Experiment performance metrics are the quantitative measures that determine whether your A/B tests are successful. Understanding these metrics is crucial for interpreting results correctly and making informed decisions about which variants to implement in your live store listing. PressPlay tracks several key performance indicators that together paint a complete picture of experiment impact.
Every experiment in PressPlay is evaluated using a standardized set of metrics that enable fair comparison across different tests, asset types, and time periods.
The conversion rate is the fundamental metric for app store optimization, measuring what percentage of users who view your store listing proceed to install your app. It's calculated simply as:
Conversion Rate = (Installs ÷ Impressions) × 100
For example, if your store listing receives 10,000 impressions and generates 500 installs, your conversion rate is 5%. Both the control variant (your current listing) and treatment variant (the test version) have their own conversion rates, which are compared to determine experiment success.
Typical conversion rates vary significantly by app category, price point, and market maturity:
Gaming Apps - Often see 15-30% conversion rates
Utility Apps - Typically range from 8-20%
Paid Apps - Generally lower at 2-10%
New Apps - May start at 5-12% and improve over time
Rather than comparing your conversion rate to industry benchmarks, focus on relative improvement. A test that improves your 10% conversion rate to 10.5% represents a meaningful 5% increase in installs for the same traffic.
Install uplift is the primary success metric for experiments, representing the relative improvement of your treatment variant compared to the control baseline. It's expressed as a percentage and calculated as:
Install Uplift = ((Treatment Conversion Rate - Control Conversion Rate) ÷ Control Conversion Rate) × 100
For example, if your control converts at 10% and your treatment converts at 10.5%, the install uplift is +5%, meaning the treatment generates 5% more installs per impression.
Understanding uplift magnitude helps prioritize which experiments to implement:
0-3% Uplift - Minor improvement, may not justify implementation effort unless highly significant
3-10% Uplift - Meaningful improvement worth implementing in most cases
10-20% Uplift - Strong improvement, clear winner to deploy immediately
20%+ Uplift - Exceptional result, investigate to understand success factors for replication
Negative uplift indicates the treatment underperformed the control. This valuable information tells you the variant actually hurts conversion and should not be implemented.
Statistical significance indicates whether the observed uplift is a genuine effect or could have occurred by random chance. PressPlay calculates significance using industry-standard hypothesis testing, typically reporting a p-value and confidence level.
Results are categorized as:
Statistically Significant (p < 0.05) - More than 95% confident the effect is real, displayed with a green checkmark
Approaching Significance (0.05 ≤ p < 0.10) - 90-95% confidence, consider extending experiment, shown with yellow indicator
Not Significant (p ≥ 0.10) - Less than 90% confidence, likely due to random variation or insufficient data, shown with red X
Statistical significance is crucial because even large uplifts can be misleading with small sample sizes. A +20% uplift with only 100 impressions per variant is far less reliable than a +5% uplift with 10,000 impressions per variant.
Best practice is to only implement experiments that are both statistically significant AND show meaningful uplift. A +1% uplift that's statistically significant may not be worth implementing, while a +15% uplift that's not significant needs more data before acting.
Beyond the core three metrics, PressPlay tracks additional measures that provide context and deeper understanding.
Impressions represent the total number of times users viewed your store listing during the experiment period. This metric is split between control and treatment variants, with PressPlay automatically balancing traffic to ensure fair comparison (typically 50/50 split).
Impression volume directly impacts statistical confidence. More impressions mean more reliable results. PressPlay's experiment duration recommendations are based on ensuring sufficient impressions for statistical validity.
Monitor impressions to ensure experiments are receiving expected traffic levels. Unusually low impressions might indicate:
Experiment was assigned to a low-traffic locale
Test period included seasonal slowdowns
App ranking dropped during test period
Store listing traffic shifted to other sources (web, ads)
Absolute install counts show how many installs each variant generated. While uplift percentage is the primary metric, absolute counts help you understand practical impact and forecast total gains.
For instance, a +10% uplift that generates an extra 50 installs per day has different business impact than the same +10% uplift generating 500 extra installs daily. Use absolute counts to calculate projected monthly and annual install gains from successful experiments.
The confidence interval provides a range within which the true uplift likely falls. For example, an experiment might show +8% uplift with a 95% confidence interval of [+3% to +13%]. This means we're 95% confident the true effect is between 3% and 13% improvement.
Narrower confidence intervals indicate more precise estimates and are achieved through larger sample sizes. Wide intervals suggest the need for more data or reveal high variability in user response.
Day-by-day conversion rate tracking shows how experiment performance evolves over time. This temporal view helps identify:
Immediate Effects - Variants that show impact from day one
Delayed Impact - Effects that take time to emerge
Trend Consistency - Whether uplift remains stable or fluctuates
Day-of-Week Patterns - Weekend vs. weekday performance differences
Stable, consistent uplift over time provides more confidence in results than volatile performance that only occasionally shows advantage.
Understanding how metrics interact helps you make nuanced decisions:
Clear winner. Implement immediately. These are your most valuable experiments, showing both meaningful improvement and statistical reliability.
Promising but unproven. Extend the experiment to gather more data before making decisions. The large uplift is encouraging, but could be due to random variation.
Small but real effect. Consider implementing if the effort is minimal and the change aligns with other goals. May not be worth prioritizing over more impactful experiments.
No meaningful effect detected. Conclude the experiment and try different variants. The tested variant doesn't appear to impact conversion.
Confirmed loser. The treatment variant demonstrably hurts performance. Do not implement. This is valuable learning about what doesn't work.
Likely neutral or negative. Don't implement, but the effect may not be as bad as it appears. Consider testing alternative variants instead.
When experiments run across multiple locales, PressPlay provides performance breakdowns by geographic market. This granular view often reveals that variants perform differently in different regions.
For example, an icon test might show:
+15% uplift in United States (significant)
+2% uplift in United Kingdom (not significant)
-5% uplift in Germany (not significant)
These patterns enable locale-specific optimization strategies, where you implement successful variants only in markets where they perform well.
Aggregate metrics across asset types help you understand which store listing elements have the greatest impact:
Icon Experiments - Often show the largest uplifts, typically 5-20% when successful
Feature Graphic Experiments - Moderate impact, usually 3-10% uplift range
Short Description Experiments - Smaller but consistent effects, 2-8% uplift typical
These patterns guide resource allocation, helping you focus testing effort on elements with highest impact potential.
Experiment duration metrics help manage your testing pipeline:
Days Running - How long the experiment has been collecting data
Days Until Significance - Projected time needed to reach statistical confidence based on current data rate
Completion Percentage - Progress toward minimum recommended sample size
These metrics help you decide whether to continue, extend, or conclude experiments based on data collection pace.
Apply these principles when evaluating experiment metrics:
Significance First - Always check statistical significance before getting excited about uplift numbers
Context Matters - Consider impression volume, test duration, and external factors
Consistency Over Time - Value stable trends more than single-day spikes
Absolute Impact - Calculate actual install gains to understand business value
Comparative Analysis - Compare results to previous experiments on similar assets
Holistic View - Consider all metrics together rather than fixating on single numbers
Experiment performance metrics are your evidence for decision-making. By understanding what each metric represents, how they interact, and what thresholds matter, you transform raw data into actionable insights that drive continuous improvement in your app store conversion rates. The combination of conversion rate, install uplift, and statistical significance provides a robust framework for evaluating tests and confidently implementing changes that genuinely improve app performance.
