HR Forecasting Methods

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

🌟 Day 6 – Forecasting Techniques in WFM 📊 Forecasting isn’t guesswork—it’s a structured process of using the right techniques to predict customer demand. Different businesses and situations call for different approaches. Let’s break down the most common techniques used in Workforce Management (WFM): 1️⃣ Moving Average – The Simple Starter This method looks at the average of past days or weeks to predict the future. 👉 Example: Last 3 Mondays had 9k, 10k, 11k calls → Next Monday forecast ≈ 10k. ✅ Best for: Stable environments with little fluctuation. ⚠️ Limitation: Doesn’t account for sudden changes (like holidays, outages, or promotions). 2️⃣ Exponential Smoothing – The Adaptive Approach Here, recent data is given more weight than older data. 👉 Example: If volumes are rising because of a new product launch, exponential smoothing picks up the trend faster than a plain average. ✅ Best for: When patterns are evolving quickly (seasonal spikes, campaigns, or changing customer habits). ⚠️ Limitation: Needs tuning (smoothing factor). If set wrong, it may overreact or underreact. 3️⃣ Driver-Based Models – The Contextual Forecaster This method uses business drivers (beyond just history) like: Marketing campaigns Product launches Seasonality (festive shopping, tax season, holidays) Economic conditions 👉 Example: If a sale campaign is expected to increase calls by 20%, you build that uplift into the forecast. ✅ Best for: Mature organizations with access to data and business intelligence. ⚠️ Limitation: Requires alignment with Marketing, Sales, and Ops to get driver insights. ⚖️ How to choose the right technique? ➡️Start simple: Small/new operations often begin with Moving Averages. Scale up: As data and volatility grow, Exponential Smoothing helps adapt to change. ➡️Mature stage: Driver-Based Models become powerful when you integrate WFM with business intelligence and cross-department inputs. In reality, many organizations use a blend of techniques depending on the channel (voice, chat, email) and business maturity. 📌 Takeaway: Forecasting techniques are like different lenses—each shows the future in its own way. The “best” technique depends on: ✔️ Business maturity ✔️ Data availability ✔️ Volatility of demand The more you refine your technique, the closer you get to forecasts that balance customer experience with business efficiency. #WorkforceManagement #WFM #Forecasting #ContactCenter #CustomerExperience #BusinessEfficiency #CapacityPlanning #Scheduling #RTA #OperationsExcellence #Analytics

How to do headcount forecasting correctly You need three things to create accurate headcount forecasts that help leaders make better decisions: #1 A Single Source of Truth  #2 The Right Workflows #3 Scenario Planning and Rapid Reconciliation Let’s dive in. #1 A Single Source of Truth You need headcount data (actuals + forecast) that is a) up-to-date, and b) accurate. That means you need a data owner who is as close to the source of headcount information as possible. Typically, that should be the recruiter or HR partner you are working with. It’s their job to ensure headcount, salary, and benefits data is classified correctly and made available to finance and business leaders at the appropriate level of detail. As a Finance leader, it’s not your job to maintain headcount data, but it is your responsibility to make recommendations about improving data quality. #2 The Right Workflows Headcount forecasts are created either top-down by using ratios, such as revenue ratios, employees per manager, or number of customers. Or you run a bottom-up process, where HR keeps you posted about the progress of filling each open role. In either case, workflows around having the right data at the right level of detail and sharing information between functions are key. These processes should be automated as much as possible and changes and decisions need to be easily trackable. #3 Scenario Planning and Rapid Reconciliation There is rarely just one scenario when it comes to headcount forecasting. You are working with several unknowns, including availability of the right talent, time to fill, resignations, and other factors that impact headcount, such as revenue growth. That means you need to be able to quickly build scenarios based on these key variables and model how they impact your headcount plan in the short and long term. Lastly, you need to ensure you can quickly reconcile headcount with budgets. That means translating possible changes to the size of the workforce to the corresponding financial impact of salaries and benefits. The bottom line is this: While you can do all of this in Excel (and I have done so for several years), you may be better served by a modern FP&A tool that automates and simplifies much of the headcount forecasting process. If you’d like to learn more about headcount forecasting, here is a fantastic article, written by Julio Martínez, the co-founder of Abacum: https://lnkd.in/erNJQW9w