Reading campaign results in context

An individual campaign report tells you what happened for one send. To understand why results are what they are — and whether things are improving, declining, or staying flat — you need to compare across campaigns. The campaign reporting dashboard gives you four tools to do that: category filters, segment filters, campaign type filters, and the time period selector. Used together, they let you cut your data in ways that reveal patterns a single report never shows.

Why individual reports are not enough

A spike in unsubscribes after a particular campaign is easy to notice. It is much harder to know whether that campaign caused it. Often it did not. A contact who unsubscribes after your Wednesday email may have been quietly irritated since Monday. The campaign that receives the blame is just the one that happened to arrive at the wrong moment.

The same logic applies to open rates, click rates, and bounces. A 2% open rate on one campaign is alarming if your usual rate is 35% and meaningless if you sent to a cold segment you had never mailed before. The number only makes sense relative to a baseline — and that baseline comes from comparing across multiple campaigns of the same type, to the same audience, over time.

Setting up categories for meaningful comparisons

Categories are labels you assign to campaigns yourself. Flexmail does not impose a fixed category structure — you create the categories that make sense for your sending programme and assign them when you create a campaign or retroactively afterwards.

The value of categories is entirely in how consistently you use them. Some common ways to categorise:

• Content type — newsletter, promotional email, product update, event invitation, re-engagement. This lets you compare how your audience responds to different kinds of content across all your sends, not just within one campaign.
• Language — EN, NL, FR. If you send to multilingual audiences, filtering by language tells you whether your results differ systematically between language groups, independent of content.
• Business unit or brand — useful if your account covers multiple products, services, or teams that send to distinct audiences.
• Campaign series — if you run a recurring monthly newsletter or a seasonal promotion, tagging all editions with the same category makes it easy to track how that series performs over time.

Using the filters to drill down

The filters appear at the top of the campaign reporting dashboard. Active filters are shown as chips and can be removed individually. You can combine multiple filters at once — the dashboard updates to show only campaigns that match all active filters. Within your filtered selection, you can sort the campaign table by any column — open rate, click rate, unsubscribe rate, send date — by clicking the column header. Sorting by open rate surfaces the strongest and weakest campaigns in your selection at a glance; sorting by date lets you read the trend chronologically.

Filter by category

Apply a category filter to compare performance across one type of campaign. For example, filtering to your newsletter category and extending the period to six months shows you whether your open rate is trending up, down, or flat across all newsletter editions — without promotional sends or other campaign types muddying the view.

Switching between categories reveals structural differences in how your audience responds to different content. If your promotional emails consistently underperform your newsletters on click rate, that is information about your offers or your promotional copy, not about email as a channel.

Filter by segment

Segment filters are particularly useful for unsubscribe and bounce analysis. The same campaign sent to your most engaged contacts and to a dormant segment will produce very different numbers. If you look at the overall unsubscribe rate without separating these groups, the dormant segment inflates the figure and makes a healthy programme look problematic.

Filtering by segment helps you answer questions like: do inactive contacts unsubscribe at a higher rate regardless of what you send them? Does your VIP segment click at three times the rate of your general list? Is your French-language segment engaging differently from your Dutch-language segment on the same campaign type?

Filter by campaign type

Campaign type distinguishes standard sends from AB tests and other campaign formats. Filtering to AB tests only lets you review all your test results in one place — useful for building up a picture of which variables (subject line length, send time, content structure) consistently move the needle across multiple tests, rather than drawing conclusions from a single result.

Combine filters for precise comparisons

The filters work together. A few examples of combinations that produce useful comparisons:

• Category: newsletter + Segment: FR contacts — shows how your French-language newsletter audience performs compared to a separate filter for NL contacts. If open rates diverge significantly and have been doing so for several months, that is a signal worth investigating.
• Category: promotional + Period: last 90 days — isolates your recent promotional sends so you can see whether a change in offer type, subject line approach, or sending frequency has had a measurable effect.
• Category: re-engagement + Segment: inactive — the most relevant combination for evaluating whether your re-engagement campaigns are actually moving the needle on your dormant contacts.

What to look for across filtered results

Once you have filtered to a meaningful slice of your data, the dashboard KPIs and campaign table reflect only those campaigns. Look for:

Trends, not individual data points

A single outlier campaign rarely tells you much. Three campaigns of the same type in a row with declining open rates tells you something is changing. Check the campaign table sorted by date and look for a direction, not just a level. A gradual climb in unsubscribes across your promotional category over three months is a signal to review frequency or offer relevance — it is not caused by any single campaign in that window.

Differences between categories

Compare the aggregate KPIs when you switch between category filters. If your newsletter category shows a 40% open rate and your promotional category shows 22%, the gap is normal — these are different types of content with different audience expectations. If both are declining together month over month, the issue is more likely list fatigue or a change in your sending cadence.

Segment-level outliers

Filtering by different segments and comparing the KPIs at the top of the dashboard quickly reveals which audiences are engaged and which are not. A segment with a consistently high unsubscribe rate across multiple campaign types is telling you it does not want to be on your list at the current frequency or with the current content — regardless of how individual campaigns perform with your other segments.

Combining filter data with unsubscribe reasons

Flexmail shows an exit survey on the unsubscribe page by default. Contacts who unsubscribe can indicate why they are leaving. You can review these responses under the Contacts tab.

Exit survey data becomes more useful when read alongside your filter results. If your campaign dashboard shows a rising unsubscribe rate on your promotional category over the past two months, and your exit survey shows "too many emails" as the top reason during the same period, those two signals together are much stronger evidence of a frequency problem than either one alone. If the exit survey shows "no longer relevant" as the dominant reason and the rising rate is concentrated in one segment, the issue is more likely targeting than frequency.

A practical starting point

If you have not used categories before, a useful first step is to go through your last three to six months of campaigns and assign categories consistently. Even a simple two-level system — content type and language — gives you enough structure to start comparing meaningfully.

From there, make a habit of checking the filtered view rather than the full dashboard when reviewing results. The full dashboard is useful for a quick health check. The filtered view is where you find out what is actually driving your numbers.

Next steps

• See "Campaign reports" for how to read an individual campaign report in detail.
• See "Building a content plan for email marketing" for guidance on structuring your sending programme in a way that makes category-based analysis useful.
• Read "The unsubscribe myth: why that one bad campaign is probably not to blame" on the Flexmail Knowledge Centre (flexmail.be/en/knowledge-center/) for a deeper look at interpreting unsubscribe data in context.