One of the areas that becomes critical as a startup hits the scalable business model phase is sales forecasting. Early on, it’s easy to build a bottom-up sales forecast using inputs like number of quota-bearing sales reps, size of quota, and estimated quota attainment. Only, as the business gets bigger, and has more current and historical sales data, forecasting needs to become more scientific.
Here are a few metrics to incorporate for more accurate sales forecasting:
- Higher in the Y-funnel metrics like number of SDR demos/appointments required for a sales accepted lead (SAL) and number of SALs to win a deal
- Historical win rate by sales rep and deal type (size, account type, etc.)
- Average sales cycle by sales rep and deal type (to be able to flag deals that are at risk due to falling outside the norms)
- Projected bookings based on statistical models of historical data
- Best case/worst case scenario planning
Sales forecasting becomes more critical as the business grows and is a key part of high performing companies. Consider reporting and analytics systems to make sales forecasting more accurate.
What else? What are some more thoughts on improving the accuracy of sales forecasting?
David Cummings on Startups 