Customer Lifetime Value And Marketing Strategy example essay topic

Introduction: As organizations expand their customer base, it becomes increasingly important for them to segment their clientele into different groups. The most logical (& practical) criteria that can be employed for this purpose is customer profitability; however, very often the difficulty in formulating a method for quantifying customer profitability makes the entire exercise prohibitive. This is mainly due to the fact that different departments in the same organization view the concept of profitability from their own characteristic viewpoints. What results is a multitude of contradicting measures and values for the same customer-base. A method of resolving this dilemma is to first come up with a definition of profitability that would find agreement in all quarters and with all the stakeholders in a firm.

This is where measures such as LTV come in. Customer Lifetime Value (LTV) is generally accepted as the total net income a company can expect from a customer. Customer value calculations help enterprises solve various fundamental problems like budgeting customer acquisition expenses, selection of recruiting media (the LTV is generally different according to the media used), or of types of offer or distributing efforts between prospecting and preserving customers. Well-conducted LTV analysis can also help build a competitive strategic advantage. This paper will review various such approaches to calculating LTV and examine their impact on CRM strategies for firms. Customer value modeling: Synthesis and extension proposals Mihai Calcium & Francis Salerno Abstract: Customer lifetime value is a key concept in relationship marketing.

This paper develops, synthesizes and organizes formulae for computing this lifetime value in a progressive manner and continues a research direction started some years ago. Calculations are organized using a double taxonomy: customer relationship behavior models (retention and migration models) and algebraic and matrix computing methods. The formulae suggested allow for the explicit representation of recency-censored customer migration processes, the flexible integration of de synchronized financial flows and extend customer value optimization procedures from the retention to the migration model. Description: The development of 'customer oriented' interactive database marketing brings to the fore, in most contexts, the need for customer value and customer portfolio assessment methods.

These methods have either been fine-tuned in direct marketing and catalogue sales or developed in sales force effort allocation models. There are many ways in which they could be improved but they require systematization and unification first. From a customer attraction and retention perspective, a profitable customer is a customer whose income, generated during the commercial relationship, exceeds with an acceptable amount costs supported to attract, satisfy and keep him. This amount is called customer lifetime value (LTV). The modeling of customer value depends on the context and on the customer's relationship behavior (whether the situation is contractual or not). A brief analysis shows that modelling are based on the distinction between customer retention and migration.

This study of existing models shows also that, in a first set of contributions, the mathematical developments are algebraic and focus on retention models while, in another set, they are based on matrix approaches and applied mainly to the migration model. The need to de synchronize financial flows (expenses and gains) in the customer relationship is also put forward by these contributions. Based upon results emerging from a broad examination of these models, this paper guides towards systematization and unification. It finds elements that are common and identifies those that are not. It adds components that seem absent from the other approaches. In this way, a set of formulae that complete existing solutions is developed and adapted.

These, existing or new, formulae are grouped in a progressive customer value calculation framework. It is presented schematically in the figure above. The algebraic approach and the matrix approach are treated apart. Formulations progressively integrate transactional flows expressing probabilities to pass orders, financial flows composed of gains and expenses and customer and prospect value optimization procedures based upon long-term calculations. Several additional aspects concerning dynamics of customer migration, like right and left censored migration processes or mechanics of purging the customer list, are treated explicitly.

Conclusions: The formulae for computing the economic value of a customer or the customer lifetime value that have been developed in this paper are organised in systematic and progressive way according to a double taxonomy: the one of customer relationship behaviour models and the one of calculation methods. The retention / migration model dichotomy is based on strong theoretical foundations in consumer behaviour that have been underlined by the previous studies. Besides the evoked behavioural characteristics, the comparative analysis of the two categories of customer relationship models reveals their economic substrata. In a migration model the retention probability is usually relatively low and companies rely less on the customer's survival probability than on the customer's reactivation probability in order to increase sales and customer value.

