Open rates are no longer enough: how to steer email marketing when your metrics get polluted

Your dashboard can look green while your funnel feels nothing

Many email marketers know the uncomfortable moment: the open rate looks fine, the click rate seems acceptable, but sales notices little difference. No extra conversations come in, forms stay quiet, and the pipeline barely moves. The dashboard says: engagement. The funnel says: very little real intent.

That does not mean email marketing is not working. It does mean that the way of measuring is under pressure. Opens and clicks are useful signals, but they are not conclusive proof that someone is ready for a commercial next step. An open can be technically influenced. A click can be curiosity, a mistake, an automated check, or a genuine action. Anyone who bases all their decisions on those two numbers is steering on too narrow a picture.

The solution is not to throw out opens and clicks. The solution is to place them lower in the hierarchy. See them as early, raw signals. Combine them with actions that are closer to intent: a reply, a completed form, a demo request, a purchase, recurring product usage, or a movement in the CRM.

Why open rates are less suitable as a primary metric

Open rates were long attractive because they gave quick feedback. After a send you could almost immediately see whether the subject line, sender, and timing appeared to be working. But inbox environments and privacy features have changed. As a result, a measured open may less clearly mean that a person has consciously read the email.

For reporting that is an important distinction. If your open rate rises, you cannot automatically conclude that your message is landing better. Perhaps your subject line is stronger. Perhaps your list is composed differently. Perhaps technical factors are at play. That is why open rate is still useful for trend observation, but weak as the sole decision metric.

A practical example: if you evaluate a reactivation campaign on opens alone, you can keep contacts active that show barely any real engagement. Conversely, you can write off silent buyers or long-cycle decision-makers too early because they show little visible inbox activity. Especially in B2B, real estate, automotive, or professional services, the orientation phase can be longer than your standard automation rule.

Clicks are useful, but not sacred either

Many teams therefore shift from opens to clicks. That is logical: a click seems closer to interest. Someone has not only seen the email but also activated a link. Yet click data is not always clean either. Some security systems and mail clients can check links. In addition, people sometimes click out of curiosity without serious intent.

The nuance matters. Clicks are not worthless. They are just not always the same as purchase intent. A click on a blog article means something different from a click on a quote request. A click on terms and conditions means something different from a click on a demo form. And a click without follow-up behaviour on the website is weaker than a click that leads to multiple page views, a form, or a reply.

That is why you need to classify clicks. Do not give every click the same value. Distinguish between informational links, commercial links, account links, preference centres, and actions with clear intent. An email dashboard that adds all clicks into one percentage gives speed, but little steering value.

Build a measurement model with three layers

A stronger measurement model starts with three layers. The first layer consists of inbox signals: deliverability, bounces, opens, clicks, unsubscribes, and spam complaints. This layer tells you whether your email arrives technically and whether there is early interaction. Important, but limited.

The second layer consists of engagement signals. Think of replies, preferences someone shares, repeated interaction with specific topics, recurring website visits, or participation in a webinar. These signals provide more context than a single open or click.

The third layer consists of funnel actions. These are actions closer to revenue or serious intent: demo requests, intake forms, quote requests, purchases, product activation, appointment confirmations, or CRM stage movements. Not every company has the same events, but every company can determine which actions carry more weight than a click.

For Funnel Adviseur the connection between layer two and three is particularly interesting. A newsletter can contribute well to trust, but if you never measure which topics later lead to forms or sales conversations, you keep optimising for cosmetic numbers.

Do not stop too quickly with silent contacts

Many email programmes use inactivity rules. For example: contacts that open or click nothing for 30, 60, or 90 days are contacted less frequently or removed from flows. That can be good for list quality, but it can also be too blunt. Not every silent contact is uninterested.

Some buyers orient slowly. Some decision-makers read via shared inboxes or have colleagues click. Some contacts see your brand but only take action later through a different channel. If you steer only on visible inbox activity, you can exclude valuable contacts too early.

Therefore distinguish between silent orientation, low intent, and genuine disengagement. A contact without opens but with a recent website visit is different from a contact with no activity at all for months. A contact at a target account is different from a random old lead. And an existing customer deserves different rules from a cold newsletter subscriber.

Where AI actually becomes useful for metrics

AI is often used in email marketing for copy. But with polluted metrics the better application is analysis and segmentation. AI can help find patterns in behaviour: which topics later lead to forms, which segments respond to educational content, which contacts appear silent but show activity elsewhere, and which flows cause unsubscribes without commercial return.

It is important that AI is not allowed to determine what is valuable without human measurement agreements. Give the system clear definitions: what is a marketing qualified action, what counts as sales intent, which events are supporting and which are decisive? Without those definitions you get nice clusters, but no better decisions.

AI can also help prepare hypotheses for tests. For example: contacts who read multiple educational articles but do not request a demo receive a gentle comparison or checklist. Contacts who visit pricing pages after an email receive a more direct follow-up instead. This way you use AI not as a trick for higher opens, but as a tool to better translate behaviour into follow-up.

A metric audit for your next send

Start with the question of which metrics actually drive decisions. Do you stop a flow because the open rate drops? Do you change subject lines because clicks disappoint? Do you pass leads to sales based on a single click? Note for each metric which action is linked to it. If a number does not influence a decision, it probably does not belong prominently on your dashboard.

Then ask which numbers may be polluted. Opens can be technically less clear. Clicks can represent different intentions. Website behaviour can be incomplete due to cookie settings. CRM data can lag if sales does not register consistently. No single metric is perfect; the art is to use combinations.

Next, review your automation rules. Are contacts being sunset too quickly? Do sales notifications fire on weak signals? Do customers receive the same nurture as prospects? Are unsubscribes and spam complaints weighted more heavily than opens? Better reporting is of little value if the automation still runs on old assumptions.

Close with a new dashboard principle: do not put vanity metrics at the top if they do not carry the most important decisions. Start with deliverability and list health, then show engagement per segment, and end with funnel actions. That turns email marketing from a reporting contest into a steerable part of the commercial funnel.