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Net Promoter Score (NPS) has been widely adopted by managers as a measure of customer mindset and predictor of sales growth. Over time, practitioners have evolved the use of NPS from its original purpose as a transaction-based customer loyalty metric, towards a metric for tracking overall brand health which includes responses from non-customers. Despite enduring managerial popularity, academics remain skeptical of NPS, citing methodological issues and ongoing concerns with NPS measurement. This study re-visits the use of NPS as a predictor of sales growth by analyzing data from seven brands operating in the U.S. sportswear industry, measured over five years. Our results confirm-within the context of our study-that while the original premise of NPS is reasonable, the methodological concerns raised by academics are valid, and only the more recently developed brand health measure of NPS (using an all potential customer sample) is effective at predicting future sales growth., Author(s): Sven Baehre [sup.1] , Michele O'Dwyer [sup.1] , Lisa O'Malley [sup.1] , Nick Lee [sup.2] Author Affiliations: (1) grid.10049.3c, 0000 0004 1936 9692, Kemmy Business School, University of Limerick, [...]

In recent years, Taylor's law describing the power function relationship between the mean and standard deviation of certain phenomena has found an increasing number of applications. We studied the characteristics of Taylor's law for branded product sales using point-of-sale (POS) data for brands sold in 72 grocery stores in the Greater Tokyo area. A previous study found that product sales follow Taylor's law with a scaling exponent of 0.5 for low sales quantities and 1.0 for large sales quantities. In the current study, we observed Taylor's law with cross-over for 54 product brands and estimated the value of the two coefficients in the theoretical curve to characterize the cross-over. The coefficients represent the fluctuations in the number of items purchased per consumer and the number of consumers in one store and in all stores. The estimated coefficients suggested the dependence of the features of Taylor's law on the category to which the brands belong. We found that brands in the same category tend to share similar features under Taylor's law. However, some brands exhibited specific features that differed from others in the same category. For example, for many brands in the Laundry Detergent and Instant Noodles categories, the number of customers purchasing the products in each store fluctuated significantly, whereas the number of purchased items per customer varied widely in the Japanese Tea category. In the coffee category, our results indicated that the degree of fluctuation in the number of purchasing customers largely depends on the brand. [ABSTRACT FROM AUTHOR]

To say that selling in today's environment is challenging is an obvious understatement after the global spread of COVID-19 has forced businesses big and small to toss even their best-laid [...]

In today's societies, given the increasing tendency to buy from malls and megastores that can meet all the buyer's needs in a limited time, we witness a large-scale growth of modern malls. Due to the breadth of services and diversity in such places, it is necessary to have a central intelligent control unit that takes control of all existing systems. The Internet of Things is one of the new technologies in the last decade, playing an essential role in intelligent business computing. The volume of customers coming to the mall, the customer service, and the customer buying behavior are factors whose analyses can dramatically increase the revenue from the stores and lead to better customer satisfaction. Many models are used to serve this purpose, one of which is the Bayesian Network model. In this research, using this model based on the interests and preferences of customers and purchasing patterns, we identify customers' needs and provide them with the desired product. After implementing this model, increasing the potential sales in the store and increasing the performance and speed of the store function are expected. [ABSTRACT FROM AUTHOR]

Within the contemporary business milieu, the discipline of selling and sales management has taken on a more prominent role in recent years. Myriad factors have contributed to the rise of interest in sales including globalization, technology, more sophisticated analytical approaches and new opportunities for co-creation of value between organizations and their customers. Over the past three decades, seven faculty consortia in sales have served as milestones to document the progress of the field, particularly the evolution of academic research. This article provides key takeaways from the most recent American Marketing Association (AMA) Faculty Consortium in Selling and Sales Management, which had the overarching goal of fostering new opportunities for building intercontinental research teams to effectively address the substantive issues for the future of the field. [ABSTRACT FROM PUBLISHER]

Paid search advertising is a critical source of traffic for sellers on e-commerce platforms. As the mobile Internet gains prominence as the main channel for reaching consumers, sellers need to know how to select and bid on keywords in online versus mobile search settings. This study compares the differential effects of paid search keywords in online and mobile settings on both direct and indirect sales, along with the moderating roles of keyword specificity and keyword cost. Two field studies and two lab experiments consistently show that a paid keyword in a mobile setting can prompt higher direct sales than it does in an online setting, but it generates lower indirect sales in general. Moreover, while keyword cost attenuates this positive relationship between mobile searching and direct sales, keyword specificity and keyword cost attenuate this negative relationship between mobile searching and indirect sales. Empirical evidence also reveals the sales and return on investment of paid search keywords in mobile and online settings. This study enriches previous literature by detailing new insights into the distinct settings of mobile and online search advertising., Author(s): Feng Wang [sup.1] , Li Zuo [sup.2] , Zhi Yang [sup.1] , Yueyan Wu [sup.1] Author Affiliations: (1) grid.67293.39, School of Business Administration, Hunan University, , Changsha, China (2) [...]

