And suddenly you realize it's working,

But how good is it actually?

The first campaign is out. The funnel is in place. The automation is running.

A small moment of satisfaction.

But then comes the big question: "Was that good? Or could there be more?"

And as is so often the case in marketing, the honest answer is: it depends. On how you measure. What you measure. And how you work with what you see.

Because automation without analysis is like an autopilot without radar. It's driving, but nobody knows where it's going.

What is a “good” success anyway?

The classic open rate is no longer everything. It is worth looking deeper - and looking at different key figures depending on the objective:

🔹 Conversion rate: How many leads or interested parties actually take the next step?
🔹 Click-to-open rate: Not just whether something was opened - but whether it was of interest.
🔹 Dwell time & scroll depth: Especially for content streams - what is really read?
🔹 Lead quality: Which profiles move through the journey - and what happens in CRM afterwards?
🔹 Abandonment rates: When and where do users drop out?

These figures do not tell the definitive truth - but they do provide clues. And clues are the best basis for optimization.

Two paths, one insight

Sometimes all it takes is a small change, a different subject line, a different button text and suddenly the click rate increases by 15 %.

A/B tests in automated processes work in the same way as with classic campaigns.

The important thing is:

• Change only one variable (e.g. subject line, imagery, call-to-action).
•  Sufficiently large target group for valid results.
•  Formulate hypotheses in advance: what should change and why?

Tip: A/B tests can also be integrated into workflows: e.g. two alternative emails that are distributed randomly. The system supplies the data. You read the story from it.

Iterative optimization - what does that mean in concrete terms?

It sounds big - “iterative” - but at its core it is quite simple:

🔁 Pausing regularly. Readjust. Start again.

An example from everyday life:

Analysis: Only 12% of the second email in a welcome section is opened.
Hypothesis: The subject line seems too technical.
Test: A more human, questioning formulation is A/B tested.
Result: Open rate increases to 21 %.
Adjustment: New subject line is adopted. Other elements (content, length, CTA) are monitored for the next run.

This turns a fixed flow into a living process - one that grows with the audience.

Tools that help you keep an overview

Depending on the setup, many platforms offer their own reporting functions - but sometimes you need more depth or an overview. Some proven tools and options:

📊 HubSpot: Detailed funnel reports, lead tracking, lifecycle stages - ideal for holistic analysis.
📈 ActiveCampaign: Good visualization of paths and engagement, simple A/B testing.
📉 Google Looker Studio (formerly Data Studio): Especially helpful for customized dashboards with data from different sources.
📬 Mailchimp: Clearly structured reporting for email flows, with focus points on opening, clicks, reactions.
Matomo or Piwik Pro: GDPR-compliant alternatives to Google Analytics, often used for in-depth web tracking.

The choice depends on the degree of automation - and whether you want the system to analyze itself or whether you prefer external tools for strategic evaluation.

Would you like to find out more about this topic and which automation you urgently need? Then arrange a consultation directly at www.asioso.com.