Copyright Thomson Professional and Regulatory Services, Inc. Sep/Oct 2009| [Headnote] |
| BECAUSE CUSTOMER RELATIONSHIP INTANGIBLE ASSETS OFTEN COMPRISE A SIGNIFICANT SEGMENT OF THE BALANCE SHEET, VALUATION PROCEDURES RECEIVE PARTICULAR ATTENTION AS PART OF FINANCIAL REPORTING REQUIREMENTS. |
describe disclosure requirements for acquired assets in a business combina- tion with fair value recognized as the standard of value. Fair value is defined by Statement of Financial Standards No. 157, Fair Value Measurements (SFAS 1 57), as the price that would be received to sell an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date. Statement of Financial Accounting Standards No. 141 R, Business Combinations (SFAS 141 R), describes separability criteria for the recognition of intangible assets apart from goodwill. Compliance with SFAS 141 R has continued the focus on intangible asset valuation for financial reporting.
| EXHIBIT 1 |
| Definition of Weibull Distribution |
| EXHIBIT 2 |
| Weibull Distribution With a Shape of 1.0 and a Scale of 8 |
| EXHIBIT 3 |
| Multi-Period Excess Earnings Method - Analytical Assumptions |
Many businesses direct significant corporate resources to the creation of intangible assets intended to improve business operations and market com- petitiveness. SFAS 141 R recognizes intangible assets apart from goodwill when they result from contractual or other legal rights that are capable of being separated from the acquired enterprise and apply regardless of whether there is intent to sell the asset discretely. The separability standard applies even if the intangible asset is individually not transferable, but instead is capable of being transferred in combination with another asset or liability. This article discusses the separability of customer relationship intangible assets from goodwill.
SFAS 141 R identifies five broad asset categories for recognition as intangible assets. The five individual intangible asset categories are listed as follows:
* Customer-related intangible assets.
* Technology-based intangible assets.
* Contract-based intangible assets.
* Marketing-based intangible assets.
* Artistic-related intangible assets.
Customer relationship intangible assets, as the most common subset of customer- related intangible assets, are consequently recognized as a major identified asset category, accorded particular attention for financial reporting purposes.
Customer Relationships
Business enterprises depend on customer loyalty as an element of financial success, with continued business patronage recognized as a valuable intangible asset. Relationships between individual customers and a business enterprise evolve as favorable experiences predispose customers to maintain the relationship. Specific examples include the media industry with newspaper and magazine subscribers, and the financial services industry with bank depositors and credit card accountholders. A variety of other industries also have significant customer relationships.
Although customers develop an affinity with a business enterprise, many terminate established business relationships for various reasons. The termination of individual customer relationships occurs despite the business resources devoted to maintaining the relationships with the expectation of continued business patronage. Terruination is driven by a number of factors, including competitive pressures, economic considerations, and geographic dislocations.
| EXHIBIT 4 |
| Multi-Period Excess Earnings Method - Customer Relationship Valuation |
| EXHIBIT 4 |
| Multi-Period Excess Earnings Method - Customer Relationship Valuation |
Separability from Goodwill
Separability from goodwill is discussed in SFAS 141 R as part of the recognition process for measurement of acquired intangible assets. The financial reporting of a business combination provides for exclusion of goodwill from identified intangible assets. Further, separation of goodwill and intangible assets is discussed in paragraph 3.k of SFAS 14 IR, which states:
An asset is identifiable if it either:
(I)Is separable, that is, capable of being separated or divided from the entity and sold, transferred, licensed, rented, or exchanged, either individually or together with a related contract, identifiable asset, or liability, regardless of whether the entity intends to do so; or
(2) Arises from contractual or other legal rights, regardless of whether those rights are transferable or separable from the entity or from other rights and obligations.
This language provides specific guidance concerning identification and separability of an intangible asset from goodwill. In addition to separability, the customer relationship intangible asset reflects the wasting nature of the acquired customer population as relationships are terminated over time.
Survivor Curves
Customer relationship retirement behavior is readily described by survivor curves such as the Iowa-type curves, as well as the exponential and WeibuJl distributions. The lowa-type survivor curves originated from research at Iowa State College in the 1920s and 1930s that evolved from empirical observation of the retirement experience for a variety of physical assets including electric, water, and gas utility property; telephone and railroad property; and motor vehicles and farm equipment. The exponential and Weibull distributions are mathematical distributions used to describe survivorship characteristics for a variety of populations.
Iowa-type survivor curves employ average life and retirement behavior patterns to describe the individuai survivor curves. The Iowa-type survivor curves are grouped into four families L, S, R, and O - based on the position of the modal frequency. The L curves are left-moded, the S curves symmet- rically-moded, the R curves right-mod- ed, and the O curves origin-moded. The Iowa-type survivor curves are fur- ther designated by a number that indi- cates the width of the dispersion pattern, with a low number indicating a wide dispersion pattern and a rela- tively low frequency of modal retire- ments. Similarly, a high number indicates a narrow dispersion pattern and a relatively high frequency of modal retirements. A revised version of Iowa Engineering Experiment Sta- tion Bulletin 125,"Statistical Analysis of Industrial Property Retirements," was published in 1967; it described the four survivor curve family types for estimating life characteristics.1
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The Weibull distribution is frequently used to describe life characteristics for a variety of applications including the engineering, biomedical science, and business professions.2 The Weibull distribution is extremely flexible, with the ability to describe a multitude of survivor curve profiles through variation of the shape and scale parameters. For valuation applications, the Weibull distribution is defined as a two-parameter distribution described by the mathematical relationship in Exhibit 1,
The Weibull distribution is a generalization of the exponential distribution with the ability to describe increasing, decreasing, and constant population retirement rates. When the shape parameter with the Weibull distribution is equal to 1 , the Weibull distribution and the exponential distribution are equivalent. Exhibit 2 illustrates a Weibull distribution profile with a shape of 1.0 and a scale equal to 8.
