Decision Framing Workshop Kit

Decision Framing Workshop Kit

Use this kit to run a focused half-day (3–4 hour) workshop that helps a team move from a vague problem to a clear, testable analytics plan with defined measures and next steps. The materials are designed for cross-functional teams who must decide what question to answer, what data is needed, and what action success looks like.

Intended outcomes

• Aligned problem statement and prioritized hypothesis to test.
• Clear KPIs and measures that define success.
• Assessment of data readiness and gaps.
• A concrete analysis or experiment plan with owners and a one-week follow-up.

Estimated duration and high-level flow

This kit is optimized for a half-day workshop (approx. 3–4 hours). Times below are suggested and should be adjusted to your team size and context.

1. Welcome, context, and outcome setting — 15 minutes
2. Stakeholder mapping & alignment — 25 minutes
3. Problem framing and conversion to hypothesis — 45 minutes
4. KPI mapping and success criteria — 45 minutes
5. Quick data-readiness check and gap identification — 25 minutes
6. Choose approach: experiment vs observational analysis — 20 minutes
7. Action planning, owners, and one-week follow-up plan — 20 minutes
8. Close and next steps — 10 minutes

Who should attend

• Problem owner or sponsor (decision-maker)
• Analytics or data practitioner(s)
• One or two cross-functional contributors (operations, product, marketing, service)
• Facilitator (neutral guide) — ideally prepared with kit artifacts

Materials included

All artifacts include fillable fields and example answers from a consumer analytics scenario.

• Facilitator agenda with timeboxes and optional scripting
• Slide template (deck) — suggested slide order and speaker notes
• Stakeholder mapping worksheet — roles, influence, information needs
• Problem → Hypothesis template — turning problems into testable statements
• KPI mapping sheet — leading indicators, lagging indicators, how to measure
• Quick data-readiness checklist — systems, access, quality, privacy considerations
• Experiment vs Observational decision guide — when to run an experiment and how to choose
• One-week follow-up plan template — owners, deliverables, mini-deadlines

Facilitator guidance (practical tips)

Be explicit about the workshop scope at the start. Set the expectation that the goal is not to complete an analysis in the room but to produce an actionable plan with clear owners and measures.

• Start with the business context: why this problem matters now.
• Use the stakeholder map to expose hidden decision criteria and information owners early.
• Encourage hypothesis-driven thinking: a good hypothesis links an action to an expected measurable outcome.
• Keep KPI mapping practical: aim for 1–3 primary measures and 2–4 supporting metrics.
• During data-readiness, separate "can we measure it now?" from "is the measurement trustworthy?"
• End with a one-week plan that creates momentum — immediate, small, verifiable steps.

Suggested facilitator script snippets

Welcome: "We have X hours to turn this open problem into a plan we can act on and measure. By the end, we'll have a hypothesis, measures of success, and a concrete next step someone will own."

Transition to KPI mapping: "Think of KPIs as the evidence that will convince the sponsor an action worked. What would success look like in numbers or observed behavior?"

Example scenario (consumer analytics)

Problem: Conversion rate on onboarding is lower than target for new users. Example hypothesis: Reducing required form fields on signup will increase new-user conversion by 8% within 30 days. KPIs: signup conversion rate (primary), onboarding completion rate (supporting), 30-day retention (lagging).

Data-readiness: systems track form submissions and onboarding completion, but retention cohort queries require a joined dataset. Privacy review required for third-party tracking. Recommended next step: analytics engineer to confirm join keys within 48 hours.

Templates and how to use them

Each template contains short instructions and an example. Suggested use:

• Stakeholder map: capture who decides, who provides data, who is affected. Use during the stakeholder mapping activity.
• Problem → Hypothesis: force the team to state the problem, propose an action, and specify the measurable effect.
• KPI mapping sheet: for each KPI note definition, data source, frequency, owner, and threshold for success.
• Data-readiness checklist: use as a quick gating tool — green/yellow/red for access, quality, and compliance.
• Experiment vs Observational guide: capture pros/cons and data needs for both approaches; pick one and record why.
• Follow-up plan: assign owners for next artifacts (e.g., data access, analysis plan, experiment design) and set check-in dates.

One-week follow-up plan (recommended)

1. Day 1 — Sponsor confirms priority and approves resources (owner: sponsor)
2. Day 2 — Analytics engineer confirms data availability and provides sample dataset (owner: analytics)
   • If missing, note specific gaps and owners to resolve them.
3. Day 4 — Draft analysis/experiment plan shared for review (owner: analyst)
4. Day 7 — Short check-in meeting to agree next 2-week sprint tasks and success measures (owner: facilitator or product owner)

Common pitfalls and how to avoid them

• Too many KPIs: pick primary measures that directly map to the hypothesis.
• No owner for data tasks: assign an analytics owner during the workshop.
• Confusing activity with impact: always ask "how will this change behavior or outcomes?"
• Expecting immediate perfect data: plan incremental steps and provisional analyses where needed.

Tailoring and optional upgrades

Customize the kit for your industry by swapping example scenarios and KPI suggestions. For regulated environments, add a privacy and compliance pre-check in the data-readiness step.

Interactive and platform opportunities

The worksheets in this kit are ready to be converted into interactive, savable forms so teams can complete and store workshop outputs inside the platform. Interactive versions can:

• Collect and save stakeholder maps, hypotheses, KPI definitions, and data-readiness answers (useful for traceability).
• Automatically generate an action list and one-week follow-up assignments that integrate with team workflows.
• Provide versioned copies so a team or site can tailor and re-run the workshop later.

Delivery notes for remote workshops

• Share slide deck and worksheets in advance. Use breakout rooms for problem framing and KPI mapping exercises.
• Use a shared whiteboard or the interactive worksheets to capture each group's outputs and then reconvene to align.

How this kit fits the Data, Analytics & Decision Making domain

This kit helps teams move beyond descriptive reporting to a hypothesis-driven decision process: ask the right question, confirm you can measure it, then choose an analysis or experiment that produces actionable evidence.

Included files and formats

• Facilitator Agenda (HTML/printable)
• Slide Deck Template (PPTX / PDF)
• Worksheets (fillable PDF + interactive form-ready JSON)
• Example filled artifacts (consumer analytics scenario)
• One-week Follow-up Plan (spreadsheet / template)

Suggested metrics to track adoption

• Number of workshops run per quarter
• Percent of workshops that produce an owner-assigned analysis plan within 7 days
• Share of workshop hypotheses that lead to an experiment or analysis within 30 days

Use this kit as a repeatable pattern: run, learn, and improve the artifacts based on your team's experience. If you want, convert the worksheets to interactive forms to capture outputs and build an organization-wide library of decision-framing outcomes.