Consequences of personalized product recommendations and price promotions in online grocery shopping

Shopping convenience can be turned into a competitive advantage for online grocery retailers. Consequently, we study how personalized product recommendations (recommendation agents) and price promotions (algorithmic pricing) compensate for the negative impact that consumer's perceived cognitive effort causes on loyalty. By default, the relationship from perceived cognitive efforts to attitudinal and behavioral loyalty is negative, yet these results demonstrate that personalized price promotions lessen the negative impact, while personalized product recommendations do not have such an influence. The findings contribute to a better understanding of personalized marketing activities in today's data-driven online grocery retailing.     

How price complexity takes its toll: The neglected role of a simplicity bias and fairness in price evaluations

Marketing managers commonly employ complex price plans. Surprisingly, limited and conflicting evidence reports how customers perceive and react to complex prices. This study examines perceptions about price complexity and shows that customers tend to prefer simple prices. Two experimental studies show that perceived price complexity negatively affects customer perceptions of price fairness and influences product choice because customers negatively evaluate the transparency of the firm's pricing practices and infer higher total prices. Customers comparing alternate offerings may therefore prefer simple over complex prices, even when the latter are less expensive. Study results suggest limiting price plan variations positively affects customer inferences about transparency and fairness, and thus customer choice.     

Price-aware enhanced dynamic recommendation based on deep learning

Price is one of the essential elements influencing consumer purchase behavior. Like consumers’ preferences in products, their price preferences also dynamically change over time. However, dynamic price preferences haven’t been fully considered in existed recommendation studies. In this study, we propose a deep learning-based dynamic recommendation model by considering consumers’ dynamic preferences in both product and price. We specially design a review-and-rating-based sequence generator to select products whose prices the consumers are satisfied with to form the new purchase sequence. We also develop a multi-level attention mechanism in the transformer layer to explore the correlations between consumers’ price choices and to combine the price preferences with the product preferences. Experimental results show the proposed model outperforms the state-of-the-art models on some real-world datasets. Our findings can help retailers understand consumers’ price preferences and make informed decisions related to pricing, discounting, and bundle sales strategies.     

Internal reference price response across store formats

Reference price models have a long tradition in marketing and consumer research. Pricing strategies can utilize consumer response to gains and losses relative to internal reference prices, which are price expectations developed from past-observed prices. Consequently, many previous studies have been devoted to analyzing differences in internal reference price response across product categories and consumer characteristics. However, knowledge about internal reference price response across different store formats is missing. Our study aims to fill this research gap. To do so, we estimate a reference price model for the same set of brands and the same sample of consumers across two store formats (discount chain vs. supermarket chain). The prices for the brands in our model are similar across store formats. Results from our proposed model show that the same consumers are loss-averse at the discount chain while gain-seeking at the supermarket chain. Predicted by previous literature, we attribute the difference in internal reference price response to the different price image of the store formats. Overall, our research contributes to the reference price literature and provides important implications for pricing strategies at stores with different price image.     

How does the learning climate affect customer satisfaction?

This study investigates the role of the learning climate in the effective management of salespersons' behaviour in service encounters. This paper reports the development and testing of a two-level model in which salespersons' perceptions of the learning climate are related to their adaptive selling behaviour and their adaptive selling behaviour is, in turn, related to customers' evaluation of the salespersons' knowledge of customers and customer satisfaction. A total of 417 service encounters involving 88 bank consultants were analysed using hierarchical linear modelling, with results that support most of the proposed model. More specifically, learning climate was positively related to adaptive selling behaviour, and adaptive selling behaviour was directly related to customer satisfaction. In addition, adaptive selling behaviour was associated with customer satisfaction through customers' perception of the salesperson's knowledge. Both theoretical and practical implications are discussed.     

Do consumers value price transparency?

We examine the role of price transparency in consumer preferences and demand. We assemble a detailed dataset on the driving school industry in Portugal to quantify how firms present the price of the course of instruction, and its individual components, to potential students. Our unique data allows us to estimate a structural model of school choice and measure the impact of varying levels of price information on demand. The results show that consumers are willing to pay a significant amount for price transparency, on average 11% of the service price, and that consumer demographics drive heterogeneous preferences for transparency.     

An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data

Retail managers have been interested in learning about cross-category purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. This paper investigates the suitability of such methods for situations when only binary pick-any customer information (i.e., choice/non-choice of items, such as shopping basket data) is available. We present an extension of collaborative filtering algorithms for such data situations and apply it to a real-world retail transaction dataset. The new method is benchmarked against more conventional algorithms and can be shown to deliver superior results in terms of predictive accuracy.     

