Building Brands

Abstract

Which marketing strategies are most effective for introducing new brands? This paper sheds light on this question by ascribing growth performance to firms' postlaunch marketing choices. We decompose the success of a new brand into its ultimate market potential and the rate at which it achieves this potential. To achieve this aim we formulate a Bayesian dynamic linear model (DLM) of repeat purchase diffusion wherein growth and market potential are directly linked to the new brand's long-term advertising, promotion, distribution, and product strategy. We perform the analysis on 225 new-brand introductions across 22 repeat-purchase product categories over five years to develop generalized findings about the correlates of new-brand success. We find that access to distribution breadth plays the greatest role in the success of a new brand, and that investments in distribution and product innovation lead to greater marginal increases in sales for new brands than either discounting, feature/display, or advertising. Moreover, distribution interacts with other strategies to enhance their effectiveness. These findings underscore the utility of extending marketing mix models of new-brand performance to include product and distribution decisions.diffusion, new products, marketing mix, dynamic linear model, empirical generalization

Drivers of and Barriers to Organic Purchase Behavior

“…Previous literature has presented conflicting evidence, with Bezawada and Pauwels (2013) finding negative price elasticities and a positive effect of the availability of organic options and Ngobo (2011) finding the opposite. In line with standard micro-economic theory we expect a negative effect of price and assume that availability positively affects the purchase of organic products (Ataman, Mela, and van Heerde 2008). We do not put forward specific hypotheses on these two variables, as these effects are rather obvious.…”

Inferring causal impact using Bayesian structural time-series models

“…Other special cases include autoregressive (AR) and moving-average (MA) models. These models define autocorrelation among observations rather than latent states, thus precluding the ability to distinguish between state noise and observation noise [Ataman, Mela and Van Heerde (2008), Leeflang et al (2009)].…”

Estimating Cannibalization Rates for Pioneering Innovations

“…Equations (3) and (4) allow for more flexibility in base sales than the random-walk specification (i.e., 0cbjt = 0cbjt−1 + 0 cbjt used in Neelamegham andChintagunta (2004), van Everdingen et al (2005), or Winer (1979). It is also more flexible than previous DLM applications in marketing (e.g., Ataman et al 2008, Lachaab et al 2006, van Heerde et al 2007) that used nonstochastic effects; i.e., cbjt = 1cbj . In §5 we compare our model with (i) a benchmark model with time-invariant (rather than time-varying) effects and (ii) a benchmark model with instantaneous (rather than gradual) adjustment.…”