The Essential Guide to Consumer Spending Data

Data-driven strategies are viewed by many companies as the key to disruption when it comes to winning in the consumer landscape. Every day, marketing, sales, product, and strategy teams are making decisions with the goal of driving growth – and data is essential to making the right decisions.

It’s generally accepted that data is crucial to most business operations, but successful execution of data-driven strategies is not as common, as many organizations still struggle with turning data into actionable insights. In this guide, we’ll give an overview of how to better understand consumer transaction data specifically, and how to augment your data strategy to leverage the power of consumer insights.

What are the different types of consumer spending data?

The main sources of consumer spending data are transaction data and syndicated retail sales data (also known as point-of-sale or “POS” data).

Consumer transaction data from credit cards, debit cards, and ACH payments

Consumer transaction data is third-party data that paints a broad picture of a consumer’s spending habits over time. This includes transaction amounts seen on credit/debit card statements, along with merchant and date info. Transactions are tied to an anonymized user account, so changes in spending behavior can be tracked over time, along with demographic information. This data is anonymized and aggregated, and then needs to be heavily cleansed, parsed, organized, and transformed by data scientists before relevant information can be derived.

While extremely valuable, unstructured data from these transactions can be virtually useless without a data engineering team capable of extracting actionable insights. Having the right team to work with unstructured data in-house can be costly for an organization to take on alone, which is why outsourced partnerships are often a more viable alternative for organizations without the internal infrastructure to deal with large amounts of raw data.

While debit/credit transaction data is useful in understanding where consumers are spending and how their preferences have changed over time, it still has pitfalls. These views are limited and don’t provide detailed insights into purchase details such as products, brands, pricing and discounts, or quantities of items purchased.

Point-of-sale (POS) data

Syndicated retail sales data, or POS data, is another viable alternative source of consumer spending data and insights. POS data pertains to transactions completed through a POS system, like checkouts at a retail store, or on handheld POS hardware. The downfall is that this data is limited and leaves gaps since it excludes purchases outside of certain retailers and specific POS systems used.

Receipt data

Transaction data alone is typically not enough to fully harness the power of consumer spending insights. Digital and in-store receipts add another level of clarity and insights when it comes to understanding consumer purchases.

Receipt data includes information on what items were purchased, down to the brand of the product and other qualifiers such as size and quantity. It also includes the price and any discounts or coupons applied. Additionally, this data sheds light on consumer demographics, but similar to transaction data, it is anonymized and aggregated.

Receipt data can be collected actively (based on incentives) or passively. An example of passive receipt data collection can be seen via apps where consumers provide email access in exchange for a service, like unsubscribing from mass marketing emails. Some views consider passive collection advantageous due to its ability to provide access to a majority of a consumer’s email receipts. Alternatively, an example of active collection can be seen via apps that incentivize consumers to self-submit their receipts in exchange for benefits, like gift cards or points. Panels including receipts from consumers’ active submission are limited, due to their reliance on the consumer to make an effort to submit his or her receipt.

Consumer spending data alone is often not enough

There are many other sources of consumer spending data, some better than others, and with different panel sizes and dynamics. However, even then, consumer spending data is not enough to inform winning strategies. Many leading data-driven companies typically combine insights from consumer spending with other forms of digital data, DTC and e-commerce data, foot traffic data, customer loyalty and engagement data, and brand awareness data for themselves and their competitors.

How M Science can help

M Science is a global data-driven research and analytics firm, with nearly 20 years of experience uncovering new insights for some of the world’s largest corporations and financial institutions. We revolutionize research by discovering new datasets and pioneering methodologies to provide actionable intelligence. Learn more at: M Science is a portfolio company of Leucadia Investments, a division of Jefferies Financial Group Inc. (NYSE: JEF).

Interested to talk to a member of our sales team for a demo of our solutions?

The Essential Guide to Clickstream Data

The digital landscape has evolved at a pace greater than many organizations’ data capabilities have, leaving gaps in their understanding when it comes to digital customer journey mapping, key word trends, browsing behavior, shopper overlap, online visibility in search and e-tailers — and most importantly, competitors’ strategies and performance in driving traffic and sales.

Every year, trillions of dollars are invested in digital commerce initiatives and marketing strategies, yet there is still gray area in measuring and understanding performance. Clickstream data provides crucial digital insights that enable companies to craft winning online strategies.

What is clickstream data? 

Clickstream data acts as a window into users’ online behavior, providing details around websites visited, time spent browsing on pages, websites purchased from, and other info about their online searches and browsing behavior. Clickstream analysis gathers information from user level online activity to paint a full picture of the customer journey over time, along with browsing details and conversion data across multiple websites. 

What insights can clickstream data provide? 

