I talk to a lot of marketing and CRM leaders in the Netherlands. Different companies, different sectors, different stages of maturity. But somewhere in almost every conversation, the same thing comes up.

It usually sounds something like: we’ve invested in the data, we’ve built the infrastructure, we’re running personalization, but honestly… we’re not seeing the impact we expected. Why is that?

What strikes me about this is that it’s never said with embarrassment. It comes from organizations that have done serious work. Teams that have spent years getting the foundations right. And they’re not wrong to feel frustrated, because from the outside, their setup looks solid.

So what’s going on?

The data isn’t the problem

Here’s what I’ve come to believe, after seeing this pattern repeat across a lot of different organizations: the data is rarely the problem.

Most companies at this stage have already solved the hard parts. They know what their customers are doing. They have the history, the behavior, the signals. The dashboards exist. The pipelines run. And if you ask the team what’s happening with a given customer segment, they can usually tell you something meaningful within minutes.

The gap shows up at the next step.

Once you have the insight, you need to do something with it. And that part — translating what you know into action, consistently, at the right moment — is where things break down.

What I typically see is this: insights get discussed. Use cases get identified. People agree something should happen. But then execution falls back into whatever the existing rhythm was. Campaigns go out according to a calendar. Channels operate independently of each other. And the action, when it does finally happen, is often too late to matter.

It’s not that organizations don’t care. It’s that there’s no clear, shared way to take a signal — a customer doing something meaningful right now — and turn it into a decision that happens every time, reliably, without needing someone to catch it and push it through manually. That missing piece is small on paper. In practice, it’s the difference between a data capability that produces reports and one that produces results.

The thing about timing

You only really feel the full weight of this problem in situations where timing is everything.

Think about a marketplace. Someone is browsing a category. They start watching an item. They place a low bid. Then another. The auction has six hours left and they’re checking back more frequently. Every one of those moments is a signal that something is building — interest turning into intent, intent building toward a decision.

If you can catch that and respond to it at the right moment, in the right way, you have a real chance of turning that interest into action. Wait too long and the moment’s gone. The auction closes, they move on, and whatever they were feeling has already faded. You can analyze it afterwards, but that’s all you can do with it at that point.

This is not a theoretical problem. We ran a campaign for Catawiki — an online auction platform — where the specific challenge was exactly this. Not to run better campaigns in the traditional sense, but to actually respond to what users were doing as they were doing it. To treat browsing patterns, early bids, and accelerating activity as live signals, not as data to be reviewed in next week’s meeting.

The results were striking. In a single week:

• 75,000 app installs — an all-time record for the platform
• +14% more bidders than forecast
• +8% more bidding activity during the campaign
• ROI of +6,000%, well above what was expected going in

Those numbers are worth pausing on. Not because they’re impossible, but because of what produced them. The organisation didn’t suddenly have better data than before. It had the same customers, the same behaviour, the same underlying intent. What changed was the ability to act on that behaviour while it was still relevant.

What this tells us about the real gap

Once you see this dynamic clearly, you start to notice it everywhere.

The organizations getting the best results from their customer data aren’t necessarily the ones with the most of it, or the most sophisticated models behind it. They’re the ones that have figured out how to act on what they know — consistently, at scale, without it depending on someone catching a signal and manually pushing it through.

That might sound obvious. But it’s actually quite rare. Because getting there requires more than good technology. It requires agreement, across teams and functions, on how decisions get made. What triggers what. Who’s responsible for what outcome. What the guardrails are. How you know if something is working or not.

Without that clarity, even great data ends up producing the same thing: interesting reports that inform the next planning meeting, rather than actions that happen in real time.

And this challenge looks different depending on where an organization is in its journey. For some, it starts with trust — data quality isn’t quite there yet, or different teams are working from different numbers, and nothing gets decided with real confidence as a result. For others, the foundation is solid, but execution is inconsistent. Decisions get made at the top but slow down or get reinterpreted by the time they actually reach a customer. And for more advanced organizations, it becomes a deeper question: is the operating model actually built around responding to customers, or just around sending to them?

The common thread, in every case, is not a lack of knowledge. It’s a gap between knowing and doing.

Something in the industry is quietly changing

It’s worth acknowledging that this problem has, for a long time, had a structural cause that wasn’t entirely the organization’s fault.

For years, the technical landscape made it genuinely difficult for signals and actions to live close together. Customer data sat in one system. Decisions happened in another. Execution was handled somewhere else again. Every handoff added time. And time — even a few hours — reduces relevance in ways that are hard to measure but very real.

That’s slowly starting to change. The systems organizations use to understand customer behavior are getting closer to the systems that act on it. The gap between recognizing a signal and responding to it is narrowing, technically at least.

What this means in practice is that the technical excuse is running out. If a system can detect that a customer is in the middle of making a decision, and can respond to that in real time, then the question becomes: why isn’t it?

Often the answer is that the organization hasn’t yet made the decisions it needs to make. Not about the technology itself, but about how it wants to operate. What actions should happen automatically. What still needs a human to approve. What the boundaries are and who sets them.

The technology has caught up. The hard work now is the organizational thinking.

The question worth asking yourself

If you’re leading marketing, CRM, or data at an organization, there’s one question I’d encourage you to sit with.

Not do we have enough data? Most of the time, the honest answer is yes.
Not do we have the right tools? Usually that’s not the core issue either.

The better question is this: when a customer does something that matters, what happens next and does it happen every time?

If the honest answer is it depends on who’s watching — or we’d bring it up in the next sprint — or it shows up in the weekly report — that’s the gap. Not a data gap. Not a technology gap. An execution gap.

