mandatory inclusive hiring training for managers in that department. OK, targeted action. Or maybe it leads to developing new partnerships with organizations focused on underrepresented talent pipelines if your applicant pool isn't diverse enough. The data guides the intervention.

10:25 Makes sense. So another moment for reflection for you listening. Looking at your own organization, which stage of that talent funnel, applicant, interview, hire, promotion do you suspect might be losing the most diversity? All right, we've got engagement pulses, turnover predictions, DEI scorecards, powerful pieces individually. But the real magic happens when you bring them all together, right?

10:48 Absolutely. That's the whole idea behind the unified people health dashboard. It integrates these different streams into one holistic view. So what does that dashboard actually look like? What are the key widgets or components you'd expect to see?

11:01 You definitely see the engagement score trend line showing how sentiment is changing over time. Okay. You'd have the turnover risk heat map probably filterable by team, tenure, maybe location showing where the risks are concentrated. Visualizing the hotspots. Right. Then the DEI metrics, those bar charts showing representation and that crucial funnel visualization tracking the pipeline. Got it. And another useful one is often a PTO burndown chart. Are people actually taking their vacation time? Low usage can be an early burnout signal.

11:31 That's a clever one. Yeah. And it's all presented simply. Yeah. Using that clear traffic light logic, green, amber, red for each key metric, it's designed to be a quick, digestible weekly digest for executives. So leaders can see the overall people health status at a glance. Exactly. No more digging through multiple reports. It's all there, synthesized. And you mentioned something about linking red KPIs to procedures.

11:55 Yes, this is really powerful. Imagine each red metric on the dashboard isn't just a warning light, but it's also a link. A link to what? A link to a Relevant Corrective Standard Operating Procedure, or SOP, stored in what you could call an SOP codex or a plea book. So if engagement is red? It could link directly to the SOP for implementing a better recognition program or the guide for running effective team listening sessions. It connects the problem directly to a documented solution or process.

12:24 That closes the loop from insight to action brilliantly. It really helps ensure that the data leads to consistent thought-through responses. Okay, this all sounds fantastic, but let's be real. Implementing this kind of data-driven approach isn't always smooth sailing. What are some common pitfalls organizations run into?

12:43 Oh, definitely. Yeah. We already touched on one big one. Survey fatigue. Right. People just stop answering. Exactly. The mitigation, as we said, is keep those poll surveys short, five questions max, and maybe rotate one question quarterly to keep it fresh while maintaining core trends. Makes sense. What else? Another big one is building or sometimes breaking data trust with employees. How so?

13:07 Well, if employees give feedback, especially on those open-ended questions, and they never hear anything back or see any changes. I feel like it just went into a black hole. Precisely. And they stop trusting the process, stop giving honest feedback. The best way to counter this is radical transparency. Meaning? After each Pulse survey cycle, share a you-said-we-did summary, show people, here's the feedback themes we heard from you, and here are the specific actions we're taking or investigating as a result. Ah, closing the feedback loop visibly.

13:37 It's critical for building and maintaining that trust. People need to see their input matters. OK, what about straight up privacy fear? Employees getting nervous about all this data being collected and analyzed about them. Yeah, that's understandable. A few things help here. Using a reputable external third party vendor for surveys can create a buffer and enhance perceived anonymity.

14:00 OK, using a neutral party. And being really clear about anonymity thresholds. For example, guaranteeing that results for a specific team or demographic cut will only be shown if there are, say, at least five responses. So individuals can't be identified. Exactly. Protecting individual privacy is non-negotiable while still allowing for aggregated insights that drive improvements for everyone.

14:22 It sounds like transparency and clear communication are threads running through all these mitigations. They absolutely are. Being open about what data is collected, why it's collected, how it's used, and crucially, how it's protected is fundamental. So bringing it all together, there's a core idea here, isn't there? A kind of mantra.

14:40 I think so. It's that healthy forward thinking companies measure their culture with the same rigor they measure their cash flow. Measure culture like cash frequently and objectively. I like that. It's about moving people insights from the nice to have column to the must have business critical column. And just to add a crucial layer. Go on.

15:01 All these powerful metrics rely on sensitive employee data, sometimes customer data, too. So protecting that data through robust cybersecurity and privacy practices isn't just a compliance issue. It's foundational. It's absolutely foundational to maintaining the trust that makes any of this work possible in the first place. That's probably a whole other deep dive, though.

15:21 Definitely. OK, so a final thought for everyone listening. As you wrap up your day or head into your week, maybe start thinking, what would your five question pulse survey look like? Could you start sketching out the inputs for a turnover risk model or maybe just mock up a simple DEI scorecard or a basic people health dashboard on paper? Yeah, just starting to visualize it.