Clickstream data can inform metrics and KPIs that aim to assess a company’s overall digital strategy. It also provides insights to help understand the customer journey, marketing efficiency, and other opportunities for strategic improvement. 

Here are some examples of questions that can be answered with clickstream data: 

  1. How does my website’s conversion rate and engagement metrics compare to similar websites?
  2. How are similar websites attracting traffic and what does that say about their digital strategies?
  3. What are the most visible and savvy paid search competitors and key word specific strategies?
  4. How does my Google search, Amazon, and e-tailer visibility compare to similar companies?
  5. Where does my customer journey start and how are they searching for my products?
  6. What other websites do my shoppers browse when shopping with me?
  7. What other brands did my customer consider before making a purchase? 
  8. Who are new entrants in my space that pose a threat to my brand share?
  9. What pre-purchase and post-purchase actions did my shoppers take? 
  10. How is my digital loyalty and retention strategy performing compared to similar websites?

How can I use clickstream data? 

While many organizations are aware of clickstream data, many fall short when it comes to properly analyzing it to extract insights and inform their strategic initiatives. Below are a couple of examples that show how successfully leveraging clickstream data can potentially lead to improved business outcomes.  

Note: The examples below are hypothetical scenarios and do not represent actual client case studies.

  • Inventory allocation and turnover: A small kitchen appliance manufacturer wants to plan their inventory and unit allocation for the upcoming quarter. By using clickstream data, the team could learn (assuming the following scenario) that Target was winning more paid traffic for air fryers and multicookers than other retailers because Target was prioritizing its marketing strategies for those products. As a result, the company could decide to allocate more units to Target and turn over inventory faster. 
  • Marketing content and investment: By using clickstream data, a grill company could learn (assuming the following scenario) that shoppers were browsing their grills but purchasing from a competitor after watching assembly instruction and grilling technique videos on YouTube featuring the competitor’s grill. The company could then launch its own YouTube channel with educational resources and video content similar to its competitor. They could also invest in influencer partnerships with YouTubers who could feature and recommend their product. These efforts could result in winning back market share from the competitor and an increase in sales. 
  • Media placement and partnership opportunities: A high end furniture company leveraged clickstream data to learn that while their shoppers were browsing for outdoor products on their website, many were also browsing for outdoor home sports equipment and installation. The company identified media placement and brand partnership opportunities by using clickstream data to determine which brands and websites shoppers have an affinity for.

The Future of Clickstream Data

The clickstream analytics market is expected to reach USD 2157.90 billion by 2026, and organizations will likely need to find experienced partners to navigate shifts in the rapidly growing marketplace– while also ensuring their efforts are in line with growing privacy concerns.

How M Science Can Help

M Science is a global data-driven research and analytics firm, with nearly 20 years of experience uncovering new insights for some of the world’s largest corporations and financial institutions. We revolutionize research by discovering new datasets and pioneering methodologies to provide actionable intelligence. Learn more at: M Science is a portfolio company of Leucadia Investments, a division of Jefferies Financial Group Inc. (NYSE: JEF).

Interested to talk to a member of our sales team for a demo of our solutions?

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M Science’s New Solution Tracks Key Metrics of the Digital Gaming Industry

M Science the pioneer in data-driven research and analytics, announced today the launch of its Digital Sports Betting and iGaming platform. This new data solution allows clients to track key performance metrics for digital sports betting and iGaming companies, state by state on a weekly basis.

“M Science brings a much-needed data solution to companies operating in this quickly growing market, which is still in its early innings”

“M Science brings a much-needed data solution to companies operating in this quickly growing market, which is still in its early innings,” noted Elizabeth Coleman, Head of Product at M Science. “Our corporate intelligence team helps our clients drill even deeper into that data to bring actionable, quantitative insights to highly specific strategic questions.”

The launch of this new offering builds upon the highly valuable research and data solutions that M Science has provided to investors and Fortune 500 companies for nearly two decades. Powered by M Science’s near real-time transaction data, this new offering significantly expands the market information available to digital sports betting and iGaming companies, which have previously relied on limited disclosures by state officials to build their understanding of overall trends in the industry.

“Our new sports betting and iGaming solution allows our clients to conduct in-depth competitive and consumer analysis – whether it’s tracking market share of overall deposits, share of new customer additions, or benchmarking competitors in terms of customer retention, loyalty, or cumulative spend across customer cohorts. This information is critical for companies as they race to win sustainable market share in each new state as it legalizes,” said Vaughan Read, corporate Technology, Media and Telecom sector lead.

M Science’s corporate intelligence serves strategy, analytics, and market research teams with data to help them understand the performance of competitors, specific products, categories, and sales channels. M Science solutions analyze the spending behavior of customers at the aggregate level, but also down to very specific cohorts and segments. For more information about M Science’s data, product, or capabilities in the sports betting and iGaming space, please contact

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