Merchant Category Research Articles

Validating Gravity-Based Market Share Models Using Large-Scale Transactional Data.

Customer patronage behavior has been widely studied in market share modeling contexts, which is an essential step toward estimating retail sales and finding new store locations in a competitive setting. Existing studies have conducted surveys to estimate merchants' market share and factors of attractiveness to use in various proposed mathematical models. Recent trends in Big Data analysis allow us to better understand human behavior and decision making, potentially leading to location models with more realistic assumptions. In this article, we propose a novel approach for validating the Huff gravity market share model, using a large-scale transactional dataset that describes customer patronage behavior at a regional level. Although the Huff model has been well studied and widely used in the context of sales estimation, competitive facility location, and demand allocation, this article is the first in validating the Huff model with a real dataset. Our approach helps to easily apply the model in different regions and with different merchant categories. Experimental results show that the Huff model fits well when modeling customer shopping behavior for a number of shopping categories, including grocery stores, clothing stores, gas stations, and restaurants. We also conduct regression analysis to show that certain features such as gender diversity and marital status diversity lead to stronger validation of the Huff model. We believe we provide strong evidence, with the help of real-world data, that gravity-based market share models are viable assumptions for retail sales estimation and competitive facility location models.

How Merchants Get Paid

Using a representative sample of actual payments made by adult U.S. consumers, this article analyzes the composition of payment methods consumers use to pay for goods and services. Consumer spending is divided into 21 main merchant categories. Results show the distributions of electronic, card, and paper payment methods and the degree of payment concentration for each merchant category.

Analysis of the geospatial activity profiles of bank customers

It this study, transactions of bank customers are used to analyze their activity profiles. In particular, Merchant Category Codes of transactions are employed to divide customers into three classes: (1) car drivers; (2) public transport users; (3) mixed. The correctness of the classification is verified by two statistical tests. Also, two metrics, Diversity and Loyalty, are calculated for all customers based on transactions’ payment amounts. Customers in each of three classes are additionally grouped by values of these two metrics. The obtained results can be used by banks, e.g., to target advertising. Using the described approach, two data sets were analyzed, each of which contains transactions of bank customers of a large Russian bank. For the second data set, the classification of customers was used in application to the problem of optimizing bank branches network. It turned out, that customers from the first two classes visit top-5 most geoavailable branches with comparable frequency (about 80%), while for customers from the mixed class that value is 70%. It is suggested to take this fact into account, resulting in a more accurate algorithm of bank branches network optimization.

The Initial Effects of EMV Migration on Chargebacks in the United States

To reduce counterfeit fraud in the card-present environment, the United States started migrating to EMV chip technology in the mid-2010s. Since October 2015, merchants have been liable for counterfeit fraud committed using EMV cards if the merchants had not adopted EMV chip-readable terminals. In particular, merchants are held liable through chargebacks. {{p}} This study examines the initial effects of the EMV liability shift on fraud chargeback and merchant loss rates using data from merchant processors and PIN debit networks. Combined with gross fraud rates?overall fraud rates regardless of who incurs fraud losses?estimated in other studies, the results of our study suggest that merchants have faced a significantly higher share of fraud losses since the shift; however, this spike will decline if merchants continue to adopt EMV. Merchant fraud loss rates for signature-based transactions in the card-present channel increased sixfold, but the rates significantly vary between magnetic stripe and chip-to-chip transactions. While merchant fraud loss rates for magnetic stripe transactions are over 9 basis points in value for all merchants combined and vary across merchant categories, the rates for chip-to-chip transactions are very low, around 0.02 basis points, across all merchant categories. Because the gross fraud rates for magnetic-stripe transactions did not increase after the liability shift, our results suggest that the higher merchant fraud loss rates for magnetic-stripe transactions are mainly due to the liability shift. {{p}} Compared with signature-based transactions, fraud chargeback rates for PIN debit transactions in the card-present channel are much lower. Our results suggest that both EMV and PIN are effective in reducing merchant fraud loss rates. However, we need detailed gross fraud rates to examine how effective EMV and PIN are in reducing fraud more generally in the card-present channel. {{p}} Our results for card-not-present fraud chargeback and merchant loss rates are mixed. Both rates increased for some merchant categories, but the rates for all merchants combined actually decreased in our data. This decline is likely due to the underrepresentation of signature-based CNP transactions in our data. The gross fraud rates for card-not-present transactions increased over the same period, and merchants are generally liable for card-not-present fraud.

