Workforce Forecasting Techniques

🌟 Day 4 – Forecasting Basics How do you know how many calls, chats, or emails to expect tomorrow? 👉 That’s Forecasting—the foundation of Workforce Management (WFM). At its core: Forecast = History (baseline) + Trend + Seasonality + Event Adjustments + Judgment 🔍 Why Forecasting matters (in plain terms) Without a forecast, everything else is guesswork: Capacity Planning: You can’t know how many people you need. Scheduling: You don’t know which hours need extra coverage. Real-Time Management: You can’t tell if you’re off-track or on-target. Reporting: You can’t measure if the plan was realistic. 🧭 What goes into a good forecast - Baseline History- Start with apples-to-apples data (same channel, same handle type). Use the closest comparable days (e.g., last 6–8 Mondays for next Monday). - Trend - Are volumes growing or shrinking month over month? Apply a gentle up/down adjustment (e.g., +2% MoM). - Seasonality - Intra-week: Mondays heavier than Fridays? Intra-day: 11:00–13:00 peak every day? Keep a pattern profile so you can shape the daily forecast by 15/30-minute intervals. - Events & External Drivers - Holidays, promos, product launches, price changes, outages, weather. Each can add/subtract volume. Use an uplift/deflation percentage based on past, similar events. - Judgment & Business Intel - Talk to Marketing, Product, and Ops. Numbers + context beats numbers alone. 🧪 Mini example (numbers you can follow) Baseline: Last 4 Mondays ≈ 10,000 calls This Monday is a holiday: Past similar holiday = +15% uplift Marketing email scheduled 10:30: Past emails add +8% for 2 hours Day total: 10,000 × 1.15 = 11,500 base for the day Apply short, time-boxed +8% uplift 10:30–12:30 to those intervals only. Shaping by intraday pattern (illustration): If 12% of Monday’s calls typically arrive 11:00–12:00, that hour ≈ 11,500 × 12% = 1,380 calls (then layer the +8% marketing effect inside that window). You now have a time-sliced forecast (by 15/30/60-min intervals), not just a day total—this is what schedulers need. 🎯 How to check if your forecast is any good MAPE (Mean Absolute Percentage Error): Average error size. Bias (Over/Under): Do you consistently over- or under-forecast? Hit Rate: % of intervals within a target error band (e.g., ±10%). Track these by channel and by interval, not just daily totals. A perfect day can still hide ugly peaks. 📌 Takeaway: Forecasting is educated prediction—never perfect, always essential. Get close, shape it by interval, adjust for real-world events, and learn fast from misses. That’s how you keep customers happy, and costs controlled. #WorkforceManagement #WFM #Forecasting #ContactCenter #CustomerExperience #BusinessEfficiency #Scheduling #CapacityPlanning #RTA #OperationsExcellence #Analytics #DataDriven

I know it's only June, but headcount planning is right around the corner. Based on 100s of discussions with finance and talent leaders, here are 4 tips for getting headcount planning right this year. 1/ Use scenario analysis to create and modify strategic workforce plans Scenario analysis allows business leaders to forecast headcount. Within the strategic workforce plan, scenario analysis can include how many people to hire, when to hire, how much to pay, what equity to offer, and so on. Here’s an example: Gusto’s team prepares scenarios based on high, medium, and low hiring budgets. They use the budget as the fixed input in each case and simulate the team composition. Then, they forecast the outcomes that the new team composition can deliver. This creates a clear line of sight between the hiring budget and business outcomes, based on which they can make decisions. 2/ Unify your headcount data Good scenario analysis incorporates all your ‘knowns.’ This could be any or all of the following: -Team composition: Employees, designations, and their reporting managers -Compensation: Salaries, bonuses, and stock options -Working model: In-office, remote, or hybrid -Location: Regional compensation trends and currency conversions -Hiring pipeline: approved roles, hiring in progress, offers sent and accepted, and backfills and internal transfers -Analytics: Offer-accept rates, burn rates, and attrition rates Does all this data live in disconnected systems, such as ATS, HRIS, and finance tools, that don’t talk to each other? If yes, it’s time to start evaluating strategic workforce planning tools to unify them. 3/ Empower business leaders to scenario plan At many companies today, if a department head wants to run three different scenarios to demonstrate their respective budget impact, they need to seek a financial analyst’s help. That usually leads to infinite back-and-forth of slightly altered spreadsheets. This is time-intensive and forces highly paid resources like VPs and Directors to do low-value, manual work. Instead, give them a self-service way to create different scenarios, see the budget impact, and adjust other variables like roles, compensation, or start date. Now they can design and modify their perfect strategic workforce plan. Finance and HR simply serve as collaborators and approvers rather than needing to perform a bulk of the work. Everybody moves faster. 4/ Enable agility in headcount planning Headcount planning is no longer a one-time activity at the beginning of the year. As the business evolves, department leaders must adapt their headcount plans to meet new demands. This requires agility. The most agile companies provide visibility into data with access control + workflows that enable them to make smarter, faster decisions. Let me know what you think in the comments! #workforceplanning #headcount #scenarioanalysis

