Planning poker example is one of the most effective ways teams estimate work in Agile. When I first learned it, a single 90‑minute session turned a winter of vague deadlines into tangible sprints. This article explains planning poker example step‑by‑step, shows a real scenario, offers tips for distributed teams, and outlines variations and mistakes to avoid. It’s written for product owners, scrum masters, developers, and anyone who wants reliable, collaborative estimates.
What planning poker is — and why it works
At its core, planning poker is a consensus‑based estimation technique that uses relative sizing (often Fibonacci numbers or a modified scale) and simultaneous reveal to reduce anchoring and groupthink. Instead of one person dictating a number, every participant privately chooses a card representing their estimate. Cards are revealed at the same time. When estimates diverge, conversation focuses on assumptions that created the differences. The result is faster alignment and often more accurate forecasts.
This planning poker example focuses on a six‑step routine: preparation, presentation, private estimate, reveal, discussion, and agreement. Each step is intentionally lightweight so teams can repeat the exercise week after week and improve velocity predictability.
Planning poker example: a real user story walkthrough
Imagine a mid‑size product team preparing for the next sprint. The user story is: “As a user, I want to filter search results by date and relevance so I can find recent items quickly.” Here’s a realistic planning poker example of how the session unfolds.
1) Preparation
- Product owner writes the story with clear acceptance criteria: date filter UI, backend filter support, tests for edge cases, and UX validation.
- Dependencies are flagged: search index update and analytics event tracking.
- Team members review the story before the meeting and note unknowns.
2) Presentation (3–5 minutes)
The product owner gives a short explanation, highlights acceptance criteria, and clarifies constraints. The team asks clarifying questions about edge cases (e.g., timezone handling, default sort) and the product owner updates the story if needed.
3) Private estimate
Each participant chooses a card privately. Common scales are Fibonacci (1, 2, 3, 5, 8, 13, 20) or T‑shirt sizes (XS–XL). In our planning poker example, the backend engineer picks 3 (straightforward filter), the frontend developer picks 5 (UI work and integration), and the QA picks 8 (lots of edge cases and cross‑browser checks).
4) Reveal
All cards are shown at once. The spread (3, 5, 8) surfaces differing assumptions immediately: the QA is accounting for edge cases the developers might have underestimated. This avoids anchoring from the first speaker.
5) Discussion (5–10 minutes)
The highest and lowest estimators explain their thinking. The QA raises timezone handling and load tests; the frontend developer mentions a library upgrade that could complicate integration. Through discussion, the team identifies a previously unnoticed dependency: the search index must expose a new filter field.
6) Re‑estimate and agreement
After the clarifications, the team re‑estimates. New estimates converge to 8, and the product owner accepts this as the final estimate. Because the team discussed hidden work, the estimate is far more reliable in the sprint planning and capacity calculations.
How to pick the right scale
Choosing a scale is part preference and part psychology. Fibonacci scales emphasize relative sizing and the growing uncertainty with larger items. T‑shirt sizes are useful at the epic or backlog grooming level. Some teams add a “?” card to show uncertainty and a “0” card for no effort. Whatever you pick, keep it consistent so velocity calculations remain meaningful.
Using planning poker to improve forecasting
One of the major benefits of planning poker is that it creates data you can use to forecast. Convert story points into average velocity across recent sprints, then plan how many stories you can take on. A planning poker example often yields better forecasts than time‑based guessing because the team’s collective judgment adjusts for complexity, unknowns, and risk.
Common variations and tools
Modern remote teams use digital tools that replicate cards and the reveal pattern. Popular practice includes:
- Integrated tools that pair with issue trackers (for example, cards inside planning screens).
- Asynchronous estimation: team members submit estimates in a window, then review divergences in a short meeting.
- Hat‑style estimation: members “explain then vote” in rounds to quickly eliminate major disagreements.
If you’d like to try a specific resource while experimenting with your first session, consider a simple web tool or a virtual whiteboard. Also, a quick reference link appears later for further reading: keywords.
