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For the above reasons (costs & risks), managers are often reluctant to approve complex CLV modeling projects.

In order to overcome the Customer survival prediction issue, the Business can calculate the retention rates achieved in the past and based on these produce an estimate for the future (alternatively, an average Customer lifecycle duration per product, can be used to simplify measurements). However, in certain markets, the calculation of the yearly retention rate is not a straight forward task: a Customer termination, cannot be clearly identified before a certain time period.

CLV or an equivalent ranking can be measured per Customer segment instead of being measured per individual Customer. This can simplify the process substantially, by applying the segment retention rate and segment average cost per

In order to serve their Customers according to their value (apply value-based-servicing), Businesses try to assess the value of each Customer. One approach to assess Customer value is by estimating the Customer Lifetime Value (hereafter CLV).

A strict approach to the definition of CLV (or LTV) is the net present value of future cash inflows and outflows or profits (based on the principles of financial management), related to a specific Customer. An important factor affecting the CLV is the retention rate (or alternatively the Customer lifecycle termination probability).

Theoretically speaking, a comprehensive assessment of customer value should comprise all different aspects of a customer’s contribution to the Business’s success (e.g. referrals to other Customers, cross selling potential).

A number of different approaches of varying complexity have been proposed for the calculation of the CLV:
• Some focus on cash inflows and do not integrate the customer survival factor
• Some estimate the cash inflows only: the revenue that this Customer is expected to contribute to the Business in the future.
• Some approaches which are more complicated, attempt to estimate both cash inflows as well as cash outflows: the revenue that this Customer is expected to produce for the Business in the future, as well as the Customer acquisition, service and marketing expenses.
• Some include also elements relating to the value gained from Customer referrals as well as cross selling & up selling.

All these approaches may or may not be suitable to the context of a specific Business. Asserting that a model which does not incorporate all CLV factors is invalid or incomplete, may be of academic value, but in the real Business world things are not so simple.

It can be easily understood that the estimation of the CLV, according to the above ‘strict approach’ definition is a very difficult task. This is due to the following reasons:
• The difficulty in the estimation of the duration before Customer lifecycle termination (survival probability). The business needs to implement a so-called survival function which is time-dependent, and apply it to each Customer. Moreover a survival function has its limitations since it only gives the probability of a Customer ‘surviving’ beyond a certain point in time.
• It can be difficult to assign costs related to a specific Customer (acquisition, marketing, serving, retaining, terminating). If the Business is not applying cost accounting at the Customer level, cost assignments can only be based on averages (e.g. cost analysed per Customer segment).
• The availability and quality of information related to the factors affecting CLV. Historical information may also be needed.

Each Business should evaluate:
• The readiness of its Organization, vis-a-vis predictive modeling for CLV
• The available data sources, relating to the factors affecting the CLV
• The cost to build and maintain each of the feasible options of a CLV model, in order to compare them
• The risk of building a complex model which is not successful in estimating accurately CLV, as promised
• The analysis of the additional insight and expected gains by the more sophisticated model vis-?-vis the additional cost incurred (not an easy task)

For the above reasons (costs & risks), managers are often reluctant to approve complex CLV modeling projects.

In order to overcome the Customer survival prediction issue, the Business can calculate the retention rates achieved in the past and based on these produce an estimate for the future (alternatively, an average Customer lifecycle duration per product, can be used to simplify measurements). However, in certain markets, the calculation of the yearly retention rate is not a straight forward task: a Customer termination, cannot be clearly identified before a certain time period.

CLV or an equivalent ranking can be measured per Customer segment instead of being measured per individual Customer. This can simplify the process substantially, by applying the segment retention rate and segment average cost per C

approaches of varying complexity have been proposed for the calculation of the CLV:
• Some focus on cash inflows and do not integrate the customer survival factor
• Some estimate the cash inflows only: the revenue that this Customer is expected to contribute to the Business in the future.
• Some approaches which are more complicated, attempt to estimate both cash inflows as well as cash outflows: the revenue that this Customer is expected to produce for the Business in the future, as well as the Customer acquisition, service and marketing expenses.
• Some include also elements relating to the value gained from Customer referrals as well as cross selling & up selling.

All these approaches may or may not be suitable to the context of a specific Business. Asserting that a model which does not incorporate all CLV factors is invalid or incomplete, may be of academic value, but in the real Business world things are not so simple.

It can be easily understood that the estimation of the CLV, according to the above ‘strict approach’ definition is a very difficult task. This is due to the following reasons:
• The difficulty in the estimation of the duration before Customer lifecycle termination (survival probability). The business needs to implement a so-called survival function which is time-dependent, and apply it to each Customer. Moreover a survival function has its limitations since it only gives the probability of a Customer ‘surviving’ beyond a certain point in time.
• It can be difficult to assign costs related to a specific Customer (acquisition, marketing, serving, retaining, terminating). If the Business is not applying cost accounting at the Customer level, cost assignments can only be based on averages (e.g. cost analysed per Customer segment).
• The availability and quality of information related to the factors affecting CLV. Historical information may also be needed.

Each Business should evaluate:
• The readiness of its Organization, vis-a-vis predictive modeling for CLV
• The available data sources, relating to the factors affecting the CLV
• The cost to build and maintain each of the feasible options of a CLV model, in order to compare them
• The risk of building a complex model which is not successful in estimating accurately CLV, as promised
• The analysis of the additional insight and expected gains by the more sophisticated model vis-?-vis the additional cost incurred (not an easy task)

For the above reasons (costs & risks), managers are often reluctant to approve complex CLV modeling projects.

