Harnessing Data to Predict the Elusive State of Flow

3 min read

https://www.linkedin.com/in/jaimegacitua/

What is Flow?

The concept of “flow,” first introduced by psychologist Mihaly Csikszentmihalyi in the 1970s, describes a mental state in which a person becomes fully immersed in an activity, achieving heightened focus, creativity, and productivity. Achieving a flow state can significantly boost work performance, so it is no surprise that businesses and individuals are eager to harness this powerful mindset. This piece will explore how we can use data analytics to predict when someone works in a flow state, empowering them to reach peak performance more frequently for better worker output and increased employee satisfaction and even customer satisfaction depending on an employee’s role in the organization.

Data Collection

To predict the state of flow, we first need data. At CKM, we have traditionally handled open problems like this using issue trees. We will break down the problem systematically, using “first principles thinking” and covering the space of possibilities with a MECE (mutually exclusive, collectively exhaustive) tree. We will not expand on this concept here, but we will state that this methodology has proven effective for decades.

The first data domain involves tracking personal performance indicators such as time on task, task completion rates, the nature of the work done, and the sequence of the tasks handled. For example, one hypothesis is that people might prefer to start their day with easier tasks before tackling the more complex challenges and achieving the desired flow state. For example, this type of data can be gathered from workflow systems like ServiceNow, HPSM, Ivanti, and Remedy in call center operations.

A second piece of data related to call center operations in this example is telephony or customer interaction software, which will additionally provide insights related to sentiment, the language used, call length, content, and more.

With the increasing popularity of wearable technology, a third data domain can include biometric information like heart rate variability, skin conductance, and sleep patterns, which can provide insight into a person’s physiological state. This piece has legal, privacy, and ethical concerns that might rule out this entire data domain for this study. We are keeping it here to explore what is possible and its limits.

A supervised learning predictive model requires a target variable. A pragmatic approach could be people’s self-reported data about when they achieved a flow state. Additionally, we could use productivity and efficiency metrics derived by T-K, such as throughput rate, perhaps coupled with biometric data.

Predictive Modeling

Machine learning algorithms derived from data and feature engineering can examine the correlations between performance indicators and flow experiences. By understanding these relationships, we can identify the conditions that lead to a flow state and establish a predictive model.

Regarding specific model architecture, the popular Gradient Boosting Machines or an AutoML engine can accelerate the research. A route that might be promising is using Hidden Markov Chains, or Kalman Filters, which are model architectures designed to predict the state of a system, even when it is impossible to measure the state directly. In this study, the state is flow, and the system is a person.

Optimizing Work Conditions

Armed with this predictive model, we can now focus on our practical objective: optimize work conditions to facilitate flow in our team. The predictive model will help determine which variables correlate with the flow state. These patterns might be widespread across the whole team or personal to the individual.

For example, managers can adjust work assignments to align with individual skills and preferences, while employees can create an environment conducive to focus on creativity. In addition, using productivity tools can help minimize distractions while providing real-time feedback on performance. Managers can know when users are interrupted by calls, chats, etc., and whether multi-tasking impacts productivity and flow positively or negatively and by how much.

Conclusion

The state of flow is a powerful driver of productivity, creativity, and satisfaction. Leveraging data to predict when someone works in a flow state allows us to optimize work conditions and maximize performance and employee fulfillment. Looking forward to seeing the future when even more advanced technologies and sophisticated tools emerge to help individuals and organizations tap into this desirable mental state.