Zooming In: Self-Emergence of Movements in New Product Growth
Coauthor(s): Oded Lowengart, Daniel Shapira.
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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-Filterbased
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.
Source: Marketing Science
Goldenberg, Jacob, Oded Lowengart, and Daniel Shapira. "Zooming In: Self-Emergence of Movements in New Product Growth." Marketing Science 28, no. 2 (Spring 2009): 274-92.