Boost Product Revenue by Focusing on User Experience and Strategic Prioritization

Once in a while every CEO does the thing where they quietly creep up behind the product manager’s seat to suggest a new approach that will definitely change things around in the company. That’s what CEOs are for, in fact. "We need to increase our product’s revenue." This staple phrase often comes with specific targets in mind. At this point the development of the product is probably stable enough — no sudden spikes in metrics, a steadily growing user base, and solid overall retention, so the product manager might think that the best solution would be to increase the marketing budget, scale up accordingly, and expect revenue to rise. But, this approach only works if your product is perfect, which is really never the case. And if you as a CEO think it is, it’s very likely that you are overlooking numerous growth opportunities within your existing user base.

Another typical situation: the product team reviews a backlog filled with features requested by various business departments, prioritizing them using the RICE model or another method and then rolling them out. Despite these efforts, you can rarely see significant changes. This, interestingly, creates only an illusion of stability and doing things right.

I want to share an approach that has repeatedly helped me uncover non-obvious problems and significantly increase product revenue. By focusing on untapped potential within your current operations and user base, you can achieve much more substantial growth.

1) Identifying Blockers and Weak Points in the User Journey

Usually, the most challenging part of enhancing revenue is actually finding the issues that lie at the core of the product's ecosystem. These are the most common problematic areas, and will help you consider the issues you are having at the moment with your product:

1. Pricing/Monetization Issues: Maybe your product is priced too high for some users and too low for others, and that is why your products can be perceived somewhat inconsistently.

   
   

2. Functionality Limitations: Functionality is key, and when a user can’t find the feature that is “supposed to be there”, or feels limited by the choice you provide – that is a good reason to re-evaluate your product.

   
   

3. Quality of Service: No one likes performance issues or persistent bugs. They can greatly degrade the user experience, which leads to dissatisfaction and reduced engagement within your user base.

   
   

4. User Interface and Usability: If navigation is too confusing, the interfaces are complex, or the flow is not streamlined all of these can deter users from fully engaging with the product.

   
   

5. Market Fit and User Expectations: Sometimes, even when you’ve done a lot of research, the product won’t align well with market needs or user expectations, leading to low adoption or satisfaction.

   
   

6. Customer Service and Support: This is big, since it is virtually the only point of human-to-human contact, and if the experience is frustrating – you can be sure to see consequences in retention and satisfaction.

   
   

7. Integration and Compatibility: Problems with integrating with other systems or incompatibility with commonly used platforms or devices is a big no-no in the current times, since people are getting increasingly used to the ability to integrate services into each other.

   
   

8. Regulatory or Compliance Issues: This is especially relevant for products in regulated industries, where failing to meet legal standards can lead to significant problems.

   
   

Refining Problem Identification with Data

If you want to effectively uncover and address the underlying problems in your product ecosystem, you need to focus. Here, both qualitative and quantitative data will allow you to concentrate your efforts on tangible solutions and not spread out too much.

• Qualitative Data: This stage is all about gathering insights that are more subjective and allow you to explore, and also help you understand the nuances of user experiences and perceptions.
  • Feedback from Customer Development, Support, and Sales Teams are highly valuable and direct insights into user issues and requests. Use it!
  • User Testing and Surveys provide you with an in-depth understanding of user interactions and satisfaction – you now know what makes them tick.

• Quantitative Data equips you with the hard numbers you will need to validate insights gained from qualitative analysis.
  • Cohort Analysis examines the behaviors and responses of different user groups over time.
  • Key Performance Metrics (for each cohort):

    • Monthly Recurring Revenue (MRR)

    • Average Revenue Per User (ARPU)

    • Retention Rates

    • Churn Rates

    • Daily Active Users (DAU) / Monthly Active Users (MAU)

      
      

By making both qualitative and quantitative data your tools, you gain a picture of the product’s performance and user engagement in each cohort and get a new direction you can further explore.

2) Measuring and Analyzing Data to Formulate Solutions

Once you've gathered all your qualitative and quantitative data available at this stage, the next step will be to translate these insights into specific, measurable goals for targeted user cohorts. For example, after analyzing feedback, you might find that users who don’t use the product very frequently feel the cost is too high for them. Here, sales data could show a high failure rate in converting this cohort, with minimal growth in daily active users (DAU).

For a cohort that shows high product usage, you might see a churn rate that is somewhat above average. Support tickets from these users could be more frequent which points to the direction of performance problems.

Do you agree that it looks warmer than “increase revenue”? Let’s make it even more close to reality.

Quantifying the Problem

1. Cohort Size: Determine how many users are affected by the problem, it’s similar to the 'Reach' metric from the RICE framework but focused on problem impact.

   
   

2. Financial Impact: Here you will need to have a collaboration with your sales and support teams to help you analyze purchase refusal rates within specific cohorts, as well as calculate potential revenue loss from churn to quantify the financial stakes.

   
   

3. User Impact: Once you resolve the issues you have identified before you can estimate the potential increase in metrics!

   
   

By bringing in numbers you can now  transform a general goal of "increase revenue" into much more specific, actionable objectives like "reduce the churn rate in a cohort of highly engaged users by addressing technical issues" or "increase DAU in a cohort with low engagement by lowering barriers to entry."

This will not only clarify the issues you are facing but also set you up with a clear path for actions that will yield significant improvements in both user experience and revenue.

3) Hypotheses and Prioritization: Turning Data into Action

After you identify and quantify specific challenges, you can build hypotheses about potential solutions, using deeper analytics to formulate several solutions for the issues.

Building Hypotheses

1. Generate Solutions: Propose actionable solutions based on the detailed insights gathered, ranging from product adjustments to pricing strategy changes.

   
   

You might need more deep analytics on this step: for example, a conversion rate analysis on different funnel stages, or in-depth analysis on your product architecture and your infrastructure  performance metrics. This is a big step that might require collaboration with other teams.

2. Model Outcomes: Employing data modeling to predict the potential impact of each proposed solution, giving you a clear picture of the benefits before implementation.

Prioritizing Projects

When you are choosing which solutions to implement first, setting your priorities straight is crucial. While you’ve estimated Reach and Impact, you can still apply the RICE scoring model to further refine your decision-making if needed:

• Confidence: Assess how confident you are in your estimates and the potential impact of the projects (solutions). This involves considering the quality of the data and the predictability of the outcomes.

• Effort: Evaluate the resources and time you will need to implement each solution – this will help you understand the feasibility and scale of the effort involved.

  
  

By focusing on Confidence and Effort, you can make sure that your decision-making is not only informed by how many users are affected and the potential benefits (already covered) but also by how likely you are to succeed and what resources will be necessary. This approach helps you allocate resources in a strategic way and prioritize projects that are not only impactful but also viable and well-supported by data. The refined application of the RICE model aids in making efficient, informed choices that align with our strategic goals and maximize the return on investment.

Conclusion: A Problem-Solving Approach to Project Prioritization

I hope that now, after reading the article you see why relying  on a traditional backlog is not always necessary. This is a problem solving approach, with which you can focus on addressing the current needs of your clients instead of just fixing surface-level issues or chasing new features that might seem impactful but don't fundamentally enhance your core product.

Once you methodically identify blockers and weak points in user journeys, you can use a balanced mix of qualitative and quantitative data, and apply targeted hypotheses with prioritization strategies (such as the refined RICE model). This way, product managers can uncover and act on high-impact opportunities, ensuring that every decision is data-driven, strategically sound, and aligned with both user needs and business objectives. And as a result, you steer clear of internal inefficiencies and focus on what really matters to drive success and satisfaction.