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Customer Transaction Data Customer Transaction Data

What Is Customer Transaction Data?

Customer Transaction Data is information about an individual customer’s transactions, past and present.

Where Does Customer Transaction Data Come From?

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.

What Types of Columns/Attributes Should I Expect When Working with This Data?

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.

What Is This Data Used For?

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.

How Should I Test the Quality of Customer Transaction Data?

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.

What Are the Most Important Factors I Should Vet when Selecting This Data?

The most important factors to consider when selecting a customer 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?

Interesting Case Studies and Blogs to Look Into

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

Tangible Examples of Impact

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

Relevant datasets

Quantcast Advertise

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Quantcast Advertise helps marketers, publishers, and others reach their target audiences. They track interest, behavior, and brand awareness, enabling companies to prospect and target customers. Quantcast claims their data are highly accurate and able to scale to any level.

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Cxense Subscription Optimization

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Subscription Optimization enables you to create a personalized user experience that drives subscription revenue.

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Cxense Content Personalization

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Content Personalization provides targeted recommendations to tailor your site to user interests and drive additional traffic and engagement

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