The two calculation methods, algebraic and matrix based are stepwise and progressively applied to both relationship behaviour models. On that occasion several stages are distinguished. a physical, quantitative level of transaction flows that are stochastically treated. a monetary value level of financial flows, the temporal effects of which are studied. a decisions level, the one of optimisation calculations. In this way calculations become easier to compare and their formalization in order to solve problems of increasing complexity becomes easier. The algebraic formulae developed for the migration model represent an alternative to the matrix formulations. They make it possible to approach in an explicit manner the de synchronisation of financial flows and the censorship of migration processes on recency. Calculations to estimate eliminations from the customer list formalism right censorship in a migration process; computing the probabilities to generate transactions for inactive customers formalised left censorship.

These aspects are only implicitly treated in the matrix approach of Pfeifer and Carraway and are absent from the algebraic approach suggested by Berger and Nasr. The optimisation procedure developed for retention models as well as the Blatt berg and Dighton procedure that optimally balances marketing efforts between customer acquisition and retention have also been adapted to the migration model using the properties of long-term transition matrices. As shown by M"ul hern customer LTV models cannot be applied to all situations. They require customers to have some persistent relations with enterprises and that financial flows (gains and expenses) can be forecast with a certain precision at an individual level.

When these conditions are not satisfied, the historic analysis of profitability can replace LTV calculations. Yet, under the combined and interdependent impulses from information technology progress and from the adoption of a customer-centred marketing, situations for which models of LTV are applicable become dominant. This incites pursuing the customer evaluation and dynamic management models's ystematisation and unification efforts. Customer Lifetime Value and Marketing Strategy: How to Forge the Link Adrian Sargeant, Henley Management College Abstract: There has been an increasing interest of late in the concept and application of customer lifetime value (LTV). Widely viewed as one of the most critical measures in assessing the performance of relationship marketing activity, many organisations now employ some form of LTV analysis. In this paper we review 4 distinct perspectives on the measurement of customer value distinguishing in particular between historic and projective approaches.

Methods for calculating projective value at both the individual and segment level are described, as are the advantages that can accrue as a result of integrating this knowledge into marketing strategy. The use of LTV analysis to inform the realm of both customer acquisition and customer development is also documented. Introduction The field of marketing has undergone rapid change since the early 1980's and has in particular seen a shift in the dominant paradigm away from transactions toward relationships (Morgan and Hunt 1994). At the core of relationship marketing is the development and maintenance of long-term relationships with customers, rather than simply a series of discrete transactions (Berger and Nasr 1998). Such a change in emphasis more accurately reflects real market behaviour in the commercial marketplace. Comparatively few purchase decisions are taken on a 'once only' basis.

Real market behaviour consists of a series of exchanges rather than purely one-off transactions (as has been effectively argued in the economics literature, see for example MacNeil 1980). Whilst the transition from a transaction to a relationship approach may seem little more than play on words, the differences in terms of the impact on marketing strategy and performance are profound. In a transaction-based approach, marketing strategy would typically be driven by the initial returns that might be expected from each campaign an organisation might run. Their strategy will be based on achieving the highest possible immediate Return On Investment (ROI) when the costs and revenues of a particular campaign are calculated.

Marketers following such a strategy tend to offer customers little choice. They can't afford to - to do so would merely add to the cost. Little segmentation takes place and customers receive a standard approach. A relationship approach by contrast recognises that it is not essential to break even on the first communication with a customer, or even the second or third. The relationship approach recognises that if treated with respect customers will want to buy again, and marketers are therefore content to live with somewhat lower rates of return in the early stages of a relationship. They recognise that they will achieve a respectable ROI, but anticipate that this will follow naturally in the longer term.

