Using Data to Inform Team Goal Setting

Bad goal setting can cripple your business (I know from firsthand experience). Here's how to set goals that propel your business forward. Step 1: Analyze last year’s performance. You can’t set the right goals without the correct information. So, take some time to gather data from the previous year to find areas of strength and weakness. Look at your: Revenue streams — what are your most profitable areas? Your biggest cost centers? Sales & marketing — can you spot trends in customer acquisition or marketing ROI? Operations — where is your business bottlenecked? Where might you be overstaffed? Employee performance — look at productivity and churn. Which direction are things going? — Step 2: Brainstorm areas for improvement. Write down all the possible things you could work on. This is a great group activity for your leadership team or even the whole company (depending on your size). The data you’ve collected in step 1 should give you some idea of opportunity areas. One tip: don’t discount an idea just because it’s hard. Often the biggest impact things are hard to do. But you should be realistic about the effort required to get something done, and its chances of success. — Step 3: Set SMART goals Specific: Define clear and precise goals. Instead of saying "increase sales," say "increase sales by 12% in the next 6 months." Measurable: Ensure each goal has quantifiable metrics. E.g. "Reduce customer acquisition costs by 15% by the end of the year." Achievable: Set realistic goals based on your resources, budget and other constraints. E.g. if you have limited cash, avoid goals that would severely impact your monthly cash flow. Relevant: Align goals with your overall business objectives. Ensure they address the key areas for improvement identified earlier. Time-bound: Set deadlines for each goal. E.g. "launch a new service by Q3." — Step 4: Develop an Action Plan For each goal, create an action plan that outlines: Steps and Milestones: Break down each goal into smaller, manageable tasks. Set milestones to track progress. Resources: Identify the resources needed (time, money, personnel) and ensure they are available. Responsibilities: Assign tasks to specific employees. Ensure everyone understands their role and what is expected of them. Timeline: Establish a timeline with deadlines for each task and milestone. Doubling down on one point there: always assign tasks to a single person. They can still bring in other people to contribute, but it’s one person’s responsibility to get it across the finish line. — Step 5: Monitor and Adjust Goals are not static. Regularly check your progress, and adjust based on new insights or changing circumstances. Schedule monthly and/or quarterly reviews to keep everything on track. Having a simple KPI tracker is a good way to keep tabs on things. Make sure you’re regularly checking in, and ask people to flag any roadblocks or necessary adjustments as soon as they identify them.

In schools today, we’re surrounded by a plethora of data - from assessments and observations to a variety of dashboards and feedback loops. But data only matters if it informs what we do next. That’s why here at American International School of Guangzhou we’ve developed the 𝐅𝐀𝐂𝐓𝐒 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 – a structured process designed to help teams move from data collection to meaningful action. FACTS guides us to: 🔎 𝐅𝐨𝐜𝐮𝐬 on the data that matters most 📊 𝐀𝐧𝐚𝐥𝐲𝐳𝐞 insights and gaps 🎉 𝐂𝐞𝐥𝐞𝐛𝐫𝐚𝐭𝐞 successes and positive trends 🎯 𝐓𝐚𝐫𝐠𝐞𝐭 strategies and interventions 🚀 Define clear 𝐒𝐭𝐞𝐩𝐬 for action and accountability We’ve recently rolled this out with faculty, middle leaders, senior leadership - as well as with our Operations Team. All with the goal of shifting the way we talk about and act on data across the whole school. A strong data protocol matters because it: * 𝐄𝐧𝐬𝐮𝐫𝐞𝐬 𝐮𝐧𝐢𝐟𝐨𝐫𝐦𝐢𝐭𝐲 – establishing consistent guidelines and vocabulary, keeping coherence across departments and educators. * 𝐂𝐮𝐥𝐭𝐢𝐯𝐚𝐭𝐞𝐬 𝐭𝐞𝐚𝐦𝐰𝐨𝐫𝐤 – giving staff a shared approach that elevates teaching and learning collaboratively. * 𝐅𝐚𝐜𝐢𝐥𝐢𝐭𝐚𝐭𝐞𝐬 𝐢𝐧𝐟𝐨𝐫𝐦𝐞𝐝 𝐜𝐡𝐨𝐢𝐜𝐞𝐬 – empowering decision-makers to rely on dependable data and implement strategies that truly improve student learning. Just as importantly, a protocol helps us 𝐝𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐯𝐚𝐥𝐮𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚 itself: Does it suit our needs? Are there important data points missing? Can we find a way to access them? Having vast amounts of data is one thing - having useful data is another. A protocol like FACTS ensures we make that distinction quickly and clearly. We’ve also dedicated significant time to our school improvement plan: our 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤. FACTS helps us implement, monitor, and analyse its impact with greater clarity. By running the full protocol, we ensure every data dive is structured, organised, and results in actionable steps - not just endless exploration. And beyond the walls of our classrooms and offices, data also helps us 𝐞𝐧𝐠𝐚𝐠𝐞 𝐨𝐮𝐫 𝐰𝐢𝐝𝐞𝐫 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 - celebrating successes, building trust, and showing the impact of our collective efforts. Ultimately, regardless of the protocol you use, the true value lies in the cycle itself - structured, collaborative, and action-driven. It’s this cycle that turns information into impact, ensuring data is never for its own sake, but always driving improvement, strengthening our community, and helping every student thrive.

