It does happen, quota setting goes wrong. There are three high-level types of errors (1) poor quality data (2) seller and business disconnect (3) payouts vs. returns are not aligned with finance.

It goes wrong because of low quality data: specifically Low Persistency and Poor Quota Relevancy

• Top Down Focus
  • The 'law of large numbers' works at the corporate level, but accuracy declines at lower levels in an organisation
  • Ignoring the outside view or bottom up view creates a disconnect between individual goals and opportunities
  • Top down goals are finance driven, not opportunity driven
• Historical Bias
  • Data inputs for quota setting largely rely on historical performance at both the corporate and individual level
  • This ignores market potential, changes in product demand, shifts in install base and share of wallet
  • Additionally, recent events are over-weighted – e.g. the big sale that happens once every five years or other ‘blue whale’ deals
• Intuition
  • Reliance on sales leadership and front line managers to intuitively guess market potential
  • Intuition stacking – intuitions of leadership are adjusted by intuition of direct reports and front line
  • Often it is good to practice to consider: “whenever we can replace human judgement by a formula, we should at least consider it". This approach drives equity in quota creation.
• Ineffective Algorithms
  • Algorithms do not properly adjust for regression to the mean and overweight most recent performance
  • Algorithms do not contain enough variables for forecasting: external data, customer data (e.g. install base, share of wallet, procurement cycles, usage, engagement)
  • Adjustments are not made to account for coverage / capacity and resource costs
• Technology Limitations
  • Technology is not in place to manage data inputs for forecasting bottom-up detail
  • Heavy reliance on excel - or gSheets - to manage the process

It goes wrong because of a disconnect between a seller and business performance

• Gap between pay and business exacerbated by quota quality, steep pay curves and high leverage
• Attainment heavily influenced by external factors such as quota quality, goal size, location, and role
• This will manifest in one of four trends in the compensation attainment and payout data:

High Variance:

High variance or abnormal performance distributions indicate poorly aligned goals

Low Persistency:

High attainment is random and rarely repeated. For example, 2% of the population sustains top 20% attainment across all 4 quarters vs. 6% sustained top 20% in two quarters

Regression to the Mean: