Consumer Transaction Data is information about an individual customer’s transactions, past and present.
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 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 consumer 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 consumer transaction data set (aside from update frequency) is 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?
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.
Content Personalization provides targeted recommendations to tailor your site to user interests and drive additional traffic and engagement
Subscription Optimization enables you to create a personalized user experience that drives subscription revenue.
Project Reaseach Software allows you to quickly search all of our datasets for donation records, compensation records and biographical information.