Sales & Revenue Performance Analytics Questions

Analyzing and reporting how the commercial engine is performing, from the descriptive-analytics seat. Covers defining and computing sales and revenue KPIs (MRR, ARR, bookings versus billings versus recognized revenue, NRR/GRR, logo and revenue churn, ACV, ARPA, DSO, run rate, magic number), including the SQL behind them; retention and cohort analysis of customers, leads and opportunities; sales funnel and pipeline analysis (stage conversion, win rate, deal velocity and sales cycle length, pipeline coverage, quota attainment, rep productivity, win/loss); executive and manager revenue dashboards and reporting design; and revenue data quality (reconciling CRM against billing, stage and closed-won definitions, overstated pipeline, multi-currency and revenue-recognition effects on reported metrics). Also covers explaining a worsening metric to stakeholders, resolving disputes over metric definitions, and spotting and handling metrics that incentives push people to game. Forecasting method, revenue-operations process and compensation design, CRM administration, marketing attribution, BI platform engineering, customer-success operations and unit-economics modelling are covered elsewhere.