Will It Ever Fly? Modeling the Takeoff of Really New Consumer Durables

Abstract

A consistent pattern observed for really new household consumer durables is a takeoff or dramatic increase in sales early in their history. The takeoff tends to appear as an elbow-shaped discontinuity in the sales curve showing an average sales increase of over 400%. In contrast, most marketing textbooks as well as diffusion models generally depict the growth of new consumer durables as a smooth sales curve. Our discussions with managers indicate that they have little idea about the takeoff and its associated characteristics. Many managers did not even know that most successful new consumer durables had a distinct takeoff. Their sales forecasts tend to show linear growth. Yet, knowledge about the takeoff is crucial for managers to decide whether to maintain, increase, or withdraw support of new products. It is equally important for industry analysts who advise investors and manufacturers of complementary and substitute products. Although previous studies have urged researchers to examine the takeoff, no research has addressed this critical event. While diffusion models are commonly used to study new product sales growth, they do not explicitly consider a new product's takeoff in sales. Indeed, diffusion researchers frequently use data only from the point of takeoff. Therefore, nothing is known about the takeoff or models appropriate for this event. Our study provides the first analysis of the takeoff. In particular, we address three key questions: (i) How much time does a newly introduced product need to takeoff? (ii) Does the takeoff have any systematic patterns? (iii) Can we predict the takeoff? We begin our study by developing an operational measure to determine when the takeoff occurs. We found that when the base level of sales is small, a relatively large percentage increase could occur without signaling the takeoff. Conversely, when the base level of sales is large, the takeoff sometimes occurs with a relatively small percentage increase in sales. Therefore, we developed a “threshold for takeoff.” This is a plot of percentage sales growth relative to a base level of sales, common across all categories. We define the takeoff as the first year in which an individual category's growth rate relative to base sales crosses this threshold. The threshold measure correctly identifies the takeoff in over 90% of our categories. We model the takeoff with a hazard model because of its advantages for analyzing time-based events. We consider three primary independent variables: price, year of introduction, and market penetration, as well as several control variables. The hazard model fits the pattern of takeoffs very well, with price and market penetration being strong correlates of takeoff. Our results provide potential generalizations about the time to takeoff and the price reduction, nominal price, and penetration at takeoff. In particular, we found that: • On average for 16 post-World War II categories: — the price at takeoff is 63% of the introductory price; — the time to takeoff from introduction is six years; — the penetration at takeoff is 1.7%. • The time to takeoff is decreasing for more recent categories. For example, the time to takeoff is 18 years for categories introduced before World War II, but only six years for those introduced after World War II. • Many of the products in our sample had a takeoff near three specific price points (in nominal dollars): $1000, $500 and $100. In addition, we show how the hazard model can be used to predict the takeoff. The model predicts takeoff one year ahead with an expected average error of 1.2 years. It predicts takeoff at a product's introduction with an expected average error of 1.9 years. Even against the simple mean time to takeoff of six years for recent categories, the model's performance represents a tremendous improvement in prediction. It represents an immeasurable improvement in prediction for managers who currently have no idea about how long it takes for a new product to takeoff. The threshold rule for determining takeoff can be used to distinguish between a large increase in sales and a real takeoff. Some limitations of this study could provide fruitful areas for future research. Our independent variables suffer from endogeneity bias, so alternative variables or methods could address this limitation. Also, the takeoff may be related to additional variables such as relative advantage over substitutes and the presence of complementary products. Finally, examination of sales from takeoff to their leveling off could be done with an integrated model of takeoff and sales growth or with the hazard model we propose. Generalizations about this period of sales growth could also be of tremendous importance to managers of new products.

A percolation-based model explaining delayed takeoff in new-product diffusion

“…These models are variants of the spatial stochastic process called Our present model is percolation-based. It is motivated by the empirical phenomenon that in the early stages of new-product diffusion low levels of sales often persist over a prolonged period of time before a "take-off" occurs (for a detailed discussion of this phe-nomenon see Golder and Tellis (1997) and Geroski (2003)). Serving as a prototypical example of this phenomenon, Figure 1 (top) depicts the cumulative number of adopters of a novel agricultural technique in Iowa in the first half of the last century.…”

Introduction of Software Products and Services Through Public 'Beta' Launches

“…This aspect has been explored by Mahajan, Muller, and Kerin (1984). Other reasons impacting the demand pattern of a new product have been studied by Golder and Tellis (1997) who focus on the takeoff portion of the sales curve where the product gets established in the market whereas Gupta, Jain, and Sawhney (1999) study the sales for a product whose demand is effected by the sales of a complementary product (e.g. hardware and software).…”

“…hardware and software). Some other papers that focus on interesting aspects of the new product adoption process are by Souza, Bayus, and Wagner (2004), Song (2003), Smith (1986), Golder and Tellis (1997), Bridges, Yim, and Briesch (1995), Narasimhan (1989) and Lee and Connor (2003).…”

Estratégias de preço na difusão de inovação: simulação baseada em agentes aplicado ao mercado brasileiro de carros elétricos

“…Por exemplo, ao contrário da homogeneidade, na qual os agentes possuem comportamentos e padrões de respostas similares, consumidores podem ter diversos graus de aceitação em inovação, sensibilidade ao preço e necessidades (GOLDER; TELLIS, 1997;SONG;CHINTAGUNTA, 2003).…”

“…Apesar da crescente evidência sobre a importância da comunicação interpessoal na adoção de produtos, uma ramificação emergiu e ele argumenta que o principal condutor no crescimento de novos produtos é a heterogeneidade do consumidor (BOHLMANN; CA-LANTONE; ZHAO, 2010;TELLIS, 1997;IYENGAR;VALENTE, 2011). Esta abordagem reivindica que cada agente são diferentes um dos outros em termos de sensibilidade ao preço,percepção e necessidades, conduzindo assim a diferentes ponderações na tomadas de decisão (BEMMAOR, 1994;SONG;CHINTAGUNTA, 2003).…”