Launching a product feels like an odyssey, full of highs, lows, and unknown challenges. As a product owner at Revolut, I had the unique experience of turning a vague idea into a credit product used by thousands across different countries. Along this journey, one of the most elusive challenges was finding the product-market fit.
How do you know if your product is viable and ready to scale? Even after completing product discovery, defining value propositions, and launching a Minimum Viable Product (MVP) with real customers, doubts lingered. There was a noticeable performance difference between our launch markets, raising questions about the best time to scale in each region.
Product-Market Fit is the ultimate success metric in product development. It means your product effectively solves a problem for a target market segment in a repeatable manner. But understanding and pinpointing exactly why you do or don’t have it can be tricky. Abstract discussions led to subjective opinions and anecdotal evidence. It was clear we needed a more structured decision-making process tailored to the unique aspects of credit products.
With my product being an unconventional one within our credit products, filled with many hypotheses about the market and customer willingness to pay, I decided to create a systematic approach to finding product-market fit. This framework, which was later extended to traditional credit products like credit cards and personal loans, may not be universally applicable but worked for us and can inspire other credit product managers.
I started by defining key questions for validation during the MVP stage:
1. Is our target market present?
2. Is the problem significant enough for customers to pay to solve it?
3. Is our model repeatable and eventually profitable?
To answer these questions, we created metrics for continuous monitoring of product-market fit and assessed the impact of any changes made.
Product-Market Fit Metrics
1. Is the market there?
The first fundamental question: Is our market truly as envisioned? We began with surveys and internal customer segmentation. The Sean Ellis test measured users’ emotional connection to our product. Segmenting internal data by factors like age, gender, income, and product usage profile helped us understand our audience better.
Assumptions: We broadly defined our target group in different countries by their convenience- and affordability-driven behaviors, lifestyle spending habits, millennial demographics, and medium+ income.
Learnings: Surveys and segmentation busted our initial assumptions. We discovered our core audience in one country was liquidity-driven millennials with medium income and medium risk, unlike our expectations. This insight led us to realign our value proposition and risk criteria.
2. Are we solving the problem for target customers?
Next, we needed to know if we were solving the customers’ problems effectively. The Net Promoter Score (NPS) was crucial here, indicating how likely customers were to recommend our product. Customer feedback collected with the NPS helped us identify areas for improvement in our product’s user interface and core features.
Assumptions: We expected consistent performance across different countries.
Learnings: NPS scores varied significantly between countries, revealing different levels of satisfaction and highlighting the need for region-specific adjustments in our product offerings.
3. Can we attract target customers at a reasonable cost?
We then turned our attention to attracting customers at a reasonable cost. The Cost of Acquiring a Customer (CAC) became key to evaluating product viability, paired with metrics like application rate, approval rate, take-up rate, and probability of default.
Assumptions: We expected high approval and application rates.
Learnings: Discrepancies surfaced. While one country aligned with our projections, another faced challenges, particularly in attracting and approving customers, prompting us to refine our approach.
4. What are the product usage patterns?
Understanding product usage was critical. We monitored metrics like spend percentage of limit, revolving rate, early repayment rate, and operational default rate to observe user behavior.
Assumptions: We expected high utilization and activation rates.
Learnings: User behavior differed, with high early repayment and low revolving rates indicating sensitivity to debt and interest. Country-specific patterns showed the need for tailored strategies.
5. Is the product sticky?
Sustaining recurring revenue required product stickiness. Metrics like Monthly Active Percentage (MAP) and Daily Active Percentage (DAP) helped gauge user retention and engagement.
Assumptions: We expected gradual engagement decay over two years.
Learnings: Dormant users were a challenge, but active users showed consistency. We identified the need for an engagement plan to convert new users into habitual ones.
6. Does the business model make sense?
Finally, we aggregated all metrics into a product viability ratio: the LTV/CAC ratio. This measure compared lifetime customer revenue with acquisition costs, guiding our investment decisions.
Assumptions: Comparable unit economics in both countries at MVP stage.
Learnings: One country showed a promising LTV/CAC ratio, while another lingered near breakeven, necessitating a review of the business model.
Growth Potential and Targets
The product-market fit framework helped test growth hypotheses before scaling. Metrics like new sign-up growth and product penetration assessed future expansion opportunities. Setting product-market fit targets depended on a variety of sources, including country practices, product benchmarks, and cross-industry standards.
Framework Application
The framework integrated into our routine, guiding regular evaluations and adjustments. Successful products received increased marketing efforts and new feature development, while underperforming ones were reevaluated.
The biggest lesson from this process was the realization that a Global MVP approach can be tricky. It performed well in one country but fell short in another, teaching me to be cautious about a one-size-fits-all strategy for launching products in different regions.