At the heart of the relational approach to marketing is the concept of 'lifetime value' (LTV). Once marketers understand how much a given customer might be worth to the organisation over time, they can tailor the offering to that customer according to the individual's needs / requirements, and yet still ensure an adequate lifetime ROI. These differences between the transaction and relational approaches to marketing are summarised in the table below. Whilst it would be puerile to suggest that a business should strive to service every potential customer need, there is no earthly reason why they should not strive to meet the most basic of these across the board and thereafter concentrate on developing the specific ingredients that appear to be attractive to its higher value segments. There are clearly very elementary requirements in respect of service that will undoubtedly be common to all the customers on the database. These may legitimately be regarded as the minimum standard of service that even those organisations following a simple transactional approach may wish to adopt.

In respect of the latter strategy, relational marketing departments make every effort to segment their customer base and to develop a uniquely tailored service and importantly 'quality of service' for each of the segments they identify. At the core of this approach is the concept of lifetime value. It is this that drives the nature of the contact strategy, the dimensions of the relationship and the initial investment that an organisation might be prepared to make to recruit its customers in the first place. What Is Lifetime Value? Bit ran and Mond schein (1997, p. 109) define lifetime value as "The total net contribution that a customer generates during his / her lifetime on a house-list (or customer database) " It is therefore a measure of the total net worth to an organisation of its relationship with a particular customer. To calculate it one has to estimate the costs and revenues that will be associated with managing the communication with that customer, during each year of his / her relationship.

If, for example, the relationship extends over a period of four years, one can subtract the costs of servicing the relationship with that customer from the revenue so generated. In essence the contribution each year to the organization's overheads and profit can be calculated. A Conceptual Framework There are two key decisions to be taken in the examination of customer value. Firstly organisations must choose between the uses of historic or projected future value.

Secondly they must elect to calculate value on either an individual basis or, more usually, on a segment-by-segment basis, examining specific groups of customers on the database. Combining each of these two dimensions yields what are essentially four approaches to the examination of value. (See figure below) Calculating the LTV of Individual Customers The formula for calculating LTV in the case of an individual customer is as follows: Where: c = net contribution (i.e. revenue minus cost) from each year's marketing activities d = discount rate i = the expected duration of the relationship (in years). This somewhat complex looking equation merely indicates that it is necessary to calculate the likely future contribution by a customer to each year's marketing activities, discount these future contributions and then add them all together.

The grand total is the LTV of a given customer. It should be noted that in examining the contribution each year, an organisation should subtract all the relevant costs of servicing the relationship with a given customer from the revenues so generated. The issue of what constitutes a relevant cost is driven by the purposes for which the analysis is being conducted. It is important to recognise from the process described above that the calculation of lifetime value is a somewhat arbitrary process. There are also assumptions about the appropriate duration over which LTV should be measured. In a sense LTV is something of a misnomer.

When organisations talk of lifetime value in this context they are therefore typically looking at value over a relatively short time horizon (Wang and Spiegel 1994). The reasons for this are threefold. Firstly, it is actually incredibly difficult to predict how customers will behave next week, let alone in four or five years time. Secondly in most consumer markets, when one discounts the value of future contributions achieved beyond a five-year period, the value of these contributions is very small compared with the early years of the relationship. The third factor pertains largely to direct marketing organisations in a consumer context.

Authors such as Derek Holder have demonstrated that the majority of customer value accrues early in the overall relationship. As a consequence the value of revenue streams generated in consumer markets beyond five years are likely to be minimal. In practice, companies should reflect on the appropriate timescale for their specific organisation to use. This can be predicated on an understanding of how long customers have remained loyal to the organisation in the past, the factors outlined above, or more likely, some combination of the two. Calculating the Lifetime Value of Discrete Customer Segments Usually, organisations want to understand whether specific segments of their database exhibit higher lifetime values than others. This calls for a more sophisticated degree of analysis.

In attempting to measure lifetime value the following process is recommended. 1. The first stage is to decide what the purpose of the analysis will be. Although this sounds rather obvious, many organisations are not clear from the outset exactly what they are hoping to gain from it. Clarity in respect of the purpose of the analysis can also guide the Organisation in assigning appropriate categories of cost for inclusion in the calculations. 2.

