Valuing customers: Part 2 — predicting customer lifetime valueCustomer lifetime value (CLTV) can be difficult to quantify, but the insight gained is powerful in informing operational and strategic management.
Editor's note: This article is the second in a two-part series examining customer profitability. It is a detailed look at customer lifetime value and follows part one — a look at profitability measured by past performance.
In the first part of this two-part series we looked at the calculation of product, service, and customer profitability and how this can start to address the deficiencies in assessing financial attractiveness based on revenues alone. Whilst this provides improved insight, it does not address three critical variants that can have a substantial effect on customer lifetime value (CLTV):
- Net cash flows at different life stages in the customer relationship;
- Likely customer retention rates through the life stages; and
- The discount rate to be applied to calculate net present values for different customers.
In this second part, we will see how to address these issues and look at the enhanced insight that then becomes available.
The variants in the calculation of value are captured in the following basic formula, which can be used to derive an indicative CLTV:
CLTV = ([Revenue – Costs] × Retention Rate) ÷ (1 + Discount Rate – Retention Rate)
This formula provides a rough idea of relative CLTV, but it is based on four major assumptions:
- Credit terms given and received have no net effect on value.
- Values of all factors in the calculation do not change over time.
- The life span of the customer relationship has an infinite time horizon.
- Once a customer is lost, it is not reacquired but is lost permanently.
Whilst this may be appropriate for the most basic indications of value, these assumptions are not realistic for most organisations. It is necessary to look at a more detailed and value-adding approach.
The two steps to derive a CLTV are (1) forecast future cash flows and then (2) calculate net present values on those cash flows.
Adjustments for cash
The timing of receipt and payment of cash is a critical component of the forecast to be used for value calculation, and this is represented by free cash flows. The adjustments required for cash will depend on how the customer profitability figures have been calculated. Profitability measures may include noncash expenses but exclude capital expenditure and changes in levels of working capital.
For instance, in the forecasting process, the indirect costs of premises and technology tangible assets may have been allocated to the cost of staff resources. Any adjustments for capital expenditure, depreciation, and amortisation will need to be made recognising how these profit-based allowances were created in the first place. Realistically, when looking at customer profitability, the key areas for adjustments will probably allow for working capital and tax.
Life stage analysis
Deriving a forecast of customer cash flows requires careful consideration of the various life stages of that customer's relationship with the organisation. Analysis of life stages can reveal vast differences in activity and profitability over time.
The relationship with a customer starts when their market segment is targeted through marketing. The customer is identified as a prospect, converted into a lead, a customer relationship agreed, and then the customer is processed through onboarding. Significant costs can be incurred with no or limited prospect of any income being gained during this period. At each stage, the value of the pipeline will increase due to the improved potential for additional future cash flows from fully converted prospects. This shows the importance of finance understanding and analysing the marketing and sales metrics of customer reach, prospect engagement, lead generation, lead conversion, and onboarding completion.
An example of how varying levels of cash income and expenditure may impact on contribution in cash terms over a customer's life stages is shown in the chart "Derived Actual Contribution" below. The example is purely indicative, based on a scenario where there is a significant customer acquisition cost.
Early on, customer contribution is affected by acquisition costs. Then, as products, services, and the relationship are being established, there are higher service needs and costs. In this period the customer may also benefit from introductory discounts or incentives to grow its relationship with the business.
As the relationship builds, then cost efficiencies may start to be achieved with the simplification of service needs and automation of processes. Meanwhile, additional costs may be incurred to achieve increases in product or service penetration, which then lead to increased future processing costs and revenues.
As the relationship reaches maturity, then product and service penetration rates may stabilise with process efficiency improving, leading to strong profitability.
The final stage is when the customer relationship ends and the organisation may incur costs for closing down the customer relationship, but this may be partly compensated by contractual penalties for the customer for ending the relationship.
These life stage dynamics are likely to vary across different segments of the customer base. Therefore, it can be beneficial to analyse the historic behaviours of the customer base by segments to provide a baseline for predicting future customer performance. When deciding the degree of segmentation of the customer population, it is necessary to ensure the size of resultant datasets remains statistically significant to provide an acceptable level of certainty for any predictions.
