⚠️ Very unlikely series B companies will have all of this info. That is totally normal. This is an exhaustive list that eventually at later stages most cos will have.
- Quarterly ARR ramp (beg. ARR, new ARR, expansion ARR, churned ARR, ending ARR)
- Quarterly customer ramp (beg. logos, new logos, churned logos, ending logos)
- Cohort data by customer count and $ spent, to get to 12-month gross dollar retention and net dollar retention + qualitatively understanding reasons for churn and mechanisms for expansion (more seats, usage based, new products/cross sell) and how consistent the cohorts look.
- Quarterly P&L (2-3 years historic) + 2-3 year forecast, including cash balance
- Top customer list & monthly MRR
- Engagement figures (mostly relevant for application companies), number of paying seats, number of engaged users, cadence of engagement (DAU/MAU or WAU/MAU ratios) -- lot's of software products are paid for but not actively used. Will show up eventually in renewals.
- Go-to-market efficiency: I think what really matters here is gross-margin burdened paybacks. Others may look at cac ratio or magic number, ultimately all of these lead to payback in months. S&M $ from the P&L relative to the net new ARR added in each period gives you payback.
- Sales funnel: how leads are generated & eventually converted to paying users & annual/multi-yr contracts. For freemium products, what's the conversion from free users to paid+understanding the levers for this conv. For bottoms up products, how do they generate top of funnel?
- Go-to-market org: Number of quota carrying reps, quotas, on-target earnings (OTE), time to ramp, historical quota attainment by rep. You want to see consistency across reps and obviously the higher the attainment the better.
- Other than go-to-market efficiency, just general cash efficiency. How much is the company burning to add $1 of net new ARR. And where is the burn coming from? Is it S&M or R&D and understanding when you'll start to get leverage on R&D spend.
- Pipeline: probability weighted and unweighted pipeline + qualitatively understanding sale cycle, conversion of bookings to ARR to cash + decision making process for the buyer (are they ripping out other solutions? if so, why? how does the buyer think about the ROI?).
- Historical win loss data, reasons for losing (e.g., no budget, internal development, lost to competitor X, Y, Z)
This is half of the process. Customer references will paint the other half and bring to life the user love.