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Krishna Poda's avatar

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.

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David Pawley's avatar

This was exactly what I needed to read about.

I’m part way through a Masters in data science and projects always require “understanding the business context” and “proposing a business case” when working with a dataset and building ML models. That’s the part I find most challenging because it’s outside my context.

More please!

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Ergest Xheblati's avatar

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

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