As finance becomes more dynamic and forward-looking, accurate forecasting has become a non-negotiable. In this masterclass, we’ll explore how high-performing finance teams approach forecasting with more precision, less bias, and smarter techniques.
Join Christian Wattig, director of FP&A certificate program at the Wharton School of the University of Pennsylvania, as he shares actionable insights from over a decade of experience in both multinational corporations and high-growth startups.
You’ll learn
- Why forecasting accuracy matters for resource allocation, risk management, and strategic planning
- The difference between top-down vs. bottom-up forecasting — and when to blend them
- Forecasting techniques including:
- Incremental approach
- Driver-based planning
- Zero-based budgeting
- Expert judgment
- Incremental approach
- How to apply the right technique based on complexity, bias, and data availability
- What do leading teams do differently to turn forecasting into a strategic advantage
Who should watch?
Finance & accounting professionals looking to improve their forecasting skills
FP&A leaders and finance executives focused on scaling predictability and planning accuracy
Controllers, CFOs, and finance business partners seeking proven frameworks
Meet your speaker
Christian Wattig
Program Director, Wharton FP&A Certificate
Christian Wattig is an FP&A expert with 10+ years of experience across multinationals and tech startups. He leads the FP&A certificate program at the Wharton School and runs Inside FP&A, a platform dedicated to training modern finance professionals. Over 30,000 professionals subscribe to his newsletter FP&A Tuesday, and more than 100,000 follow his insights on LinkedIn.
Webinar Summary
1. How do you ensure the accuracy of financial forecasts in a rapidly changing market?
When I was working with Squarespace, we noticed the unpredictability of certain revenue streams, especially in B2B marketing. To address this, I adopted rolling forecasts. Instead of sticking to an annual plan, we continuously updated our forecast monthly and adjusted it based on real-time data. This allowed us to pivot faster when we saw changes in consumer behavior. My advice: Implement rolling forecasts to stay nimble and ensure that your forecasting is always up-to-date with current market conditions.
2. Can you share an example of a time when forecasting helped you make critical business decisions?
During my time at Unilever, we faced a challenge with the North American freight and warehousing operations. The market was fluctuating, and our predictions for transportation costs were continuously off. We integrated driver-based forecasting, which looked at key business drivers like fuel prices and demand volumes. This gave us a more granular view and helped adjust our logistics spend in real time. The key takeaway: break down forecasting into the specific drivers that influence your costs, and revisit them regularly.
3. How do you manage the tension between top-down and bottom-up forecasting?
At one point, there was a significant gap between top-down and bottom-up forecasts. The leadership had ambitious growth goals, while the departments were more conservative. Instead of dismissing the discrepancy, I used it as a starting point for dialogue. I brought both sides together, discussed the underlying assumptions, and asked departments to justify their numbers. The result was a more collaborative approach to forecasting. My advice: always treat the gap as an opportunity to improve communication, not just a discrepancy.
4. What steps did you take to improve forecast accuracy across departments?
At Chegg, we rolled out an integrated training program for our global FP&A team, which emphasized consistent forecasting methodologies. We adopted a combination of historical data analysis and scenario modeling to assess risks. Each department’s forecasts were aligned with company-wide objectives. The feedback loop between departments became much stronger. My takeaway: Consistency in forecasting methods across departments ensures alignment and minimizes discrepancies.
5. How do you incorporate operational metrics into financial forecasting?
I’ve always believed that financial forecasts should not only be about numbers but also reflect the operations behind those numbers. For instance, at Take-Two Interactive, I worked closely with the marketing and sales teams to integrate key operational metrics such as ad spend efficiency and sales conversion rates into our forecasts. This made it easier to predict how changes in marketing investments would impact revenue. My advice: Ensure that operational data informs your financial forecasts. It leads to a much more accurate and actionable view of the future.
