GTM for DDPs

Deep Domain Problems (DDPs) require specialized knowledge, extensive expertise, and innovative technical solutions. Drawing from real-world experiences in lending, supply chain optimization, and forex risk management, here's a comprehensive guide on taking your DDP solution to market effectively.

1. Validate Market Readiness

Assess Solution Maturity

Follow Betalectic's two-phase approach: first make it work, then make it work well, as described in The Evolving Role of Programmers
Example: In our supply chain case study, the initial solution reduced delays by 7% and fuel costs by 10% in six months, proving market viability
Validate that edge cases are handled—like the loan approval system managing cases with strong business plans despite weak credit scores, as detailed in our lending case study

Evaluate Market Timing

Monitor regulatory changes that might affect adoption, as discussed in our guide to identifying DDPs
Consider market evolution—as seen in India's transformation from manual processes to digital solutions over the past 15 years
Assess if the industry is ready for change, like how the lending industry evolved to accept automated decision-making systems

2. Build Domain-Expert Partnerships

Identify Key Stakeholders

Partner with industry veterans who understand domain complexities, following our systematic approach
Example: In our lending analysis, engage underwriters, loan officers, and analysts who each bring unique perspectives:
- Underwriters focus 70% on data
- Loan officers weight 50% on qualitative factors
- Analysts rely 90% on models

Create a Knowledge Transfer Framework

Document domain expertise using ubiquitous language
Example: In supply chain optimization, clearly define terms like "Route" (different meanings for planners vs. drivers)
Build decision trees that capture expert knowledge, like the comprehensive loan approval process tree from our lending case study

3. Structure Your Go-to-Market Strategy

Define Your Market Position

Focus on deep domain challenges rather than surface-level problems
Example: Betalectic's experience shows success comes from avoiding simple e-commerce apps in favor of complex domain problems
Position solutions as transformative rather than just digitizing existing processes

Target Market Segmentation

Identify industries most impacted by the deep domain problem
Example: In forex risk management, target global businesses dealing with multiple currencies and complex regulatory frameworks, as demonstrated by WiredUp's experience
Consider market size—India's large population presents unique scaling opportunities

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Best Practices When Working with AI

As discussed in Coding with AI and Sunk Cost Fallacy:

Avoid the sunk cost fallacy when using AI tools
Set clear directions before engaging AI
Use AI as a consultant rather than a solution provider
Balance AI capabilities with domain expertise

Conclusion

Taking a DDP solution to market requires balancing technical excellence with domain expertise. Success comes from strong partnerships with domain experts, comprehensive support structures, and a long-term focus on solving deep industry challenges. As demonstrated by successful implementations at Betalectic, WiredUp, and Finezzy, the key is to maintain deep domain understanding while leveraging technology to create scalable, effective solutions.

Remember that market entry is just the beginning—continuous evolution and adaptation to changing domain needs are essential for long-term success. The most valuable opportunities come from solving complex, high-impact challenges that demand both domain expertise and technical innovation.