As organizations feverishly plan the next year, it presents a vital opportunity for data teams to shape and drive this process analytically. It is one of their key jobs-to-be-done. But, what does this look like? Let's consider a base financial model that outlines the desired direction for the business. The metrics of interest at this level are usually the highest-level outputs such as revenue and costs. 1) Breakdown Outputs: The first area where a data team can help is in breaking down these outputs into more granular and operational input pieces. How should we assess the contributions from various cohorts of users or accounts? From existing or new product lines? From new features? From different markets? By increasing supply? By driving engagement? By improving application performance? Or upgrading the operations? Data teams as one of very few teams with a holistic view of the business, can translate these top-line KPIs into targets for specific teams. 2) Resolve Conflicts: A second role for data teams is identifying and resolving conflicts. It is tempting to want all metrics to move up and to the right - but in reality, metrics are often in conflict. For instance, if you focus on driving traffic, you may see a drop in conversion rates. If you want to drive higher revenue per account, expect higher churn. If you want to improve margins, new acquisition efforts may slow down. Balancing these metric equations is vital for establishing metric goals, as failing to do so can demotivate even high performing teams who will struggle to connect their work to overall progress. 3) Inform Trade-offs: Data teams can help in making informed trade-offs. Drawing upon their experience of what’s worked, they can shape strategy discussions. A consequence of this is focus - deciding what to worry about, and what to de-prioritize can be liberating for operational teams. All these pieces of work are ultimately accomplished with a significant amount of data and code. Apart from spreadsheets or notebooks, which are both do-whatever-you-please tools, there aren’t many options for analytics or business teams. The flip side of having open-ended flexibility is that these operations are expensive - requiring experts to hand-craft queries, retrieve data, build models, and execute calculations. In practice, due to these productivity constraints, the planning process usually does not end up as analytically rigorous as desired. Worse yet, it can be half-baked where executives believe they are thorough, but the numbers are backed by false precision. All said, it is worth noting we are just getting started. Data teams are playing a greater role in shaping how organizations debate strategy, allocate capital, make bets, create plans, establish tactics, and set and monitor metric goals. I’m excited to see this elevate the visibility and ROI of data teams.

