Install projections are forward-looking estimates that translate experiment results into forecasted business impact. When you identify a winning variant, projections help you quantify exactly how many additional installs you can expect by implementing the change permanently across your store listing. This capability transforms abstract percentage uplifts into concrete numbers that inform business planning and resource allocation.
Install projections use historical traffic data and experiment uplift percentages to estimate future install gains. The calculation combines three key inputs:
Historical Impression Volume - Average daily impressions your store listing receives
Current Conversion Rate - Your baseline install rate (control variant performance)
Measured Uplift - The improvement demonstrated by the winning variant
For example, if your app receives 10,000 daily impressions, converts at 10%, and you have a winning variant showing +8% uplift, the projection would be:
Current daily installs: 10,000 × 10% = 1,000 installs
Projected daily installs with winning variant: 10,000 × 10.8% = 1,080 installs
Additional installs per day: 80 installs
Extend this across time periods to forecast cumulative impact: 80 additional installs daily equals 2,400 extra installs per month or 29,200 annually.
Install projections appear in multiple locations within PressPlay:
When viewing a completed experiment with statistically significant results, the projection panel shows estimated impact for various time horizons. This appears automatically for experiments meeting significance thresholds, providing immediate visibility into potential gains.
The Reports Dashboard includes a dedicated projections column showing estimated monthly install lift for each successful experiment. Sort by this column to prioritize implementation based on forecasted impact.
When approving experiments for permanent implementation, projections help you understand the expected return on your testing investment and prioritize among multiple successful experiments.
PressPlay generates install projections for standard time periods that align with business planning cycles:
Immediate impact estimate showing additional installs per day. This is the most direct translation of uplift and is calculated as:
Additional Daily Installs = Average Daily Impressions × Control Conversion Rate × Uplift Percentage
Daily projections are useful for understanding short-term impact and detecting implementation effects quickly.
Seven-day cumulative forecast, simply daily projection multiplied by seven. Weekly projections help track impact in the first period after implementing a winning variant.
30-day forecast providing a standard business reporting period. Monthly projections are most commonly used for performance reporting and business case development. These account for day-of-week traffic variations by using 30-day historical averages rather than simple daily multiplication.
90-day forecast showing sustained impact over a business quarter. Useful for planning and measuring against quarterly objectives. Consider confidence intervals wider for longer time frames.
365-day forecast demonstrating long-term cumulative value of optimization efforts. Annual projections are powerful for demonstrating ROI of testing programs, but should be interpreted cautiously as they assume stable traffic and conversion patterns over an extended period.
Not all projections have equal reliability. PressPlay indicates confidence levels based on the underlying experiment data quality:
Based on experiments with:
Statistical significance > 95%
Sample size > 10,000 impressions per variant
Experiment duration > 7 days
Consistent daily uplift trends
High confidence projections are shown with a green indicator and represent your most reliable forecasts.
Based on experiments meeting significance thresholds but with smaller sample sizes or shorter durations. These projections are reasonable estimates but acknowledge greater uncertainty. Shown with a yellow indicator.
Based on experiments approaching but not yet reaching full significance, or showing high day-to-day variability. These projections are speculative and should be used cautiously. Shown with an orange indicator, often suggesting the experiment should continue collecting data.
Several factors influence how closely actual results match projections:
App store traffic fluctuates with seasons, holidays, and app category trends. Projections based on summer data may not accurately predict winter performance. PressPlay accounts for this by allowing seasonal adjustment factors and noting when projections span seasonal boundaries.
Changes in competitive landscape, such as new apps launching in your category or competitor campaigns, can affect baseline traffic and conversion rates. Projections assume stable competitive conditions.
Google Play algorithm updates or policy changes can shift traffic patterns. Major platform changes may require projection recalibration.
New app versions with feature changes or quality improvements can affect conversion rates independently of store listing changes. Projections work best when app quality remains consistent.
Paid user acquisition campaigns or PR events can temporarily increase traffic from different sources with different conversion characteristics. Projections are most accurate for organic store traffic patterns.
When experiments show varying results across locales, PressPlay generates locale-specific projections reflecting geographic performance differences:
For example, an icon experiment might project:
United States: +800 installs/month (from +12% uplift)
United Kingdom: +120 installs/month (from +8% uplift)
Germany: +50 installs/month (from +3% uplift)
Total: +970 installs/month across all markets
This granularity enables sophisticated optimization strategies where you implement variants selectively in high-performing locales while maintaining current assets in markets where results were neutral or negative.
Beyond individual experiment projections, PressPlay provides portfolio-level forecasts showing cumulative impact of all successful experiments:
Calculates total additional installs generated by all implemented winning variants so far this year. This metric demonstrates the cumulative value of your optimization program.
Sums projections from all running experiments that are trending toward significance, showing potential future impact if current trends hold. This helps quantify the value of your testing pipeline.
Compares total projected install gains against program costs (platform fees, resource time, asset creation) to demonstrate return on testing investment. This business case metric justifies continued optimization efforts.
Install projections are invaluable for deciding which experiments to implement first when you have multiple successful tests:
Sort by Impact - Implement highest-projection experiments first for maximum gain
Consider Effort - Balance projected impact against implementation complexity
Account for Dependencies - Some variants may conflict; projections help choose the more valuable option
Resource Allocation - Direct design and development resources to highest-impact opportunities
Projections are estimates, not guarantees. PressPlay provides ranges showing best-case, expected, and worst-case scenarios based on experiment confidence intervals:
Expected - Most likely outcome based on measured uplift
Conservative - Lower bound estimate using confidence interval bottom
Optimistic - Upper bound estimate using confidence interval top
For example, a projection might show 500 expected additional monthly installs, with a range of 300-700 installs at 95% confidence. This range helps with risk-aware planning.
After implementing winning variants, PressPlay tracks actual performance against projections to measure forecast accuracy and refine future predictions:
Post-Implementation Tracking - Monitors conversion rates after deploying winning variants
Variance Analysis - Compares actual gains to projections, identifying when reality exceeds or falls short of forecasts
Model Calibration - Uses realized results to improve projection algorithms over time
Attribution Confidence - Confirms that observed gains are truly from the implemented variant
Maximize the value of install projections with these approaches:
Base Decisions on High-Confidence Projections - Prioritize experiments with strong statistical backing
Consider Conservative Estimates - Use lower-bound projections for business planning to avoid disappointment
Update Regularly - Refresh projections as traffic patterns evolve
Combine with Qualitative Factors - Don't ignore strategic considerations in favor of pure numbers
Track Realization Rates - Learn from past projection accuracy to calibrate expectations
Account for Interactions - Understand that implementing multiple changes may have non-additive effects
Install projections translate technical experiment results into business language that stakeholders understand. Use projections to:
Justify continued investment in optimization programs
Set realistic performance targets for upcoming periods
Celebrate wins by quantifying their impact
Prioritize roadmap items based on projected value
Demonstrate marketing efficiency improvements
When presenting projections to stakeholders, always include confidence levels, time frames, and assumptions. Transparent communication about projection uncertainty builds credibility and manages expectations appropriately.
Install projections bridge the gap between experiment metrics and business outcomes. By forecasting how many additional users will install your app as a result of optimization efforts, projections transform abstract uplift percentages into tangible growth numbers that drive decision-making and demonstrate the value of systematic A/B testing. Whether planning next quarter's targets or justifying optimization resources, install projections provide the forward-looking insights needed to run a data-driven app store optimization program.
