Bet Impact Model Report

Overview

The Bet Impact Model helps you score, prioritize, and measure the effectiveness of strategic initiatives (bets) based on their expected or actual impact on metrics. The model supports two distinct modes: Hybrid Mode for pre-release scoring based on forecasts, and Statistical Mode for post-release analysis using regression to measure actual effects.

When to Use This Report

Use the Bet Impact Model report when you need to:

Prioritize Your Roadmap

Problem: You have more ideas than resources and need to decide which initiatives to pursue
Solution: Score all potential bets using expected impact, confidence, reach, and effort to identify the highest ROI opportunities

Make Resource Allocation Decisions

Problem: Multiple teams are competing for limited engineering, design, or budget resources
Solution: Objectively compare bets across teams using a standardized scoring methodology to allocate resources where they’ll have the most impact

Balance Quick Wins vs. Big Bets

Problem: You need a mix of short-term results and long-term strategic initiatives
Solution: The effort-normalized scoring reveals both high-impact/low-effort quick wins and high-impact/high-effort transformational bets

Account for Uncertainty

Problem: Some bets are proven concepts while others are speculative, but you’re comparing them equally
Solution: Confidence levels risk-adjust your scores, so proven bets score higher than equally-sized but uncertain bets

Measure Actual Impact After Launch

Problem: You launched an initiative but don’t know if it actually worked or if metrics changed due to other factors
Solution: Statistical mode uses regression to isolate the true causal effect of your bet, controlling for confounding variables

Validate Your Predictions

Problem: You want to improve your team’s ability to forecast impact
Solution: Compare pre-launch expected impact (Hybrid mode) with post-launch measured impact (Statistical mode) to calibrate future estimates

Consider Reach and Scope

Problem: A bet might have huge per-user impact but only affect a tiny segment, or vice versa
Solution: Reach weighting ensures you account for how many users or how much of your business is affected

Build an Evidence-Based Culture

Problem: Prioritization is based on opinion, politics, or who argues loudest
Solution: Replace subjective debates with data-driven scoring that anyone can understand and trust

Track Portfolio Performance

Problem: You’ve launched multiple bets but don’t know which ones delivered and which ones flopped
Solution: Use Statistical mode to measure all your bets consistently and build a knowledge base of what works

What It Measures

Hybrid Mode (Pre-Release Scoring)

Used before launching a bet to prioritize which initiatives to pursue.

Bet Total Score
- Aggregates impact across all metrics the bet influences.
- Formula: score = (delta × confidence × reach) / effort
- Higher scores indicate better return on investment.
Per-Metric Impact
- Delta: The absolute expected change in each metric.
- Percentage Change: The relative change from baseline.
- Shows which metrics will be most affected by the bet.
Confidence Weighting
- Based on your assessment of how certain you are about the impact.
- Levels: High (1.0), Medium (0.7), Low (0.4).
- Reduces scores for uncertain bets to account for risk.
Reach Weighting
- Measures how many users or how much of the system the bet affects.
- Can be explicitly set or inferred from strategy type:
  - Core Product: 0.9 (affects most users)
  - New Feature: 0.6 (affects subset of users)
  - Experiment: 0.3 (limited rollout)
  - Optimization: 0.7 (affects active users)
Effort Normalization
- Divides impact by effort (person-weeks) to calculate ROI.
- Helps identify high-impact, low-effort opportunities.
Aggregate Impact
- Sum of absolute deltas across all affected metrics.
- Shows total expected change from the bet.

How to Create This Report

Add bets and metrics to your board
- Add the strategic initiatives (bets) you want to evaluate
- Add the metrics these bets are expected to impact
Connect bets to metrics
- Draw connections from each bet to the metrics it aims to influence
- Configure each connection with confidence level and expected impact
Generate the report
- Select a bet on the board
- Click on the “Report” button in the popup menu
- Choose “Bet Impact Analysis” from the report options

Statistical Mode (Post-Release Analysis)

Used after launching a bet to measure actual effectiveness.

Effect Size
- The measured impact of the intervention on the metric.
- Calculated using regression: metric = β₀ + β₁×intervention + β₂×control₁ + ... + ε
- β₁ represents the effect size of your bet.
Statistical Significance
- P-value: Probability that the observed effect is due to chance.
- P < 0.05 indicates statistically significant impact.
- Helps distinguish real effects from random variation.
Confidence & Uncertainty
- Standard Error: Measures precision of the effect estimate.
- Confidence Weight: Derived from p-value, used in scoring.
- Lower p-values yield higher confidence weights.
Controlled Analysis
- Accounts for other factors (control metrics) that might influence the outcome.
- Isolates the true causal effect of the bet from confounding variables.
- Ensures you’re measuring the bet’s impact, not external factors.
Score & Interpretation
- Combines effect size, significance, and reach into a single score.
- Provides human-readable interpretation of results.
- Example: “moderate positive effect (significant, p=0.032, confidence weight=0.95)“