Forecasting is hard. Finding analysts who do it well is even harder. Too often, I see forecasting either: 1. Overcomplicated: Applying complex ML models just to predict a moving average (?!), or 2. Oversimplified: Running regressions without understanding what the coefficients even mean. I personally use 4 forecasting methods to model a range of outcomes, from conservative to aggressive: 1. ARIMA - Smooths time series data, w/o seasonality adjustment. 2. SARIMAX -  Like ARIMA, but accounts for seasonality. Likely to be the safest and conservative forecast. 3. Prophet -  Captures non-linear trends and seasonality. Often the most accurate. My favorite model for growth forecasts. 4. Manual Projection – aka Olga's secret, overly complicated manual projection. I plot every available metric’s historical D/D, W/W, M/M, and Y/Y % change and analyze their: (a) correlations and relationships (b) seasonal thresholds. It takes ages to complete, but it delivers the most precise forecast. If done right. If I can account for everything the teams are doing. Which is rarely the case. 😬 When reporting, I typically present only Prophet alongside my Projection, keeping ARIMA and its variations for myself as checks. There are many time series models out there: MA, AR, ARMA, ARIMA, SARIMA, Exponential Smoothing, VAR, and more. Forecasts are fun.

Most small businesses default to two forecasting methods: top-down or bottom-up. But they both share the same problem. The "why" behind performance isn't explained. These approaches are easy to model and are used all the time. But they can easily fail as companies grow larger and more driver based. (1) Top-down forecasting Many companies favor top-down because it's simple and aligned with strategic goals. But the biggest drawback is it's often completely disconnected from an operational reality. I use it for high-level financial forecasting and hardly ever for operational planning. • Leadership sets growth or margin targets • The P&L is segmented into business units • These targets cascade down the statements • Line-items are forecast on high-level assumptions (2) Bottom-up forecasting Bottom-up forecasting is based upon detailed inputs such as sales to customers, sales by SKU, hiring plans by individual versus job category or department, expense budgets, etc. The benefit of bottoms-up is it's detailed and grounded in operations. But it's usually time-consuming, fragmented, and hard to roll up consistently. • Individual contributors come up with their numbers • They share it with an accountant or financial analyst • The accounting/finance person puts it into a model • The model is updated constantly with new details (3) Driver-based forecasting Rather than come up with high-level assumptions that don't tie into operations, or granular detail that doesn't separate signal from noise, driver-based combines the best of both. In this example for a professional staffing company, we can tie future revenue to placements per recruiter, contract duration, markup percentage, bill rates, and recruiter headcount. This allows FP&A the ability to flex operating assumptions, test them, and quickly see what can be done on the ground to influence. Differences between the 3 methods matter: Top-down may set revenue at $50 million based upon an 8% growth rate. We can ask "how do we increase growth?" Bottoms-up may set revenue at $50 million based upon a monthly forecast of 200 customers. We can ask "what do we expect from each customer?" Driver-based planning may arrive at the same $50 million but ask "what operational levers can we press to truly move revenue and margin?" The result is forecasts that are faster, more explainable and easier to update. 💡 If you want to explore next-level modeling techniques, join live with 200+ people for Advanced FP&A: Financial Modeling with Dynamic Excel Session 2. https://lnkd.in/emi2xFdZ

Workforce Planning (WP). Here's my cheat sheet for using aspects of scenario planning for WP. Many WP efforts still operate as static, once-a-year exercises often built around a single business scenario. But what if that scenario doesn't happen? My cheat sheet has examples to help you think through: 👉 BUSINESS CONTEXT 1/ Business Scenarios ↳ What plausible business scenarios might we face over the next 24 months? 2/ Scenario Assumptions ↳ What evidence, assumptions, data, or trends suggest these scenarios are likely and worth planning for? 3/ Scenario Triggers ↳ What leading indicators would suggest a scenario is more likely to occur? 4/ Scenario Business Impact ↳ How would each scenario affect business goals (e.g., growth, sales)? 5/ Base Scenario (Most Likely) ↳ Which scenario do we believe is most likely to happen? What are we basing this on? 👉 TALENT IMPLICATIONS 6/ Plan for Base Scenario ↳ For our base business scenario (what we expect), what are the key aspects of the workforce plan? 7/ Directional Plan for Alternate Scenarios ↳ For each alternate scenario, what directional adjustments would be required in our base plan? 8/ Common Talent Themes ↳ Are there shared or common talent-related needs or risks that appear across multiple scenarios? 9/ Common Talent Actions ↳ What talent actions will be required across all of our possible scenarios? (Helps prioritize shared actions.) 👉 EXECUTION FACTORS 10/ Decision Triggers ↳ Based on the scenario triggers, what thresholds would indicate we should begin shifting from the base plan to an alternate one? (Helps get a head start). 11/ Risk Mitigation ↳ What talent-related risks are introduced by each scenario, and how can we mitigate them proactively? 12/ Communications Needs ↳ What communications guidance would different stakeholders need under each scenario? 13/ Key Stakeholders ↳ Who needs to be involved in scenario-based workforce planning and execution? How do we align? 👉 A few more thoughts: ↳ This isn’t about creating multiple workforce plans ↳ It’s about planning for the base scenario while... ↳ gaining directional insights into how plans might flex ↳ This helps us respond effectively if scenarios shift ↳ Even high-level insights are better than none at all ↳ Whether you use these questions or not, start today ↳ Doing so will prepare you for what the future brings ❓Did anything here resonate with you? What would you add or change? Let me know. ♻️ Repost to help others strengthen workforce planning 🔔 Follow Brian Heger for daily HR insights #hr #humanresources #workforceplanning