Case Study 1 - Root Cause Analysis - Order Cancelation is up by 20%

Order cancellations on the Personal Care Online Retailer platform have increased by 20%. The platform allows cancellations within a 24-hour window, but this spike is specifically seen after the 24-hour mark—when products are prepared for shipping. Key Observations & Clarifications: • The spike in cancellations is sudden and occurred within the past week. • It is specific to the app (not the website). • Primarily affects makeup products, not skincare. • Payment method (e.g., COD or EMI) is not a factor. • No recent external changes (e.g., competition, pricing shifts, regulations, weather, social sentiment) were identified as causes. • No experiments or changes in analytics tools have been introduced. • The measurement method for cancellations has not changed. Potential Internal Factors Explored: 1. New Feature Releases: • Some new features were added recently. • Among them, a virtual try-on tool was launched to help users choose the right product shade. However, it may inadvertently lead to cancellations if users realize post-purchase that they made the wrong selection. 2. Customer Feedback on Cancellation: • Users mention “product not as expected” as the reason, though this may be a generic response rather than the actual issue. 3. Product Selection Errors: • Potential confusion around shades or sizes despite the virtual try-on. 4. Sale Timing: • It’s not linked to upcoming sales—there’s an ongoing sale, which is always pre-announced. 5. Changes in Address Entry: • No significant recent updates to this process; not seen as a likely cause. 6. Reviews and Perception: • While the platform is surfacing more positive reviews, customers may still be influenced by negative reviews elsewhere. • Additionally, the removal of the authenticity badge image (replaced with a text label to improve page load speed) could be reducing trust in product authenticity. Conclusion: The likely drivers of the cancellation spike appear to be internal UX and trust-related issues, such as: • Misuse or misinterpretation of the virtual try-on feature. • Perceived loss of trust due to removal of visual authenticity cues. • Discrepancy between what’s promoted (e.g., positive reviews) and what users find externally. The next step would be to conduct a deep dive into user behavior post-virtual try-on, A/B test the reintroduction of visual authenticity indicators, and gather more qualitative feedback to validate these hypotheses. ⸻ Let me know if you’d like this adapted for a stakeholder deck or interview-ready framework (e.g., root cause analysis format).