How to Generate Hypotheses for Root Cause?
Building on the foundational understanding of Root Cause Analysis (RCA) and the structured approach provided by Metric Hierarchy and Exploratory Data Analysis (EDA), the next critical step in RCA is generating hypotheses for the root cause.
This involves forming potential explanations for the identified issues, which can then be tested and analyzed further using specific RCA techniques that will be covered in the upcoming concepts.
Generating Hypotheses:
Generating hypotheses in RCA is a critical thinking exercise that involves looking at the patterns, anomalies, or changes identified during EDA and considering what underlying factors might be causing them. It's about asking the right questions and forming educated guesses that can later be tested.
For example, if an e-commerce site has noticed a drop in sales recently. During EDA, it was found that the website traffic was stable and so was average transaction value, while the e-commerce conversion rate dropped.
Using metric hierarchy, the conversion rate was further broken down into its independent components. It was found that the dip is limited to checkout completion % and the conversion rates across other funnel stages are stable.
This EDA result narrows down the focus of hypothesis generation. A hypothesis might be that recent changes to the checkout process have introduced new friction for customers.
Example in Internet Businesses:
Consider an online content platform that sees a sudden decrease in user engagement.
After applying EDA and metric hierarchy, the issue is narrowed down to a drop in average time spent on the platform and a decrease in content interaction rates. This data points us toward several potential hypotheses:
→ Recent UI changes may have created a poorer user experience, leading to shorter sessions
→ Server upgrades might have inadvertently increased load times, frustrating users
→ A shift in content strategy may not align with user preferences, reducing interaction
Each hypothesis stems from our data analysis and focuses on potential root causes that could explain the observed decrease in engagement..
Testing the hypotheses:
While generating hypotheses is an imaginative and informed speculation process, the validity of these hypotheses needs to be tested.
The subsequent concepts will go into specific RCA techniques such as correlation analysis, regression analysis, and time series analysis, among others. These techniques provide the tools to rigorously test the generated hypotheses, allowing analysts to confirm or refute them with data and evidence.
Takeaway:
Generating hypotheses is a pivotal step in RCA that bridges the initial understanding of the problem with deeper, targeted analysis. It sets the direction for the subsequent investigative efforts and ensures that the RCA process is focused and efficient.
By formulating thoughtful hypotheses based on the insights gained from EDA and metric hierarchy, businesses can more effectively uncover the real reasons behind their challenges and take corrective actions that are informed and impactful.