- In this paper, we propose an individual-level approach to diffusion and growth models. By zooming in, we refer to the unit of analysis, which is a single consumer instead of segments or markets and the use of granular sales data daily instead of smoothed e.g., annual data as is more commonly used in the literature. By analyzing the high volatility of daily data, we show how changes in sales patterns can self-emerge as a direct consequence of the stochastic nature of the process. Our contention is that the fluctuations observed in more granular data are not noise, but rather consist of accurate measurement and contain valuable information. By stepping into the noise-like data and treating it as information, we generated better short-term predictions even at very early stages of the penetration process. Using a Kalman-Filter-based tracker, we demonstrate how movements can be traced and how predictions can be significantly improved. We propose that for such tasks, daily data with high volatility offer more insights than do smoothed annual data.