A data explorer is a report built on a single dataset. Think of it like a spreadsheet for marketing data: you can rearrange the table, filter it, sort it, and keep digging until the numbers make sense. A data explorer shows one period at a time and does not compare across periods. That is a dashboard job.
Dimensions and metrics
Section titled “Dimensions and metrics”The two building blocks in a data explorer are dimensions and metrics.
- Dimensions are the labels you group by. They answer “which slice of the data?”
- Metrics are the numbers you measure. They answer “how much?”
That is what makes aggregation work. The more dimensions you add, the more detailed the data becomes. A metric is still the same metric, but it gets split across the groups you choose.
For example, if you use one dimension, Country, and one metric, Sent:
| Country | Sent |
|---|---|
| NL | 1,200 |
| BE | 800 |
| DE | 1,000 |
If you add a second dimension, Mailing type, the same sends are split again:
| Country | Mailing type | Sent |
|---|---|---|
| NL | Newsletter | 700 |
| NL | Transactional | 500 |
| BE | Newsletter | 450 |
| BE | Transactional | 350 |
| DE | Newsletter | 650 |
| DE | Transactional | 350 |
The totals are still there, but now you can see how each country breaks down by mailing type.
Working the grid
Section titled “Working the grid”A data explorer is flexible. You can sort and filter the fields in the grid, then show or hide dimensions from the right-hand Columns tab. That lets you move from a broad view to a focused one without rebuilding the report.
The right side of the explorer has two tabs:
- Columns to choose which fields appear in the grid
- Filters to narrow the data
The filter tab can filter on any dimension or metric, regardless of whether that field is in the grid. That makes it easy to answer a new question without changing the structure of the table.
Choosing a date field
Section titled “Choosing a date field”A dataset can carry more than one date, for example when a campaign was sent versus when a conversion happened. In a data explorer, that usually means choosing between Send Date and Event Date.
- Send Date groups the metrics by the day the campaign was sent.
- Event Date groups the metrics by the day the interaction or conversion actually happened.
The choice changes how the same numbers are grouped. Set a date range as usual; the explorer shows that one period without comparison. For a fuller explanation, see Send date vs event date.
Baselines
Section titled “Baselines”Baselines are a way to compare your data against benchmark values you set yourself. They are useful when you want to keep a manual benchmark in view while you work through the table. Each baseline belongs to one metric and gives you a number to compare the rows against. In the grid, the metric header shows that baseline value, and the cells can be styled against it so you can see at a glance whether a row is above or below the mark.
You can add baselines only for metrics that support them. That keeps the feature focused on the numbers that make sense to benchmark.
Baselines can be applied in two ways:
- Current view: save the baselines only on the explorer you are working in.
- Template: save the same set of baselines as a reusable template and apply it to other views later.
That makes baselines a good fit when you want to compare a report against a manual benchmark, then reuse the same comparison again later.
Exporting
Section titled “Exporting”When you need the numbers outside SEINō, export the data explorer from the toolbar. Use Export to CSV for the raw data, or Export to Excel for a formatted workbook.
Saving and reusing
Section titled “Saving and reusing”Once you have a useful view, you can save it and come back to it later. That makes a data explorer a working surface, not just a one-time lookup: build the view, keep it, and reuse it whenever you need the same slice again.