In the 1990s, business focus on customer satisfaction was in its infancy and the satisfaction decline between 1994 and 1997 came at a time when profits were generated from efficiencies in production rather than from satisfied customers. As the profitability from customer satisfaction and customer retention became more apparent, except for the financial crisis in 2009, a long period of strong economic growth and increasing customer satisfaction followed. However, both economic growth and customer satisfaction began to flatten about a decade ago. Since 2019, there has been a sharp decline in customer satisfaction.
There are several reasons for the flattening and subsequent decline of customer satisfaction. While COVID-19 has certainly played a role, the fall in customer satisfaction began before the advent of the pandemic. From 2010 to 2019, about 70% of the companies tracked by ACSI had declining or flat customer satisfaction scores. Since then, American customers became even more dissatisfied. As of the fourth quarter 2021, almost 80% of the companies failed to increase the satisfaction of their customers since 2010.
By mid-2022, customer satisfaction had plummeted to levels not seen in 17 years. However, the final quarter of 2022 might be breaking this downward trend as customer satisfaction is showing signs of recovery. The fourth quarter 2022 GDP growth, along with increasing customer satisfaction, are encouraging signs that things may be moving in the right direction for the U.S. economy.
Welcome as these results are, the near-term economic progress remains uncertain, but the risk of a recession is a bit lower. While price inflation remains too high, it is nevertheless reduced. Further, the levels of customer satisfaction and spending, while improving, are not yet robust enough.
The primary reason for the previous decade-long stagnation in customer satisfaction among major companies and the subsequent sharp decline in customer satisfaction is not a result of lack of attention by business. Companies devote more effort than ever before to enhancing the shopping, purchasing, and consumption experience. It is not for lack of data either. Companies assemble, organize, combine, transmit, store, and display more customer data now than they have in the past. There is no evidence that consumer expectations have risen either.
While companies today have more data about their customers, the analytics employed to turn data into information are for the most part not good enough. Customer satisfaction data have certain characteristics that make it difficult to obtain accurate estimates, to pinpoint what aspects of the customer experience need attention, and to gauge the financial impact of actions contemplated. Traditional statistical methods assume normal frequency distributions among the residuals, moderate multicollinearity, and low levels of data noise. Customer satisfaction data don’t meet these assumptions.
ACSI Analytics is designed to overcome these problems and thereby turning raw data into financially relevant information by:
- Separating signals from noise
- Moving from correlations and artificial intelligence (AI) patterns to cause-and-effect interpretations
- Calibrating measurement instruments toward profitability
Data is not the same as information—especially not data from consumer surveys. Management decisions require information; raw data must be filtered in order to be useful for decision-making. ACSI technology filters out data noise.
Management decisions require cause-and-effect information—something that current CX tools, whether based on AI or descriptive statistics, don’t provide. ACSI Analytics, on the other hand, is based on a causal model.
There is a wide disparity in the amount of consumer data collected by companies today. Some data suppliers use surveys with more than 200 questions per respondent, while others focus on responses to a single question. Neither is appropriate.
Survey respondents are generally unable to provide reliable or valid information for more than 30 questions. According to University of Michigan research, long questionnaires should not be used as they lead to straight-line responses. At the other end of the spectrum, good measurement techniques—whether in the social or physical sciences—typically require several measures (survey questions in this case) per product feature or service dimension.
Accuracy and relevance are what matters. To contribute to the business objectives at hand, the measurement instruments need calibration in ways similar to the physical sciences. This is why companies with high scores in the American Customer Satisfaction Index, which is calibrated to maximize customer loyalty, are financially successful, most notably in terms of stock returns and profitability.
National ACSI Score
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