Customer choices of manufacturer versus retailer brands in alternative price and usage contexts

This article offers a context-dependent theory of how price changes influence consumer purchase choice for fast moving consumer goods (FMCGs) for manufacturer (large household share) and retailer (small household share) brands. The theory proposes that the influence of price on demand is systematically very sensitive to context effects; more specifically, the theory includes the hypothesis that elasticity is much greater when the price change results in the manufacturer and retailer brands having the same price compared to when the price change keeps the manufacturer brand price above the retailer brand price. The implicit and/or explicit association with higher quality with the manufacturer versus retailer brand may be the main reason for buying the higher priced manufacturer brand. Decreasing the price of the manufacturer brand to equal the retailer brand's price takes away the primary reason for buying the retailer brand (i.e., saving money); increasing the price of the retailer brand to equal the manufacturer brand's price has the same effect. The empirical findings in the studies that this article reports support the hypothesis and confirm Scriven and Ehrenberg's [2004. Consistent consumer responses to price changes. Australas. Mark. J. 12(3), 21–39] major conclusion that relative order of price is more important than relative distance.     

Complex services and choice criteria: an example from the life assurance market

This study analyses customer choice criteria in the context of a complex service, more specifically, endowment life assurance policies. The study draws upon previous literature which has suggested that service complexity may have implications for consumer behaviour in general and customer choice criteria in particular. A quantitative study is employed to investigate the relative importance of choice criteria in endowments and differences in importance with respect to various demographic and related factors. Findings indicate that the consumer's choice of institution is most often influenced by use of an existing service with that firm, followed closely by choosing on the basis of recommendation or family relationship. Factors such as performance and charges are shown to be relatively unimportant choice criteria. In addition, differences in the importance of choice criteria with respect to gender, class, household income, educational attainment, age and financial maturity are apparent.     

The role of machine learning analytics and metrics in retailing research

This research presents the use of machine learning analytics and metrics in the retailing context. We first discuss what is machine learning and explain the field’s origins. We then demonstrate the strengths of machine learning methods using an online retailing dataset, noting key areas of divergence from the traditional explanatory approach to data analysis. We then provide a review of the current state of machine learning in top-level retailing and marketing research, integrating ideas for future research and showcasing potential applications for practitioners. We propose that the explanatory and machine learning approaches need not be mutually exclusive. Particularly, we discuss four key areas in the general scientific research process that can benefit from machine learning: data exploration/theory building, variable creation, estimation, and predicting an outcome metric. Due to the customer-facing nature of retailing, we anticipate several challenges researchers and practitioners might face in the adoption and implementation of machine learning, such as ethical prediction and customer privacy issues. Overall, our belief is that machine learning can enhance customer experience and, accordingly, we advance opportunities for future research.     

Customer segmentation by web content mining

This article introduces a new dimension, Interpurchase Time (T), into the existing RFM (Recency, Frequency, and Monetary) model to form an expanded RFMT model for parsing consumers' online purchase sequences in a long period to implement customer segmentation. The proposed RFMT model can track and discern changes in customer purchasing behaviors during their whole shopping cycle. Firstly, a web content retrieving system was developed to fetch publicly available customer data on a retailer's website, including demographic information (gender, age, location, etc.) and product information (name, price, date, etc.) of each purchase in a period from 2008 to 2019. The RFMT values of a customer were then computed from the retrieved data and subsequently analyzed by the hierarchical clustering to derive seven homogeneous clusters with specific customer profiles. Subsequently, demographic features and product preferences were identified for each cluster with business insights that can help the retailer to improve customer relationships and to implement targeted recommendation strategies.     

Supermarket self-checkout service quality,customer satisfaction,and loyalty: Empirical evidence from an emerging market

Supermarket shoppers around the world are increasingly encountering and using self-service technologies (SSTs) during their shopping process. The SSTs are mainly offered to reduce retailer costs and enhance customer's experience. Among the many different SSTs available, self-checkout systems (SCS) have become an extremely popular choice of supermarkets around the world. Although some of the main motivations of the supermarkets for offering SCSs are cost cutting, speed, and convenience, supermarkets are also assuming that these services would enhance customer experience, satisfaction, and ultimately loyalty. However, empirical evidence is needed to better understand customer expectations of SCS service quality and how technology based service quality impacts retail patronage. Therefore, the purpose of this research is to examine the service quality of supermarket/grocery store SCSs and its impact on customer satisfaction and loyalty in an emerging market, namely Turkey. Using the SSTQUAL scale (Lin and Hsieh, 2011), data (n=275) for the study is collected from shoppers who had just completed going through the self-checkout counter in a large supermarket chain. The results of this study show that SCS service quality positively influences loyalty through the customer satisfaction path. Managerial and research implications of the findings are discussed.     

The influence of supermarket attributes on perceived customer satisfaction: an East Asian study

Despite the proliferation of theoretical developments in the area of western consumer behaviour, to date the likelihood of Asian variants to existing consumer behavioural models has not been explored in any detail. Based on prior research in the field of retail attribute testing this empirical study investigates the influence of supermarket store attributes on Chinese (Macau) customer satisfaction moderated by the biographical factors of age and income. The results indicate that the store attributes of distance and speed of check out were significant for age, and that delivery service and price reductions were significant by income. Total independent variable rankings indicated store location, price and product variety were perceived by Chinese respondents as being the most important store attributes influencing satisfaction.     

