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# Two Products, Two Different Math Engines

**[Medium](https://daily.dev/sources/medium_js)** · 6 min read · 0 upvotes · 0 comments

## Summary

Consumer and enterprise AI products require fundamentally different estimation frameworks. B2C forecasting relies on multiplicative rate chains (conversion, retention, ARPDAU) where small rate shifts cause outsized revenue impact, while B2B enterprise forecasting deals with discrete, lumpy outcomes where losing two accounts can break projections entirely. Three real-world scenarios illustrate how applying the wrong mental model — consumer intuitions to enterprise math or vice versa — leads to plans that are internally consistent but entirely wrong. The piece also explores how AI agents affect each model differently: improving personalization in B2C at the cost of inference spend, and flattening compliance/delivery step-functions in B2B to unlock mid-market segments.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@ankursaran_59645/two-products-two-different-math-engines-58e26b2cc1b3>

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Tags: [#business](https://daily.dev/tags/business), [#product-management](https://daily.dev/tags/product-management)

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