Consumer preferences for payment methods: Role of discounts and surcharges

Abstract We use new data from the 2015 Diary of Consumer Payment Choice to analyze the role of consumer preferences for specific payment instruments, and how price discounts and surcharges based on payment method affect payment instrument choice. We test whether consumer demand for payment instruments is price elastic, namely whether consumers are likely to deviate from their preferred methods in order to get a discount or to avoid a surcharge. We find that the occurrence of price incentives is low, but that given cash discounts, the probability that a cash transaction is conducted by a consumer who prefers other payment methods increased by 19.2 percent, after controlling for merchant category and dollar value of the transaction. Payment method choice is affected very strongly by consumer individual preferences, but steering by merchants may be effective under some circumstances. Both merchants’ reluctance to offer price discounts and consumers’ limited response to them lead to the low observed occurrences of such incentives.

Correlations and dynamics of consumption patterns in social-economic networks

We analyse a coupled dataset collecting the mobile phone communications and bank transactions history of a large number of individuals living in a Latin American country. After mapping the social structure and introducing indicators of socioeconomic status, demographic features, and purchasing habits of individuals, we show that typical consumption patterns are strongly correlated with identified socioeconomic classes leading to patterns of stratification in the social structure. In addition, we measure correlations between merchant categories and introduce a correlation network, which emerges with a meaningful community structure. We detect multivariate relations between merchant categories and show correlations in purchasing habits of individuals. Finally, by analysing individual consumption histories, we detect dynamical patterns in purchase behaviour and their correlations with the socioeconomic status, demographic characters and the egocentric social network of individuals. Our work provides novel and detailed insight into the relations between social and consuming behaviour with potential applications in resource allocation, marketing, and recommendation system design.

Social Bridges in Urban Purchase Behavior

The understanding and modeling of human purchase behavior in city environment can have important implications in the study of urban economy and in the design and organization of cities. In this article, we study human purchase behavior at the community level and argue that people who live in different communities but work at close-by locations could act as “social bridges” between the respective communities and that they are correlated with similarity in community purchase behavior. We provide empirical evidence by studying millions of credit card transaction records for tens of thousands of individuals in a city environment during a period of three months. More specifically, we show that the number of social bridges between communities is a much stronger indicator of similarity in their purchase behavior than traditionally considered factors such as income and sociodemographic variables. Our findings also suggest that such an effect varies across different merchant categories, that the presence of female customers in social bridges is a stronger indicator compared to that of their male counterparts, and that there seems to be a geographical constraint for this effect, all of which may have implications in the studies of urban economy and data-driven urban planning.

Chargebacks: Another Payment Card Acceptance Cost for Merchants

Although chargebacks are perceived as one of the major cost components for merchants to accept card payments, little research has been conducted on them. To fill that gap, this paper describes the current chargeback landscape by generating detailed statistics on chargebacks for signature-based transactions. Our data are from merchant processors, which, altogether, processed more than 20 percent of all signature-based transactions in the United States. For Visa and MasterCard transactions, chargebacks merchants receive are, on average, 1.6 basis points (bps) of sales number and 6.5 bps of sales value. About 70 to 80 percent of chargebacks are resolved as merchant liability. The most common chargeback reason is fraud, which accounts for about 50 percent of the total chargebacks. The merchant fraud loss rate is 0.7 bps in number and 2.6 bps in value. For American Express and Discover transactions, the total and fraud chargeback rates are somewhat lower. For all of the four networks, the total and fraud chargeback rates are significantly higher for card-not-present transactions than for card-present transactions. They also vary by merchant category. Our fraud results are generally consistent with other available fraud statistics.

Chargebacks: Another Payment Card Acceptance Cost for Merchants

Although chargebacks are perceived as one of the major cost components for merchants to accept card payments, little research has been conducted on them. To fill that gap, this paper describes the current chargeback landscape by generating detailed statistics on chargebacks for signature-based transactions. Our data are from merchant processors, which, altogether, processed more than 20 percent of all signature-based transactions in the United States. For Visa and MasterCard transactions, chargebacks merchants receive are, on average, 1.6 basis points (bps) of sales number and 6.5 bps of sales value. About 70 to 80 percent of chargebacks are resolved as merchant liability. The most common chargeback reason is fraud, which accounts for about 50 percent of the total chargebacks. The merchant fraud loss rate is 0.7 bps in number and 2.6 bps in value. For American Express and Discover transactions, the total and fraud chargeback rates are somewhat lower. For all of the four networks, the total and fraud chargeback rates are significantly higher for card-not-present transactions than for card-present transactions. They also vary by merchant category. Our fraud results are generally consistent with other available fraud statistics.

Changes in The Image of Merchants and Merchant Category Represented in Yadam of the late Chosun Dynasty

Changes in The Image of Merchants and Merchant Category Represented in Yadam of the late Chosun Dynasty