Growth Product Manager: Skills, Characteristics, and Responsibilities

Overview of the Growth Product Manager Role

A growth product manager (GPM) is a professional responsible for driving specific metrics across acquisition, activation, conversion, retention, and monetization to achieve product-led growth. In this section, we will outline the key skills, characteristics, and responsibilities of a GPM.

Key Skills

• Standard PM skills:
  • Roadmapping
  • Prioritization
  • Leadership
  • Communication
  • Customer research
  • Stakeholder management
  • Shipping products
• Growth-related skills:
  • Acquisition
  • Activation
  • Conversion
  • Retention
  • Monetization
  • A/B testing
  • Growth models and loops

Characteristics

• Curious
• Adaptable
• Collaborative
• Influential
• Resilient

Responsibilities

1. Drive specific metrics across acquisition, activation, conversion, retention, and monetization
2. Develop growth strategies, including channel distribution, growth models, and loops, and monetization strategies
3. Use data-informed and analytical processes, including rapid A/B testing and productizing learnings to drive iterations
4. Understand customer struggles and pain points to complement data-informed insights and hypotheses

Becoming a Growth Product Manager

To become a GPM, individuals should focus on developing the necessary skills and characteristics. Product management experience is typically expected, and aspiring GPMs can benefit from training programs, online courses, and thought leadership content.

Future of the Growth Product Manager Role

The future of the GPM role is likely to involve continued growth in popularity as more businesses shift to product-led growth. As the role evolves, we may see new insights and strategies for success, as well as shifts in product management career trajectories.

Example of A/B Testing in Python

import numpy as np

# Define the sample size
sample_size = 1000

# Generate random samples for the control and treatment groups
control_group = np.random.normal(0, 1, sample_size)
treatment_group = np.random.normal(0.5, 1, sample_size)

# Calculate the mean and standard deviation of each group
control_mean = np.mean(control_group)
control_std = np.std(control_group)

treatment_mean = np.mean(treatment_group)
treatment_std = np.std(treatment_group)

# Print the results
print("Control Group:")
print(f"Mean: {control_mean}")
print(f"Standard Deviation: {control_std}")

print("\nTreatment Group:")
print(f"Mean: {treatment_mean}")
print(f"Standard Deviation: {treatment_std}")