The American Customer Satisfaction Index (ACSI): Implications for the Economy
Record Increase in Customer Satisfaction Impacts Consumer Spending and GDP Growth
November 7, 2023
- Largest quarterly ACSI bounce in 25 years
- Customer satisfaction increase drives big GDP growth from strong consumer spending
- Nevertheless, customer satisfaction still remains at a low historical level.
Fueled by strong consumer demand, U.S. Gross Domestic Product (GDP) grew by 4.9% in the third quarter of 2023 – well above most predictions and driven by a 4% increase in consumer spending. Customer satisfaction – a major driver behind consumer spending – posted its largest increase in 25 years.
Since customer satisfaction impacts consumer spending – by far the largest component in GDP – ACSI changes also affect GDP. A year ago, ACSI fell to its lowest point in nearly 20 years. For the first two quarters of that year, GDP growth was negative as well.
But the tide seems to have turned. ACSI leaps by 1.3% to a score of 75.1 (on a 0-100 scale) in the third quarter – in part due to improvements in health care, which is up by 8%, and to non-durables and personal care products that also had major improvements in customer satisfaction.
Amid the positive news, however, it is peculiar that portions of the economy do not seem to follow central patterns of the past. For example, a recession, which was predicted by many, has yet to materialize. High interest rates have not reduced consumer demand. Nor has it weakened the labor market. Over the past 2 years, another anomaly is that fewer of the high scoring ACSI companies have realized superior stock returns relative to their competitors.
“The most fundamental tenet of a well-functioning market economy is that companies are rewarded for treating their customers well and penalized for treating them poorly,” said Claes Fornell, founder of the ACSI and the Distinguished Donald C. Cook Professor (emeritus) of Business Administration at the University of Michigan. “This is still true with respect to customers, as companies with high customer satisfaction continue to have greater revenue growth than competitors with lower ACSI scores. Accordingly, the current stock market anomaly is unlikely to last.”
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.
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