Customer satisfaction and financial analysts earnings forecast errors

This paper examines the relevance of customer satisfaction for the financial 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 - ACSI (University of Michigan), which also appear on the Institutional Brokers Estimate System (I/B/E/S) files. 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. More specifically, both levels and changes in ACSI are associated with higher levels of Actual Earnings per Share (EPS) and higher levels of analysts’ earnings forecasts, making the analysts’ forecasts to be closer to the business reality. The robust impact of customer satisfaction on forecast earnings and errors suggests that analysts who undervalue customer satisfaction information may deprive themselves of an important proxy of non-financial information.