Does Customer Satisfaction lead to Accurate Earnings Forecasts?

Jean-François Casta (1) , Olivier J. Ramond (2) , Paul-Valentin Ngobo (3)

1 DRM - Dauphine Recherches en Management
2 DRM-Finance
3 Valorem

This paper examines the usefulness of customer satisfaction to analysts when preparing their earnings forecasts. We draw on theory in marketing to predict how customer satisfaction should be associated with earnings forecasts and forecast errors. 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 1,875 analysts following 90 companies yielding 8,034 year-firm-analyst observations. We control for factors known to influence the earnings forecasts, such as firm profitability and risk, as well as potential unobservable factors using a Mixed-effects regression. We find that customer satisfaction has a negative association with the analysts' forecast errors because it allows analysts forecasts to be closer to the business reality. The influence of customer satisfaction varies across sectors. Specifically, we found that in the Information technology sector (i.e. Computer, the Internet Software & Services - e.g. EBay), customer satisfaction has the largest negative impact on earnings forecast errors. In sum, our findings suggest that analysts that neglect customer satisfaction information may deprive themselves of an important proxy of non-financial information, specifically in the information technology sector.

Mots clés: Customer satisfaction; forecast accuracy/error; multilevel modeling; forecast earnings; American Customer Satisfaction Index

Domaines: Economies et finances

Jean-François Casta, Olivier J. Ramond, Paul-Valentin Ngobo. Does Customer Satisfaction lead to Accurate Earnings Forecasts?. " Marketing Strategy Meets Wall Street ", Marketing Science Institute, Academic Conference at Emory University, Jan 2009, Atlanta, United States. pp.full session. ⟨halshs-00680002⟩