How to monitor the long term effects of your experiment

Understanding the long-term effects of experiments is crucial in the tech and e-commerce sectors, as decisions based on short-term data can lead to unforeseen setbacks. Long-term effects monitoring involves assessing changes in user behavior and satisfaction over time, despite challenges like evolving user behavior and external factors like market shifts. Methodologies such as Ladder Experiment Assignment and Difference-in-Difference help isolate the impact of experiments over time, while predictive modeling and surrogate indexes offer insights into long-term outcomes using short-term data. Overcoming challenges like engaged user bias and selective sampling requires diversifying data sources and employing strategies like stratified sampling and A/B/n testing. Case studies, like Linktree's seamless transition to Statsig’s platform, highlight the benefits of using modern experimentation platforms, which offer advanced features and foster collaboration among cross-functional teams. These platforms allow companies to make informed decisions, improve user engagement, and adapt to changing market dynamics, underscoring the importance of choosing the right tools for effective experimentation.