Consumer confidence data—also called consumer sentiment data—measures citizen confidence in their personal financial and national economic states. This data measures both current and future economic confidence.
The original purveyor of this data is the Conference Board’s 1985 Consumer Confidence Index. Subsequent measures use this index as a benchmark.
The Conference Board’s monthly report relies on household surveys; most other consumer sentiment reports follow suit. However, this confidence data can also be measured by company or industry sales reports, stock market trades, and web data, particularly NLP data.
Consumer confidence data presents customer sentiment as a number out of 100. The higher a number rises, the more confident the population. It also measures month-to-month and year-to-year change as a positive or negative percentage. Experts, however, disregard any change of less than 5% in either direction.
Data providers often break down the data into demographics. These include age, income bracket, and political affiliation—essentially, whatever the country or NGO collecting the data considers useful.
Businesses use consumer confidence data in a number of ways. In particular, they use the data to prepare future inventories and make hiring decisions. Meanwhile, economists in think tanks or governments use the data to improve or protect their economy, whether that means changing existing tax policy to lifting lockdown orders.
Economists and business leaders generally consider this data accurate and representative of the population measured. Therefore, you do not have much testing to do at all, unless you are building a dataset out of several different surveys.
If that is the case, you will need to standardize and cleanse the data. If you aggregate data from many different countries, you may find demographic breakdowns especially difficult to standardize, as every country has unique demographics and different standards for meeting them (especially regarding income brackets). In the end, you may consider disregarding all demographics except age range and gender.
Additionally, evaluating consumer confidence data presents another difficulty. Economists themselves disagree about the predictive nature of consumer sentiment: though companies use this data to make business decisions, the data itself follows existing economic trends that may have been acting upon citizens for months.
“Fear and loathing now dominate the mood of consumers, producing a false sense of confidence,” Curtin said. “Fears have increased due to rising rates of COVID infections and deaths, and loathing has been generated by the hyperpartisanship that has driven the election to ideological extremes.
“Moreover, the impacts of the covid virus and the extremes of hyperpartisanship will continue long past next week’s election, with the potential to permanently alter the economic and political landscape. Restoring optimism requires progress against the coronavirus and mitigating its uneven impact on families, firms and local governments.”
PreciseTarget Retailer Taste Insights – USA Audiences for 70 Retailers (>220 Million consumers) dataset provides information regarding: Retail & Commerce Data,Consumer Confidence Data and more.
Data Axle B2C Link connects individual and business personas, revealing data on people where they spend most of their waking hours
Brand Management helps your brand online to understand customer perception, spot changes in sentiment, and measure brand visibility – all in real time.