Retail Spending Insights

Retail Spending Insights
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Understanding consumer behavior and retail spending patterns has always been a cornerstone for businesses aiming to thrive in the competitive market. Historically, gaining insights into these patterns was a daunting task. Before the digital revolution, firms relied on manual surveys, point-of-sale data, and anecdotal evidence to gauge consumer spending. These methods were not only time-consuming but also often resulted in outdated or inaccurate data. The advent of sensors, the internet, and connected devices, alongside the proliferation of software and databases, has dramatically transformed the landscape. Now, businesses can access real-time data, offering a granular view of consumer transactions across both online and offline retailers.

The importance of data in understanding consumer spending cannot be overstated. Previously, businesses were in the dark, waiting weeks or months to analyze changes in consumer behavior. Today, with the right data, changes can be understood in real-time, allowing for swift and informed decision-making. This shift has not only improved the accuracy of insights but also the speed at which they can be obtained, providing a significant competitive advantage.

However, navigating the vast ocean of available data to find relevant insights can still pose challenges. This article aims to shed light on specific categories of datasets that can help business professionals better understand US consumer transaction data, focusing on aggregate spending by merchant. By exploring the historical context, technological advancements, and current applications of these data types, we aim to provide a comprehensive guide for those looking to track retail sales in the US.

Alternative Data for Retail Insights

The rise of alternative data has been a game-changer for understanding consumer spending. This category includes online and offline transactions by merchant across millions of US consumers, offering a detailed picture of spending patterns. Historically, such detailed transaction data was not accessible, but technological advancements have made it possible to aggregate and analyze this information, providing businesses with unprecedented insights into consumer behavior.

Examples of alternative data include:

  • Online and offline transaction data: Captures consumer purchases across various merchants, providing a holistic view of spending habits.
  • Email receipt data: Offers insights into online and buy online pick up in store (BOPIS) transactions, complementing traditional credit card data for a more complete picture of consumer spending.

Industries and roles that benefit from alternative data include:

  • Market researchers
  • Retail analysts
  • Consumer behavior specialists

Technological advances such as big data analytics and machine learning have played a crucial role in harnessing the power of alternative data. The amount of data available is accelerating, enabling deeper insights into consumer spending trends.

Specific applications of alternative data in understanding US consumer transaction data include:

  • Tracking aggregate spending by merchant to identify trends and shifts in consumer preferences.
  • Analyzing spending patterns to forecast future consumer behavior.
  • Understanding the impact of economic events on consumer spending at a granular level.

Economic Data for Consumer Insights

Economic data providers offer another valuable source of insights into consumer spending. This data is aggregated by merchant and location, available down to the weekly level, and provides a historical view back to January 2018. Such data allows businesses to understand how broader economic trends impact consumer spending on a macro and micro level.

Examples of economic data include:

  • Consumer spending products: Aggregated data that highlights spending patterns across different merchants and locations.
  • Gross Merchandise Value (GMV): Data indicating the total sales value for retailers, offering insights into their performance.

Roles and industries that benefit from economic data include:

  • Financial analysts
  • Economic researchers
  • Retail strategists

The advent of cloud computing and advanced analytics has facilitated the processing and analysis of large economic datasets, enabling more nuanced insights into consumer spending.

Specific applications of economic data in understanding US consumer transaction data include:

  • Identifying spending trends by location to tailor marketing and product strategies.
  • Assessing the impact of economic policies on consumer spending habits.
  • Comparing spending across different merchant categories to prioritize business investments.


The importance of data in understanding consumer spending patterns and making informed business decisions cannot be overstated. With access to alternative and economic data, businesses can gain a comprehensive view of US consumer transactions, enabling them to respond swiftly to market changes. The shift towards a more data-driven approach in business is not just a trend but a necessity in today's fast-paced market.

As organizations continue to seek out and monetize useful data, we can expect to see the emergence of new data types offering even deeper insights into consumer behavior. The future of data discovery and application is bright, with technologies such as AI poised to unlock the value hidden in decades-old documents and modern datasets alike.

Ultimately, the ability to understand and predict consumer spending patterns through data will be a key differentiator for businesses looking to succeed in the competitive retail landscape.


Industries and roles that stand to benefit significantly from access to detailed consumer transaction data include investors, consultants, insurance companies, market researchers, and more. These stakeholders face various challenges, such as understanding market trends, assessing risks, and identifying growth opportunities. Data has transformed these industries by providing actionable insights that were previously inaccessible.

Looking ahead, the potential for AI to further enhance the value of consumer transaction data is immense. By automating the analysis of vast datasets, AI can help uncover patterns and insights that would be impossible for humans to detect, opening up new avenues for understanding and leveraging consumer behavior.

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