What innovative features could Goibibo add to its hotel reviews system to provide more valuable insights for travelers?

Introduction

To improve Goibibo's hotel reviews system and provide more valuable insights for travelers, we need to focus on innovative features that enhance the user experience and decision-making process. I'll outline a strategic approach to address this challenge, considering user needs, market trends, and technological possibilities.

Step 1

Clarifying Questions (5 mins)

• Looking at Goibibo's position in the travel market, I'm curious about the current user engagement with the review system. Could you share some insights on the average number of reviews users read before booking and the percentage of users who leave reviews after their stay?

Why it matters: This helps us understand the current impact of reviews on booking decisions and identify potential areas for improvement. Expected answer: Users read an average of 5-7 reviews before booking, and about 20% leave reviews post-stay. Impact on approach: Low review engagement would prioritize features to encourage review submission, while high engagement might focus on improving review quality and relevance.

• Considering the competitive landscape, I'm wondering about Goibibo's unique value proposition in the hotel booking space. How does Goibibo currently differentiate its review system from competitors like MakeMyTrip or Booking.com?

Why it matters: Helps identify gaps in the current offering and opportunities for innovation. Expected answer: Goibibo offers verified reviews but lacks advanced filtering or personalization features. Impact on approach: Would focus on developing unique features that leverage Goibibo's strengths and address competitor weaknesses.

• Thinking about Goibibo's broader strategy, I'm curious about the company's data capabilities. What types of user data does Goibibo currently collect and analyze that could be leveraged to enhance the review system?

Why it matters: Determines the feasibility of implementing data-driven personalization features. Expected answer: Goibibo collects booking history, search patterns, and user preferences. Impact on approach: Rich data availability would enable sophisticated personalization and AI-driven insights.

• Considering the diverse needs of travelers, I'm interested in understanding the primary user segments for Goibibo's hotel bookings. Could you provide insights into the main traveler types (e.g., business, leisure, family) and their relative proportions?

Why it matters: Helps tailor review features to meet the needs of different user segments. Expected answer: 50% leisure, 30% business, 20% family travelers. Impact on approach: Would prioritize features that cater to the largest segments while ensuring relevance for all user types.