Reading campaign results in context

An individual campaign report tells you what happened during a single send. To understand why the results are what they are, and whether they are improving, worsening, or remaining stable, you need to compare across multiple campaigns.

The campaign reports dashboard gives you four tools to do that: category filters, segment filters, campaign type filters, and the date range selector. Used together, they allow you to slice your data in ways that reveal patterns a single report never shows.

Do not directly compare an open rate from before and after activating the open tracking consent policy. After activation, the open rate only includes tracked recipients, and Flexmail no longer counts opens inferred from clicks for contacts without a pixel. Use the click rate to compare campaigns across this transition.

Why individual reports are not enough

A spike in unsubscribes after a particular campaign is easy to spot. It is much harder to know whether that campaign caused it. Often it did not. A contact who unsubscribes after your Wednesday email may already have been quietly annoyed since Monday. The campaign that gets the blame is simply the one that arrived at the wrong time.

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

Setting up categories for meaningful comparisons

Categories are labels that you assign to campaigns yourself. Flexmail does not impose a fixed category structure. You create the categories that suit your sending programme and assign them when creating a campaign or afterwards.

The value of categories depends entirely on how consistently you use them. Here are several common ways to categorise:

• Content type: newsletter, promotional email, product update, event invitation, re-engagement. This allows you to compare how your audience responds to different types of content across all your sends, not just within a single campaign.
• Language: NL, FR, EN. If you send to multilingual target groups, filtering by language shows you whether your results differ systematically between language groups, regardless of content.
• Business unit or brand: useful if your account covers multiple products, services, or teams that send to separate target groups.
• Campaign series: if you send 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 reports dashboard. Active filters are displayed as chips and can be removed individually. You can combine multiple filters at the same time.

The dashboard updates to show only campaigns that meet 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 directly reveals the strongest and weakest campaigns in your selection. Sorting by date lets you read the trend chronologically.

Filter by category

Apply a category filter to compare the performance of one type of campaign. For example, filtering by your newsletter category and extending the period to six months shows whether your open rate is rising, falling, or remaining stable across all newsletter editions, without promotional sends or other campaign types clouding the view.

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

Filter by segment

Segment filters are particularly useful for analysing unsubscribes and bounces. The same campaign sent to your most engaged contacts and to a dormant segment produces very different numbers. If you view 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 such as: do inactive contacts unsubscribe faster regardless of what you send them? Does your VIP segment click three times as often as your general list? Does your French-speaking segment respond differently from your Dutch-speaking segment to the same campaign type?

Filter by campaign type

Campaign type distinguishes between standard sends, AB tests, and other campaign formats. Filtering only by AB tests lets you view all test results in one place. This helps build a picture of which variables, such as subject line length, send time, or content structure, consistently make a difference across multiple tests, rather than drawing conclusions from a single result.

Combining filters for precise comparisons

The filters work together. Here are a few examples of combinations that produce useful comparisons:

• Category: newsletter + Segment: FR contacts. Shows how your French-speaking newsletter audience performs compared to a separate filter for NL contacts. If open rates diverge significantly and have done so for 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 send frequency had a measurable effect.
• Category: re-engagement + Segment: inactive. The most relevant combination to assess whether your re-engagement campaigns actually make a difference with dormant contacts.

What to look for across filtered results

Once you have filtered to a meaningful selection of your data, the KPIs and the campaign table on the dashboard reflect only those campaigns. Pay attention to:

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 indicate that something is changing. Review 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 the frequency or relevance of your offer. It is not caused by one specific campaign in that period.

Differences between categories

Compare the aggregated KPIs when you switch between category filters. If your newsletter category shows an open rate of 40% and your promotional category shows 22%, the difference is normal. These are different types of content with different audience expectations. If both decline at the same time month over month, the issue is more likely list fatigue or a change in your sending rhythm.

Segment-level outliers

Filtering by different segments and comparing the KPIs at the top of the dashboard quickly shows which target groups are engaged and which are not. A segment with a consistently high unsubscribe rate across multiple campaign types indicates that 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 displays an exit survey on the unsubscribe page by default. Contacts who unsubscribe can indicate why they are leaving. You can review these responses via the Contacts tab.

Exit survey data becomes more useful when read alongside your filter results. If your campaign dashboard shows a rising unsubscribe rate in your promotional category over the past two months, and your exit survey shows "too many emails" as the primary reason in the same period, those two signals together provide much stronger evidence of a frequency issue 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 yet, a useful first step is to review your campaigns from the past three to six months and assign categories consistently. Even a simple two-level system, content type and language, gives you enough structure to start comparing meaningfully.

After that, get into the habit of reviewing the filtered view rather than the full dashboard when evaluating results. The full dashboard is useful for a quick status check. The filtered view is where you discover what actually drives your numbers.

Next steps

• See "Campaign reports" for more information on reading an individual campaign report.
• 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" on the Flexmail Knowledge Centre for a deeper look at interpreting unsubscribe data in context.