Context & Challenge
Allma is an AI platform that helps Product Marketing teams generate and manage go-to-market content at scale. Their technology uses LLMs to transform product knowledge (docs, slides, websites) into various marketing assets while maintaining brand voice and accuracy.
- Early-stage AI startup with a barebone MVP and 50 beta customers
- Engineering team responding to random user requests
- No clear prioritization framework or strategic direction
- Limited resources requiring careful prioritization
Approach
- Created comprehensive jobs-to-be-done framework by analyzing sales discovery calls and interviewing beta users (PMMs at tech companies).
- Mapped the product marketing pain points to identify highest-value automation opportunities.
- Developed prioritization matrix using Impact/Confidence/Effort scoring to evaluate features
Strategic Recommendations
Based on the analysis, I recommended three strategic priorities:
- Prioritize Content Quality
- Implement configurable templates and persona support
- Highest confidence in driving adoption
- Clear differentiation from generic AI tools
- Expand Knowledge Integration
- Critical for reducing time-to-value
- Foundation for advanced features
- Selective Feature Expansion
- Start with high-impact personalization capabilities
- Postpone resource-heavy features like slide deck building
- Maintain engineering focus on improving core AI generation
What We Decided Not to Do
- Launch management workflow (despite customer requests)
- Slide deck builder (too resource-intensive)
- Asset repository features (low strategic value)
- Complex approval workflows (outside core value proposition)