Predicting Customer Lifetime Value and its underlying short-term cash flow effects
(2018) MIOM05Production Management
- Abstract
- Background The intense competition in the Telecom industry makes service providers
invest heavily in acquiring new customers. In Telavox case, the majority of
these initial expenses are related to the sales process. To be profitable it is
necessary for the customers to generate a net profit during its time at Telavox
that compensates for this initial expense, i.e. a positive Customer Lifetime
Value. However, it can be difficult to predict a potential customer’s future
lifetime value.
Purpose The purpose is to broaden or review the academia’s understanding
concerning modelling and predict the CLV of customers in the Telecom
industry and investigate the short-term cash flow effects of the underlying
CLV factors.
Research questions... (More) - Background The intense competition in the Telecom industry makes service providers
invest heavily in acquiring new customers. In Telavox case, the majority of
these initial expenses are related to the sales process. To be profitable it is
necessary for the customers to generate a net profit during its time at Telavox
that compensates for this initial expense, i.e. a positive Customer Lifetime
Value. However, it can be difficult to predict a potential customer’s future
lifetime value.
Purpose The purpose is to broaden or review the academia’s understanding
concerning modelling and predict the CLV of customers in the Telecom
industry and investigate the short-term cash flow effects of the underlying
CLV factors.
Research questions What short-term effect has CLV from a cash flow perspective?
Is it possible to create a model for predicting CLV of new customers in the
Telecom industry?
What are the possibilities for detecting a change in CLV customers in the
Telecom industry?
Method A cash flow analysis was conducted based on the average Telavox customer
to investigate the short-term cash flows.
A retention model based on current literature was developed for the
characteristics of the largest segment (61% of revenues) at Telavox. The
model gives insights about the lifetime of customer cohorts from this
segment. To fully predict the CLV it is also needed to predict future revenues
and costs.
Results The payback time for an expansion was found to be significantly longer than
the payback time for a single customer. Increasing the rate of expansion will
generate a lower minimum of cumulative cash flow but will not affect the
time to reach break even.
The retention model was able to give a prediction of the lifetime of the
largest segment at Telavox. The model was not tested but a sensitivity
analysis indicated that the resulting values are reasonable. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8939247
- author
- Boris-Möller, Henrik
- supervisor
- organization
- course
- MIOM05
- year
- 2018
- type
- M1 - University Diploma
- subject
- keywords
- Customer Lifetime Value, CLV, Modelling, Predicting, cash flow effect
- other publication id
- 18/5594
- language
- English
- id
- 8939247
- date added to LUP
- 2018-05-03 14:57:32
- date last changed
- 2018-05-03 14:57:32
@misc{8939247, abstract = {{Background The intense competition in the Telecom industry makes service providers invest heavily in acquiring new customers. In Telavox case, the majority of these initial expenses are related to the sales process. To be profitable it is necessary for the customers to generate a net profit during its time at Telavox that compensates for this initial expense, i.e. a positive Customer Lifetime Value. However, it can be difficult to predict a potential customer’s future lifetime value. Purpose The purpose is to broaden or review the academia’s understanding concerning modelling and predict the CLV of customers in the Telecom industry and investigate the short-term cash flow effects of the underlying CLV factors. Research questions What short-term effect has CLV from a cash flow perspective? Is it possible to create a model for predicting CLV of new customers in the Telecom industry? What are the possibilities for detecting a change in CLV customers in the Telecom industry? Method A cash flow analysis was conducted based on the average Telavox customer to investigate the short-term cash flows. A retention model based on current literature was developed for the characteristics of the largest segment (61% of revenues) at Telavox. The model gives insights about the lifetime of customer cohorts from this segment. To fully predict the CLV it is also needed to predict future revenues and costs. Results The payback time for an expansion was found to be significantly longer than the payback time for a single customer. Increasing the rate of expansion will generate a lower minimum of cumulative cash flow but will not affect the time to reach break even. The retention model was able to give a prediction of the lifetime of the largest segment at Telavox. The model was not tested but a sensitivity analysis indicated that the resulting values are reasonable.}}, author = {{Boris-Möller, Henrik}}, language = {{eng}}, note = {{Student Paper}}, title = {{Predicting Customer Lifetime Value and its underlying short-term cash flow effects}}, year = {{2018}}, }