Demographics,psychographics, price searching and recall in retail shopping

Retailing environments have gone through physical changes and the Internet revolution which intensifies price search and comparison behaviour, however, what customer characteristics – demographics or psychographics – affect price searching and recall? Further, what changes the relationship between the price-searching tendency and price recall? This research develops a framework that integrates the views of the economics of information and psychosocial returns to address these questions. Through point-of-purchase surveys, this study finds that psychographics affect the price-searching tendency directly while demographics do so indirectly through psychographics. In addition, the price-searching tendency has a positive effect on price recall and this relationship is stronger when consumers buy discounted products. Finally, in contrast to previous research, consumers' high tendency to search for price and price recall accuracy and confidence are found.     

Using animated agents in learner‐controlled training: the effects of design control

Animated agents have the potential to increase engagement and learning during online training by acting as personalized tutors. However, little is known about the conditions that make these agents most effective. In this study, 183 e‐learners completed a Microsoft Excel training course. Approximately half were assigned an agent with predetermined features. The others were allowed to choose their agent's appearance, personality, feedback style or all of the above features. Offering multiple choices increased learning. Unexpectedly, choice of feedback style alone decreased self‐efficacy. Choosing the agent's appearance increased self‐efficacy and the number of training modules completed. Overall, this study expands the learner control literature, identifying a new form of learner control that has some beneficial effects on knowledge acquisition.     

Culture, product type, and price influences on consumer purchase intention to buy personalized products online

With personalization, consumers can choose from various product attributes and a customized product is assembled based on their preferences. Marketers often offer personalization on websites. This paper investigates consumer purchase intentions toward personalized products in an online selling situation.The research builds and tests three hypotheses: (1) intention to purchase personalized products will be affected by individualism, uncertainty avoidance, power distance, and masculinity dimensions of a national culture; (2) consumers will be more likely to buy personalized search products than experience products; and (3) intention to buy a personalized product will not be influenced by price premiums up to some level. Results indicate that individualism is the only culture dimension to have a significant effect on purchase intention. Product type and individualism by price interaction also have a significant effect, whereas price does not. Major findings and implications are discussed.     

Internal and external price search in industrial buying: The moderating role of customer satisfaction

Despite price's great relevance in organizational buying decisions, prior research primarily studies price search in business-to-consumer environments. This research conceptualizes both internal and external price search in a business-to-business setting and focuses on how price importance affects these two types as well as how customer satisfaction moderates the relationships. Results show that purchasing managers' satisfaction associates positively with the effect of price importance on internal price search. Also, their satisfaction negatively associates with the effect of price importance on external price search. Price search reduces the price premium paid by customers (especially external price search).     

Does Value for Money Create Advocates? A Study in the International Airline Services Industry

ABSTRACT

New carriers and business models have dramatically transformed the aviation industry. Using feedback ratings from 109 carriers and 36,710 passengers, this study first shows that emerging market airlines typically offer better value-for-money propositions than their established competitors. The study then relates perceived value for money with recommendation intention. The relationship is very strong without significant outliers; true loyalty shown by such customer advocates drives a service firm's profitability. Even the best entrenched airlines should not ignore this change in competitive climate, but respond by focusing on value for money as the standard for their business model's value proposition.     

Trading protocols and price discovery: Implicit transaction costs in Indian single stock futures

We show how trading protocols impede the price discovery process in single stock futures as implicit trade costs outweigh explicit costs. Despite the trade volume dominance, trade costs advantage and leverage efficiency in futures markets, single stock futures account for only 35% of the price discovery vis-á-vis the spot market. Futures market's informational efficiency is adversely affected by market frictions in the form of marketwide position limits, minimum contract values, and margin requirements.     

Purchase frequency,sample selection,and price sensitivity: The heavy-user bias

We study the relationship between purchase frequency and volume and choice behavior as summarized by brand preferences and price sensitivity. Our most striking finding is that consumers with high purchase frequency or high purchase volume are much more price sensitive and have more sharply defined preferences for national brands than consumers with low frequency or low volume of purchase. In much of the choice literature, analysis is confined to households that have on average a larger number of purchases than is representative of the ERIM panel. Our findings suggest that some caution should be exercised in interpreting studies that employ purchase-frequency or purchase-number sample inclusion rules. Our findings also support an information theoretic point of view in which households become more price sensitive via costly acquisition of information about the distribution of prices.We acknowledge helpful comments from Greg Allenby and Bob Blattberg. Support from the Micro-Marketing Project at the Graduate School of Business at the University of Chicago is gratefully acknowledged. Byung-Do Kim is the corresponding author.