6. How do you approach M&A forecasting and integration from a finance perspective?
When I worked on an M&A deal at Unilever, forecasting became crucial. We had to incorporate the newly acquired company’s financials, which had different forecasting practices. To streamline the integration, we harmonized the reporting process and implemented a unified forecasting model that included both companies’ data. This made the transition smoother. The lesson: Align forecasting methods early when you’re involved in M&A to avoid confusion later.
7. How do you use AI in forecasting to improve decision-making?
At Datarails, I worked with AI-driven tools that integrated with our forecasting models. The AI helped analyze historical trends and run simulations for various market conditions. This allowed us to test different scenarios and fine-tune our forecasts based on more advanced data. My advice: Leverage AI tools for predictive analytics to increase the speed and accuracy of your financial forecasts.
8. What advice do you have for managing cash flow with uncertain forecasts?
At Procter & Gamble, when we had volatile markets, cash flow management became challenging. We adopted a scenario-based approach to forecasting, which included a high, mid, and low-case scenario for cash flow. This helped us plan for the worst and prepare for the best. The key takeaway: Always build cash flow forecasts with multiple scenarios, especially when market uncertainty is high.
9. How do you balance short-term needs with long-term forecasting goals?
While working at Unilever, balancing the immediate operational needs with long-term planning was always a challenge. We used a “strategic cascading” approach where we’d set long-term targets but would break them down into smaller, more achievable milestones. This allowed us to make short-term decisions that still aligned with our long-term objectives. My advice: Make sure short-term actions are tied to long-term strategic goals through clear milestones.
10. Can you explain how you use rolling forecasts to respond to rapidly changing financial conditions?
At Squarespace, we moved from an annual forecast to a rolling forecast. Every month, we would update our forecast based on the latest financial and market data. For instance, when we saw a shift in consumer behavior, we adjusted our marketing spend forecasts and reallocated resources. This agile approach gave us better control over the business. My advice: Switch to rolling forecasts to respond faster to market dynamics and avoid being caught off-guard by changes.
11. How do you ensure that AI-powered forecasting tools are aligned with business needs?
When introducing AI forecasting tools at Datarails, I made sure that the tools were configured to reflect the specific drivers of our business. For instance, in marketing, we linked the AI’s predictions to key performance indicators such as customer acquisition costs. This ensured that AI-generated insights were relevant and useful. My recommendation: Customize AI tools to reflect your business drivers for more accurate and actionable forecasts.
12. What role does collaboration between finance and other departments play in improving forecasting accuracy?
Collaboration is key. At Unilever, we included the operations and procurement teams in the forecasting process. We ensured that their input on supply chain risks and opportunities was factored into our financial forecasts. This not only improved accuracy but also fostered cross-departmental alignment. My advice: Build strong partnerships with other departments to bring their expertise into the forecasting process for better accuracy.
13. How do you tackle forecasting when working with remote teams across different time zones?
At Take-Two Interactive, I had to manage FP&A across multiple time zones, with teams in the U.S. and Europe. To address this, we set up a shared forecasting dashboard and regular check-ins to ensure alignment. This also helped streamline communication. The key takeaway: Use collaborative tools that allow remote teams to stay aligned in real-time, especially when managing complex forecasting across multiple regions.
14. What challenges did you face when implementing a new forecasting model and how did you overcome them?
One challenge I faced was implementing driver-based forecasting at Take-Two Interactive. Initially, there was resistance from teams who were used to a more traditional approach. We overcame this by running pilot programs that demonstrated the benefits of this method, showing teams how it linked directly to their operations and decision-making. My advice: Start small with pilots to demonstrate the value of new forecasting models before rolling them out company-wide.
15. How do you deal with discrepancies between forecasted and actual results?
At Unilever, discrepancies between forecasted and actual results were inevitable. The key was to review the drivers that contributed to the forecast. We would hold a “root cause analysis” session with the relevant teams, where we would evaluate what went wrong and how we could adjust for future forecasts. My advice: Always conduct a post-mortem analysis to identify what caused discrepancies, and use that knowledge to refine your forecasting process moving forward.
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