Customer retention rates
As the customers move through the life stages, there will be a level of attrition that leads to a reduced number of customers reaching subsequent life stages. This attrition will affect the likely level of future cash flows from the customer and is shown in the chart "Derivation of Retention-Adjusted Contribution" below.
Retention rates should be applied at variable rates in forecasts depending on customer type and life stage. This enhancement can have a massive impact on improving the quality of any insight given.
Net present values
Having created a forecast of retention-adjusted free cash flow, the values need to be discounted at a rate based on the appropriate weighted average cost of capital. Ideally, the discount rate used will differ for the level of perceived risk with various customer segments. Finally, the discounted cash flows are summed up across the lifetime to give a net present value prediction for the customer relationship (CLTV), as shown in the chart "Derivation of Discounted Contribution and Customer Lifetime Value".
Having completed one iteration of calculating the individual CLTV for each customer, the individual results should then be aggregated. The resultant total can then be compared to the perceived organisational value as a sense-check that the results are materially correct.
The next stage is to identify common characteristics for all customer groupings at differing levels of value. This will assist the identification of the drivers of varying levels of profitability and the strategic actions to be taken. It will also improve the development of acquisition strategies for customers that are more likely to prove profitable.
Organisations tend to hold a wealth of information on personal customers that can be used for segmentation, as indicated in the sidebar, "Personal Customer Segmentation Criteria". However, for customers that are organisations, the information held tends to be sparser but may include industry code, geography, organisational size, purchasing behaviours, use of the products or services, and benefits sought.
Customer transactional and descriptive data is normally scattered across various operational systems. Hierarchies for this segmentation data will often only exist on paper or in people's heads. This needs to be formalised and digitised into approved hierarchy tables.
When calculating customer profitability, it is beneficial to complete this at the lowest level of detail in order that the results can subsequently be aggregated by any groupings that are available to enable multi-dimensional reporting.
CLTV by life stage
Customer acquisition costs reduce the CLTV, meaning that the same client will have a higher remaining CLTV for a period after acquisition. This is demonstrated in the chart "Lifetime Value of Customer at Various Life Stages". This means that a recently acquired customer will have a higher CLTV than a prospective customer. If customer attrition rates are high at the start of the relationship, then these higher levels of CLTV can be seen for a significant proportion of the anticipated customer lifetime.
This demonstrates the importance of having a robust strategy for both customer acquisition and retention.
Practical applications of CLTV
Having invested the time in developing this view of CLTV, it is worth taking a moment to consider the variety of applications for this invaluable insight, such as:
- Developing product and service portfolio strategies.
- Determining a better pricing structure.
- Defining a segmented customer strategy, whilst considering the level of market saturation of those segments.
- Calculating a return on investment in marketing.
- Completing M&A valuations and understanding how to realise improved value post-transaction.
- Reducing risk and improving business certainty, leading to lower costs of funding.
CLTV is more ambiguous and difficult to quantify than looking simply at revenue or profitability, but the resultant insight can be far more powerful in informing operational and strategic management. Similar to the animals in George Orwell's Animal Farm, it will become evident that all customers are equal, but some are far more equal than others.
Personal customer segmentation criteria
- Demographic, which includes gender, age, income level, marital status, education, race, and religion.
- Geographic, which includes postal/ZIP code, area code, city, province/state, region, and country.
- Psychographic, which includes socio-economic class, personality, lifestyle preferences, social status, opinions/attitude, interests, and activities.
- Needs-based, which includes product and service features and bespoking.
- Behavioural, which includes purchase frequency, amount, returns, and complaints.
— Paul Ashworth, FCMA, CGMA, is a Jersey, British Isles-based practising management accountant providing strategic insight and enabling business intelligence systems in financial and business services and in public sector organisations. To comment on this article or to suggest an idea for another article, contact Oliver Rowe at Oliver.Rowe@aicpa-cima.com.