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Is customer satisfaction a relevant metric for financial analysts?

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Abstract

This study examines the effects of customer satisfaction on analysts' earnings forecast errors. Based on a sample of analysts following companies measured by the American Customer Satisfaction Index (ACSI), we find that customer satisfaction reduces earnings forecast errors. However, analysts respond to changes in customer satisfaction but not to the ACSI metric per se. Furthermore, the effects of customer satisfaction are asymmetric; for example, analysts are more willing to use good news (i.e. an increase in customer satisfaction information) than bad news (i.e. a decrease in satisfaction). Similarly, customer satisfaction reduces negative deviation more than positive deviation of the analysts' forecasts from actual earnings. Furthermore, the effects of customer satisfaction depend upon the base level of satisfaction that the firm has achieved. Finally, the effects of customer satisfaction on analysts' forecast errors differ across firms with volatile satisfaction scores and those with stable satisfaction scores. We discuss the implications of our results for marketers and participants in financial markets.

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Notes

  1. The ACSI results from a survey of customers of a sample of large US companies. Questions concern customers’ expectations, their perceptions of product/service quality, value, their satisfaction with various goods and services, loyalty intentions and complaints. It is released every year in February, April, August and November by the University of Michigan Business School.

  2. For example, in 2005, Safeway Inc. announced an investment of $100 million for developing its image of a retailer with “quality perishables, strong proprietary brands, redesigned stores, and world-class service”. Safeway’s chairman was expecting that: “The changes the company was doing in its stores will entice households that have defected to rivals, as well as inspire the grocer’s still-loyal customers to spend more money than they have been” (Progressive Grocer, April 6, 2005). The analysts following Safeway Inc. may have picked up this information and revised their earnings forecasts before the ACSI, which would capture and report such changes in the store base only in the next ACSI release (i.e., in April of the next year).

  3. If analysts react slowly to customer satisfaction, it may be possible to observe significant effects of customer satisfaction even after the ACSI publication. In this case, satisfaction information obtained through other sources may confound with satisfaction information revealed by the ACSI. Nevertheless, while the existence of significant effects of the ACSI during the period following its publication are not necessarily evidence of its use by analysts, the pre-release effects of the ACSI are an indication that analysts had access to satisfaction information. It also implies that the ACSI is just capturing these changes but with a lag.

  4. We use the terms positive and negative satisfaction information in line with the accounting literature that assimilates a “loss” to negative information and a “gain’ to positive information. Thus, positive satisfaction information refers to an increase in satisfaction while negative satisfaction refers to a decrease in satisfaction.

  5. The incentive-based explanation suggests that as earnings become less predictable (e.g., following a loss or a decrease in earnings), analysts issue optimistic forecasts to please managers and consequently gain (or limit the loss of) access to managers’ private information (Das et al. 1998). This is questionable because optimistic earnings forecasts result in negative earnings surprises and negative market reactions (e.g., Kasznik and McNichols 2002) but also because of the enactment of the Regulation Fair Disclosure (RFD), that now prohibits selective disclosure by corporate officials to analysts.

  6. We thank one reviewer for suggesting this hypothesis.

  7. We use the ACSI publication as a cutoff period to examine whether an increase in customer satisfaction is reflected into the earnings forecasts faster than a decrease in customer satisfaction.

  8. We checked the sensitivity of the results by estimating the effects of customer satisfaction with a first-difference GMM specification. The estimated model tried to answer the following question: Does a change in ACSI lead to a change in forecast errors between two consecutive quarters? We use two consecutive quarters because for each company, the ACSI data is announced once a year. When we estimate the effects of a change in ACSI on a change in forecast error between current quarter and previous year’s corresponding quarter, we end up with a very small sample size of 1,432 observations. Instead, when we estimate the effect of a change in ACSI (say quarter 1, 2000, versus quarter 1, 1999) on the difference in forecast error between two consecutive quarters (say between quarter 2 of 2000 and quarter 1 of 2000), we find that a change in satisfaction is still associated with a decrease in forecast errors (number of observations = 3,610). Nevertheless, we prefer our model in equation (1), as it measures analysts’ immediate response to new information.

  9. When we used a change in the residuals of an AR (1) model of ACSI as new information, our results did not change. Similarly, when we use the percentage changes (Itner et al. 2009), we still find that ACSI has a negative effect on forecast errors.

