(Recherche menée avec JF casta et Olivier Ramond et financée par le marketing Science Institute)
Résumé managérial
A growing number of papers have been reporting significant associations between customer satisfaction and various firm performance metrics such as profitability and Tobin’s q. However, while marketers generally agree on its impact, it is unclear whether investors use information on customer satisfaction when they predict firm performance. Much of the evidence on the effects of customer satisfaction comes from direct analyses of its influence on financial performance, bypassing the analysis of whether and how this information translates into stock pricing and valuation. An important way customer satisfaction efforts could translate into the stock pricing is through the financial analysts’ forecasts and recommendations. Analysts play an important role as information intermediaries for the investors. Financial analysts aggregate complex information for other market participants (e.g. macroeconomic data, business plans, and possibly non-financial information) and provide (1) earnings forecasts, (2) price targets, and (3) buy-sell-hold recommendations.
However, the extent to which analysts use non-financial information has received limited attention despite some frustration over traditional financial statements. In this study, we examine the value relevance of customer satisfaction to financial analysts, where value relevance refers to the usefulness of customer satisfaction to analysts when preparing their earnings forecasts. We assembled a dataset of companies studied in the American Customer Satisfaction Index (University of Michigan), which also appear on the Institutional Brokers Estimate System (I/B/E/S) files. By combining these sources, we were able to analyze the forecast errors of over 1300 analysts following 89 companies in 22 industries. We developed a Latent Factor Regression Model, which allows us to estimate the mean effect of customer satisfaction but also the analyst heterogeneity and industry heterogeneity through latent factors and factor scores.
Résumé managérial
A growing number of papers have been reporting significant associations between customer satisfaction and various firm performance metrics such as profitability and Tobin’s q. However, while marketers generally agree on its impact, it is unclear whether investors use information on customer satisfaction when they predict firm performance. Much of the evidence on the effects of customer satisfaction comes from direct analyses of its influence on financial performance, bypassing the analysis of whether and how this information translates into stock pricing and valuation. An important way customer satisfaction efforts could translate into the stock pricing is through the financial analysts’ forecasts and recommendations. Analysts play an important role as information intermediaries for the investors. Financial analysts aggregate complex information for other market participants (e.g. macroeconomic data, business plans, and possibly non-financial information) and provide (1) earnings forecasts, (2) price targets, and (3) buy-sell-hold recommendations.
However, the extent to which analysts use non-financial information has received limited attention despite some frustration over traditional financial statements. In this study, we examine the value relevance of customer satisfaction to financial analysts, where value relevance refers to the usefulness of customer satisfaction to analysts when preparing their earnings forecasts. We assembled a dataset of companies studied in the American Customer Satisfaction Index (University of Michigan), which also appear on the Institutional Brokers Estimate System (I/B/E/S) files. By combining these sources, we were able to analyze the forecast errors of over 1300 analysts following 89 companies in 22 industries. We developed a Latent Factor Regression Model, which allows us to estimate the mean effect of customer satisfaction but also the analyst heterogeneity and industry heterogeneity through latent factors and factor scores.
Our findings indicate that customer satisfaction is negatively associated with analysts’ forecast errors. The influence of customer satisfaction varies across analysts and industries. Specifically, we found in industries such as the Internet Software & Services (e.g. Ebay), Managed Health Care (e.g. Aetna), Integrated Telecommunications Services (e.g. AT&T), customer satisfaction has the largest negative impact on earnings forecast errors. In other industries, such as the Broadcasting & Cable TV, the Airlines, and the Electric Utilities, customer satisfaction has a weaker impact. The structure of these industries provides the explanation. Industries, which are less sensitive to customer satisfaction, such as Electric utilities, are in concentrated markets. The former industries are differentiated markets where repeat purchase is sensitive to customer satisfaction. These results are robust even if we examine the influence of the unanticipated component of customer satisfaction. An increase in customer satisfaction has a negative association with the forecast errors and a decrease in customer satisfaction has a positive association with earnings forecast errors. In sum, our findings suggest that by neglecting customer satisfaction information, analysts have deprived themselves of an important proxy of non-financial information.
Aucun commentaire:
Enregistrer un commentaire