Practical tips that make planning poker work
- Limit the session length. Keep each story discussion to 5–15 minutes to maintain focus and momentum.
- Break down large stories. If a story repeatedly lands at large numbers, split it into smaller, deliverable slices before estimating again.
- Stop debating the estimate. Once estimates converge, capture the context (assumptions, dependencies) in the ticket and move on.
- Use planning poker for relative sizing, not precise hours. It’s about comparative effort and uncertainty.
- Invite cross‑functional participation. QA, UX, and operations bring perspectives that reveal hidden work.
Addressing common objections
Teams sometimes say planning poker takes too long or yields the same number as a quick discussion. The point is less about the number and more about surfacing assumptions. If you conduct planning poker well, you’ll find fewer mid‑sprint surprises and more predictable delivery. If a session feels like theater, shorten stories, require pre‑reading, or switch to asynchronous votes.
Scaling planning poker for large or distributed teams
Large initiatives need additional structure. When multiple teams estimate related work, consider the following approach in your planning poker example:
- Run a cross‑team estimation session with senior representatives who understand local constraints.
- Use reference stories: pick a few well‑understood stories as anchors for scale calibration across teams.
- Document assumptions and handoffs explicitly—these are often the real causes of variance between team estimates.
For distributed teams, asynchronous estimation tools let members vote across time zones. Follow up with a short synchronous meeting to resolve large discrepancies. Recording sessions can help new members learn how the team reasons about complexity.
How to turn estimates into reliable plans
Good estimates combined with disciplined sprint planning create predictability. Here are steps that help transform planning poker output into execution confidence:
- Keep a rolling velocity average and adjust based on team changes (e.g., onboarding, holidays).
- Prioritize stories by value and risk; estimate the riskiest items early so unknowns are exposed.
- Revisit estimates if scope changes—an unchanged point value with added scope is misleading.
- Track actual time and outcomes to refine future estimates and identify biases (optimism, technical debt).
Common pitfalls and how to avoid them
Even with the best intentions, teams can misuse planning poker. Watch out for these traps:
- Anchoring: avoid open discussion of numbers before the first vote.
- Overprecision: refusing to split large items keeps uncertainty in the backlog.
- Role imbalance: product owners or senior engineers dominating the conversation can stifle contributions.
- Ignoring non‑functional work: make sure testing, deployment, and observability are included in the estimate.
My personal experience with a planning poker turnaround
I once coached a team whose sprint completion rate was inconsistent. Their stories were either trivial or monsters that never finished. In the first session, we introduced a strict definition of “done,” used reference stories, and enforced a three‑minute limit for initial clarifying questions. Within three sprints, the team’s sprint predictability improved markedly. The secret was not the cards themselves but the discipline to surface assumptions and split large work early—a direct outcome of consistently using planning poker.
Checklist: running a productive planning poker session
- Prepare clear stories with acceptance criteria.
- Ensure everyone reads the stories in advance.
- Use a consistent scale and reference stories.
- Privately vote, then reveal simultaneously.
- Discuss only the outliers and re‑vote once.
- Record assumptions and dependencies on the ticket.
Further reading and tools
There are many digital tools and plugins that replicate the planning poker flow and integrate with issue trackers. If you want a simple starting point to practice with your team, try a virtual planning board or a lightweight online planning poker app. For quick access to a demo or external resources during experimentation, see this link: keywords.
Wrapping up
This planning poker example shows why the technique is valued: it reliably exposes assumptions, leverages collective knowledge, and improves estimation accuracy over time. The goal is not perfect prediction but a shared understanding of work, risk, and dependencies. Start small, enforce concise discussions, and use the results to improve backlog hygiene and forecasting. If you’re experimenting with your first session, bookmark a tool or template and invite one cross‑functional stakeholder to join—small changes create big gains.
If you’d like a quick reference while building your backlog or facilitating your next session, here’s a helpful resource to revisit: keywords.
Ready to run your next session? Gather a few reference stories, pick a scale, and commit to one sprint of disciplined planning poker. The difference in clarity and delivery will show up fast.
