Improving Data Analysis by Decoding Human Behavior
The Art of Predicting User Behavior: A Data Scientist’s Journey
Years ago, while working as a data scientist at an email application startup, I encountered a formidable challenge: predicting user actions. Despite employing conventional machine learning techniques, the results fell short of expectations. Unbeknownst to me, a profound lesson in understanding human behavior was about to unfold.
The Struggle with Numbers
For weeks, I immersed myself in tweaking model parameters and experimenting with various methods such as parameter tuning, feature manipulations, and augmentation processes. Yet, the needle barely moved. Frustrated, I approached my team leader, armed with proposals for more advanced mathematical models.
To my surprise, he wasn’t interested in complex algorithms. Instead, he offered a perspective that would change my approach entirely.
“I don’t think the problem is the model,” he said. “I have a feeling you need better features.”
A Shift in Perspective
Confused, I argued that we were using all available data. But my team leader saw beyond the numbers. “You’re viewing this as a math problem,” he explained. “It’s not. Users are people, like you and me.”
He then gave me an unconventional assignment: spend the next day using our application as a regular user, documenting my actions on paper.
The Revelation
As I followed his instructions, a pattern emerged. When we analyzed my actions along a time axis, it became clear that one action often predicted the next. This insight led us to incorporate features based on recent user actions into our model. The result? A significant improvement in prediction accuracy.
The Human Element in Data Science
My team leader smiled when I shared the news. “The best way to do feature engineering is to understand your users,” he said. “Put yourself in their shoes.”
He went on to explain a crucial concept: humans tend to see patterns and act accordingly. However, we often mistake randomness for patterns — a cognitive bias known as the gambler’s fallacy. This bias leads people to believe that past events influence the probability of future random events. In reality, humans tend to act in patterns, even when trying to avoid them.
Lessons Learned
This experience offered several valuable insights:
- Data Science and Human Behavior: Data science goes beyond mere numbers; it’s fundamentally about understanding human behavior.
- User Perspective: Some of the most profound insights emerge when you step away from the computer and engage with the product as a user.
- Pattern Recognition: Human actions often follow recognizable patterns, and identifying these patterns can significantly enhance predictive models.
- Gambler’s Fallacy: The gambler’s fallacy serves as a potent reminder of how the human mind processes randomness. It’s important to note that this is just one of many cognitive biases, and leveraging these biases can lead to substantial improvements in outcomes.
Conclusion
As data scientists, we often get caught up in the technical aspects of our work. However, this experience reminded me that at the heart of every data point is a human being making decisions. By understanding and modeling these human tendencies, we can create more accurate and useful predictive models.
The next time you’re stuck on a data science problem, consider stepping into your users’ shoes. You might just find the solution you’ve been looking for.
