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Found’s rapid user growth exposed unclear pricing logic and declining retention.I led a quant + qual conjoint study identifying high-value feature bundles from 50 K+ combinations, aligning Product, Marketing, Finance, and Data Science on a scalable pricing roadmap.The work increased CLTV 14 %, reduced month-one churn 9 %, and raised pricing comprehension 22 %.

• Unit economics were unsustainable (CLTV/CAC = 2.7) and growth was slowing.
• Feature-value hierarchy was unclear, leading to confused buyers and inconsistent adoption.
• Leadership teams lacked a shared, evidence-based pricing strategy.

• Designed and led end-to-end conjoint and discrete-choice modeling study with D2C customers.
• Partnered with PM, Finance, Marketing, and GTM to model feature value and test MVP bundles.
• Unified teams around an experiment-driven pricing framework and accelerated roadmap adoption.



CLTV/CAC ↑ 22 % | CLTV +14 % | M1 Churn −9 % | Pricing comprehension +22 %
These findings clarified which features to elevate, which to retire, and how to align our roadmap around true value drivers. The universal win was organizational: we unified the company around a shared lexicon and mental model of our optimal package, transforming a once-messy pricing model into a clear, trust-based offering that improved conversion quality and reduced early-stage churn through transparency and clarity.

Unified the company around a shared lexicon and mental model of our optimal package, transforming a once-messy pricing model into a clear, trust-based offering that improved conversion quality and reduced early-stage churn through transparency and clarity.