The next stage is to decide the period of analysis to use. It is not essential here that the chosen time period selected be based on the longest standing customers (Carpenter 1995). 3. The next step should be to divide, or segment, the database into a manageable but distinct group of cells on the basis of the primary variable to be explored. Suppose one wished to explore the LTV of customers who elect to make their first purchase at differing monetary values. To investigate this issue the database should be divided into a number of cells based on the level of the initial purchase.

In theory, the greater the number of cells the more accurate the predictive capability of the eventual model. But in practice, there is a trade-off between accuracy and simplicity. As a general rule, in the marketing context, between 10 and 30 cells are recommended. 4.

It is then necessary to establish the buying behaviour of each of the cells identified at (3) above. 5. The final stage is to outline the intended development strategy, including, for example, the number of mailings and the projected costs thereof. 6. The preceding information can then gainfully be employed to predict future value.

Armed with information about likely attrition rates, the future costs of servicing customers, the predicted revenue streams and an appropriate discount rate, one can then proceed to make predictions about the projected lifetime value of each cell, or category of customer. Conclusion In calculating customer value it is important to draw a distinction between historic and projected future measures. Whilst both approaches will undoubtedly offer some utility, the latter, in particular, may be used to great effect in enhancing subsequent marketing strategy. Indeed, lifetime value can play a pivotal role in the development of a relationship marketing strategy.

It can, for example, guide the identification of appropriate customers with whom to develop a relationship and delineate the monies that an organisation should be prepared to invest therein. Indeed, one of the central themes of this paper has been that by using LTV analyses valuable marketing resources can be deliberately targeted at those facets of activity where it is likely to have the greatest impact. As this becomes increasingly evident to both marketers and financiers alike, the LTV bandwagon will undoubtedly begin to roll. Modeling Customer Lifetime Value Using Survival Analysis- An Application in the Telecommunication Industry Jun xiang Lu, PhD Abstract Increasingly, companies are viewing customers in terms of their lifetime value - the net present value of customers calculated profit over a certain number of months. Customer lifetime value is a powerful and straightforward measure that synthesizes customer profitability and churn (attrition) risk at individual customer level. For existing customers, customer lifetime value can help companies develop customer loyalty and treatment strategies to maximize customer value.

For newly acquired customers, customer lifetime value can help companies develop strategies to grow the right customers. The calculation of customer lifetime value varies across industries. In the telecommunications industry, customer monthly margin and customer survival curve are the two major components to calculate the customer lifetime value. Since customer monthly margin is from accounting models, the key to estimate customer lifetime value is the customer survival curve. In this study, survival analysis is applied to estimate customer survival curve, therefore customer lifetime value is calculated. Introduction In the telecommunications industry, customers are able to choose among multiple service providers and actively exercise their rights of switching from one service provider to another.

In this fiercely competitive market, customers demand tailored products and better services at fewer prices, while service providers constantly focus on acquisitions as their business goals. Given the fact that the telecommunications industry experiences an average of 30-35 percent annual churn rate and it costs 5-10 times more to recruit a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining highly profitable customers is the number one business pain. Many telecommunications companies deploy retention strategies in synchronizing programs and processes to keep customers longer by providing them with tailored products and services. With retention strategies in place, many companies start to include churn reduction as one of their business goals. With the telecommunications market getting more and more mature, telecommunications companies do not satisfy themselves with predicting customer churn; they instead start viewing customers in terms of customer lifetime value.

Not only do the telecommunications companies distinguish between which customers stay longer and which ones stay shorter, they also distinguish between which customers are highly profitable and which ones are low profitable or not profitable. Customer lifetime value is therefore developed to satisfy telecommunications companies need to evaluate their customer value. Conventional statistical methods (e.g. logistics regression, decision tree, and etc.) are very successful in predicting customer survival / churn. These methods could hardly predict when customers will churn, or how long the customers will stay with. However, survival analysis was, at the very beginning, designed to handle survival data, and therefore is an efficient and powerful tool to predict customer survival / churn. The goal of this study is to calculate customer lifetime value through estimating customer survival curve using survival analysis techniques.

