Effective performance appraisal in hypergrowth environments

There are many challenges in driving effective performance appraisal in hypergrowth environments. Some of them are

• Very likely, leaders have expanded spans
• The constant change in people's goals and projects
• Inability to observe input behaviors of your span consistently

To be effective, one should start with non-negotiable goals of performance appraisal. In my opinion, there ate two

1. Fairness
2. Disproportionate rewards to the people with the most impact

Viewed from this lens, it becomes apparent that career ladder and cross-calibrations are essential elements of the appraisal process.

The argument against making a career ladder in a rapidly growing org is around the rapidly changing expectations (outcomes and i/p behaviors) from different levels. But, I found without a base layer describing the scope and impact expected for each role and a good delineation of i/p behaviors at L0 and L1 levels, it becomes tough to ensure fairness.

We should revisit the career ladder every performance cycle and update it with the emerging behaviors that show a solid correlation to outcomes in that role.

It is also imp to invest in cross calibrations across spans of different leaders. In those cross-calibration discussions, we should spend a disproportionate amount of time on people in the top quartile of the performance bar so you direct the disproportionate rewards effectively.

The standard argument against investing in cross-calibrations is that the impact delivered by people is transparent in an early-stage setup. However, I found that outcomes are not always correlated to the quality of i/p. Without cross-calibration, the focus on the quality of i/p is lost.

This opens up another can of worms. Whether rewards should be based entirely on outcomes or whether the quality of i/p has a role. That's an idea of a post for another day.