In my first year leading campus recruiting, I had to set my own hiring goals. Predicting demand 12 months out in a constrained headcount world was a challenge. Here’s how I used data to build a headcount plan in 3 steps: 📈 Step 1: Estimate Monthly Planned Headcount by Function for the Next 18 Months I used the sales forecasting approach we had mastered in the supply chain. Even though I was estimating staffing demand, it followed a similar seasonal pattern to sales, growing at a similar rate. The only big change was adjusting for demand – engineering teams couldn’t hire enough whereas business teams were operating within a strict budget. 📉 Step 2: Estimate Monthly Attrition by Function and Level for Those 18 Months Before we had a long-range attrition forecast, I had to guess how many backfills we needed to add. Fortunately, I had access to attrition and promotion data. Mapping monthly or quarterly attrition percentages to the planned headcount produced a good estimate for the number of additional roles to be filled. 📊 Step 3: Calculate Recruiting Capacity Needed to Fill All Roles Once all of the roles are added together, the easiest way to figure out how many recruiters you need is to divide hires needed each month by expected hires per recruiter. But with data, you can get much more accurate while setting activity goals early. Looking at application-to-hire ratios for each role, then mapping activity needed to process those applications/screens/offers each month, can show you what’s feasible. It’s how we almost doubled the number of hires per recruiter without increasing workload. A lot of recruiting leaders are being asked to do more with less this year. The best way to set expectations, and exceed performance targets, is to start with data. I was brand new to recruiting when I ran this for the first time. Drop a comment below if you’ve found a more effective approach! #hiring #headcount #planning

Most teams don’t fail because they lack data. They fail because they set vague goals that lead nowhere. Here’s a common example: “We need to know how our competitors are pricing their products.” Sounds strategic… but it’s not. It’s actually a dead end. A good goal doesn’t just collect information, it connects data to decisions. Instead, try: “𝐁𝐮𝐢𝐥𝐝 𝐚 𝐰𝐞𝐥𝐥-𝐢𝐧𝐟𝐨𝐫𝐦𝐞𝐝 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲.” Now you're framing the research around an actual business outcome. Good goals are clear and purposeful. They ask the right questions, like: “𝘏𝘰𝘸 𝘩𝘢𝘷𝘦 𝘰𝘶𝘳 𝘵𝘰𝘱 𝘵𝘩𝘳𝘦𝘦 𝘤𝘰𝘮𝘱𝘦𝘵𝘪𝘵𝘰𝘳𝘴 𝘱𝘳𝘪𝘤𝘦𝘥 𝘵𝘩𝘦𝘪𝘳 𝘦𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦 𝘱𝘭𝘢𝘯𝘴? 𝘞𝘩𝘢𝘵 𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘩𝘢𝘷𝘦 𝘵𝘩𝘦𝘺 𝘮𝘢𝘥𝘦 𝘳𝘦𝘤𝘦𝘯𝘵𝘭𝘺?” They also clarify who needs to be involved: in this case, product and sales leadership. And most importantly, they lead to something tangible: 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. Poor goal-setting leads to research that sits in a slide deck. Good goal-setting drives action.

