Introduction
The 30% decrease in customer engagement with DataProphet's prescriptive maintenance dashboard among mining industry users is a critical issue that demands immediate attention. To address this problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term fixes and long-term strategic implications.
My analysis will follow a structured framework, beginning with clarifying questions to gather essential context, followed by a thorough examination of external factors, product understanding, metric breakdown, and data-driven hypothesis formation. We'll then conduct a root cause analysis, propose validation methods, and outline a comprehensive resolution plan.
Framework overview
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: This helps us identify if the issue is universal or specific to certain sub-segments. Expected answer: There may be variations among different mining types. Impact on approach: If variations exist, we'll need to analyze each sub-segment separately.
Why it matters: Recent changes could directly impact user engagement. Expected answer: There might have been some updates or changes. Impact on approach: If changes occurred, we'll focus on their potential impact on user behavior.
Why it matters: Ensures we're comparing apples to apples in our analysis. Expected answer: The metric definition has remained consistent. Impact on approach: If the definition changed, we'd need to recalibrate our analysis based on the new metric.
Why it matters: External factors could be influencing user behavior independently of the product. Expected answer: There might be some industry-wide challenges or changes. Impact on approach: If external factors are significant, we'll need to factor them into our root cause analysis.
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