We engineer predictable revenue models for high-growth SaaS companies. Explore how we apply data rigour to complex marketing challenges.
How we re-architected DataSynthetix's lead generation pipeline, moving from a volume-based approach to a strict account-based model targeting Fortune 500 AI labs.
High churn rate during the initial 14-day trial period due to complex setup requirements.
Implemented a progressive profiling flow and automated setup using predictive defaults.
Stagnant growth in mid-market segment; needed to move upmarket to enterprise clients.
Developed an ABM strategy targeting specific compliance pain points of Tier 1 banks.
Traditional marketing tactics were rejected by the core developer audience.
Shifted to utility-first content, creating open-source tools that solved adjacent problems.
Value misalignment in the pricing tiers leading to massive downgrade velocity.
Conducted a Van Westendorp analysis and restructured tiers based on feature usage data.
Transitioning from a traditional sales-led motion to product-led growth without cannibalizing enterprise deals.
Implemented a hybrid model, using the self-serve product as a lead qualification engine for sales.
Scaling paid search spend resulted in diminishing returns and unacceptable CAC levels.
Restructured account taxonomy, implemented offline conversion tracking, and launched hyper-specific landing pages.
Proof of Impact
Audited data from a Series A SaaS client following the GrowthEngine sprint.
| Key Growth Metric | Baseline | Post-Sprint (Day 90) | Performance Delta |
|---|---|---|---|
| Qualified Pipeline ARR | $420,000 | $1,850,000 | +340% |
| Blended CAC | $4,200 | $2,150 | -48.8% |
| CAC Payback Velocity | 16.5 Months | 7.2 Months | -56.4% Faster |
| Demo-to-Opportunity | 18% | 41% | +127% |
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