Start by acknowledging the lack of a controlled experiment and the challenges of causal inference. Then outline a multi-pronged approach: define success metrics, use quasi-experimental methods like difference-in-differences or synthetic control, and validate with qualitative and quantitative evidence. Emphasize the importance of understanding the rollout pattern and potential confounders.
Pro tip: Propose a 'pre-post with comparison group' design using regions or user segments where the new version was not yet rolled out, and discuss how to test for parallel trends. Also, mention the value of instrumenting the product to capture call drop reasons for future analysis.
Clarify what 'reducing call drops' means: define call drop rate, maybe segmented by call type, device, network, etc. State the hypothesis that the new version reduces call drops compared to the old version.
Since no A/B test, find natural comparison groups: users/regions not yet upgraded, or historical data from before the release. Determine pre- and post-release periods, ensuring sufficient data.
Use difference-in-differences, synthetic control, or interrupted time series to estimate the causal effect. Check assumptions like parallel trends and robustness to confounders.
Look at qualitative feedback, support tickets, and internal logs to understand why drops occur. Check if other metrics (e.g., call duration, user satisfaction) moved consistently.
Present the estimated effect with confidence intervals, discuss limitations of observational methods, and recommend next steps (e.g., run a proper A/B test for future changes).
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