The authoritative guide to consumer transaction data

Arguably the most popular data category within alternative data, the consumer transaction data space is multi-faceted and encompasses a range of data sub-categories from e-receipts and point-of-sale data to credit and debit card data.

Data users from all different backgrounds, including professional investors like hedge funds, asset managers and private equity / venture capital funds, as well as consultancy firms and corporations, regard consumer transaction data as some of the most valuable data that exists – and they’re willing to pay big bucks to get their hands on it!

As with many alternative datasets, Neudata research has recently found that this data type’s popularity is increasing among non-experienced alternative data users, which can be attributed to the growing maturation of the alternative data industry.

Keep reading to discover the different types of consumer transaction data, how it’s collected and the best potential use cases for each category.

Key takeaways include:

• Consumer transaction data can be sourced via a variety of complementary methods, including credit & debit card transaction data, point-of-sale data, and e-receipt and physical receipt data.
• Credit and debit card transaction datasets are some of the most valuable datasets that exist and typically command very high prices.
• The card transaction data ecosystem continues to grow with new entrants entering the market.

What is consumer transaction data and why is it interesting?

As the name suggests, consumer transaction data provides visibility into financial transactions made by consumers, such as the purchase of a product or service or a deposit into / withdrawal from an account.

This type of information is extremely valuable to many different types of businesses, because it provides insight into the behaviour of consumers and shows the products and industries that are gaining traction or falling out of favour. In other words, it can provide a window into company performance or be used to analyse macroeconomic trends. You may find a hedge fund using consumer transaction data to discover the profitability of a specific company so it can make a bet about its future earnings, a private equity firm using it to determine whether a specific sector is growing or contracting, or a beverage corporation using the data to assess the value of the sales its competitors made last year.

Data vendors can either access consumer transaction data via a relationship with a bank or by directly sourcing the information from consumers. Banks and data vendors can enter into explicit data-sharing agreements, or the vendor could just be providing a tool or service to a financial institution that allows it to access transactional data. If a data vendor wishes to source data directly from consumers, it may do so by providing a personal finance management app that a consumer would use — in exchange, the consumer would grant the vendor access to his or her data.

Data users should think of consumer transaction as falling into three main categories:

• Card and bank transaction data – the information that a consumer would be shown when viewing his or her personal bank account transaction history or credit card statement
• POS transaction data – data collected when a customer makes a payment at a physical or online store
• Receipt data (e-receipt & offline receipt) – data sourced from consumer receipts, both collected from emailed receipts and offline receipts that are uploaded to a data platform

1) Card and bank transaction data

Credit card, debit card and bank transaction data reflects what a consumer would be shown when viewing his or her personal bank account transaction history or credit card statement. It shows the amount of money a consumer spends at a retailer per transaction.

This category of transaction data offers merchant-level detail and can provide insight into:

• consumer spend at merchants/retailers, or
• consumer account deposits/withdrawals

However, card and bank transaction data doesn’t show users which items consumers are spending money on. If a user’s use case requires them to know this level of information, they would be better off exploring some other types of transaction data that can provide more granularity.

Due to the lack of granularity, this type of data is best deployed to understand macro use cases like “How much money are consumers spending at Starbucks in Texas?” or “Is the market for grocery delivery services growing or declining in France?”

Potential users should also be aware that this type of data is typically the most expensive of all transaction datasets, which can be attributed to how difficult it is to source. It is also the data type that’s most highly in demand among hedge fund and asset management data users, which contributes to the high prices that data vendors are able to charge for its use.

2) Point of sale (POS) transaction data

Point of Sale (POS) transactions occur when customers visit a physical or online location and make a payment in exchange for a product or a service. That payment is recorded in a POS system, which is the software that processes and maintains a record of each transaction. Typically, POS systems will use a product code that links to a price and specific SKU (stock keeping unit).

Within the data space, retailers allow intermediaries to aggregate this type of data, as long as those groups agree to anonymise the data. Intermediaries will capture data from multiple retailers and anonymise the contributors before turning the insight into data products. Data users will then buy this data — which can show total market sales and market share values for brand manufacturers — and apply it to their specific use case.

Users of POS data have benefited greatly over the last few years, as a number of new entrants have begun selling this type of data. These new entrants are typically more quant-friendly, meaning they deliver daily transaction-level data with a low lag time.

One popular use case for POS transaction datasets is to track the sales of consumer packaged goods (CPG) products. Using this type of data allows firms to track spending on specific products and brands, regardless of where those items are purchased.

Other relevant use cases for POS transaction datasets include consumer discretionary products (like home appliances, smartphones, gaming hardware and software) and healthcare products.

3) Receipt data: E-receipt and offline receipt

Over the last several years, companies have become more adept at emailing receipts to their consumers, opening a new potential source of consumer transaction data. This trend has been accelerated by the introduction of mobile payment systems, like Square and Stripe, which offer small businesses an easy way to email receipts to their customers.

E-receipt (email receipt) datasets extract information from the receipts found in consumers’ email inboxes (after receiving consent from consumers to access that information). E-receipts contain details on the specific purchases that people make, as well as any delivery fees and whether a customer has returned any parts of his or her order.

Historically, e-receipts have been mostly associated with purchases made online, but physical retailers are also wading more into the e-receipt space.

All of the e-receipt panels that Neudata has covered to date rely on consumer email accounts, which allow users to see a full picture of an individual's overall spending habits, regardless of which card they use.

Data providers will either gather this data themselves or form partnerships with app developers that gather the data on their behalf.

In addition to e-receipts, many data vendors also collect physical receipts that are given to customers after making a purchase at a brick-and-mortar retailer. These receipts typically contain details about the purchases that are made at physical retail stores and contain item-level granularity.

In the case of offline receipt panels, vendors have mobile apps that incentivise consumers to submit physical receipts from brick-and-mortar retailers. The value proposition in most cases is some form of promotional discount or monetary incentive, so data users must be aware that this incentive system may result in data being collected from a non-representative sample of the population.

Both online and offline receipt datasets capture both item- and merchant-level sales, so they can provide insights into consumer spend on specific products as well as spend at specific merchants.

How can Neudata help?

As of June 2022, Neudata has identified 250+ consumer transaction datasets from 200+ unique vendors and has made thousands of introductions between the buyers and sellers of that data.

Neudata is an alternative data-focused research platform that specialises in the objective and neutral assessment of data vendors and datasets. We connect institutional investors, corporate clients and leading global organisations to the most relevant alternative data sources that are used in their internal data ingestion processes.

Our core philosophy is simple — we catalogue and assess datasets based on over 100 unique factors and deliver metadata reports & advice for a fee.

We don’t buy data, sell data, or require revenue-shares or commissions from the data vendors that we catalogue. That means you get unbiased intelligence that’s tailored to your specific research goals and strategies. Neudata users are also able to leverage insights from our research experts based in London, New York and Shanghai.

Since 2016, we have helped our clients understand the landscape of available datasets, increasing the efficiency of their data spending budgets. Neudata’s data buyer clients represent 60-70% of industry-wide spending on alternative data.

If you have data like this and want advice on monetising it, or if you're looking to buy this type of data, please reach out to info@neudata.co to learn more about how Neudata can help.

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