Direct marketing decision support through predictive customer response modeling

2012 ◽  
Vol 54 (1) ◽  
pp. 443-451 ◽  
Author(s):  
David L. Olson ◽  
Bongsug(Kevin) Chae
Keyword(s):  
Decision Support ◽  
Direct Marketing ◽  
Marketing Decision ◽  
Response Modeling

Effective customer selection for marketing campaigns based on net scores

2017 ◽  
Vol 11 (1) ◽  
pp. 2-15 ◽  
Author(s):  
René Michel ◽  
Igor Schnakenburg ◽  
Tobias von Martens
Keyword(s):  
Decision Trees ◽  
Direct Marketing ◽  
Real Data ◽  
Business Case ◽  
Added Value ◽  
Scoring Method ◽  
Content Type ◽  
Effective Selection ◽  
Selection For ◽  
Response Modeling

Purpose This paper aims to address the effective selection of customers for direct marketing campaigns. It introduces a new method to forecast campaign-related uplifts (also known as incremental response modeling or net scoring). By means of these uplifts, only the most responsive customers are targeted by a campaign. This paper also aims at calculating the financial impact of the new approach compared to the classical (gross) scoring methods. Design/methodology/approach First, gross and net scoring approaches to customer selection for direct marketing campaigns are compared. After that, it is shown how net scoring can be applied in practice with regard to different strategical objectives. Then, a new statistic for net scoring based on decision trees is developed. Finally, a business case based on real data from the financial sector is calculated to compare gross and net scoring approaches. Findings Whereas gross scoring focuses on customers with a high probability of purchase, regardless of being targeted by a campaign, net scoring identifies those customers who are most responsive to campaigns. A common scoring procedure – decision trees – can be enhanced by the new statistic to forecast those campaign-related uplifts. The business case shows that the selected scoring method has a relevant impact on economical indicators. Practical implications The contribution of net scoring to campaign effectiveness and efficiency is shown by the business case. Furthermore, this paper suggests a framework for customer selection, given strategical objectives, e.g. minimizing costs or maximizing (gross or lift)-added value, and presents a new statistic that can be applied to common scoring procedures. Originality/value Despite its lever on the effectiveness of marketing campaigns, only few contributions address net scores up to now. The new χ2-statistic is a straightforward approach to the enhancement of decision trees for net scoring. Furthermore, this paper is the first to the application of net scoring with regard to different strategical objectives.

Application of Multidimensional Data Analysis in Power Marketing Decision Support System Based on Big Data

2016 ◽  
Vol 9 (10) ◽  
pp. 295-304
Author(s):  
Sun Zhineng ◽  
Yu Fang ◽  
Zhou Kaihe ◽  
Li Jiangang ◽  
Fang Yunhui
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support System ◽  
Support System ◽  
Multidimensional Data ◽  
Multidimensional Data Analysis ◽  
Marketing Decision

scholarly journals Hierarchical scaling of marketing decision support systems

1994 ◽  
Vol 12 (3) ◽  
pp. 219-232 ◽  
Author(s):  
Berend Wierenga ◽  
Peter A.M. Oude Ophuis ◽  
Eelko K.R. Huizingh ◽  
Peter A.F.M. van Campen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Marketing Decision

Marketing-Decision-Support-Systeme im Pharma-Markt

1995 ◽  
pp. 667-687 ◽  
Author(s):  
Klaus D. Wilde
Keyword(s):  
Decision Support ◽  
Marketing Decision

Capability Criteria for Marketing Decision Support Systems

1986 ◽  
Vol 3 (1) ◽  
pp. 81-95 ◽  
Author(s):  
Martin D. Goslar
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Marketing Decision
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