Why is aha moment wrong?

In the realm of product growth, the concept of ‘Aha Moments’ has gained significant traction. Inspired by notable examples from Facebook and Dropbox, professionals have come to identify these moments as the pivotal junctures in a user’s journey with a product. It’s the instant they comprehend the product’s value proposition. For instance, Facebook’s aha moment occurs when users connect with more than seven friends, making them realize its potential as a connectivity tool.

Aha moments are appealing because they represent a harmonious blend of quantitative and qualitative insights into user behavior. These events can be delineated and monitored on dashboards, allowing teams to channel resources into amplifying these moments. However, a critical oversight exists in this approach.

The Issue with Aha Moments

The common belief is that aha moments catalyze users to transition from being passive to active. This assumption justifies the vast resources dedicated to promoting such moments. The presumption is simple: expose users to these moments, and they’ll shift from passivity to activity.

But, more often than not, this isn’t the case. Active users experience these aha moments primarily because they’re already engaged. For instance, individuals predisposed to using Facebook will inevitably connect with friends quicker. This correlation extends to monetization. Users who exhibit certain behaviors might seem more inclined to become paid users. In reality, those with a higher intent to pay often demonstrate these purchase indicators from the outset.

Therefore, resources invested in fostering these moments might not yield the anticipated ROI. Essentially, businesses might merely be targeting users already predisposed to engagement, inundating them with notifications, marketing tactics, and incentives. This doesn’t genuinely add substantial value to the business.

The Inherent Flaws

Two inherent problems make this mistake nearly unavoidable:

1. They are users’ choice: These moments are essentially user behaviors, stemming from user choices laden with latent variables. One dominant latent variable is the user’s inherent inclination to be active. Hence, high-intent users will naturally gravitate towards these moments, while their low-intent counterparts might not.
2. High Correlation with Desired Outcomes: A strong correlation usually exists between aha moments and the desired user activity. Given that these moments arise from active user choices, an endogeneity issue arises, making it challenging to establish a definitive causal relationship.

Rethinking the Approach

Given this knowledge, how should businesses proceed?

1. Use Aha Moments as the proxies they are: Accept aha moments for what they truly are — indicators of user intent. They can serve as early signals of a user’s potential activity or inclination towards monetization. When viewed as indicators or predictors, these moments retain their utility. For instance, they can be employed to train machine learning models, allowing them to achieve scale sooner.
2. Establishing Causality: If the goal is to establish a causal relationship, then the treatment needs to be exogenous. This requires placing certain features or experiences under the purview of the business, rather than leaving them to user discretion. One could randomly assign users to specific features or incentive programs, then compare long-term performance metrics with those lacking access. This methodology underpins AB testing and online experimentation.

In Conclusion

When evaluating aha moments:

• Recognize that if they’re based on user choice, causality can’t be definitively established.
• View them as indicators or predictors and leverage them accordingly.
• To draw causal connections, ensure the treatment is exogenous.

As the digital landscape evolves, it’s crucial for stakeholders to understand the true nature of aha moments. They might not be the game-changers they’re perceived to be, but when approached with a discerning eye, they can still offer valuable insights.

For further discussions or queries, please reach out. — Pragmatic Data Scientists