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Customer Lifetime Value Prediction Using Statistical Modeling: Predicting Online Payments in an Industry Setting / Kundens livstidsvärde förutsägelse med statistisk modellering: predikera online betalningar i en industriell miljöSUSOYKINA, Alina January 2018 (has links)
Customer lifetime value (CLV) provides a measure of customer revenue contribution. It allows to justify marketing campaigns, overall budgeting, and strategy planning. CLV is an estimated cash flow attributed to the entire relationship with customers in the future. Ability to utilize information gained from CLV analysis at the most efficient way, provides a strong competitive advantage. The concept of CLV was studied and modeled with application to the online payments industry which is relatively new and at its growing phase. Ability to predict CLV accurately conveys a great value for guiding the industry (i.e. emerging companies) to maturity. CLV analysis in this case becomes complex due to the fact that usually the databases of such companies are huge and include transactions from different industries: e-commerce, financial services, travel, gaming etc. This paper aims to define an appropriate model for CLV prediction in the online payments setting. The proposed model segments customers first in order to improve performance of the predictive model. Then Pareto/NBD model was applied to predict CLV at the customer-level for each customer segment separately. Although the results show that it is possible to predict CLV at some extent, the model needs to be further improved and possible pitfalls need to be scrutinized. Discussion on these issues is provided in the following sections. / Kundens livstidsvärde (Customer lifetime value) är ett mått på hur en kund bidrar till företagets omsättning. Det tillåter att åskådliggöra försäljningskampanjer, företagets budget och företagets strategi. Kundens livstidsvärde är en estimering av betalningsflöde som ett företag kan tjäna av kunder i framtiden. Möjligheten att nyttiggöra informationen från kundens livstidsvärde analys ger företag en starkt konkurrenskraftig fördel. Kundens livstidsvärde var studerat och modellerat med anknytning till online betalningstjänster industri, vilken har utvecklats kraftigt inom senaste åren. Möjligheten att predikera kundens livstidsvärde med hög noggranhet medför ett starkt värde för företag som erbjuder tjänster inom online betalningar och kan driva dessa till mognad. Att predikera kundens livstidsvärde inom denna bransch anses vara en komplex process, då databaser hos såna föratag är stora och inkluderar information om transaktioner från olika industrier såsom: elektronisk handel, finansiella tjänster, rese- och spelbolag. I denna studie definieras en modell för att kunna predikera kundens livstidsvärde baserat på data från ett företag som tillhandahåller online betalningstjänster. För att uppnå bättre prestanda, segmenterar den föreslagna modellen kunder först. Därefter en Pareto/NBD modell används, för att predikera kundens livstidsvärde för varje kundsegment. Trots att resultat visar att kundens livtidsvärde kan modelleras till en viss nivå, modellen behöver förbättras och möjliga blindskär måste granskas.
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Stochastic process customer lifetime value models with time-varying covariatesHarman, David M. 01 December 2016 (has links)
Customer lifetime value (CLV) is a forecasted expectation of the future value of a customer to the firm. There are two customer behavioral components of CLV that represent a particular modeling challenge: 1) how many transactions we expect from a customer in the future, and 2) how likely it is the customer remains active. Existing CLV models like the Pareto/NBD are valuable managerial tools because they are able to provide forward-looking estimates of transaction patterns and customer churn when the event of a customer leaving is unobservable, which is typical for most noncontractual goods and services.
The CLV model literature has for the most part maintained its original assumption that the number of customer transactions follows a stable transaction process. Yet there are many categories of noncontractual goods and services where the stable transaction rate assumption is violated, particularly seasonal purchase patterns. CLV model estimates are further biased when there is an excess of customers with no repeat transactions.
To address these modeling challenges, within this thesis I develop a generalized CLV modeling framework that combines three elements necessary to reduce bias in model estimates: 1) the incorporation of time-varying covariates to model data with transaction rates that change over time, 2) a zero-inflated model specification for customers with no repeat transactions, and 3) generalizes to different transaction process distributions to better fit diverse customer transaction patterns. This CLV modeling framework provides firms better estimates of the future activity of their customers, a critical CRM application.
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