The Impact of Customer Lifetime Value Metrics on the Decision Making of Customer Facing Employees

2010 ◽  
Author(s):  
Pablo Casas-Arce ◽  
Francisco de Asis Martinez-Jerez ◽  
V. G. Narayanan
Keyword(s):  
Decision Making ◽  
Customer Lifetime Value ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
The Impact

The Impact of Forward-Looking Metrics on Employee Decision-Making: The Case of Customer Lifetime Value

2016 ◽  
Vol 92 (3) ◽  
pp. 31-56 ◽  
Author(s):  
Pablo Casas-Arce ◽  
F. Asis Martínez-Jerez ◽  
V. G. Narayanan
Keyword(s):  
Decision Making ◽  
Default Risk ◽  
Customer Value ◽  
Customer Lifetime Value ◽  
Customer Relationship ◽  
Significant Shift ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
The Impact ◽  
Forward Looking

ABSTRACT This paper analyzes the effects of forward-looking metrics on employee decision-making. We use data from a bank that started providing branch managers with the customer lifetime value (CLV)—an estimate of the future value of the customer relationship—of mortgage applicants. The data allow us to gauge the effects of enriching the employees' information set in an environment where explicit incentives and decision rights remained unchanged. On average, customer value increased 5 percent after the metric's introduction. The metric's availability resulted in a significant shift in attention toward more profitable client segments and some improvement in cross-selling. However, the use of CLV did not negatively impact pricing or default risk, as the literature predicts. Finally, branch managers with shorter tenure displayed a stronger response, consistent with information substituting for experience.

scholarly journals Customer Profitability Analyses and Customer Lifetime Value

2015 ◽  
Vol 89 (7/8) ◽  
pp. 265-273
Author(s):  
Ashok Sridhar ◽  
Michael Corbey
Keyword(s):  
Decision Making ◽  
Retrospective Analysis ◽  
Customer Lifetime Value ◽  
Customer Profitability ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
Managerial Decisions ◽  
Doing Business ◽  
Customer Lifetime ◽  
Lifetime Value

The main objective of this paper is to compare two key approaches in the !eld of Customer Accounting (CA), namely Customer Pro!tability Analysis (CPA) and Customer Lifetime Value (CLV). While CPA is a retrospective analysis of past accruals that represent the results of doing business with a customer over a certain, mostly single-period of time, CLV is a predictive measure of future customer-related cash "ows over a certain (multi-)period of time. This paper draws on the state-ofthe- art knowledge in the Customer Accounting (CA) literature to identify the impacts of CPA and CLV on managerial decision-making. It also offers recommendations as to the scenarios in which these metrics should be deployed in order to arrive at meaningful managerial decisions, and highlights their collective limitations.

The Relevance of Positive Word-of-Mouth Effects on the Customer Lifetime Value - A Replication and Extension in the Context of Start-ups

2019 ◽  
Vol 3 (3) ◽  
pp. 148-160 ◽  
Author(s):  
Manuel Grossmann ◽  
Christian Brock ◽  
Marco Hubert ◽  
Thomas Reimer
Keyword(s):  
Word Of Mouth ◽  
Customer Lifetime Value ◽  
Positive Word ◽  
Customer Lifetime ◽  
Service Setting ◽  
Start Up ◽  
Lifetime Value ◽  
Positive Word Of Mouth ◽  
Start Ups ◽  
The Impact

This paper investigates the importance of positive word-of-mouth (WOM) effects on estimating the customer lifetime value (CLV) in start-up businesses. In line with prior research, we assume that, especially in young companies such as start-ups, managers and investors neglect the impact of WOM and therefore underestimate the CLV. To examine this assumption, self-collected WOM data is integrated into calculation of the CLV of a one-yearold online grocery retailer start-up. The CLV of 632 customers is combined with a survey about positive WOM activities. The study shows the high relevance of WOM for start-ups in a noncontractual as well as service setting, thereby calling for integration of WOM into calculation of the CLV.

scholarly journals PEMODELAN DAN PENERAPAN METODE RFM PADA ESTIMASI NILAI KONSUMEN (CUSTOMER LIFETIME VALUE) MENGGUNAKAN K-MEANS CLUSTERING MACHINE LEARNING

2021 ◽  
Vol 2020 (1) ◽  
pp. 1277-1285
Author(s):  
Bagaskoro Cahyo Laksono ◽  
Ika Yuni Wulansari
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Learning Model ◽  
Customer Lifetime Value ◽  
Customer Lifetime ◽  
Machine Learning Model ◽  
Lifetime Value

Krisis Covid-19 berdampak pada revenue perusahaan, jika perusahaan tidak meningkatkan strategi pemasaran yang tepat terhadap konsumen, akan beresiko gulung tikar karena tidak memiliki target pasar yang jelas. Disamping itu, perusahaan dapat mengembangkan bisnisnya menggunakan big data untuk mendukung decision making. Big data dalam industry e-commerce yang mencakup ukuran dan kecepatan transaksi yang tinggi dapat digunakan untuk menganalisis perilaku konsumen bahkan memprediksi nilai konsumen. Pada zaman sekarang perusahaan mulai mengembangkan ketertarikan bisnis yang berorientasi konsumen daripada berorientasi produk. Salah satu cara yang dapat digunakan untuk menentukan nilai konsumen yaitu dengan menghitung Customer Lifetime Value (CLV). Dengan mengetahui CLV di level individu, akan berguna untuk membantu pengambil keputusan untuk mengembangkan segmentasi konsumen dan alokasi sumber daya. Penting dilakukan segmentasi atau pengelompokkan konsumen yang menggambarkan kelompok loyalitas konsumen. Oleh karena itu tujuan dalam penelitian ini adalah melakukan penghitungan CLV dan segmentasi konsumen dengan menggunakan metode analisis RFM dengan K-Means Clustering Machine Learning Model. Tahapan analisis diantaranya mendefinisikan RFM Segmentation Value yang merupakan clustering yang dibangun dari angka kumulatif yang berisi penjumlahan Recency, Frequency dan Monetary Level yang dimiliki masing-masing konsumen. Kombinasi nilai level yang tercipta berkisar antara 0,1,2,3,4,5,6 yang artinya semakin tinggi nilainya maka semakin berharga konsumen tersebut. Pada akhirnya, metode segmentasi konsumen yang di bangun penulis dapat digunakan untuk optimasi strategi perusahaan untuk mendapat profit yang maksimum. Metode ini dapat diterapkan pada berbagai kasus dan perusahaan lain. Hasil penelitian ini diharapkan dapat membantu perusahaan untuk bertahan di tengah krisis akibat Covid-19.

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