Improving customer churn prediction by data augmentation using pictorial stimulus-choice data

The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures

Target the ego or target the group: evidence from a randomized experiment in proactive churn management

We propose a new strategy for proactive churn management that actively uses social network information to help retain consumers. We collaborate with a major telecommunications provider to design, deploy, and analyze the outcomes of a randomized control trial at the household level to evaluate the effectiveness of this strategy. A random subset of likely churners were selected to be called by the firm. We also randomly selected whether their friends would be called. We find that listing likely churners to be called reduced their propensity to churn by 1.9 percentage points from a baseline of 17.2%. When their friends were also listed to be called, their likelihood of churn reduced an additional 1.3 percentage points. The client lifetime value of likely churners increased 2.1% with traditional proactive churn management, and this statistic becomes 6.4% when their friends were also listed to be called by the firm. We show that, in our setting, likely churners receive a signal from their friends that reduces churn among the former. We also discuss how this signal may trigger mechanisms akin to both financial comparisons and conformity that may explain our findings.info:eu-repo/semantics/acceptedVersio

Churn Management Optimization with Controllable Marketing Variables and Associated Management Costs

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 success- ful churn management program. A PLS prediction model is first calibrated to estimate the churn proba- bilities 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 control- lable 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 pre- diction 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 customers based on churn probability or management costs are presented and discussed

Intelligent data analysis approaches to churn as a business problem: a survey

Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition and, with it, a growing preoccupation among service-providing companies with creating stronger customer bonds. In this context, anticipating the customer’s intention to abandon the provider, a phenomenon known as churn, becomes a competitive advantage. Such anticipation can be the result of the correct application of information-based knowledge extraction in the form of business analytics. In particular, the use of intelligent data analysis, or data mining, for the analysis of market surveyed information can be of great assistance to churn management. In this paper, we provide a detailed survey of recent applications of business analytics to churn, with a focus on computational intelligence methods. This is preceded by an in-depth discussion of churn within the context of customer continuity management. The survey is structured according to the stages identified as basic for the building of the predictive models of churn, as well as according to the different types of predictive methods employed and the business areas of their application.Peer ReviewedPostprint (author's final draft

Characterization of the clients retention in the tlecommunications companies

The ability of a company to be able to do a precisely churn prediction, so it can act on it, is paramount. For this reason, Deloitte addressed me the challenge of characterizing the client’s retention in the telecom companies. To do so, it was created a comprehensive tool that enables Deloitte to evaluate the churn management maturity level of a telecom operator and highlight its strengths and weaknesses. The development of this matrix was based on a depth churn research, a market research based on 40 interviews and 2 focus group and the valuable feedback from Deloitte consultants