Customer Lifetime Value: the best measure of loyalty?

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By: Wise Marketer Staff |

Posted on December 11, 2006

Customer Lifetime Value: the best measure of loyalty?

Customer Lifetime Value (CLV) is increasingly being recognised as one of the most important measures of the worth of a customer, as it takes into account not only the customer's value now but the expected value over their projected lifetime with your company. And it's arguably the best way you can demonstrate that your loyalty programme is working: the CLV of targeted customers always rises, according to Peter Clark, co-editor of The Wise Marketer and co-author of The Loyalty Guide reports.

Customer Lifetime Value is the value of the customer to the business over his or her lifetime as a customer. It is quite sobering to see how big the CLV can be. It is particularly important in high ticket value, low frequency businesses (motor sales or insurance, for example). The costs of setting up an account and establishing the customer are offset over many years so, in terms of profitability, the customer becomes annually more valuable as time passes. It is also true to say that loyalty is a two way investment: both parties must buy into the relationship and invest time, effort and probably money in it. The longer the relationship lasts, the more unlikely it is to break down over some fairly small error or annoyance. Both parties have too much to lose.

Many different ways of measuring and calculating CLV have been developed, to suit different trading situations and purposes. While the values calculated can only be a guide to real future value, using the same formula over many customers does at least give a fairly accurate idea of the comparative value of different customers, or groups of customers. The calculation and use of CLV should be an essential part of every business if maximising future profit is the objective.

Loyalty's relationship with profit In theory, building customer loyalty increases company profits. But how can we measure accurately how effective it is in practice? How can we predict the effect an investment in loyalty will have on the future of the business? No method is perfect, but measuring the effect on Customer Lifetime Value (CLV) is one of the best ways - particularly if managing and maximising profitability over the complete customer life cycle is the goal.

The positive correlation between customer loyalty and company profit is not a new and revolutionary idea: thirty to forty years ago international management guru, Peter Drucker, said: "It is the purpose of a firm to create satisfied customers" and Theodore Levitt said: "... satisfaction is an important basis for loyalty, which is the key to profitability". Within the marketing arena, the correlation has been expressed even more directly by statements like: "It's 3-5 times as expensive to acquire a new customer as it is to keep an existing customer", and "The best 20% of customers contribute 80% of the company's net profit".

But while most managers and marketers agree with such statements, few companies are able, due to a historical lack of focus on the correlation, to confirm them using their own data. In addition, the calculations required to analyse long term correlations are quite complicated.

Questions to ask - and how to answer them Over the past decade business managers and marketers have become increasingly interested in learning about the loyalty/profitability correlation, leading to more research of customer loyalty (e.g. the causes of customer churn and defection), and the implementation of many loyalty programmes and CRM initiatives.

But despite this interest, most managers still have a number of unanswered questions, including:

  1. Who are the most loyal customers and who are the most disloyal?
  2. Who are the profitable customers and who are the unprofitable customers?
  3. How do we calculate the yield from an investment in customer loyalty?
  4. How much do we have to invest to acquire each new customer?
  5. How much do we have to invest to retain an existing customer?
  6. How much do we have to invest to develop existing customers into 'total' customers?
  7. How much do we have to invest to win back each defected customer?
  8. How should we allocate our marketing budget for the optimal return?

In order to answer those questions in the real world, a business would have to:

  1. Define major correlations between investments in increased customer loyalty and investments in increased profitability;
  2. Define, collect, and analyse key data that is relevant to customer loyalty and profitability;
  3. Predict the results of different strategies, budgets, and campaigns;
  4. Measure the results of those investments;
  5. Report marketing contribution to company growth, profitability and shareholder value.

Investment in both B2C and B2B customers is needed at all stages of the customer lifecycle in order to acquire new customers (and then to wait for an eventual return), to retain and develop customers, and to win back customers who defect.

CLV supports the loyalty business case In order to establish a reliable business case that shows the connection between loyalty and profitability it is necessary to analyse the economics of the customer throughout the entire customer lifecycle. This might seem difficult, particularly for companies that have previously focused mainly on acquisition and product sales, aiming at short term customer profitability. It is also difficult for those companies that market their products through independent distribution channels (and consequently don't have direct customer contact or even a customer database). Over the past decade or so some companies (e.g. retailers) have overcome this problem by using a loyalty programme and a loyalty card to gather data about customers for analysis.

There is a growing interest from management in the value of long term customer relationships, and the value of tools that enable them to effectively implement a loyalty strategy. They are beginning to realise that calculating their customers' lifetime value provides a more solid basis for deciding what strategy is best; for implementing the strategy; and for evaluating the results of the strategy. In the last decade, the development of CLV software models has simplified the calculation of CLV and the use of it to make major marketing decisions, like budget level, segmentation, and channel choice.

