Modeling Within- and Across-Customer Association in Lifetime Value with Copulas

Recent advances in linking Recency-Frequency-Monetary value (RFM) data to Customer Lifetime Value (CLV) in non-contractual settings rely on the assumption of independence between the transaction and spend processes. We propose to model jointly the inter- and intra-customer dependency between both processes using copulas, hereby accounting for the double correlation within and across customers. Applied to a unique data set of securities' transactions, we nd that modeling both associations enhances the accuracy of CLV predictions, thus improving customer valuation and selection tasks.Association;Copula;Customer Lifetime Value;Across and Within Customers

Modeling customer loyalty using customer lifetime value.

The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution will be to redefine the notion of customer's loyalty by considering it from a customer-centric point-of-view instead of a product-centric point-of-view. We will hereby use the customer lifetime value (CLV) defined as the discounted value of future marginal earnings, based on the customer's activity. Hence, a churner will be defined as someone whose CLV, thus the related marginal profit, is decreasing. As a second contribution, the loss incurred by the CLV decrease will be used to appraise the cost to misclassify a customer by introducing a new loss function. In the empirical study, we will compare the accuracy of various classification techniques commonly used in the domain of churn prediction, including two cost-sensitive classirfiers. Our final conclusion is that since profit is what really matters in a commercial environment, standard statistical accuracy measures or prediction need to be revised and a more profit oriented focus may be desirable.Churn prediction; Classification; Customer lifetime value; Prediction models;

Autocrine Transforming Growth Factor β Signaling Regulates Extracellular Signal-regulated Kinase 1/2 Phosphorylation via Modulation of Protein Phosphatase 2A Expression in Scleroderma Fibroblasts

BACKGROUND. During scleroderma (SSc) pathogenesis, fibroblasts acquire an activated phenotype characterized by enhanced production of extracellular matrix (ECM) and constitutive activation of several major signaling pathways including extracellular signal-related kinase (ERK1/2). Several studies have addressed the role of ERK1/2 in SSc fibrosis however the mechanism of its prolonged activation in SSc fibroblasts is still unknown. Protein phosphatase 2A (PP2A) is a key serine threonine phosphatase responsible for dephosphorylation of a wide array of signaling molecules. Recently published microarray data from cultured SSc fibroblasts suggests that the catalytic subunit (C-subunit) of PP2A is downregulated in SSc. In this study we examined the role and regulation of PP2A in SSc fibroblasts in the context of ERK1/2 phosphorylation and matrix production. RESULTS. We show for the first time that PP2A mRNA and protein expression are significantly reduced in SSc fibroblasts and correlate with an increase in ERK1/2 phosphorylation and collagen expression. Furthermore, transforming growth factor β (TGFβ), a major profibrotic cytokine implicated in SSc fibrosis, downregulates PP2A expression in healthy fibroblasts. PP2A-specific small interfering RNA (siRNA) was utilized to confirm the role of PP2A in ERK1/2 dephosphorylation in dermal fibroblasts. Accordingly, blockade of autocrine TGFβ signaling in SSc fibroblasts using soluble recombinant TGFβ receptor II (SRII) restored PP2A levels and decreased ERK1/2 phosphorylation and collagen expression. In addition, we observed that inhibition of ERK1/2 in SSc fibroblasts increased PP2A expression suggesting that ERK1/2 phosphorylation also contributes to maintaining low levels of PP2A, leading to an even further amplification of ERK1/2 phosphorylation. CONCLUSIONS. Taken together, these studies suggest that decreased PP2A levels in SSc is a result of constitutively activated autocrine TGFβ signaling and could contribute to enhanced phosphorylation of ERK1/2 and matrix production in SSc fibroblasts.National Institutes of Health (AR-44883

Ensembles of probability estimation trees for customer churn prediction

Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both

Modeling Within- and Across-Customer Association in Lifetime Value with Copulas

Recent advances in linking Recency-Frequency-Monetary value (RFM) data to Customer Lifetime Value (CLV) in non-contractual settings rely on the assumption of independence between the transaction and spend processes. We propose to model jointly the inter- and intra-customer dependency between both processes using copulas, hereby accounting for the double correlation within and across customers. Applied to a unique data set of securities' transactions, we nd that modeling both associations enhances the accuracy of CLV predictions, thus improving customer valuation and selection tasks.

A modified Pareto/NBD approach for predicting customer lifetime value

Valuing customers is a central issue for any commercial activity. The customer lifetime value (CLV) is the discounted value of the future profits that this customer yields to the company. In order to compute the CLV, one needs to predict the future number of transactions a customer will make and the profit of these transactions. With the Pareto/NBD model, the future number of transactions of a customer can be predicted, and the CLV is then computed as a discounted product between this number and the expected profit per transaction. Usually, the number of transactions and the future profits per transaction are estimated separately. This study proposes an alternative. We show that the dependence between the number of transactions and their profitability can be used to increase the accuracy of the prediction of the CLV. This is illustrated with a new empirical case from the retail banking sector

Modeling customer loyalty using customer lifetime value

The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution will be to redefine the notion of customer's loyalty by considering it from a customer-centric point-of-view instead of a product-centric point-of-view. We will hereby use the customer lifetime value (CLV) defined as the discounted value of future marginal earnings, based on the customer's activity. Hence, a churner will be defined as someone whose CLV, thus the related marginal profit, is decreasing. As a second contribution, the loss incurred by the CLV decrease will be used to appraise the cost to misclassify a customer by introducing a new loss function. In the empirical study, we will compare the accuracy of various classification techniques commonly used in the domain of churn prediction, including two cost-sensitive classirfiers. Our final conclusion is that since profit is what really matters in a commercial environment, standard statistical accuracy measures or prediction need to be revised and a more profit oriented focus may be desirable.status: publishe