{"title":"Customer-Base Analysis in a Discrete-Time Noncontractual Setting","authors":"P. Fader, Bruce G. S. Hardie, J. Shang","doi":"10.2139/ssrn.1373469","DOIUrl":"https://doi.org/10.2139/ssrn.1373469","url":null,"abstract":"Many businesses track repeat transactions on a discrete-time basis. These include (1) companies for whom transactions can only occur at fixed regular intervals, (2) firms that frequently associate transactions with specific events (e.g., a charity that records whether supporters respond to a particular appeal), and (3) organizations that choose to utilize discrete reporting periods even though the transactions can occur at any time. Furthermore, many of these businesses operate in a noncontractual setting, so they have a difficult time differentiating between those customers who have ended their relationship with the firm versus those who are in the midst of a long hiatus between transactions. We develop a model to predict future purchasing patterns for a customer base that can be described by these structural characteristics. Our beta-geometric/beta-Bernoulli (BG/BB) model captures both of the underlying behavioral processes (i.e., customers' purchasing while “alive” and time until each customer permanently “dies”). The model is easy to implement in a standard spreadsheet environment and yields relatively simple closed-form expressions for the expected number of future transactions conditional on past observed behavior (and other quantities of managerial interest). We apply this discrete-time analog of the well-known Pareto/NBD model to a data set on donations made by the supporters of a nonprofit organization located in the midwestern United States. Our analysis demonstrates the excellent ability of the BG/BB model to describe and predict the future behavior of a customer base.","PeriodicalId":344096,"journal":{"name":"Qnt Mkt: Measurement & Data Analysis (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128678729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Physicians' Persistence and its Implications for Their Response to Promotion of Prescription Drugs","authors":"R. Janakiraman, S. Dutta, C. Sismeiro, P. Stern","doi":"10.1287/mnsc.1070.0799","DOIUrl":"https://doi.org/10.1287/mnsc.1070.0799","url":null,"abstract":"Motivated by the medical literature findings that physicians are inertial, we seek to understand (1) whether physicians exhibit structural persistence in drug choice (structural persistence occurs when the drug chosen for a patient depends structurally on the drug previously prescribed by the physician to other patients) and (2) whether persistence, if present, is a physician-specific characteristic or a physician state that can change over time. We further explore the role of promotional tools on persistence and drug choice, and we investigate whether physicians who exhibit persistence respond differently to three forms of sales promotion: one-to-one meetings (detailing), out-of-office meetings, and symposium meetings. \u0000 \u0000Our results show significant levels of physician persistence in drug choice. We find that persistence is mostly a cross-sectional physician feature. Nonpersistent physicians appear to be responsive to detailing and symposium meetings, whereas persistent physicians seem to be responsive only to symposium meetings. Out-of-office meetings, such as golf or lunch, have no effect on physicians' drug choice. We also find that (1) older physicians and those who work in smaller practices are more likely to be persistent and (2) physicians who are more willing to receive sales force representatives have a lower likelihood of being persistent. Finally, we discuss implications for public policy from our rich set of results.","PeriodicalId":344096,"journal":{"name":"Qnt Mkt: Measurement & Data Analysis (Topic)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130950435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Measuring Forecast Errors in the Percentage of Sales Method","authors":"Fernando E. Arellano, Yasir Agha","doi":"10.2139/ssrn.1095652","DOIUrl":"https://doi.org/10.2139/ssrn.1095652","url":null,"abstract":"The percentage of sales method is commonly used to forecast income statements and balance sheets, assuming that costs change in the same proportion as the change in sales and using the sales forecast as a proxy. Since fixed cost is present in the short run, the percentage of sales method can result in errors when forecasting the short run. This paper derives two equations that quantify the forecast errors inherent in forecasting one-period income statements using the percentage of sales method. As expected, the equations show that errors can be significant when fixed cost or sales growth rate are high. An unexpected result is that profit margin also plays a role in determining the profit forecast error. A table showing forecast errors for a range of the main variables causing the errors is included.","PeriodicalId":344096,"journal":{"name":"Qnt Mkt: Measurement & Data Analysis (Topic)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124788723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Persuasion Bias, Social Influence, and Uni-Dimensional Opinions","authors":"P. DeMarzo, Jeffrey Zwiebel, Dimitri Vayanos","doi":"10.2139/ssrn.293139","DOIUrl":"https://doi.org/10.2139/ssrn.293139","url":null,"abstract":"We propose a boundedly rational model of opinion formation in which individuals are subject to persuasion bias; that is, they fail to account for possible repetition in the information they receive. We show that persuasion bias implies the phenomenon of social influence, whereby one's influence on group opinions depends not only on accuracy, but also on how well-connected one is in the social network that determines communication. Persuasion bias also implies the phenomenon of unidimensional opinions; that is, individuals' opinions over a multidimensional set of issues converge to a single \"left-right\" spectrum. We explore the implications of our model in several natural settings, including political science and marketing, and we obtain a number of novel empirical implications.","PeriodicalId":344096,"journal":{"name":"Qnt Mkt: Measurement & Data Analysis (Topic)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134224422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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