CLV and customer profitability This article looks at customer loyalty and customer profitability in theory and in practice. Different methods of calculating the effects on return on investment (ROI) that increasing customer loyalty has are detailed. The main emphasis is on the calculation of customers' lifetime value, including the basic theory, hypotheses, models, and the tools that help managers to use these calculations in practice.

All references made here to 'customers' could apply equally to members, subscribers, visitors, donors, or indeed almost any other group that deals with a business. Incidentally, the concept of CLV has been in use for many years by organisations that raise money by fundraising, sponsorships, and donor contribution.

The increased interest that managers have shown over the past decade or so in CRM, customer databases, data warehouses, and data mining is a positive development in the area of customer data that will increase their ability to make the right decisions based upon customer lifetime value. There is light at the end of the tunnel, however: several graduates of the Copenhagen Business School over the past couple of years have included detailed descriptions of CLV theory and concrete calculations of CLV in their final reports.

Factors for CLV model success The effectiveness of an investment in customer loyalty, profitability, and CLV will differ depending on various factors:

  • The sector When comparing data, or conclusions based on its analysis, it is necessary to define the market sector, the type of company, the market segment and the company strategy, etc. Research shows that in different market sectors there are different correlation curves between, say, customer satisfaction and loyalty, or between customer satisfaction and profitability. Furthermore, the drivers of loyalty will be different - as will the drivers of profitability. For example, the correlation curve (satisfaction vs. loyalty) in a stable monopoly market will differ significantly from the correlation curve for a turbulent market with fierce competition.  
  • The product The correlation curve for a short term tangible product will differ significantly from the correlation curve for a long-term service product. Clearly, this means that the expected outcome of investment in increased satisfaction will yield different results depending on the market sector.  
  • The future period on which the analysis is based Results for CLV will differ depending on the projected length of time into the future or past (e.g. 3 years, 5 years, 10 years, etc.)

Essential correlations for CLV There is a relatively large number of business models, research studies, and other data that can be used to form an overall picture of the kind of relationships and correlations that are most essential when dealing with CLV. Moreover, ideas can also be drawn from the many articles and business books that indirectly, if not directly, describe the different elements of CLV. So, even if a company does not have a substantial amount of actual and past customer data, it should still be possible in most cases to build a CLV model and to calculate (or at least evaluate) the results of an investment in increasing customer loyalty.

In order to begin using CLV in practice it is necessary to first construct some hypotheses and rules of thumb about loyalty and profitability for the specific market sector and company involved. Ideally, conclusions should be drawn about all the elements in a loyalty model such as the Loyalty & Profitability Chain (see Figure 7-G). The conclusions may be drawn on the basis of analysis or, if that is not possible, from general knowledge of customers in the company.

Factors relating to CLV Experience shows that CLV will often be correlated with factors that are relatively easy to define, measure and allocate the marketing budget to, based on rules of thumb - for example:

  • Sales channel (e.g. internet, telephone, or mail vs. mass marketing); First product bought;
  • The amount of first order;
  • The number of orders in the first period of customer relationship (e.g. one year);
  • The number of different products bought in the first period of customer relationship (e.g. one year);
  • The number of relationship contacts between customer and company.

Defection rates As an example, the defection rate will often depend on the type of sales channel (for example, a higher defection rate from a mass marketing channel than from a more personalised channel); the defection rate of a new insurance customer with an automobile insurance will often be higher than the defection rate of a customer with a house insurance.

In the telecoms sector, a prepaid mobile subscription will have a low retention rate while a fixed phone subscription will have a relative high retention rate. A post-paid mobile subscription will have a higher retention rate than a prepaid subscription. The reason for this kind of correlation is that the factors mentioned are all an indication of motives for buying, buying behaviour, relationship strength, customer income and product characteristics, which directly or indirectly influence both customer loyalty and buying patterns.

By collecting data from different sources it is possible to get an overview of the different correlations between loyalty and profitability. It is also possible to construct the first company hypotheses and rules of thumb, so that they can be analysed further to confirm their validity in the given sector and company. This will provide a solid platform on which to base future work on the development of a company-adapted CLV model and possibly eventually an IT-supported CLV system (i.e. a CLV simulator).

The Holy Grail This, then, is the 'Holy Grail' of CLV: to collect the right data to support the continuous calculation of CLV on a per-customer basis, and to be able to monitor the trends in each customer's CLV sequence to spot the strengths, weaknesses, opportunities, and threats that affect your customers' loyalty.

The practical calculation of CLV, both as a snapshot and as a time-series, is explained in detail in The Loyalty Guide report, along with all the formulae, variables, data types, and management reports you will need to apply CLV to your company's customer loyalty and financial models. Apart from two full chapters covering CLV and the maths, metrics and reporting of CLV and customer loyalty, the report also provides you with internet-based "what if" formulae calculators that you can use, as well as spreadsheet-based working models of the formulae to download and use offline. To see the facts, figures and tools included, and to have your own copy of the report delivered by express courier, visit

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