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National Economic Indicator

U.S. Overall Customer Satisfaction

Small Gain Keeps Customer Satisfaction at Record Level

Q1 2024: The American Customer Satisfaction Index (ACSI)

Less than two years ago, customer satisfaction in the United States was at its lowest point in 20 years. The decline began about a decade ago and was exacerbated by inflation, supply chain problems, and shortages caused by COVID and the war in Ukraine. Since then, things have changed dramatically. ACSI at the national level has increased sharply over the past seven quarters. In the first quarter of 2024, customer satisfaction improves again, albeit at a much slower pace, advancing 0.3% to a score of 78.0 (on a 0-100 scale) that is an all-time high.

The slowdown in customer satisfaction growth is consistent with slower growth in consumer spending and GDP. Nevertheless, customer satisfaction is at a record level, but so are customer expectations. Historically, expectations have always been higher than subsequent satisfaction. But when the gap is large, it often signals a coming decline in customer satisfaction.

While inflation has abated, it is probably still a factor because food and housing are now a much higher percentage of household income than they used to be. Even if the proportion of discretionary consumer spending declines, customer satisfaction will still have a major impact on how consumers spend much of their money. Consequently, the economic outlook is fairly positive, but not without caution.

The ACSI results appear to be inconsistent with most political polls, which typically find that people are not pleased with the economy and are particularly concerned about inflation. Clearly, inflation has contributed to the decline in customer satisfaction, but only between 2020 and 2022. Before then, the rate of inflation was rarely above 2% and it seems close to returning to that level again. The biggest threat to the economy now is not weak customer satisfaction leading to lower consumer spending, but rather interest rates and high consumer expectations.

1st Quarter 2nd Quarter 3rd Quarter 4th Quarter
2024 78.0
2023 75.4 76.7 77.1 77.8
2022 73.1 73.0 73.5 74.4
2021 73.9 73.8 73.5 73.1
2020 74.3 74.1 73.9 73.6
2019 75.6 75.7 75.7 75.2
2018 76.7 76.2 75.9 75.6
2017 76.6 76.9 76.9 76.9
2016 76.3 76.2 76.4 76.7
2015 76.2 76.1 76.1 76.1
2014 76.5 76.4 76.5 76.5
2013 76.6 76.5 76.7 76.8
2012 75.9 75.9 75.9 76.3
2011 75.6 75.7 75.7 75.8
2010 75.9 75.9 75.7 75.3
2009 76.0 76.1 76.0 75.9
2008 75.2 75.1 75.0 75.7
2007 75.2 75.3 75.2 74.9
2006 74.1 74.4 74.4 74.9
2005 73.0 73.1 73.2 73.5
2004 74.4 74.4 74.3 73.6
2003 73.8 73.8 73.8 74.0
2002 73.0 73.0 73.1 72.9
2001 72.2 72.1 72.0 72.6
2000 72.5 72.8 72.9 72.6
1999 72.1 72.0 72.1 72.8
1998 71.9 72.2 72.3 72.6
1997 70.7 71.1 71.1 70.8
1996 73.0 72.4 72.2 72.0
1995 74.1 73.7 73.7 73.7
1994 74.8* 74.2

*Baseline measurement taken in summer 1994

Download Excel file of National ACSI Scores

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. Excessively long surveys may lead to straight-line responses. 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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