In order to overcome the Customer survival prediction issue, the Business can calculate the retention rates achieved in the past and based on these produce an estimate for the future (alternatively, an average Customer lifecycle duration per product, can be used to simplify measurements). However, in certain markets, the calculation of the yearly retention rate is not a straight forward task: a Customer termination, cannot be clearly identified before a certain time period.

CLV or an equivalent ranking can be measured per Customer segment instead of being measured per individual Customer. This can simplify the process substantially, by applying the segment retention rate and segment average cost per

e all CLV factors is invalid or incomplete, may be of academic value, but in the real Business world things are not so simple.

It can be easily understood that the estimation of the CLV, according to the above ‘strict approach’ definition is a very difficult task. This is due to the following reasons:
• The difficulty in the estimation of the duration before Customer lifecycle termination (survival probability). The business needs to implement a so-called survival function which is time-dependent, and apply it to each Customer. Moreover a survival function has its limitations since it only gives the probability of a Customer ‘surviving’ beyond a certain point in time.
• It can be difficult to assign costs related to a specific Customer (acquisition, marketing, serving, retaining, terminating). If the Business is not applying cost accounting at the Customer level, cost assignments can only be based on averages (e.g. cost analysed per Customer segment).
• The availability and quality of information related to the factors affecting CLV. Historical information may also be needed.

Each Business should evaluate:
• The readiness of its Organization, vis-a-vis predictive modeling for CLV
• The available data sources, relating to the factors affecting the CLV
• The cost to build and maintain each of the feasible options of a CLV model, in order to compare them
• The risk of building a complex model which is not successful in estimating accurately CLV, as promised
• The analysis of the additional insight and expected gains by the more sophisticated model vis-?-vis the additional cost incurred (not an easy task)

For the above reasons (costs & risks), managers are often reluctant to approve complex CLV modeling projects.

In order to overcome the Customer survival prediction issue, the Business can calculate the retention rates achieved in the past and based on these produce an estimate for the future (alternatively, an average Customer lifecycle duration per product, can be used to simplify measurements). However, in certain markets, the calculation of the yearly retention rate is not a straight forward task: a Customer termination, cannot be clearly identified before a certain time period.

CLV or an equivalent ranking can be measured per Customer segment instead of being measured per individual Customer. This can simplify the process substantially, by applying the segment retention rate and segment average cost per

s not applying cost accounting at the Customer level, cost assignments can only be based on averages (e.g. cost analysed per Customer segment).
• The availability and quality of information related to the factors affecting CLV. Historical information may also be needed.

Each Business should evaluate:
• The readiness of its Organization, vis-a-vis predictive modeling for CLV
• The available data sources, relating to the factors affecting the CLV
• The cost to build and maintain each of the feasible options of a CLV model, in order to compare them
• The risk of building a complex model which is not successful in estimating accurately CLV, as promised
• The analysis of the additional insight and expected gains by the more sophisticated model vis-?-vis the additional cost incurred (not an easy task)

For the above reasons (costs & risks), managers are often reluctant to approve complex CLV modeling projects.

In order to overcome the Customer survival prediction issue, the Business can calculate the retention rates achieved in the past and based on these produce an estimate for the future (alternatively, an average Customer lifecycle duration per product, can be used to simplify measurements). However, in certain markets, the calculation of the yearly retention rate is not a straight forward task: a Customer termination, cannot be clearly identified before a certain time period.

CLV or an equivalent ranking can be measured per Customer segment instead of being measured per individual Customer. This can simplify the process substantially, by applying the segment retention rate and segment average cost per

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For the above reasons (costs & risks), managers are often reluctant to approve complex CLV modeling projects.

In order to overcome the Customer survival prediction issue, the Business can calculate the retention rates achieved in the past and based on these produce an estimate for the future (alternatively, an average Customer lifecycle duration per product, can be used to simplify measurements). However, in certain markets, the calculation of the yearly retention rate is not a straight forward task: a Customer termination, cannot be clearly identified before a certain time period.

CLV or an equivalent ranking can be measured per Customer segment instead of being measured per individual Customer. This can simplify the process substantially, by applying the segment retention rate and segment average cost per Customer. However, this can only be meaningful, if the segmentation mode is sufficiently aligned to the factors affecting the CLV.

The efficient application of CLV is not an easy task. A Customer about to churn has a reduced CLV, if the CLV model takes into account a churn probability. Should a business lower the Customer service level, according to a value-based servicing approach, or try to retain this Customer? The Business should try to retain a profitable Customer.

Before implementing a value-based servicing strategy, Organizational issues should also be evaluated:
• Organizational readiness should be evaluated: business processes in place, CTP’s structured to handle the value based approach, CRM information systems supporting the value ranking, trained people adopting the value-based approach
• The value-based servicing capability, related to a sophisticated CLV valuation which entails many different service levels
• How often should the CLV or equivalent value ranking, be updated. Is this realizable by the business infrastructure: does the Business have a dedicated infrastructure to calculate analytics like the Customer value rankings, in order to feed the operational CRM without computationally loading it.

A Business with no prior experience on the use of CLV should start with a simple approach: a limited complexity Customer value model. An RFM (recency – frequency – monetary) score can be used as a proxy for the CLV. This score can be a cost-effective initial Customer value ranking. In certain cases even a recency score may be a complex start. Frequency and/or monetary scores may be more suitable as a starting point.

The use of a Customer value ranking is only a means to the efficient use of business resources (value-based management). The accurate estimation of the Customer Lifetime value may be not only very difficult but also of limited business value.

Copyright 2006 – Kostis Panayotakis