A survivor curve results from statistical analysis of the retirement data, and yields a measure of customer life characteristics. Survivor curves depict the retirement profile of the customer population and reflect the dispersion of customer relationship life expectancy. Customer retirement knowledge facilitates understanding of the influences that drive customer retirement activity to produce a pattern of retirement activity. The survivor curve indicated from the retirement profile is used to describe customer behavior expectations for estimating customer relationship value.
Customer Relationship Valuation
The estimation of a customer relationship retirement profile as described by the survivor curve directly influences the value associated with the customer relationship intangible asset. Customer relationship value is dependent on population life characteristics due to the impact of retirement behavior on the available economic benefits from the customer population. Because retirement behavior influences the earnings available from the customer population, estimation of customer life characteristics has a direct influence on the magnitude of the customer relationship intangible asset.
With the multi- period excess earnings method, customer population retirement characteristics are used to project the number of surviving customers in future years for the customer population. After the estimation of expected population retirement behavior, revenue per customer and growth in revenue per customer are forecast along with the expenses associated with servicing the customer population. The expected future excess earnings are forecast from consideration of the remaining customers from the population in addition to the corresponding revenue and expenses for each future year. The projected excess earnings are converted to present value through application of a discount rate, and aggregated to estimate the customer relationship value.
Example
The attrition aspect of customer relationships is illustrated through an analytical look at the multiperiod excess earnings method. The expected economic benefits associated with the customer-based intangible asset are measured through application of this method to the customer population. Based on statistical analysis, a Weibull distribution with a shape of 1.0 and scale of 6.0 is the best descriptor of survivor characteristics for the customer population. Future survivor characteristics are forecast for the customer population by applying the selected survivor curve to the active customer population in order to estimate the number of surviving customers in each future year.
Expected revenue for the customer population is estimated from the projected future surviving customers and the forecast revenue per customer. Annua) customer population revenue is calculated as the product of revenue per customer and the number of surviving customers, with consideration given to the expected revenue growth per customer. Revenue growth per customer is estimated from anticipated trends in customer business activity for the duration of the projection period.
After the forecast of expected customer revenue, expenses associated with servicing the customers are estimated based on consideration of historical and projected financial information. Cost of sales expenses, along with general and administrative expenses, include expenses associated with the manufacture of a product or provision ot services and the indirect expenditures incurred to generate revenue. Selling expenses represent the marketing expenditures associated with maintaining the customer relationship. Contributory asset charges for fixed assets, working capital, and trade names represent a charge to the intangible asset benefit stream for the use of a normalized level of contributory assets to support the intangible asset.3 The income tax rate reflects a combination of federal and state income taxes, with state taxes dependent on the applicable jurisdiction. The pro forma analysis estimates the available annual excess earnings associated with the customer population, including the amortization tax benefit.
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The excess earnings attributable to the customer population are estimated from projections oí revenues and expenses tor the duration ot the analysis. The annual earnings projections are then converted to present values and summed to estimate the customer population value. The discount rate provides a satisfactory return for the risk associated with the customer relationship intangible asset and considers an appropriate capita! structure along with the cost of each capital component to estimate the rate-of-return requirements. The sum of the present values provides an estimate of the customer relationship intangible asset value.
The customer population consists of 5,000 active customer relationships with average revenue per customer of $3,000. The base revenue for the customer population is then calculated as the product of the number of customers and the average revenue per customer. Additional assumptions for the application of the multi-period excess earnings method are presented in Exhibit 3.
Application of the selected survivor curve to the customer population produces a forecast of the remaining number of customers in future years of the cash How projection. The multi-period excess earnings method yields a value indication of $10,062,000 for the customer population as presented in Exhibit 4.
Conclusion
Financial reporting requirements have focused attention on separability of intangible assets from goodwill. SFAS 141 R specifies that intangible assets are recognized apart from goodwill when they result from contractual or other legal rights that are capable of being separated from the acquired business enterprise. Customer relationships have historically been recognized as a valuable intangible asset arising from the efforts of businesses to cultivate customer loyalty and a legacy of a customer's positive experiences with the business enterprise.
Along with separability considerations, customer relationship intangible assets are recognized as wasting assets capable of being described by survivor curves. Statistical methods and application of survivor curves facilitate the description of customer population retirement characteristics. Survivor curves such as the lowa-type curves, along with the exponential and Weibull distributions are popular descriptors of' customer retirement behavior. The economic attributes of customer relationship intangible assets are described through the multi-period excess earnings method to estimate the customer relationship value. Intangible asset separability and retirement behavior of the customer population represent important considerations when estimating the value of customer relationship intangible assets.
| [Reference] |
| 1 Winfrey. "Statistical Analysis of Industrial Property Retirements," Engineering Research Institute Revised Bulletin 125 (Iowa State University, 19671 |
2 Hahn and Shapiro. Statistical Models m Engineering ( John Wiley & Sons, 1967). ? 109; Lee Statistical Methods for Survival Data Analysis. (John Wiley & Sons. 1992). pp 135-136 |
| 3 Gooch, "Capital Charges and the Valuation of Intangibles." It Bus val. Rev. 5 (March (9921. |
| [Author Affiliation] |
| RICHARD K. ELlSWORTH. PE, ASA, CFA. is a director with Deloitte Financial Advisory Services LLP in the New York City office, specializing in leasing ami structured finance transactions. Mr. Ellsworth is a member of Valuation Stratifies" editorial advisory hoard. The views expressed in this article are those of the author and do not necessarily represent the views oj Deloitte Financial Advisory Services LLP. |
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