Unleashing the Power of Social Apps and AI: A Benchmarking Guide

Unleashing the Power of Social Apps and AI: A Benchmarking Guide

Introduction:
Social apps have become an integral part of our daily lives, offering us the opportunity to connect, share, and engage with others. However, achieving sustainable growth and success in the competitive social app landscape requires careful benchmarking and analysis of key metrics. Similarly, the emergence of advanced AI models like GPT-3.5 series has sparked a new wave of possibilities and debates about the future of artificial general intelligence (AGI). In this article, we will explore how to benchmark social app growth and delve into the birth of AGI.

Benchmarking Growth for Social Apps:
When it comes to benchmarking growth for social apps, defining the core metric is crucial. For most consumer social apps, the daily active users (DAUs) metric is essential as it signifies consistent product usage. Alternatively, apps with less frequent use cases may consider weekly active users (WAUs). The desired growth rates for monthly user growth in seed-stage consumer social companies are as follows: OK - 20%, Good - 35%, Great - 50%. It is important to note that organic growth is preferable, as social apps should inherently be viral and organically attract users. Relying heavily on paid sources may indicate a need to reevaluate acquisition strategies.

Additionally, the DAU/MAU (Daily Active Users to Monthly Active Users) ratio is a significant benchmark for social apps. A smile-shaped or skewed-right L-ness curve indicates that users are actively incorporating the app into their daily lives. The benchmark for L5+ performance, measuring near-daily use behavior, is as follows: OK - 30%, Good - 40%, Great - 50% or higher.

Retention is another critical metric for social apps. N-day retention, focusing on the percentage of users from an original cohort who continue using the app on specific days (d1, d7, and d30), is a primary indicator. The benchmark for n-day retention is as follows: OK - d1 50%, d7 35%, d30 20%, Good - d1 60%, d7 40%, d30 25%, Great - d1 70%, d7 50%, d30 30%. It is common to observe a flattening of the retention curve between d7 and d14, followed by a plateau.

For companies transitioning from a tool to a network, weekly retention becomes relevant. The benchmark for weekly retention is as follows: OK - w1 40%, w4 20%, Good - w1 55%, w4 30%, Great - w1 75%, w4 50%. It is important to analyze metrics over time and evaluate cohort trends for a comprehensive understanding of a social app's performance.

The Birth of AGI:
The emergence of advanced AI models like GPT-3.5 series has brought us closer to the realm of artificial general intelligence (AGI). These models excel in generating text based on user instructions, showcasing improved zero-shot capabilities compared to their predecessors. With long-term memory and increased input-output capacity, GPT-3.5 holds promise for creative tasks, such as brainstorming, drafting, and presenting information in innovative ways.

However, limitations remain. GPT-3.5 struggles with mathematical operations, generates false information about the real world, writes subpar code, and falls short of passing Turing, SAT, or IQ tests. While it may not replace Google's search capabilities entirely, GPT-3.5 exhibits the ability to provide more direct and legible answers. Nevertheless, caution must be exercised as these answers may not always be accurate or sourced.

The rapid advancements in Reinforcement Learning via Human Feedback have played a significant role in accelerating progress towards AGI. This newfound speed has contributed to the belief that we are witnessing the birth of AGI. However, the debate surrounding the true potential and implications of AGI remains unresolved.

Actionable Advice:

1. Foster Organic Growth: Prioritize strategies that encourage organic user acquisition and retention. Focus on creating a compelling and viral product that users naturally want to share with their friends.

2. Continuously Monitor and Analyze Metrics: Regularly track and evaluate key metrics such as DAU/MAU ratio, retention rates, and cohort trends. Identify areas for improvement and make data-informed decisions to optimize growth.

3. Embrace AI's Creative Potential: Explore the creative capabilities of advanced AI models like GPT-3.5 series. Utilize them in tasks that prioritize creativity over precision, while considering external assets to compensate for limitations in accuracy.

Conclusion:
Benchmarking growth for social apps and understanding the potential of advanced AI models like GPT-3.5 series are essential in today's tech landscape. By defining core metrics, monitoring key indicators, and embracing the creative potential of AI, developers and entrepreneurs can unlock new levels of success and innovation. As we navigate the ever-evolving digital landscape, it is crucial to stay informed, adapt to emerging trends, and leverage data-driven insights to drive growth and shape the future of social apps and AI.