From Business Intelligence to Competitive Intelligence: Inferring Competitive Measures Using Augmented Site-Centric Data
Published Online:3 Nov 2011https://doi.org/10.1287/isre.1110.0385
References
- Is your brand loyalty too much, too little, or just right? Explaining deviations in loyalty from the Dirichlet norm. Internat. J. Res. Marketing (1997) 14(5):421–435Crossref, Google Scholar
- The distribution of survey contact and participation in America: Constructing a survey-based estimate. J. Marketing Res. (1999) 36(2):286–294Crossref, Google Scholar
- A customer relationship management roadmap: What is known, potential pitfalls, and where to go. J. Marketing (2005) 69(10):155–166Crossref, Google Scholar
- Estimating disaggregate models using aggregate data through augmentation of individual choice. J. Marketing Res. (2007) 44(November):613–621Crossref, Google Scholar
- Comparison of online and offline consumer brand loyalty. Marketing Sci. (2003) 22(4):461–476Link, Google Scholar
- Measuring Marketing: 103 Key Metrics Every Marketer Needs (2007) (John Wiley and Sons (Asia), Singapore) Google Scholar
- Size and share of customer wallet. J. Marketing (2007) 71(4):94–113Crossref, Google Scholar
- An investigation of the assumptions of the NBD model as applied to purchasing at individual stores. J. Roy. Statist. Soc. Ser. C (Applied Statistics) (1983) 32(3):249–259Google Scholar
- The pattern of consumer purchases. Appl. Statist. (1959) 8(1):26–41Crossref, Google Scholar
- Repeat buying. J. Empirical Generalisations Marketing Sci. (2000) 5(2):p1–p375Google Scholar
- Repeat Buying: Theory and Applications (1988) (Charles-Griffin, London) Google Scholar
- Empirical generalizations, theory and methods. Marketing Sci. (1995) 14(3):20–28Link, Google Scholar
- Understanding brand performance measures: Using Dirichlet benchmarks. J. Bus. Res. (2004) 57:1307–1325Crossref, Google Scholar
- A note on modeling underreported poisson counts. J. Appl. Statist. (2000) 27(8):953–964Crossref, Google Scholar
- Accounting for heterogeneity and nonstationarity in a cross-sectional model of consumer purchase behavior. Marketing Sci. (1993) 12(2):304–317Link, Google Scholar
- A cross-category analysis of category structure and promotional activity of grocery products. J. Marketing (1990) 54(3):52–65Crossref, Google Scholar
- Excess behavioral loyalty for high-share brands: Deviations from the Dirichlet model for repeat purchasing. J. Marketing Res. (1993) 30:478–493Crossref, Google Scholar
- Estimating CLV using aggregated data: The Tuscan lifestyles case revisited. J. Interactive Marketing (2007) 21(3):55–71Crossref, Google Scholar
- Marketing Metrics; 50+ Metrics Every Executive Should Master (2006) (Wharton School Publishing, Philadelphia) Google Scholar
- Predicting retail customers' share-of-wallet using shopper loyalty card data. (2006) . Working paper, Marketing Department, Southern Methodist University, DallasGoogle Scholar
- The Dirichlet: A comprehensive model of buying behavior. J. Roy. Statist. Soc., Ser. A (1984) 147(5):621–655Crossref, Google Scholar
- A multi-brand stochastic model compounding heterogeneous Erland timig and multinomial choice processes. Oper. Res. (1980) 28:255–277Link, Google Scholar
- Univariate Discrete Distributions (1992) 2nd ed.(John Wiley & Sons, New York) Google Scholar
- Competitive intelligence: How to gather, analyze, and use information to move your business to the top. Touchstone (1998) (Touchstone, New York) Google Scholar
- Niching versus change-of-pace brands: Using purchase frequencies and penetration rates to infer brand positioning. J. Marketing Res. (1988) 25(11):384–390Crossref, Google Scholar
- The value of private sector credit information sharing: The U.S. case. J. Banking Finance (2003) 27(3):449–469Crossref, Google Scholar
- The lognormal distribution of buying frequency rates. J. Marketing Res. (1980) 17:212–220Crossref, Google Scholar
- Customer information sharing among rival firms. Eur. Econom. Rev. (2006) 50:1571–1600Crossref, Google Scholar
- Minimizing information loss and preserving privacy. Management Sci. (2007) 53(1):102–116Link, Google Scholar
- Generalizing the NBD model for customer purchases: What are the implications and is it worth the effort? J. Bus. Econom. Statist. (1988) 6(2):145–159Crossref, Google Scholar
- Who's got the coupon? Estimating consumer preferences and coupon usage from aggregate information. J. Marketing Res. (2008) 45(December):715–730Crossref, Google Scholar
- Bayesian estimation of random-coefficients choice models using aggregate data. J. Appl. Econometrics (2009) 24(3):490–516Crossref, Google Scholar
- Personalization from incomplete data: What you don't know can hurt. Proc. 7th ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (KDD01) (2001) San Francisco:154–163Crossref, Google Scholar
- An empirical analysis of complete information for eCRM models. MIS Quart. (2006) 30(2):247–267Crossref, Google Scholar
- Modeling browsing behavior at multiple websites. Marketing Sci. (2004) 23(3):280–303Link, Google Scholar
- Consideration: Review of research and prospects for future insights. J. Marketing Res. (1997) 34(3):406–410Crossref, Google Scholar
- Why does the NBD model work? Robustness in representing product purchases, brand purchases and imperfectly recorded purchases. Marketing Sci. (1985) 4:255–266Link, Google Scholar
- How Brands Grow: What Marketers Don't Know (2010) (Oxford University Press, Oxford, UK) Google Scholar
- Repeat buying and the generalized inverse Gaussian Poisson distribution. Appl. Statist. (1982) 31:193–204Crossref, Google Scholar
- Using external aggregate ratings for improving individual recommendations. ACM Trans. Web (TWEB) (2011) 5(1):22–26Google Scholar
- Patterns of buyer behavior: Regularities, models, and extensions. Marketing Sci. (1995) 14(3):71–78Link, Google Scholar
- Econometrics Analysis of Count Data (2008) 5th ed.(Springer, Berlin) Google Scholar
- Privacy-preserving classification of customer data without loss of accuracy. Proc. 5th SIAM Conf. Data Mining (2005) 92–102Crossref, Google Scholar
- Selectively acquiring customer information: A new data acquisition problem and an active learning based solution. Management Sci. (2006) 52(5):697–712Link, Google Scholar

