Introduction
The recent 15% drop in engagement rate for Sprinklr's AI-powered content recommendations is a critical issue that demands immediate attention. This analysis will systematically investigate potential root causes, validate hypotheses, and propose targeted solutions to address the engagement decline while considering both short-term fixes and long-term strategic implications.
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: Algorithm changes can significantly impact recommendation quality and user engagement. Expected answer: Yes, there was a minor update to improve personalization. Impact on approach: If confirmed, we'd focus on analyzing the algorithm change and its effects.
Why it matters: Identifying specific affected segments can help pinpoint the root cause. Expected answer: The drop is more pronounced among newer users. Impact on approach: We'd investigate onboarding processes and initial user experience if this is the case.
Why it matters: Content quality directly impacts engagement rates. Expected answer: No major changes reported from content providers. Impact on approach: If confirmed, we'd focus more on internal factors and user behavior.
Why it matters: Technical issues can severely impact user engagement. Expected answer: Some intermittent slowdowns were reported but deemed minor. Impact on approach: We'd investigate the correlation between performance issues and engagement drops.
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