In a recent webinar, Lokesh Mahajan, Group Product Manager at Glovo, shared valuable insights on tackling complex business problems using data and user research. He emphasized the importance of understanding user behavior and leveraging data to create impactful products.
Key Takeaways
- Understand the balance between revenue and costs in a subscription model.
- Explore user behavior to enhance product offerings.
- Use data-driven insights to inform product strategies.
Introduction to Jimmy
Let’s dive into a fictional company called Jimmy. This company offers a monthly subscription service for gym access at a flat rate of 79 Euros. Operating in a fictional country, Jimmy has no competition and boasts 50,000 monthly active users with 600,000 check-ins across its gyms. Each user averages about 12 check-ins per month, indicating a solid engagement level. With over 7,500 gyms in its network, Jimmy seems to be doing well. So, what’s the catch?
The CEO’s Dilemma
One day, the CEO of Jimmy sends a letter to the senior management outlining a pressing issue: they want to expand into new countries and tap into the B2B market, but they lack the funds for user acquisition. This is a common challenge for startups that are profitable but need more cash flow to grow. The CEO’s main concern boils down to increasing revenue while managing costs effectively.
Breaking Down Revenue and Costs
To understand Jimmy’s financials, we need to look at how revenue is generated and what costs are incurred.
- Revenue Generation:
- Cost Structure:
Strategies for Increasing Revenue
To boost revenue, Jimmy has two main options:
- Increase Monthly Active Users: This involves acquiring new users, which is currently not feasible due to budget constraints.
- Increase Subscription Revenue: This could be done by raising subscription prices or introducing tiered plans. However, raising prices might lead to user churn, which is risky.
Cost Management Strategies
On the cost side, reducing the average check-in fee is crucial. Here are some strategies:
- Negotiate Lower Fees: Approach gyms to lower the fees, but this could risk losing gym partnerships.
- Analyze Check-in Fees: Understanding the distribution of fees can help identify opportunities for negotiation. For instance, if 90% of gyms charge 3 Euros, but a few charge 6 Euros, focusing on those high-fee gyms could yield savings.
User Behavior Insights
Understanding user behavior is key to improving Jimmy’s business model. Users tend to stick to familiar gyms, especially big brands, which can lead to high check-in fees. Here’s what the data shows:
- New Users: They often check in at big brands due to trust and familiarity.
- Repeat Users: As users become more experienced, they tend to explore other gyms, but many still rely on big brands.
Discovery Avenues for Users
Jimmy has three main avenues for users to discover gyms:
- Gym Listings: 55% of discoveries happen here, with a high concentration of top brand reservations.
- Activity Discovery Page: This page shows various fitness activities and has a better mix of brand reservations.
- Search Functionality: About 30% of discoveries come from search, with a significant portion leading to top brand reservations.
Qualitative Insights from User Research
User research reveals several reasons why users prefer big brands:
- Trust Issues: Users are often skeptical about lesser-known gyms.
- Group Dynamics: Gym visits are often social, leading users to choose popular options.
- Familiarity: Regular visits to the same gym create a routine.
Defining Success Metrics
A good metric to optimize for is the average number of gyms tried per user. This encourages users to diversify their gym visits, which can help reduce dependency on big brands.
Hypotheses for Improvement
To increase the average number of gyms tried, consider these hypotheses:
- Awareness Campaigns: Promote various activities across different gyms to encourage exploration.
- Building Trust: Implement trust markers for lesser-known gyms to enhance user confidence.
- Optimize Discovery: Adjust the gym listing algorithm to promote smaller brands more effectively.
Potential Solutions
Here are some potential solutions to implement:
- Boost Activity Discovery: Highlight unique activities offered by smaller gyms.
- Revamp Gym Listings: Use machine learning to optimize gym visibility based on user behavior.
- Introduce Trust Markers: Use ratings, reviews, and tags to build trust in smaller gyms.
Conclusion
In summary, tackling complex business problems like those faced by Jimmy requires a clear understanding of both quantitative and qualitative data. By breaking down the problem, defining success metrics, and exploring user behavior, companies can create effective strategies to enhance product discovery and ultimately drive growth. Lokesh Mahajan’s insights remind us that understanding our users is key to building impactful products.