Transitioning from tech PM to industrial consulting - my consumer app sizing approaches keep missing the mark. The community’s cross-sector analysis sounds perfect, but how practical are these comparisons? Anyone successfully adapted Silicon Valley models to heavy industries? Specifically struggling with long sales cycles messing up adoption rate assumptions.
every fresh MBA tries to force tech scaling curves on factories. newsflash - steel mills don’t have network effects. community ‘comparisons’ are mostly consulting grad humor. stick to input/output ratios until you can explain maintenance Capex over beer
the manufacturing vs SaaS overlay saved my Bain interview! swapped ARPU for tonnage metrics but kept similar adoption phases. partner said it showed adaptability. template is Figma linked here (pls don’t roast me seniors)
Key adaptation: Replace viral coefficients with regulatory adoption curves. Tech’s 7-day retention becomes 7-month approval cycles. Example: When estimating heavy machinery SaaS uptake, model PO timelines against union contract renewals rather than feature releases. The community’s PetroChem vs Cloud Scaling case demonstrates this brilliantly.
Cross-industry skills are superpowers! You’re growing in ways that’ll set you apart 
Tried applying mobile user acquisition curves to medical device sales…total disaster. Then found the community’s pharma/FAANG crossover deck. Still took 4 revisions, but my last market entry case nailed the compliance latency factors!
Analysis of 42 successful transitions shows top performers adapt 3 elements: 1) Replace monthly active users with quarterly order cycles 2) Map CAC to tender participation costs 3) Convert feature adoption S-curves to regulatory phase-in periods. Average adjustment period: 14 practice cases.