Diagnose a drop in retention

Question

The activation rate dropped by 15% this week. Walk through your investigation process.

Codemia · Accepted Answer

The immediate problem is a **15% drop in activation rates** this week at Expedia, particularly concerning the initial booking process. To scope this problem, I would first define 'activation' as users who complete a booking after searching for flights or accommodations. I would investigate the timeline of this drop, focusing on user behavior before and after the drop, and analyzing whether it correlates with any changes in the product (e.g., UI updates, pricing adjustments) or external factors (e.g., competitor promotions, travel restrictions). Additionally, I would segment users by demographics and behavior to identify if certain groups are more affected than others.

The primary users of Expedia are travelers who are price-sensitive and value convenience. Understanding their needs is crucial. For instance, users may prioritize finding the best deals, ease of booking, and trust in the platform. Given the competitive landscape with players like Booking.com and Airbnb, users also expect a seamless, fast experience. I would gather qualitative feedback from user reviews and conduct user interviews to understand their pain points. Are users facing issues with the booking interface? Are they concerned about pricing transparency? This insight will guide the investigation.

Based on the investigation findings, I would recommend implementing a **personalized booking assistant feature** that leverages user behavior data to suggest tailored options based on their search history and preferences. This feature could address user concerns about overwhelming choices and improve engagement during the crucial activation phase. Additionally, I would enhance the UI to simplify the booking process, reducing the number of steps needed to complete a transaction. This dual approach not only targets the immediate drop in activation rates but also aligns with Expedia's long-term vision of becoming a more user-centric platform.

To track the effectiveness of the proposed solution, I would focus on the following metrics:
1. **Activation Rate**: Measure the percentage of users completing a booking after implementing the new feature.
2. **User Engagement Time**: Analyze the time users spend on the booking page before and after the change; ideally, this should decrease if the feature is effective.
3. **Churn Rate**: Monitor the percentage of users who abandon the booking process to identify if the new assistant reduces drop-offs.
4. **User Satisfaction Score (NPS)**: Gather feedback through surveys to understand how users perceive the new feature.
5. **Conversion Rate**: Track the percentage of users who move from searching to booking, as this will indicate the success of personalization.

Implementing the personalized booking assistant may involve several trade-offs:
1. **Resource Allocation**: Building and testing this feature requires time and engineering resources, which could delay other priority projects.
2. **Complexity vs. Usability**: While personalization can improve user experience, it may also complicate the UI if not executed properly, potentially leading to confusion.
3. **Data Privacy Concerns**: Utilizing user data for personalization may raise privacy concerns among users, necessitating transparent communication and potentially affecting user trust.
4. **Initial Investment vs. Long-term Gains**: The upfront investment in developing this feature must be weighed against its potential to improve retention and long-term user engagement.

Diagnose a drop in retention

Expedia

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