The results from this study are helpful for telecommunications companies to develop customer loyalty and treatment strategies to maximize customer value. It is also useful for telecommunications companies to develop strategies to grow the right customers. Objectives The objectives of this study are in two folds. The first objective is to develop the concept of customer lifetime value in the telecommunications industry. The second one is to demonstrate how survival analysis techniques are used to estimate customer lifetime value. Definitions & Exclusions This section clarifies some of the important concepts and exclusions used in this study.

Survival / Churn - In the telecommunications industry, the broad definition of churn is the action that a customer's telecommunications service is cancelled. In this study, both service-provider initiated churn and customer initiated churn are included. An example of service-provider initiated churn is a customer's account being closed because of payment default. Customer initiated churn is more complicated and reasons behind vary from customer to customer.

Customer survival is the opposite of customer churn, and both terms are used in the study. Active - Active is a customer status. Customers whose service being involuntarily terminated and are in collection stage are not in "active" status. Granularity - This study examines customer survival / churn at the account level.

Customer Contract - This study does not distinguish customers with or without contracts, although separate models may be desirable for each contract status. Exclusions - This study does not include employee accounts Customer Lifetime Value The calculation of customer lifetime value (LTV) varies across industries. In telecommunications industry, customer monthly margin and customer survival curve are the two major components of customer lifetime value. The customer lifetime value is the net present value of customers calculated profit over a certain number of months.

Here is the formula to calculate customer lifetime value: Where MM is the monthly margin for the last three months for existing customers, or the last month's monthly margin for newly acquired customer. MM is either calculated from accounting models or estimated through a set of regression models. The calculation of monthly margin is not the focus of this study and therefore not covered. T is the number of months in consideration to calculate customer lifetime value; it could be 24, 36, or some other number that makes the most business sense. R is the discount rate. pi is the series of customer survival probabilities (customer survival curve) from month 1 through Month T, where p 1 = 1. pi is estimated through customer survival model.

Survival Analysis & Customer Churn / Survival Survival analysis is a clan of statistical methods for studying the occurrence and timing of events. From the beginning, survival analysis was designed for longitudinal data on the occurrence of events. Keeping track of customer churn is a good example of survival data. Survival data have two common features that are difficult to handle with conventional statistical methods: censoring and time-dependent covariates. Generally, survival function and hazard function are used to describe the status of customer survival during the tenure of observation. The survival function gives the probability of surviving beyond a certain time point t.

However, the hazard function describes the risk of event (in this case, customer churn) in an interval time after time t, conditional on the customer already survived to time t. Therefore the hazard function is more intuitive to use in survival analysis because it attempts to quantify the instantaneous risk that customer churn will take place at time t given that the customer already survived to time t. For survival analysis, the best observation plan is prospective. We begin observing a set of customers at some well-defined point of time (called the origin of time) and then follow them for some substantial period of time, recording the times at which customer churns occur. It's not necessary that every customer experience churn (customers who are yet to experience churn are called censored cases, while those customers who already churned are called observed cases). Not only do we predict the timing of customer churn, we also want to analyze how time-dependent covariates (e.g. customers calls to service centers, customers change plan types, customers change billing options, and etc.) impact the occurrence and timing of customer survival / churn.

Implementation Customer lifetime value can be implemented in multiple ways. It could be used as a customer segmentation tool to segment customer value. It could also be used to segment customers by their churn behavior. Customer lifetime value could be used to measure marketing campaign efficiency as well. The results from the customer survival curve estimation could be used to devise strategies to extend customers expected life span. Conclusion This study presents an application to model customer lifetime value - the net present value of customers calculated profit over a certain number of months, using survival analysis techniques.

Customer lifetime value in this study is essentially based upon customers "single product" value. It can be extended to incorporate cross-sell probabilities to estimate customer lifetime value in a multiple product scenario.