Scoring Formula Details

Hybrid Mode Score


score = (|delta| × confidence_weight × reach_weight) / effort

Where:

delta: Expected change in metric from baseline forecast
confidence_weight: 0.4 (Low), 0.7 (Medium), or 1.0 (High)
reach_weight: 0.3 to 0.9 based on strategy type or explicit value
effort: Person-weeks or similar effort measure

Statistical Mode Score


score = (|effect_size| × significance_weight × reach_weight) / effort

Where:

effect_size: Regression coefficient for the intervention
significance_weight: Function of p-value (higher for p < 0.05)
reach_weight: Same as hybrid mode
effort: Actual effort spent on the bet

Configuration Options

Confidence Levels

High: You have strong evidence or historical data supporting the expected impact
Medium: Reasonable assumptions but with some uncertainty
Low: Speculative or unproven hypothesis

Strategy Types

Core Product: Changes to fundamental product features used by most users
New Feature: Addition of new functionality for a specific use case
Experiment: Test or pilot with limited rollout
Optimization: Improvements to existing features or processes

Reach Scale

Massive (0.9): Affects >75% of users or system
Large (0.7): Affects 50-75% of users
Medium (0.5): Affects 25-50% of users
Small (0.3): Affects < 25% of users

Effort Metrics

Measured in person-weeks, person-months, or story points
Should include all phases: design, development, testing, rollout
Be consistent across bets for accurate comparison

Interpreting the Results

Pre-Release (Hybrid Mode)

Prioritizing Bets
- Rank bets by total score to identify highest ROI opportunities
- Review per-metric contributions to understand value drivers
- Consider risk-adjusted scores (confidence weighting) vs. pure potential
Resource Allocation
- Focus on high-score, low-effort bets for quick wins
- Balance portfolio between high-confidence incremental bets and low-confidence transformational bets
- Use aggregate impact to understand scale of change
Expectation Setting
- Share delta and percentage change with stakeholders
- Communicate confidence levels to set realistic expectations
- Document assumptions for post-launch validation

Post-Release (Statistical Mode)

Validating Hypotheses
- Compare effect size to expected delta from hybrid mode
- Check if p-value < 0.05 for statistical significance
- Review interpretation for directional accuracy (positive vs. negative)
Learning & Iteration
- For significant positive effects: Scale up and replicate
- For non-significant effects: Investigate confounds or insufficient data
- For significant negative effects: Roll back or adjust approach
Improving Estimates
- Track actual vs. predicted impact to calibrate future forecasts
- Adjust confidence levels based on accuracy of past predictions
- Refine effort estimates based on actual time spent

Applying Insights

Short-Term Actions

Bet Prioritization
- Create a ranked list of bets using total score
- Start with top-scoring bets that fit current capacity
- Queue lower-scoring bets or deprioritize permanently
Resource Planning
- Allocate team capacity to highest-impact bets
- Balance workload using effort estimates
- Plan for dependencies between related bets
Stakeholder Communication
- Share scoring rationale and methodology
- Present per-metric breakdowns for transparency
- Set expectations using confidence levels

Medium-Term Strategy

Portfolio Management
- Monitor distribution of bets across strategy types
- Ensure mix of quick wins and transformational initiatives
- Track aggregate impact across all active bets
Continuous Measurement
- Run statistical analysis for all launched bets
- Compare predicted vs. actual impact systematically
- Build a knowledge base of what works
Process Improvement
- Refine scoring based on actual outcomes
- Adjust confidence and reach calibration
- Improve effort estimation accuracy

Long-Term Planning

Strategic Direction
- Identify patterns in high-impact bet characteristics
- Double down on strategy types that consistently deliver
- Develop organizational capabilities in high-leverage areas
Data-Driven Culture
- Use scoring to replace subjective prioritization
- Make bet impact measurement a standard practice
- Train teams to think in terms of expected value and ROI
Predictive Capability
- Build forecasting models based on historical accuracy
- Develop intuition for effort and impact estimation
- Create playbooks for common bet types

Example Use Cases

Product Roadmap Prioritization

Score all proposed features to determine quarterly roadmap. Balance quick wins (high score, low effort) with strategic bets (high impact, higher effort).

Marketing Campaign Selection

Evaluate different campaign ideas pre-launch using hybrid mode. After launch, use statistical mode to measure actual lift and refine future campaign selection.

A/B Test Analysis

Use statistical mode to rigorously measure experiment effects, accounting for confounding factors. Prioritize rollout of winning variants based on effect size and significance.

Resource Allocation

When deciding between competing initiatives, use bet scoring to make objective, data-driven decisions about where to invest limited resources.

By leveraging the Bet Impact Model, you can move from opinion-based to evidence-based prioritization, systematically improve prediction accuracy, and maximize the return on your strategic investments.