TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Tillmanns, Sebastian
A1  - Hofstede, Frenkel ter
A1  - Krafft, Manfred
A1  - Götz, Oliver
T1  - How to separate the wheat from the chaff: improved variable selection for new customer acquisition
JF  - Journal of marketing : JM
N2  - Steady customer losses create pressure for firms to acquire new accounts, a task that is both costly and risky. Lacking knowledge about their prospects, firms often use a large array of predictors obtained from list vendors, which in turn rapidly creates massive high-dimensional data problems. Selecting the appropriate variables and their functional relationships with acquisition probabilities is therefore a substantial challenge. This study proposes a Bayesian variable selection approach to optimally select targets for new customer acquisition. Data from an insurance company reveal that this approach outperforms nonselection methods and selection methods based on expert judgment as well as benchmarks based on principal component analysis and bootstrap aggregation of classification trees. Notably, the optimal results show that the Bayesian approach selects panel-based metrics as predictors, detects several nonlinear relationships, selects very large numbers of addresses, and generates profits. In a series of post hoc analyses, the authors consider prospects’ response behaviors and cross selling potential and systematically vary the number of predictors and the estimated profit per response. The results reveal that more predictors and higher response rates do not necessarily lead to higher profits.
KW  - new customer acquisition
KW  - address selection
KW  - variable selection
KW  - big data
KW  - campaign optimization
Y1  - 2017
SN  - 0022-2429
SS  - 0022-2429
U6  - https://doi.org/10.1509/jm.15.0398
DO  - https://doi.org/10.1509/jm.15.0398
VL  - 81
IS  - 2
SP  - 99
EP  - 113
S1  - 15
PB  - American Marketing Association
CY  - Chicago, Ill.
ER  -