Zooming In: Self-Emergence of Movements in New Product Growth

  • Authors:

  • Jacob Goldenberg;Oded Lowengart;Daniel Shapira

  • Affiliations:

  • School of Business Administration, Hebrew University, Jerusalem, Israel 91905, and Columbia Business School, Columbia University, New York, New York 10027;Department of Business Administration, Guilford Glazer School of Business and Management, Ben-Gurion University, Beer Sheva, Israel 84105;Department of Business Administration, Guilford Glazer School of Business and Management, Ben-Gurion University, Beer Sheva, Israel 84105

  • Venue:
  • Marketing Science
  • Year:
  • 2009

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Abstract

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.