당기의 판매량 변화가 얼마나 차기의 판매량 변화에 영향을 미치는지를 나타내는 판매관성계수 (Sales Inertia Coefficient)는 마케팅 활동의 장기 효과 분석을 위한 다양한 계량 모형에서 동적관계를 파악하기 위한 측정변수로 활용되어 왔다. 그러나, 이 판매관성계수 자체에 대한 분석은 비교적 부족한 것이 현실이다. 본 연구에서는 판매관성계수가 시간의 흐름에 따라 변할 수 있도록 허용한 적응적 모형을 도입하고, 22개 제품군의 60개 브랜드에 대해 7년 기간의 주간 판매자료로 실증분석 함으로써, 판매관성계수가 각 브랜드의 다양한 속성과 어떠한 관계를 가지는지에 대해 고찰하였다. 분석 결과, 판매관성계수는 제품군과 브랜드에 따라 그 값에 있어서 차이를 보였으며 시간의 흐름에 따라서도 변화하는 모습을 보였다. 특정 브랜드의 판매관성계수는 그 브랜드의 시장점유율과 판촉 심도 (promotion depth)와는 음의 상관관계를, 가격과 판촉 빈도 (promotion frequency) 와는 양의 상관관계 보이는 것으로 추정되었다. 또한, 본 연구에서는 기존 지표들의 단점을 보완한 새로운 브랜드 성과 지표로서 판매관성계수의 실무적 활용 가능성도 제시하고 있다.
To achieve healthy and organic growth of the firm in a rapidly changing business environment, marketers aim at designing marketing activities that can contribute to the long-term value of the firm. Therefore, the impact of marketing actions on performance metrics such as sales or market share should be considered not only in the short-run but in the long-run context. Academics in marketing have developed various models that can accommodate this requirement including Koyck models and persistence models (e.g., vector autoregression). We investigate the sales inertia coefficient, one of the key model components in many extant long- term models, in this study. The sales inertia coefficient, also referred as a carryover effect or state dependence, is a metric that indicates the direct impact of past performance (e.g., sales) on current one. Though sales inertia has been frequently used as a mediating variable in various econometric models to analyze the long-term effect of marketing (e.g., advertising effects on sales), studies on sales inertia per se are scarce in the extant literature. Since the sales inertia coefficient plays a significant role in describing the dynamic relationship between marketing activities and market responses, understanding the relationship between sales inertia and various brand characteristics is important for practitioners and academics as well. We propose an adaptive model which allows a time-varying sales inertia coefficient, which enables analysts to understand the relationship between sales inertia and various brand-level characteristics, which may vary cross-sectionally and longitudinally. More specifically, we focus on four important brand characteristics that show a brand`s market status and marketing activities-promotion frequency, promotion depth, price, and market share-and develop corresponding research hypotheses as follows. H1:There exists a positive relationship between a brand`s promotion frequency and its sales inertia coefficient. H2:There exists a negative relationship between a brand`s promotion depth and its sales inertia coefficient. H3:There exists a positive relationship between a brand`s price and its sales inertia coefficient. H4:There exists a negative relationship between a brand`s market share and its sales inertia coefficient. Based on a time series dataset from 22 product categories, 60 brands, and 7 years of observations, we find that sales inertia coefficients vary across product categories and across brands (mean=0.22, SD=0.26). We also find that sales inertia coefficients change over time (coefficient of variation=1.40), which suggests that ignoring the time-varying aspect of the sales inertia coefficient may generate biased results in measuring the long-term relationship between marketing activities and market responses. All research hypotheses are confirmed by our empirical data. The sales inertia coefficient of a particular brand is found to be positively associated with promotion frequency (p < 0.01) and the price level (p < 0.1), and negatively with promotion depth (p < 0.01) and market share (p < 0.01). That is, a frequently-promoting brand tends to have lower contemporaneous marketing effects on sales, which results in high sales inertia. In contrast, a brand with a deeper promotion practice is expected to have higher current effects of marketing on sales, which results in low sales inertia coefficient. In addition ceteris paribus, price (market share) increase is positively related to the increase (decrease) of sales inertia coefficient. We also perform a simulation to explore more managerial implications of sales inertia coefficients: how do the changes in sales inertia coefficients affect a brand`s expected sales given a price level? We show that brands are expected to show a ``high-risk-high-return`` pattern as sales inertia coefficients decrease, i.e., both the sales level and its fluctuations are expected to increase as the sales inertia coefficient declines. Our results indicate that the sales inertia coefficient can be managerially used as a new brand performance metric which supplements conventional indices such as market share and brand loyalty. Conventional flow variables such as units sold can only describe a brand`s performance at a certain point in time. On the contrary, conventional stock variables such as customer satisfaction are difficult and costly to be estimated repeatedly. We propose that marketing managers may diagnose the health and competitive position of their brand by the sales inertia coefficient.