Churn management optimization with controllable marketing variables and associated management costs

Abstract

In this paper, we propose a churn management model based on a partial least square (PLS) optimization method that explicitly considers the management costs of controllable marketing variables for a successful churn management program. A PLS prediction model is first calibrated to estimate the churn probabilities of customers. Then this PLS prediction model is transformed into a control model after relative management costs of controllable marketing variables are estimated through a triangulation method. Finally, a PLS optimization model with marketing objectives and constraints are specified and solved via a sequential quadratic programming method. In our experiments, we observe that while the training and test data sets are dramatically different in terms of churner distributions (50% vs. 1.8%), four controllable variables in three marketing strategies significantly changed through optimization process while other variables only marginally changed. We also observe that the most significant variable in a PLS prediction model does not necessarily change most significantly in our PLS optimization model due to the highest management cost associated, implying differences between a prediction and an optimization model. Finally, two marketing models designed for targeting the subsets of custo mers based on churn probability or management costs are presented and discussed.

Rinkodaros programų optimizavimo metodinių prielaidų analizė

“…Analizuojami darbai pagal ūkio sektoriaus požymį pasiskirsto taip: gamyba ( 2008; Even et al 2010;Krishnamoorthy et al 2010;Asllani, Halstead 2011;Luo 2011;Ohlmann, Jones 2011;Xiao et al 2011;Kristensen et al 2012), bankininkystė (Cohen 2004;Bhaskar et al 2009;Nobibon et al 2011), telekomunikacijos (Owczarczuk 2011), socialiniai tinklalapiai , avialinijos (Heilporn et al 2009), kruizų veikla (Sun et al 2011), aukštasis mokslas (Kim et al 2012). Šiame kontekste pastebima tendencija -tyrimų poslinkis į paslaugų sektorių -koreliuoja su šio sektoriaus santykinės dalies ekonomikoje didėjimu.…”

“…2) kainodara (Lewis 2005;Liu et al 2008;Heilporn et al 2009;Kristhnamoorthy et al 2010;Schön 2010;Nobibon et al 2011;Sadjadi 2012); 3) tikslinių vartotojų ar jų grupių nustatymas (Cohen 2004;Ryals et al 2007;Bhaskar et al 2009;Asllani, Halstead 2011;Nobibon et al 2011;Owczarczuk 2011;Xu et al 2012); 4) kiti pavieniai objektai (Heilporn et al 2009;Shang et al 2009;Even et al 2010;Ginevičius 2011;Ohlmann, Jones 2011;Sun et al 2011;Xiao et al 2011;Kim et al 2012;Kristensen et al 2012).…”

“…-euristiniai algoritmai (Ryals et al 2007;Owczarczuk 2011;Kim et al 2012). Taigi šios grupės mokslo darbų analizė taikomų metodų aspektu sustiprino ankstesnio analizės etapo įžvalgas dėl diskrečiojo programavimo.…”

Regaining drifting mobile communication customers: Predicting the odds of success of winback efforts with competing risks regression

“…In academia, quite a number of researchers have sought to identify variables contributing significantly to the discrimination of churning MNO subscribers from their staying counterparts (e.g., Ahn, Han, & Lee, 2006;Benedek, Lublóy, & Vastag, 2014;Keramati & Ardabili, 2011;Kim, Lee, & Johnson, 2013;Rehman & Ali, 2014;Seo, Ranganathan, & Babad, 2008). MNOs exploit findings of this kind of studies to improve their customer retention management programs.…”