Bing shares model for improving website performance despite new feature challenges

In the face of the common challenge of maintaining fast website performance while adding new features, Bing has presented a helpful model that tackles this dilemma. The model comprises different components and provides valuable insights that other teams can use to enhance their web performance.

We have simplified it into key points for easy understanding:

Organizational Commitment

• • Performance isn’t compromised for new features; both are prioritized.
  • Adequate resources allocated for performance efforts.

Data & Tools

• • Quantitative data and tools are crucial for monitoring performance.
  • User-perceived metrics tracked to ensure desirable business outcomes.
  • Real User Monitoring (RUM) data collected from actual users.
  • Performance tools integrated throughout the development pipeline.

A/B Experimentation

• • A/B testing platform evaluates performance in real-world scenarios.
  • Pre-experiment gates are set to maintain performance standards.
  • Automated alerts trigger if performance significantly regresses during experiments.

Live Site Tools

• • Dashboards and reports track real-world performance trends.
  • Anomaly detection tools identify unusual patterns for rapid issue identification.

Team & Process

• • A dedicated performance team guides improvement efforts.
  • Workstreams focus on enhancing performance in partnership with other teams.
  • Clear goals and accountability mechanisms ensure performance targets are met.

Policy of No Regressions

• • A stringent policy prevents shipping performance regressions.
  • New features must maintain or improve performance; formal sign-off process.
  • Exceptions are allowed with VP approval within a limited regression budget.

Continuity Over Time

• • Consistency over time is ensured despite organizational changes.
  • Knowledge transfer or institutionalization helps maintain execution.

All seven components of the Bing Model for Driving Performance are essential and interdependent in their ways. However, if we were to prioritize the most critical ones, they would be:

1. Organizational Commitment: Strong organizational commitment sets the foundation for the entire model. Other efforts may not receive the necessary resources and attention without this commitment.
2. Data & Tools: Excellent quantitative data and tools are crucial for monitoring and improving performance. This component enables the identification of issues and tracking improvements over time.
3. Policy of No Regressions: Enforcing a policy that prevents shipping performance regressions ensures that new features or changes do not compromise the hard-earned gains in performance. This policy sets a high bar for maintaining performance excellence.

These three components lay the groundwork for the model’s success.