LTV model in consultant sector. Case study: mental health clinic

Tarokh, Mohammad Jafar; Sekhavat, Aydin Akbari

doi:10.1080/01449290500168087

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…Therefore, there are some difficulties in assessing the number of contacts of each customer. In general, a customer's needs could be classified into four classes:permanent needs;temporary needs with limited period services;temporary needs once only; andcase‐based temporary needs.For each case, we should consider a special definition for customer life cycle and loyalty (Tarokh and Sekhavat, 2006).…”

Section: Case Studymentioning

confidence: 99%

“…The ideal lift chart is the ideal state of calculating the lift value in which all of the customers with special behavior have been placed in the high‐risk customers segment. If the special behavior rate in the society increases, the ideal lift will be skewed to the right and will complete the baseline concept for model validation (Tarokh and Sekhavat, 2006). In this work, the lift charts of all 11 models are tested, and concludes that all of them are indeed valid.…”

Section: Model Validationmentioning

confidence: 99%

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Lifetime value model in the medical sector: a case study of a restoration and beauty clinic

Zare‐Hoseini

Tarokh

Nooghabi

2011

International Journal of Pharmaceutical and Healthcare Marketing

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PurposeAcquiring and retaining profitable customers are major concerns of a business. In this paper, the customers of the Restoration and Beauty Clinic of Iran University are segmented using three value types: current value, expected value, and loyalty in a case study to predict the probability of customer churn and future purchase services in the clinic.Design/methodology/approachThis study utilized customers' data records with nine data fields (socio‐demographic and transactional) from three year's transactions of the clinic. Logistic regression as a data mining technique is then used to predict the future behavior of the customers. In addition, the verification and the validation of the models are done using lift charts.FindingsThis research segments the customers of the clinic into four categories based on three values (current value, expected value, and loyalty). Then simple marketing strategies that might be adopted are suggested. These strategies might help the shareholders and experts of the clinic to promote relationships with patients and deliver better services to attract and retain their customers.Originality/valueThe results of this research enable public health agencies to evaluate the effectiveness of their policies and detect their shortcomings in order to better serve patients. Also, it will help to increase their profits from the clinics and raise customer satisfaction.

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Section: Case Studymentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

Lifetime value model in the medical sector: a case study of a restoration and beauty clinic

Zare‐Hoseini

Tarokh

Nooghabi

2011

International Journal of Pharmaceutical and Healthcare Marketing

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“…Due to development in information technology and E-Business, customer data accumulates by an increasing quantity. Analytical Customer Relationship Management (CRM) consists of strategic and effective use of data mining tools to provide management with suitable decisionmaking possibilities, so it requires robust and accurate data processing technologies such as data mining tools and data ware housing (Tarokh & Sekhavat, 2006).…”

Section: Customer Lifetime Valuementioning

confidence: 99%

“…Yamamoto, Sublaban, and Aranha (2008) described CLV asestimated monetary value that the client will bring to the firm during the entire lifespan of his/her commercial relationship with the company, discounted to today's value. A simple mathematical model of CLV for a customer (omitting customer subscript) is (Gupta et al, 2004;Reinartzet al, 2003;Sheth et al, 2002;Tarokh, & Sekhavat, 2006).…”

Section: Fundamentals Of CLV Modelingmentioning

confidence: 99%

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Citizen Lifetime Value in E-Government

Nikkhahan

Tarokh

2011

International Journal of Customer Relationship Marketing and Management

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Customer Lifetime Value (CLV) is one of the most important measures of valuing the customers in private sector. CLV calculates customer contribution in the profits of organization. In this paper Citizen Lifetime Value (CzLV) is introduced to measure the financial value of citizens for the government. CzLV evaluates citizen contribution in cost reduction of the organization. This measure can be calculated based on past behavior of citizens in using the service and cost reduction of using online services rather than offline ones. Logistic regression is employed as a data mining technique to predict future use of online services by citizens. A service of Tehran.ir called “137,” one of the most important portals of Iran’s E-government, is considered as a case study. CzLV for the citizens of this service is calculated and four citizen segments are specified. Then each segment is evaluated based on different characteristic of citizens, and suitable strategies are presented to build more financial values for the organization.

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The impact of online purchase behaviour on customer lifetime value

Kvíčala,

Králová,

Suchánek

2024

J Market Anal

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This paper investigates customer lifetime value (CLV) in e-shops, particularly those operated by small on-platform evolving financially independent online resellers (SOEFIOR) e-shops. The aim is to identify factors predicting CLV and assess their associations with CLV. Given the nested structure of the data, where transactions by customers are clustered within e-shops, a multilevel model is employed as the analytical framework. While classical linear regression assumes independence of observations within a sample, our dataset operates across three hierarchical levels: transaction level (I), customer level (II), and e-shop level (III). This hierarchical structure challenges the validity of inferences drawn from linear regression models, as transactions by one customer are not independent, and customers within a single e-shop may exhibit interdependencies. Therefore, a multilevel model is utilised to appropriately address the dependence among transactions within this nested data structure. The analysis reveals that the “number of transactions” exhibits the strongest positive association with CLV, followed by “days to transaction” and “session duration”. Furthermore, we discovered that “direct access” exhibits a positive association with CLV compared to access through Google campaigns, whereas access through Facebook campaigns demonstrates a negative association with CLV when compared to Google campaigns. Additionally, using the e-shop on mobile and landing on the product details page both show negative associations with CLV compared to desktop usage and landing on the e-shop’s home page, respectively. Our research identifies several variables that are associated with CLV in e-shops. This enables e-shop managers to effectively target and engage customers through marketing activities, thereby maximising revenues, financial performance, and customer CLV.

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LTV model in consultant sector. Case study: mental health clinic

Cited by 5 publications

References 7 publications

Lifetime value model in the medical sector: a case study of a restoration and beauty clinic

Lifetime value model in the medical sector: a case study of a restoration and beauty clinic

Citizen Lifetime Value in E-Government

The impact of online purchase behaviour on customer lifetime value

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