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.
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: mscience.com/corporateintelligence. M Science is a portfolio company of Leucadia Investments, a division of Jefferies Financial Group Inc. (NYSE: JEF).
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