Email performance drops for one of three reasons:
1. your list has gone stale
2. your content has stopped being relevant to segments who used to engage with it
or
3. the mailbox providers have changed how they judge you and your sending programme hasn’t caught up.
But, it’s actually rarely just one of these – it’s usually the second and third happening together, with the first quietly feeding both.
What’s actually changed with your email performance?
Email Performance Metrics: Measurement Focus
Most marketers reach for open rate first when performance dips, and that’s the wrong instinct.
Open rate has become one of the least trustworthy numbers in the report
Apple’s Mail Privacy Protection now inflates opens by 15 to 20% across a huge share of any list, because Apple’s own servers pre-load images regardless of whether a human ever looks at the email.
Gmail has been quietly reducing image prefetching too, which pushes recorded opens down in a way that has nothing to do with subscriber behaviour.
So a brand can genuinely be sending better emails to a more engaged list and still watch its open rate fall, purely because the tracking pixel is firing less often.
Chase that number and you’ll “fix” a problem that was never really there, while missing the one that is.
Here’s the part most dashboards and most agencies won’t tell a client outright: blaming the algorithm is comforting because it’s external. It lets a marketing team say the platform changed rather than admit the list has been over-mailed, under-segmented, or drifting for two years while everyone watched a healthy-looking open rate and assumed all was well.
It may be an uncomfortable truth, but it’s the pattern I see whenever I see with clients regularly.
The technical shifts from Gmail, Yahoo and Microsoft in 2026 haven’t created a relevance problem: they’ve exposed one that was already there and had been hidden by inflated metrics.
AI Powered Inboxes Impact on Email Performance Metrics
The big story has been around Gmail rolling out Gemini-powered inbox features at the start of this year (limited availability) that go well beyond old-fashioned spam filtering.
The system now prioritises messages based on each subscriber’s own engagement history and quietly pushes low-value senders further down the inbox, sometimes without ever touching the spam folder.
But, none of this is really about the AI technology.
It’s mailbox providers building, at scale, the version of customer judgement that a good CRM strategy should already be applying manually – which customers still want to hear from this sender, and which ones stopped caring months ago.
The provider is now doing the segmentation the brand didn’t do. Every subscriber who hasn’t opened, clicked or engaged recently is a signal, and providers are finally acting on that signal on the customer’s behalf, whether the sender likes it or not.
Customer Behaviour & Attention
The first thing that’s changed for the customer is attention, not taste.
Inboxes are noisier than they’ve ever been, filled with AI-generated content from every brand that decided a large language model was a cheaper way to write more emails.
People aren’t opening less because they suddenly dislike email as a channel. They’re opening less because there’s more competing for the same ten seconds of attention, and most of what lands in that inbox has stopped earning it.
A customer who’s ignored your last six campaigns isn’t undecided. They’ve made a decision. The inbox provider is simply the first system to notice and respond to it before you did.
Secondly, and most importantly, customer behaviour has changed.
Every email adds to a customer’s cognitive load – the mental effort required to process, prioritise and decide what deserves their time.
When people are busy, overwhelmed or simply trying to get through their day, they instinctively ignore anything that doesn’t feel immediately relevant.
That’s why understanding where someone is in their customer journey is so important.
A first-time buyer doesn’t need the same message as a loyal customer.
Someone actively researching a product has different priorities to someone who hasn’t engaged with your brand for six months.
Relevance isn’t just about personalisation – it’s about recognising a customer’s current context, reducing the effort required to engage and delivering the next best message for that stage of their relationship with you.
The commercial cost of not seeing this in your email performance
This isn’t a vanity metrics story.
From a behaviour point of view, every irrelevant message chips away at attention, trust and future engagement, making it less likely that your next campaign, product launch or important customer update will be seen.
Customers begin to disengage, inbox providers recognise those changing behaviours and your deliverability can suffer.
A shrinking share of inbox placement and engagement compounds – fewer emails land where they can be seen, which lowers opens, clicks and in turn, lowers conversions, and the same send volume produces less revenue for the same cost.
Worse, provider algorithms treat continued poor engagement as a reason to suppress you further (beyond deliverability suppression to the junk folder or blocked), so the decline accelerates rather than levelling off, and recovering sender reputation once it’s damaged can take months of disciplined sending before results return to where they were.
Over time, acquisition costs rise because you’re forced to replace customers who might otherwise have stayed, repeat purchase rates decline, customer lifetime value falls and marketing efficiency deteriorates.
What starts as a messaging problem quickly becomes a revenue problem.
The brands that outperform aren’t necessarily communicating more – they’re making better decisions about who to communicate with, when to do it and why that interaction matters at that specific point in the customer relationship.
A pattern I often see in CRM audits: a brand sending to its full list (or broad segments therein only), watching revenue per email fall year on year, and assuming it’s just a creative or subject line problem.
Nothing about their emails had suddenly got worse. The instrument measuring them had simply stopped lying and customer behaviour and expectations have fundamentally changed.
What to do next to understand and increase your email performance
I’d recommend to stop treating open rate as a north star metric altogether.
I’d build a live, honest view of who’s actually engaged, who’s drifting, and who’s already gone dark, and I’d rebuild the sending programme around that reality rather than around list size.
A smaller list that opens, clicks and converts will always outperform a bigger one that mostly gets ignored, and in 2026 the mailbox providers will actively reward you for making that switch rather than penalise you for a shorter subscriber count.
1. Start to discover what’s really happening for your audience
Pull email performance metrics including last-open and last-click dates across the full list (and by segment) for the last 30, 90, 180 and 365 days of silence and review it – how many people have and haven’t opened or clicked in this time?
You want an honest number for how much of the list has genuinely gone quiet, not the number the open rate report is telling you.
2. Decide on your Next Best Customer Decision
What will you do with your list going forward?
Where are the segmentation and personalisation opportunities to increase relevance?
Set a real suppression or re-permission threshold for anyone silent past 180 days ( – for example; base this on your audience lifecycle and activity data), and commit to acting on it rather than leaving them in the send file “just in case.”
3. Grow & Evolve
Cut frequency to your most disengaged segment while increasing relevance and personalisation to your most engaged one, then measure revenue per email sent, as the scorecard for whether it worked.
Ensure you have a clear single message focus and modular template layout that drives engagement.
If this drop is showing up in your own numbers, the real question is whether your team would catch it early or explain it away as an algorithm change.
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