Objective: Extensive marketing of 'toddler milks' (sugar-sweetened milk-based drinks for toddlers) promotes unsubstantiated product benefits and raises concerns about consumption by young children. The present study documents trends in US toddler milk sales and assesses relationships with brand and category marketing. Design: We report annual US toddler milk and infant formula sales and marketing from 2006 to 2015. Sales response models estimate associations between marketing (television advertising spending, product price, number of retail displays) and volume sales of toddler milks by brand and category. Setting: US Nielsen retail scanner sales and advertising spending data from 2006 to 2015. Participants: Researchers analysed all Universal Product Codes (n 117·4 million) sold by seven infant formula and eight toddler milk brands from 2006 to 2015. Results: Advertising spending on toddler milks increased fourfold during this 10-year period and volume sales increased 2·6 times. In contrast, advertising spending and volume sales of infant formulas declined. Toddler milk volume sales were positively associated with television advertising and retail displays, and negatively associated with price, at both the brand and category levels. Conclusions: Aggressive marketing of toddler milks has likely contributed to rapid sales increases in the USA. However, these sugar-sweetened drinks are not recommended for toddler consumption. Health-care providers, professional organizations and public health campaigns should provide clear guidance and educate parents to reduce toddler milk consumption and address misperceptions about their benefits. These findings also support the need to regulate marketing of toddler milks in countries that prohibit infant formula marketing to consumers. [ABSTRACT FROM AUTHOR]

Abstract Retail atmospherics is becoming an increasingly important strategic tool for stores and restaurants. Ambient music and background noise are especially important atmospheric elements given their ubiquity in retail settings. [...]

Abstract. In this paper, I introduce a framework of price promotions by firms that preschedule their sale dates. I set up a dynamic model of demand accumulation in which high- [...]

Purpose: The factors that influence the true market demand and their extent of influence are highly elusive and erratic. This paper aims at applying Artificial Intelligence (AI) techniques to identify and predict complex sales patterns and compare the results with traditional forecasting models. Design/methodology/approach: Sales data related to the sample firm was used in the study for forecast modelling using both, advanced forecasting method (ARIMA) and Artificial Neural Networks (ANNs). The latter considers inputs of influential/causal factors and revisits every time to identify new trends related to those factors, thus providing a more robust forecast. This is compared with results from the ARIMA model. Findings: It is inferred that neural networks with the input of influencing/causal factors has a greater accuracy when compared to ARIMA model. It is also time adaptive and changes according to trends experienced in the causal factors. Research limitations/implications: Though the purpose of the research is achieved, a few limitations exist because of the limited availability of data. Besides, the factors that affect the prediction are generalised and based on previous research works. A more customised approach towards the firm under study would greatly improve the accuracy. Practical implications (if applicable): Machine learning and big data are bringing paradigm changes in developing a better forecasting model. It has the potential to analyse huge amounts of data and provide instant insights that can greatly improve business performances. Originality/Value: This paper extends the scope of previous literature by taking into account the factors that influence the sales of the automobile industry, considering the Indian market during the time period (1988-2016). [ABSTRACT FROM AUTHOR]

We document that consistent patterns of high or low sales growth that are incongruent with underlying fundamentals are followed by significant stock price reversals. In contrast, no stock return reversals are found for firms that achieve the same level of sales growth in an inconsistent manner. Furthermore, future earnings announcement returns mimic the pattern of abnormal stock returns documented for the Consistent- and Inconsistent-Growth portfolios, respectively, corroborating our main findings. Our results are robust to controls for the magnitude of sales growth, fundamental strength, business cycle risk exposures, and standard risk factors. Our evidence is consistent with representativeness bias affecting investor interpretation of consistency in sales growth patterns. History: Accepted by Mary Barth, accounting. Keywords: representativeness * behavioral finance * asset pricing * fundamental analysis * stock return predictability, 1. Introduction A central issue in capital markets research is whether psychological biases affect investor processing of financial information. In a recent comprehensive study of behavioral finance theories, Chan et [...]

The purpose of this article is to find out the most important factors for sales of dairy products. Transactional data for sales are used. This article is published for the first time. The results of the analysis will show how transactional data may be used for data mining. SPSS is used to analyze the data. Statistical methods (such as independent samples t-test, Chi-square tests, Mann-Whitney U test, one-way ANOVA test) are applied to find dependencies. We suppose that the quantity of sales depends on the year, month, group, item and average price. At the end of the study we found out that “year” and “group” are factors which mostly influence the quantity of a sale. Factors “month”, “item” and “average sale price” do not affect the quantity of a single sale. Mathematical formulas are derived to predict the quantity of a future sale on the basis of independent variables such as year and group of stock.

We examine the effectiveness of Korea's Sunday superstore shopping regulation, whose purpose is to protect small- and medium-sized retailers as well as traditional markets. Applying a two-way random-effects regression model to the daily sales data of four megastores and four super supermarkets, we find the net sales decrease of superstores is on average 5.61% of daily sales after taking into account sales increases due to consumers' switching to weekdays. Only about J7%-19% of the reduced superstore sales are transferred to traditional markets. Based on these estimates, the compensating variation per year is predicted to be 2.4-2.5 trillion Korean won (around US $2.1-2.2 billion). (JEL L51, L88), I. INTRODUCTION The Korean government implemented a Sunday shopping regulation in 2012. This restriction is unique in its purpose and implementation; while most other countries have enforced Sunday shopping regulations [...]