Understanding the Difference between Transaction Data and Behavioral Data

10 September 2024 | 3 minutes of reading time

Transaction data and behavioral data are essential for understanding user interactions with your product or service, but they serve different purposes.

Transaction data is often straightforward and solid—it records concrete actions like purchases, sign-ups, or subscriptions. This data is reliable because it reflects final, completed actions, providing a clear “truth” about what users have done. Transaction data is generated on the application’s server side. The quality of transaction data is typically high; if a subscription fails to be recorded, it simply doesn’t exist in the system, which in turn is unacceptable from a business point of view.

Behavioral data, on the other hand, is more nuanced and complex. It is often generated at the clients’ side, on devices of users, and captures the various actions users take leading up to a transaction—such as clicks, page views, or interactions with specific features. However, the quality of behavioral data can be less consistent. There are many reasons why this data might fail to reach the database—network issues, browser settings, tracking errors, or system limitations. Despite these potential discrepancies, behavioral data remains incredibly valuable because it provides deeper insights into user behavior that transaction data alone cannot offer.

Why Behavioral Data Matters

Behavioral data is crucial because it provides context to transaction data. It reveals patterns, preferences, and user journeys that transaction data alone cannot. Knowing that a user made a purchase is important, but understanding how they navigated your site, what content they engaged with, and what led them to complete the purchase is even more valuable. Despite occasional inconsistencies, behavioral data allows for better decision-making and optimization of the user experience, making it an essential component of any data strategy.

What’s the difference between transaction data and behavioral data?

While both transaction and behavioral data are important, they serve different functions. Transaction data provides hard facts—what happened and when, with high reliability. Behavioral data, meanwhile, offers richer context by showing how and why those transactions occurred, even if some might not be captured perfectly. Transaction data is more straightforward and easier to quantify, whereas behavioral data, though more complex and occasionally messy, provides deeper insights that require careful analysis.

Where transaction data is generated by back-end applications and often automatically stored in pre-defined formats in a database, capturing and processing behavioral data isn’t that straightforward. In almost all cases behavioral data is being presented in the form of an event, generated at the time of an action of an user. Some events are out-of-the-box available/generated in most browser applications, such as link clicks and page views. But for other common use cases a custom event implementation is necessary to make it work. That might require the involvement of developers.

So, to summarize it:

Transaction data refers to concrete, completed actions like purchases or sign-ups. It is generated server-side, highly reliable, and provides a clear record of user actions.

Event/behavioral data, on the other hand, captures user interactions, such as clicks or page views, before a transaction. Generated client-side, it may be less reliable due to tracking errors or network issues, but it offers deeper insights into user behavior.

While transaction data provides facts, behavioral data gives context, showing the “how” and “why” behind user actions.

Common Solutions for Capturing Event Data

Capturing and analyzing event data requires the right tools that help to store the less clearly defined data in a more structured manner. Here are some commonly used solutions:

• Snowplow: A platform for collecting and processing behavioral data in real-time.
• Hightouch: Syncs data from your data warehouse to business tools, making event data accessible where it’s needed.
• Segment: Simplifies the process of collecting and routing behavioral data to various analytics and marketing platforms.
• Google Analytics 4 (GA4): Focuses on event-based data collection, offering insights into user behavior across different devices and platforms.

Industries That Rely on Event Data

Several industries depend heavily on event data to understand user interactions and drive their business strategies:

• Digital Media and Entertainment: To track how users engage with content.
• Technology: For continuous innovation and improving user engagement.
• Professional Services: To understand client interactions and optimize service delivery.
• E-commerce: To refine the customer journey and enhance the shopping experience.
• Life Sciences and Energy: To improve operational efficiency and research outcomes.

Implementing Event Data in Your Ecosystem

To fully leverage event data, it must be integrated into a robust data platform. At i-spark, we can design and deliver these platforms for you, whether they are built  on Snowflake, Databricks, or another solution. However, before the event data can start flowing into tools like Snowplow or Hightouch, some development work might be required on your product(s). This might involve adding data layers or sending specific events to the right tools. While i-spark doesn’t handle product or app development, our sister company, Morphe.io, can take care of these technical needs, ensuring everything is set up correctly to capture and utilize your event data effectively. Despite the potential for discrepancies in event data, its ability to offer deep, actionable insights makes it an invaluable resource for understanding and improving user experiences.