This article contains frequently asked questions that arise when working with Timestamps in TapClicks. You can quickly navigate to a particular FAQ by using the table of contents below.
How does TapClicks handle timestamps from different data sources?
Why does TapClicks store timestamps in relative format?
Can you give an example of how this works?
How does this affect data visualizations?
How does TapClicks handle timestamps from different data sources?
When working with data from many APIs, timestamp formatting can vary. Most APIs provide an event_time field in ISO 8601 format, which includes the full date, time, and time zone offset. For example:
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2025-01-01T14:20:05-05:00
This indicates 2:20:05 PM on January 1st, 2025 in Eastern Time (UTC−05:00).
At TapClicks, we store these timestamps in a relative format, preserving both the local time and the time zone from the original data source.
Why does TapClicks store timestamps in relative format?
This approach helps ensure:
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Consistency Across Campaigns and Connectors
- Many users manage campaigns across different regions. By storing timestamps exactly as they come in, complete with time zone, we keep data aligned with its original source. This avoids unintentional shifts in reported time.
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Clarity for End Users
- Relative timestamps show data in the local time where the event occurred. For example, “10 PM” means “10 PM” in that campaign’s region. No need for time zone conversions or mental math when analyzing cross-regional performance.
Can you give an example of how this works?
Absolutely. Let’s say two events occur on January 1st:
- Event A at 10 PM Eastern Time
- Event B at 10 PM Pacific Time
Both will appear in TapClicks as “10 PM on January 1st,” correctly aligned with their local context.
If the timestamps were converted to a fixed time zone like UTC, they might appear as “3 AM” or “1 AM” on the next day, causing confusion when comparing or aggregating data.
How does this affect data visualizations?
Widgets that group by hour use the preserved local time. This provides more accurate and intuitive insights. For instance:
- Activity at midnight in New York shows as 12 AM
- Activity at midnight in Los Angeles shows as 12 AM
Even though the events happened hours apart, the reporting reflects the local reality, making your hourly trends easier to interpret and act on.