KPI Design and Strategy AI Prompts

Design a KPI framework for {{business_area}} aligned to the strategic objective: {{strategic_objective}} 1. Define the measurement hierarchy: - Outcome KPIs (2–3): the ultimate business results we want to achieve - Driver KPIs (4–6): the leading indicators that predict and drive the outcomes - Activity metrics (6–8): the operational inputs we control directly 2. For each KPI define: - Name and plain-English definition - Formula or calculation method - Data source: where does the data come from? - Measurement frequency: daily / weekly / monthly - Owner: who is accountable? - Current baseline value (if known) - Target value and timeframe - Direction of improvement: higher is better / lower is better 3. Map the causal relationships: draw a simple KPI tree showing how activity metrics drive driver KPIs which drive outcome KPIs 4. Flag any KPI that cannot currently be measured (data gaps) Return: KPI framework table, KPI tree diagram (text-based), and a data gap report.

Step 1: Align to strategy — map the business's stated strategic objectives to measurable outcomes. What does success look like in 12 months? Step 2: Define the North Star Metric — identify the single metric that best captures customer value and predicts long-term business health. Step 3: Build the metric tree — decompose the North Star into driver metrics and then into actionable input metrics. Map ownership at each level. Step 4: Audit current metrics — review the existing KPI set against the new framework. Classify each as keep, modify, or retire. Step 5: Fill gaps — identify business dimensions with no metric coverage. Propose new metrics with data source, owner, and measurement method. Step 6: Set targets — for each metric in the final framework, set a baseline, a 12-month target, and an interim 90-day milestone. Step 7: Write the KPI strategy document: one page covering — strategic alignment, NSM, metric framework summary, ownership table, targets, and reporting cadence.

Help set data-driven targets for the KPI: {{kpi_name}} for {{time_period}}. Current performance data is provided. Use these four approaches and compare: 1. Historical trend extrapolation: - Fit a trend line to the last 12 months of data - Project forward to {{time_period}} end - Suggested target: trend projection + {{stretch_factor}}% improvement 2. Benchmarking: - Compare current performance to industry benchmarks (provide if known, otherwise note as assumption) - Target: close the gap to the industry median by 50% within {{time_period}} 3. Bottom-up driver modeling: - Identify the 2–3 key drivers of this KPI - Model the KPI impact of realistic improvements in each driver - Suggested target: sum of driver improvements 4. Top-down from business goal: - Start from the business revenue or growth target for {{time_period}} - Work backwards: what level of {{kpi_name}} is needed to achieve that goal? Compare all four targets. Recommend a final target with rationale. Return: target comparison table, recommended target, and confidence level (High / Medium / Low).

Classify and balance the KPI set for {{business_area}} into leading and lagging indicators. KPI list: {{kpi_list}} Definitions: - Lagging indicator: measures what has already happened (outcome). Reliable but too late to act on. Example: monthly revenue, customer churn rate. - Leading indicator: predicts what will happen (predictor). Actionable now but harder to measure. Example: NPS, pipeline coverage ratio, trial activation rate. For each KPI: 1. Classify: Leading / Lagging / Mixed 2. Time lag: how far in advance does this metric predict or reflect business performance? 3. Reliability: how strongly correlated is this metric with the business outcome it's supposed to predict? Then: 4. Assess balance: most teams over-index on lagging metrics. Is this set balanced? 5. For each lagging KPI, suggest a corresponding leading indicator 6. Identify the 2 leading indicators with the strongest predictive relationship to the most important outcome metric Return: classification table, balance assessment, and leading/lagging pairs.