The unprecedented proliferation of data stands as a testament to human ingenuity and technological advancement. Every digital interaction, every transaction, and every online footprint contributes to this ever-growing ocean of data. The value embedded within this data is immense, capable of transforming industries, optimizing operations, and unlocking new avenues for growth. However, the true potential of data lies not just in its accumulation but in our ability to convert it into meaningful information and, subsequently, actionable insights. The challenge, therefore, is not in collecting more data but in understanding and interacting with it effectively. For companies looking to harness this potential, the key lies in asking the right questions. Here are three pieces of advice to guide your journey in leveraging data effectively: 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟏: 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐆𝐨𝐚𝐥-𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 • Tactic 1: Define specific, measurable objectives for each data analysis project. For instance, rather than a broad goal like "increase sales," aim for "identify factors that can increase sales in the 18-25 age group by 10% in the next quarter." • Tactic 2: Regularly review and adjust these objectives based on changing business needs and market trends to ensure your data queries remain relevant and targeted. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟐: 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐂𝐫𝐨𝐬𝐬-𝐃𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭𝐚𝐥 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 • Tactic 1: Conduct regular interdepartmental meetings where different teams can present their data findings and insights. This practice encourages a holistic view of data and generates multifaceted questions. • Tactic 2: Implement a shared analytics platform where data from various departments can be accessed and analyzed collectively, facilitating a more comprehensive understanding of the business. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟑: 𝐀𝐩𝐩𝐥𝐲 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 • Tactic 1: Utilize machine learning models to analyze current and historical data to predict future trends and behaviors. For example, use customer purchase history to forecast future buying patterns. • Tactic 2: Regularly update and refine your predictive models with new data, and use these models to generate specific, forward-looking questions that can guide business strategy. By adopting these strategies and tactics, companies can move beyond the surface level of data interpretation and dive into deeper, more meaningful analytics. It's about transforming data from a static resource into a dynamic tool for future growth and innovation. ******************************************** • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Setting effective goals is challenging, especially at the scale of a company like Meta. In a recent blog post, Meta’s analytics team shares their approach to goal setting through a tool called the Goal Map: a conceptual framework that connects team-level metrics to company-wide outcomes, helping teams prioritize the right work and measure impact more effectively. Here’s how it works: teams align their goals with broader objectives, use a tiering system to prioritize what matters most, set targets that balance ambition and achievability, and track progress through experiments, milestones, and long-term trends. Everything ties back to the Goal Map—ensuring that all efforts remain connected to meaningful outcomes. The result is greater focus, stronger coordination, and more informed decision-making across teams. It’s a valuable framework for any data-driven organization looking to scale without losing sight of its strategic goals: a recommended reading. #DataScience #Analytics #BusinessMetrics #GoalSetting #ProductAnalytics #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gHFkBEPY

Are you making progress, or just moving through tasks? Activity looks productive but Outcomes tell the truth. It’s easy to fill your calendar and inbox. But real growth comes from aligning effort with impact. High-performing teams don’t just stay busy. They use systems like OKRs and KPIs to stay focused, accountable, and results-driven. OKRs = Direction (Objectives & Key Results) ➟ Set clear, outcome-based objectives ➟ Define measurable key results ➟ Align efforts across teams ➟ Focus on what moves the business, not just what gets done KPIs = Validation (Key Performance Indicators) ➟ Track the few metrics that truly matter ➟ Use real-time insights to steer decisions ➟ Replace gut feelings with data ➟ Bring clarity to what success actually looks like How to apply OKRs effectively: 1. Anchor objectives in outcomes, not tasks 2. Set 2–4 measurable key results 3. Align across functions to eliminate silos 4. Review often—refine based on what’s real How to make KPIs useful, not reactive: 1. Choose metrics tied to your goals 2. Set clear targets with timelines 3. Monitor in real time, not just quarterly 4. Use insights to adjust, not just report OKRs define where you’re going. KPIs confirm you’re getting there. It’s not about doing more. It’s about measuring what matters. How are you using OKRs and KPIs in 2025? Share what’s working—or what still feels unclear. Let’s swap insights. 📌 Save this for your next planning session 🔁 Repost to help your team align with purpose 👤 Follow Jay Mount for more strategy systems that drive real outcomes

"We'll reach every village by next year," the team promised. Six months later, they’d reached just three out of fifty. The problem wasn’t lack of effort—it was setting targets in the dark. Good intentions don’t make good targets—data does. Learn how to set realistic, achievable targets with these tips ✔ Start with a Baseline If you don’t know where you’re starting from, you can’t set realistic targets. Collect baseline data first. ✔ Make Targets Specific & Measurable "Reach every village" is vague. A better target: "Reach 25 villages with clean water access by Year 2." ✔ Align Targets with Available Resources Do you have enough funding, staff, and infrastructure to meet your goals? Match ambition with capacity. ✔ Break Big Goals into Milestones Instead of "50 villages in a year," set quarterly targets to track progress and adjust as needed. ✔ Factor in Context & Barriers Travel, cultural norms, and government approvals can slow progress. Build flexibility into your targets. ✔ Involve Stakeholders Early Communities, local leaders, and funders should help shape realistic targets. Co-create goals for better buy-in. ✔ Review & Adjust Targets Regularly If external factors change, so should your targets. Monitor progress and refine goals accordingly. 🔔 Follow me for more tips ♻️ Sharing is caring #targets