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Company focus

LTK
Product Improvement Medium Member-only

What features could LTK add to its shopping app to increase user engagement and time spent browsing?

Prepared by NextSprints

15 mins
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Feature Prioritization User Analysis Product Strategy E-commerce Social Media Fashion User Engagement Feature Development Social Commerce Mobile Apps
Product Management Improvement Question: LTK shopping app feature enhancements for increased user engagement

Introduction

To increase user engagement and time spent browsing on LTK's shopping app, we need to identify key features that enhance the user experience and provide value to our target audience. I'll approach this challenge by analyzing our user segments, identifying pain points, generating innovative solutions, and prioritizing them based on potential impact and feasibility.

Step 1

Clarifying Questions

  • Looking at LTK's position in the market, I'm curious about our current user retention rates. Could you share some insights on our user churn and how it compares to industry standards?

Why it matters: This helps us determine if we should focus more on acquiring new users or retaining existing ones. Expected answer: Churn rate is slightly higher than industry average at 15% monthly. Impact on approach: We'd prioritize features that increase stickiness and provide long-term value to users.

  • Considering the evolving landscape of social commerce, I'm wondering about our users' cross-platform behavior. How do our users typically discover products on LTK - through the app, social media integrations, or other channels?

Why it matters: This informs our strategy for feature development and integration with other platforms. Expected answer: 60% of discoveries happen through social media integrations, 30% directly in the app, and 10% through other channels. Impact on approach: We might focus on enhancing social media integrations and improving the in-app discovery experience.

  • Given the importance of personalization in e-commerce, I'm interested in understanding our current capabilities. How sophisticated is our recommendation engine, and what types of data are we currently leveraging for personalization?

Why it matters: This helps us identify opportunities to improve the relevance of product recommendations. Expected answer: Basic recommendation engine based on browsing history and purchase data, with limited use of social graph data. Impact on approach: We might prioritize enhancing our recommendation algorithms and expanding the types of data used for personalization.

  • Considering the competitive landscape, I'm curious about our unique selling proposition. What features or aspects of LTK do users consistently rate as superior to our competitors?

Why it matters: This helps us identify our strengths and areas where we can further differentiate ourselves. Expected answer: Users appreciate our curated content from influencers and the seamless shopping experience. Impact on approach: We might focus on enhancing these strengths while addressing areas where we lag behind competitors.

Tip

At this point, you can ask interviewer to take a 1-minute break to organize your thoughts before diving into the next step.

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Updated Mar 29, 2025