  10. In other analyses not reported here, we examined analysts’ responses to changes in customer satisfaction across sectors. Our findings regarding the utilities were interesting as well. We found that analysts following utilities are not responsive to (not affected by) a negative change in customer satisfaction. In these industries, customer dissatisfaction does not automatically affect revenues, due to switching costs (Fornell 1992). We found that the analysts of companies in information technology and telecommunications sectors respond to a decrease in customer satisfaction with a lag as well. This may explain why Jacobson and Mizik (2009c) report a mispricing effect in the computer and Internet sectors. If analysts initially undervalue customer satisfaction information and readjust in the long term, then it becomes possible to beat the market.

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Acknowledgements

A Marketing Science Institute Grant supported this research. The authors thank Chiraz BenAli and Sebastien Lorenzini for valuable research support. We also thank the “Marketing Strategy Meets Wall Street” conference participants at Emory University. This has significantly benefited from the comments of IJRM’s reviewers.

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  1. Institut d’Administration des Entreprises, Laboratoire Orléans de Gestion (LOG), Faculté de Droit, Economie et Gestion, Rue de Blois, B.P. 6739, 45067, Orléans Cedex 2, France

    Paul-Valentin Ngobo

  2. Université Paris-Dauphine, DRM-Finance, Place du Général du Lattre de Tassigny, Paris, France

    Jean-François Casta

  3. Université Paris-Dauphine, DRM-Finance, Place du Maréchal de Lattre de Tassigny, Paris, France

    Olivier Ramond

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Appendix 1: matching datasets

Appendix 1: matching datasets

The American Customer Satisfaction Index reports quarterly customer satisfaction data for each company on its website once a year. For example, data for Prudential Financial, Inc. is published in February only once per year (which the ACSI project calls the fourth quarter). I/B/E/S, however, reports data for Prudential Financial, Inc. for every quarter of the year. Therefore, we need to reorganize the data. We proceed as follows. First, using the ACSI data, we refer to customer satisfaction scores reported in February as first-quarter data, in May as second-quarter data, in August as third-quarter data, and in November as fourth-quarter data. Second, we relate the customer satisfaction data made available in February to earnings forecasts for the quarter closing in March. Consequently, for companies whose satisfaction scores appear in February, we use only their forecasts for the quarter ending in March. For companies whose satisfaction scores appear in May, we use only their forecasts for the quarter ending in June, and so on.

We need to combine the ACSI date with the earnings forecast date. However, we do not know the exact date where ACSI scores are available to analysts or whether they are even available to analysts on the forecast date recorded by I/B/E/S. We examined the ACSI publication and commentary dates on the www.theacsi.org. For example, in 2000, commentaries appeared on August 19, May 20, February 22, and November 22. However, Fornell et al. (2006) suggest (footnote 3) possibilities of the prior leakage of customer satisfaction information because the “ACSI results were routinely provided under embargo to the public relations and market research units of corporate subscribers and to The Wall Street Journal about 2 weeks before the release” (p. 7–8). Therefore, we distinguished between two periods. In period 1, we retain only the forecasts made by the analyst from the month of ACSI release. This is also justified by the fact that these latest forecasts should capture all the information at the disposal of the analyst. For Coca Cola Company, for example, we have 12 analysts who made at least one forecast in the fourth quarter (October through December). However, some of the analysts made their forecasts before November, the ACSI publication month for Coca Cola Company. Therefore, in this period, we exclude all the forecasts made prior to November and retain only the analysts with forecasts in December. In period 2, we include only the forecasts made before the month of the ACSI release. Here, we assume that the market may have already reacted to information about customer satisfaction that is reflected in measures other than the ACSI metric. That is, information about customer satisfaction is available to market participants on an earlier and timelier basis. We join the two periods in our estimation equations.

Fornell et al. (2006) state that: “although ACSI has measured customer satisfaction since 1994, before the second quarter of 1999, the results were published once a year in Fortune magazine, making it difficult to pinpoint the event date because readers received the magazine on different dates.” Indeed, the ACSI data were the object of significant press coverage in 1995 (Stewart 1995) and later, in 1998 (Grant 1998; Lieber 1998; Martin 1998), in a series of articles published in Fortune. The first publication of the ACSI data in Fortune was on December 11, 1995. However, subscribers may have obtained the issue 2 weeks earlier (Itner and Larcker 1998). Therefore, we added the forecasts made from November 27, 1995 to December 1, 1995 and combined them with the forecasts made in 1996 through 2004. We distinguished between the two periods (i.e., 1995–1999: 1 versus 1999:2 through 2004) with a dummy variable but found no significant differences in the effects of ACSI.

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Ngobo, PV., Casta, JF. & Ramond, O. Is customer satisfaction a relevant metric for financial analysts?. J. of the Acad. Mark. Sci. 40, 480–508 (2012). https://doi.org/10.1007/s11747-010-0242-1

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  • DOI: https://doi.org/10.1007/s11747-010-0242-1

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