When assessing the impact of a new feature, product, performance improvement, flow, or other change to your product environment or experience, accuracy is paramount. However, many teams still make important product decisions based on simple Before and After analysis, also known as pre post analysis. While straightforward, this approach often leads to misleading conclusions due to its failure to account for key factors like seasonality and overall trends, time dynamics such as a user mix change due to marketing campaigns, and multiple product launch situations..
This technical white paper highlights the limitations of Before After analysis in measuring the impact of product releases and demonstrates how the Loops Release Impact model offers a stronger alternative, accurately isolating the causal effect of product releases by adjusting for confounding factors.
Lessons from Real-Life Cases:
We took three real-life examples of A/B tests, which are the gold standard in Product Analytics, and analyzed whether a Before and After analysis (pre post analysis) approach would have led to the same conclusion or resulted in a misleading decision. For comparison, we also ran the analyses using the Loops Release Impact Model.
The findings reveal how relying on Before and After analysis alone can often lead to the wrong decision. This paper explores these cases and comparisons, offering insights on how teams can enhance accuracy in their decision-making.
Use Cases:
- When a concurrent, positive trend is occurring during the launch period
- The user mix changes during the launch period
- There are multiple surrounding releases and a very short test period
Learn about:
- Measuring Release Impact with Greater Accuracy
- Loops Release Impact Analysis - A Proven Methodology
- Use Case 1: A Positive Trend and a False Positive
- Use Case 2: A Changing User Mix
- Use Case 3: Accuracy amidst Multiple Releases
- Before-and-After Analysis Summarized
- Unlocking the Full Potential of Your Product Data
