Customer Transaction Data is information about an individual customer’s transactions, past and present. This includes the amount spent, when, and on what.
This data also frequently comes linked to consumer identity data. After all, accurate customer transaction data must identify the customer behind the transactions, even when they use different devices.
Transaction records and receipts are the main sources of this data. Additional sources are cookies, rewards/loyalty programs, registration details, and reports from sales and support staff.
Obviously, the most common transaction data attributes are the amount of money spent on your products or services. Other easily accessible data attributes include how often the customer returns to your store, whether they use rewards points or coupons, whether they request refunds or make chargebacks, when they tend to make purchases, and how they make purchases (online, via mobile, in person).
Other common attributes include explicit data on your customer: their name, phone number, email address, etc. Finally, you can expect data like the location of purchase, the length of time the customer spent on your site or app, and whether they visited your store in person after viewing an ad.
Your business can use this data for customer relations and marketing purposes, such as customer service improvement, marketing analysis and campaign planning, and improving the online recommendation engine.
You can also use this data for fraud identification and prevention. By identifying a customer’s location and behavior, you can recognize when someone else is using their card without their knowledge.
The best test of the quality of customer transaction data is the update frequency and consistency of data within the dataset. This data category is one of the fastest-moving fields in the universe of big data, with more information coming every day (at least).
It is also good to collect a set of historical transaction data and test that against newer data or against the rate of conversions to see if the dataset is still good.
The most important factors to consider when selecting a customer transaction data set (aside from update frequency) are the experience and methodology of the vendor. Does the vendor use a mix of offline and online data? Do they rely more on probabilistic or deterministic data? Do they have historical data to compare to past data?
MIT: Sloan Review: A Data-Driven Approach to Customer Relationships
Bournemouth University: Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales
One way that banks protect businesses from fraud is through keeping a log and examining regular transactional history. Any transactions which appear suspicious based on location, amount, the beneficiary, and so on will be reported.
Global Banking and Finance: Bank fraud prevention in a post-COVID-19 world
With a focus on marketing and product development, AI Internet Data helps businesses mine and manage data. They primarily track customer journey, web, event, and other online data and offer resources and training for business professionals. They also offer specific products for marketers, product developers, and executives.
Random Walk Data tracks customer interaction and engagement data, identifying when consumer demand rises and what services they most enjoy
PredictHQ Demand Intelligence combines a company’s historical transaction data with external events data that impacts customer demand