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Journal of the Academy of Marketing Science
Published in: Journal of the Academy of Marketing Science 5/2011

01-10-2011 | Original Empirical Research

Sales margin and margin capitalization rates: linking marketing activities to shareholder value

Authors: Steve C. Lim, Robert F. Lusch

Published in: Journal of the Academy of Marketing Science | Issue 5/2011

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Abstract

Using customer level data, prior marketing research has developed a micro or bottom up approach to link marketing activities with shareholder value. This study develops a macro or top down approach using longitudinal firm level data from publicly available financial statements. Test results show that the earnings component supported by sales has higher pricing multiples than other components of earnings in firm specific time-series data. We also test hypotheses of five marketing-related drivers of sales capitalization rate (the rate at which sales increases are converted into increased shareholder value). From the research effort, we develop three managerially useful tools. First, we suggest enterprises develop alpha or sales margin strategies and beta or margin capitalization strategies, and we show that one can map these strategies into a planning matrix. Second, using financial statements of publicly traded firms, we develop an alternative method of estimating customer equity. Third, we show how a company may use our macro approach and compare its performance with its industry competitors to develop insights into competitive dynamics.
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Appendix
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Footnotes
1
Christie (1987) concludes that the market value of equity at the beginning of the returns period is the correct deflator in returns or change studies to control the effect of scale differences. In level studies where the dependent variable is the market value of equity, though, Barth and Kallapur (1996) suggest that including a scale proxy as an independent variable is more effective than deflation at mitigating coefficient bias.
 
2
Considerable research on earnings response coefficient (ERC) in accounting has focused on the relation between price changes (or stock returns) and earnings changes (or unexpected earnings) to assess the information content of earnings (Collins and Kothari 1989). We added sales revenue to the ERC model to investigate the impact of sales activities to earnings and stock returns in Eqs. (4) and (5).
 
3
The cash cycle also involves trade payables to suppliers, which we ignore in this discussion since our focus is on customers; however, we do include trade or accounts payable when measuring the cash cycle.
 
4
Dechow (1994) predicts and finds that the longer the cash cycle, the more useful the earnings in equity valuation relative to cash flows. She uses a ship building firm to present the intuition behind her findings. Assume that the construction takes several accounting periods and the payment occurs on completion of the contract. The revenue recognition for this type of long-term construction projects is based on engineer’s estimate of the degree of completion. Therefore, realized cash flows for the firm could easily be negative in the early periods due to the acquisition required for the construction contract and earnings will be positive due to the revenue recognition and matching principle under U.S. generally accepted accounting principles. This example shows that earnings will better reflect the value-relevant events relative to cash flows when the cash cycle is long and earnings and cash flows differ by the greatest magnitude.
 
5
We use other COMPUSTAT variables for identifying drivers of market impact of sales-supported earnings. The following lists all COMPUSTAT variables we used with numbers in the parenthesis representing data item numbers in annual COMPUSTAT. Sales (#12), income (#18), market value of equity (#25 x #199), research and development expense (#46), advertising expense (#45), cost of goods sold (#41), accounts receivable (#2), inventories (#3), accounts payable (#70), current assets (#4), and current liabilities (#5). In addition, we delete firm/years when sales are less than $10 million or the stock price is less than $1 following Barth et al. (2001). Finally, we delete observations when the book value of equity is negative because the market-to-book ratio makes no sense in this case.
 
6
The Kolmogorov-Smirnov D statistics show that 343 firms (22.67%) fail to satisfy the normality assumption at the 5% level of significance. Note that no statistical tests are performed on Eq. 1.
 
7
The mean (median) correlation between stock return and ∆SMAR is 0.301 (0.405). The mean (median) correlation between stock return and ∆ERFS is 0.237 (0.302). The firm-specific correlation between ∆SMAR and ∆ERFS is zero due to our orthogonalization schedule. We performed two diagnostics (error normality and heteroscedasticity). The Kolmogorov-Smirnov D statistics show that 85 firms (8.96%) fail to satisfy the normality assumption at the 5% level of significance. Other test results were insensitive when we limited our analysis to those firms satisfying the normality assumption. When we estimated the heteroscedasticity consistent standard errors using ACOV option in PROC REG of SAS, at the 5% level of significance, the number of firms with b1 greater than b2 has increased from 175 firms to 267 firms and the number of firms with b1 less than b2 has increased from 78 firms to 141 firms. Note that White’s (1980) asymptotically consistent estimator has no impact of the parameter estimates.
 
8
Teets and Wasley (1996) compare mean earnings response coefficients (ERC) from linear time-series regressions to earnings response coefficients from pooled linear regressions and argue that firm-specific regressions are superior to pooled regressions. Freeman et al. (2002) provide an alternative interpretation on the findings of Teets and Wasley (1996) and report the average ERC of 2.71 from the firm-specific linear regression and ERC of 0.43 from the pooled linear regression.
 
9
Test results were insensitive when we replaced the median values with the mean values of each company.
 
10
The analyst could, of course, make a different assumption if warranted. For instance, sales could be assumed to continue for t time periods and not an infinite time period and one could assume the annuity of sales-supported margins are not even over time.
 
11
The present value of $ in annuity can be written as \( {b_1} = 1 + \frac{1}{{1 + i}} + ... + \frac{1}{{{{(1 + i)}^n}}} = 1 + \frac{{1 - \frac{1}{{{{(1 + i)}^n}}}}}{i}, \) where i is the discount rate. Solving the equation for n, you get \( n = \frac{{ - \log \left[ {1 - \left( {{b_1} - 1} \right)i} \right]}}{{\log \left( {1 + i} \right)}}. \) The present value of $1 in perpetuity can be written as \( {b_1} = 1 + \frac{1}{{1 + i}} + \frac{1}{{{{(1 + i)}^2}}}... = 1 + \frac{1}{i}. \)
 
12
One industry (electronic equipment) shows about even margin capitalization rates between small and large firms.
 
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Metadata
Title
Sales margin and margin capitalization rates: linking marketing activities to shareholder value
Authors
Steve C. Lim
Robert F. Lusch
Publication date
01-10-2011
Publisher
Springer US
Published in
Journal of the Academy of Marketing Science / Issue 5/2011
Print ISSN: 0092-0703
Electronic ISSN: 1552-7824
DOI
https://doi.org/10.1007/s11747-010-0226-1

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