استفاده از مدل های انتشار برای پیش بینی اندازه بازار در بازارهای نوظهور با برنامه های کاربردی برای بازار خودرو چین

Marketing managers have to forecast the market size and this forecast guides strategic decisions whether to continue exporting, open new factories or expand existing production operations. Forecasting sales and the market size is a challenging task; even more so in emerging markets where data is limited and the market demand is changeable. This research proposes a novel approach that applies diffusion models using car ownership data to forecast car sales. Car ownership data may be easier to access than sales data in emerging markets but marketing managers are more interested in the sales forecast. Researchers propose using diffusion models to forecast the adoption of new products or products which are new to consumers in a market. This research demonstrates that marketing managers can use diffusion models to predict car sales in China where cars are new products to most consumers in this market. Since the majority of car buyers in China are first time buyers, car manufacturers and retailers must also forecast when the market composition will change. This effectively means predicting when first time car buying will start to slow down and repeat/replacement purchase or second hand car purchase will become more important. To forecast both sales and market composition change, marketing managers must choose a robust model. Managers want insights from models that have been tested robustly especially in less stable market conditions. In this context, this study illustrates the value of using a rolling forecast instead of a fixed horizon approach when comparing and choosing which model to use to forecast both sales and market composition change for the Chinese car market.

Forecasting the market size for any product or service informs strategic and investment decisions. The market size forecast influences strategic decisions of firms whether they should enter new markets or if they should expand existing capacity in markets where they already operate. Multinational car companies that are interested in manufacturing and selling cars in China also have to make such strategic decisions. Decision makers, in such instances, want to use data and insights that they can rely upon with confidence. This is particularly important when the market conditions are changing rapidly. The Chinese car market provides an interesting context for this research, as it is an example of a typical market in an emerging economy. The Chinese car market has experienced exponential growth in the last decade compared to other more mature markets. In 2010, China overtook the United States to become the largest car market in the world when comparing annual car sales. Annual sales of new cars in China, the United States and Japan are shown in Fig. 1. This clearly depicts a non-linear growth pattern of car sales in China. The Chinese car market is far from reaching market saturation where growth slows down and sales/car ownership reaches a plateau. The average car ownership has increased from 90 cars per 1000 inhabitants in the early1990s to 118 cars per 1000 inhabitants in 2005 (World Development Indicators published by World Bank (see Table 1)). Car markets in more mature economies, represented by the US, UK, Germany and Japan, have high car ownership rate with approximately 500 cars per 1000 inhabitants in 2005. China has a much lower car ownership level: every 1000 inhabitants only owned 15 cars in 2005 but this statistic has increased by more than 10 times since 1990.

Forecasting the market size and at what point that market will eventually slow down are important insights for managers. Also, the change in the growth rate of sales for a durable product that is new to a market, such as cars in China, may indicate an important change in the composition of that market which will influence the marketing and new product development strategies Sales data may be unavailable in emerging markets which means that secondary sources of data on ownership level of the new product may have to be used to forecast market size and growth. Furthermore, when market conditions are unstable, managers will demand models which are robustly tested to reduce the risk of making bad strategic decisions. Researchers typically use diffusion models to forecast ownership level of a new product. This paper illustrates how ownership data on cars, i.e. secondary data, can be used to forecast sales. The proposed method can be replicated in emerging markets when first time buyers dominate the market. The diffusion models are also useful because they can accommodate the inflection points in sales growth. The prediction of the inflection point also provides an important forecasting insight: a change in market composition. Prior studies highlight the importance of using models that suit the market dynamics and characteristics (Fildes et al., 1998 and Fildes et al., 2008). The diffusion models are better than the models that forecast sales directly because the diffusion models can accommodate the dynamics of the market and in particular the non-linearity feature of the data. This research also illustrates the value of using a rolling forecast horizon instead of using a fixed horizon for the validation sample. The study here demonstrates that, if forecasters use a fixed horizon to assess the different models, they will end up choosing an inferior model and wrongly predict the future market size for first time buyers. More specifically, this study recommends the use of rolling forecasts when past market conditions may significantly differ from future market conditions which is typically the case in emerging markets.