Build a full metric decomposition tree for the top-level metric: {{top_metric}} A decomposition tree breaks a top-level metric into its component parts, making it possible to diagnose exactly which lever to pull when the metric moves. 1. Level 1 decomposition: break {{top_metric}} into its arithmetic components - Example: Revenue = Users × Conversion Rate × Average Order Value 2. Level 2 decomposition: break each Level 1 component further - Example: Users = New Users + Returning Users 3. Level 3 decomposition where meaningful 4. For each leaf node in the tree: - Current value (if available) - Which team or person owns it - How quickly it can realistically change - What specific actions move it 5. Identify which leaf nodes have the highest leverage — a 10% improvement in which node would move the top metric the most? 6. Identify which leaf nodes are currently unmeasured Return: full decomposition tree (text format), leverage analysis table, and measurement gap list.

Audit the existing KPI set for {{business_area}} and assess its health. Existing KPIs: {{kpi_list}} For each KPI, evaluate: 1. Relevance: does it still align to current business strategy? (Yes / Partially / No) 2. Actionability: if this metric moves, does the team know what to do? (High / Medium / Low) 3. Measurability: is it reliably measured with good data quality? (High / Medium / Low) 4. Frequency: is it measured and reviewed frequently enough to drive action? 5. Gaming risk: can someone make this metric look good without creating real value? (High / Medium / Low) 6. Overlap: is it redundant with another metric in the set? After auditing each KPI: - Recommend which KPIs to keep, modify, retire, or replace - Identify any important business dimensions that have no KPI coverage - Suggest the ideal total number of KPIs for this team (rule of thumb: 5–8 for a team, 3–5 for an individual) Return: KPI health scorecard, recommended actions per KPI, and gaps in metric coverage.

Help define the North Star Metric (NSM) for this business or product: {{business_description}} 1. Explain what makes a good North Star Metric: - It measures value delivered to customers, not just revenue - It is a leading indicator of long-term growth - The whole company can influence it - It is a single number everyone understands 2. Propose 3 candidate North Star Metrics for this business, with for each: - Metric name and definition - Why it captures customer value - How it connects to revenue - How measurable it is today - Potential gaming risks (can it be gamed without delivering real value?) 3. Recommend the best candidate with a clear rationale 4. Define the input metrics (3–5) that the teams would manage to move the NSM 5. Write a one-paragraph NSM narrative: 'We grow revenue by doing X, which creates value for customers by Y, and our North Star Metric captures this by measuring Z.' Return: candidate comparison table, recommended NSM, input metrics, and NSM narrative.

Design a set of OKRs (Objectives and Key Results) for {{team_or_department}} for {{time_period}}. Context: {{strategic_context}} For each OKR: 1. Write the Objective: inspiring, qualitative, direction-setting. It should answer 'where do we want to go?' 2. Write 3–4 Key Results per objective: - Quantitative and measurable: must include a specific number - Outcome-focused, not activity-focused: 'Increase NPS from 32 to 45' not 'Run 10 customer surveys' - Ambitious but achievable: 70% achievement should feel like success - Time-bound: achievable within the OKR period 3. For each Key Result: - Current baseline value - Target value - Measurement method - Owner 4. Check OKR quality: - Do the Key Results, if all achieved, guarantee the Objective is met? - Are any Key Results actually activities or outputs rather than outcomes? - Do they connect to the company-level OKRs? Return: formatted OKR set, quality check assessment, and an alignment map showing how these OKRs ladder up to company goals.

Review this list of metrics and classify each as a vanity metric or an actionable metric: {{metrics_list}} Definitions: - Vanity metric: looks impressive, grows over time, but doesn't tell you what to do or predict business success (e.g. total registered users, page views, app downloads) - Actionable metric: directly tied to business outcomes, tells you what to do when it changes, and can be influenced by specific team actions (e.g. activation rate, revenue per user, week-2 retention) For each metric: 1. Classification: Vanity / Actionable / Context-dependent 2. Why: one sentence explanation 3. If vanity: suggest the actionable version (e.g. replace 'registered users' with 'weekly active users') 4. If actionable: confirm which team owns it and what actions move it Then: 5. Identify the 3 most important actionable metrics from the list 6. Flag any critical business dimension (revenue, retention, acquisition, engagement) with no actionable metric coverage Return: classification table, recommended replacements, and coverage gaps.