In our D2C analytics work, I've seen how connecting unit economics to customer behavior transforms decision-making. Your three pillars approach cuts through complexity and focuses on what matters. The demand generation vs. capture distinction is especially relevant for our clients who struggle to measure marketing effectiveness. Would love to see you expand on identifying which metrics truly deserve dashboard real estate versus vanity metrics.
In that case I recommend focusing on operations, anything that makes the customer journey (aka value chain) run smoother, faster, or look for levers. Check out this case study from HubSpot. What they call the "customer file" is basically a training dataset that could be fed to any ML algo.
In our D2C analytics work, I've seen how connecting unit economics to customer behavior transforms decision-making. Your three pillars approach cuts through complexity and focuses on what matters. The demand generation vs. capture distinction is especially relevant for our clients who struggle to measure marketing effectiveness. Would love to see you expand on identifying which metrics truly deserve dashboard real estate versus vanity metrics.
In that case I recommend focusing on operations, anything that makes the customer journey (aka value chain) run smoother, faster, or look for levers. Check out this case study from HubSpot. What they call the "customer file" is basically a training dataset that could be fed to any ML algo.
https://wrap-text.equals.com/p/an-analysts-quest-to-improve-retention