How to use Metric Hierarchy to Guide the EDA?
In Root Cause Analysis (RCA), it's crucial to approach the problem systematically. One effective approach to make the Exploratory Data analysis (EDA) more focused and systematic is by using the concept of metric hierarchy.
This method helps to break down complex issues into manageable components, allowing growth analysts to systematically examine each part and understand how it contributes to the overall trend or problem.
Integrating Metric Hierarchy in RCA:
Metric hierarchy is a structured representation of metrics from the most overarching ones down to the more granular or contributing factors.
In the context of RCA, it involves deconstructing the problem into its constituent metrics, identifying which parts of the business they relate to, and understanding how they interact with each other.
This breakdown can significantly narrow the focus during the exploratory phase, guiding growth analysts to potential root causes by systematically isolating and examining each contributing factor.
Applying Metric Hierarchy to an Example:
Let's revisit the example of the online streaming service experiencing unexpected fluctuations in viewer engagement.
Suppose the top-level metric of concern is 'Average Viewer Engagement Time.' Using a metric hierarchy approach, we might break this down into several independent metrics such as 'Time of Day Viewed,' 'Type of Content Watched,' and 'Viewer Demographics.'
These can further be broken down, for instance, 'Type of Content' might include 'Genre,' 'Length of Episode,' and 'New vs. Returning Series.'
By examining these independent variables systematically, growth analysts can focus their EDA on specific areas.
For example, they might start by looking at engagement time across different times of the day or analyzing if certain genres are consistently associated with higher or lower engagement.
This focused approach allows for a more manageable and structured investigation, making it easier to identify where significant changes or anomalies are occurring that could point to the root cause.
Enhancing EDA with Metric Hierarchy:
Incorporating metric hierarchy into EDA helps avoid a scattergun approach to data analysis, where growth analysts might randomly explore various aspects of the data without a clear strategy.
By providing a structured pathway of investigation, metric hierarchy ensures that EDA is both comprehensive and focused, covering all relevant areas systematically but also honing in on the most likely sources of the problem.
It enhances the efficiency of the analysis and increases the likelihood of uncovering the true root causes behind observed issues.
Takeaway:
Using metric hierarchy in RCA refines the exploratory process, directing attention to the right places and ensuring a thorough yet efficient analysis. It's an essential tool in the growth analyst's arsenal, particularly when dealing with the complex and often large datasets typical of internet businesses.