Start by acknowledging that forecasting is inherently uncertain, then describe a structured, data-driven approach that combines historical data, team input, and continuous refinement. Emphasize how you break down work, account for unknowns, and use feedback loops to improve accuracy over time.
Pro tip: Highlight that accurate forecasting is less about perfect estimates and more about reducing variance through small, well-understood work items and regular calibration. Mention that you track forecast accuracy metrics to learn from misses.
Decompose features into small, independent tasks that are similar in size and complexity. This reduces estimation error and makes forecasts more reliable.
Leverage past sprint data, cycle time, and throughput to inform forecasts. Avoid relying solely on gut feel; use empirical evidence from your team's performance.
Provide forecasts as ranges (e.g., best case, likely, worst case) rather than single-point estimates. This communicates uncertainty and sets realistic expectations.
Use techniques like planning poker or affinity estimation to gather diverse perspectives. Ensure the whole team is involved to improve accuracy and buy-in.
After each sprint or project, compare actuals to forecasts, identify biases, and adjust your process. Continuously refine estimation techniques based on lessons learned.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.