And the encouraging thing is that it’s solvable. It doesn’t require starting over. It requires getting specific about how decisions get made, who owns them, and what consistent looks like in practice. That work is less glamorous than buying a new tool or hiring more analysts. But it’s the work that actually changes outcomes.

The organizations that compound their advantage over time aren’t doing something magical. They’re just acting on what they know, reliably, while it still counts.

So what is actually holding you back: technology, governance, or decision ownership?

This is an article by EMAS 2026 Gold Sponsor Dignify.

Author:

Just Greve

Country Director NL at Dignify

How do marketing teams use AI in emails? It’s mostly about subject lines, copy, and sometimes images. Well, it’s a great starting point that saves time, but it introduces the next problem: Your headlines, CTA copy, or imagery may be decent, but does AI see the full picture? What do email sections mean? What can it change, and what must always stay the same? What’s the structure?

We need to shift our perspective from mere content generation to an operating system. In this article, we’ll look at AI on different levels (copywriter, orchestrator, and strategist), highlight a missing layer between user intent and a production-ready email, and clarify where your team stands and what you need to move forward.

Level 1. AI as copywriter

This is where many companies begin: brainstorming, subject-line generation, copywriting, and adapting emails for different audiences, you name it. It’s a beginner level, but it brings value to teams. At this level, it is important for the organization to have streamlined processes and workflow to allow AI to make decisions within a framework and guardrails set by the organization.

HUMAI’s work with FMCG brands is a good example. Their creative CRM workflow uses AI across briefing, copy creation, asset creation, and email development. Of course, humans still control the output at each step. This approach saves time and costs: according to HUMAI, it can cut briefing and content creation time by up to 70 percent.

But there are some limitations:

• Lack of context: AI doesn’t know your brand library, design tokens, or available sections and generates them from scratch every time.
• Brand consistency depends on humans: Without governed rules, AI can drift from guidelines. The human reviewer is the only quality gate.
• The “email assembly” step: You still need to manually assemble generated content into a production-ready email.
• No learning loop: Campaign results don’t feed back into the system. They stay in an email marketer’s memory instead of a knowledge base.

So, yes, this first level is useful, but it’s still local optimization. It speeds up separate steps without changing the email production architecture.

The missing layer

Here’s the catch: The problem isn’t content generation. The issue is the absence of a semantic layer between user intent and production-ready email. Without it, AI is kind of blind. Yes, it will generate your copy and images, but it doesn’t know which email parts are unchangeable brand assets, which it can edit, or how to put together an email for different segments. This prevents scalability—and that’s where the next level begins.

Level 2. AI as orchestrator

The shift to level 2 needs a governed composition. Here, AI doesn’t improvise an email structure but works within a semantic system. At Stripo, this is the idea behind the Email Domain System (EDS), a semantic composition framework built on top of Stripo’s editor infrastructure. It treats an email as a composition of semantic elements, each with a role, content slots, and controlled variants.

The pipeline looks something like this:

Intent → Plan → Composition → Rendering

• Intent: Parse the campaign goal, audience, and references.
• Plan: Build a list of semantic sections with roles, variants, and rules (we’ll call it a brief).
• Composition: Choose components, apply decorators, and fill content into typed slots.
• Rendering: Transform the semantic structure into production-ready HTML.

Note that the brief is important here. It’s a semantic plan that defines which sections your email needs, their order, and the data they require.

Here’s what you might specify in your brief for a back-to-school campaign:

• Hero banner for attention and framing
• Product showcase for the main offer
• Benefits block for proof
• Countdown timer for FOMO
• Footer for compliance

Brand consistency by construction

At Level 1, people check the design, content, and layout. At Level 2, the architecture itself helps us stay consistent. Governed tokens handle colors, typography, and spacing. Atoms are the smallest semantic units (e.g., for CTAs, titles, and prices). Components combine these building blocks into structures. Variant swap lets the team change the layout without rebuilding the content.

This means A/B testing can become a configuration change instead of a redesign. With one brief, one set of content, and two variant overrides, you get two distinct emails. Tone of voice is also part of your system. It’s like a guardrail for generated copy that keeps the brand sounding like itself, regardless of which model or prompt you used. Human validation isn’t eliminated; it becomes more structured and less repetitive.

Interactive content as a new dimension

At this second level, you can also move from static emails to new content types. Widgets open the door to interactive content, a new type of email module in which content is configured through natural-language conversation with an AI assistant.

You just describe what you want (e.g., “a Wheel of Fortune with a seasonal promo for dormant subscribers”), AI turns it into a structured configuration, and a microservice renders it into email-safe HTML.

This gives you access to interactive elements like polls, questionnaires, quizzes, and scratch cards that are hard to create and scale manually, all without requiring an email or coding expertise. Rather than creating another static campaign under time pressure, you can use everything that email has to offer.

Level 3: AI as strategist

This is the level at which the system becomes autonomous and starts making decisions, still with human guidance. For organisations this implies clear roles, processes and governance models to ensure AI gets to reach it’s maximum potential with’human-in-the-loop’ roles clarified. For instance, a person defines a business goal (“increase retention by 20 percent”), and AI determines what to send, to whom, through which channel, with what content, and at what frequency. It makes these choices based on goals, feedback, and accumulated knowledge.

This isn’t science fiction, but it’s where many teams are not yet operating. It requires both agentic-ready organisation, as well as an Experiment Knowledge Base (EKB) that stores A/B test results with hypotheses and outcomes, audience segments, content rules, brief modifiers, campaign strategy presets, and a feedback loop. That loop closes the cycle:

Brief → Email → Send → Metrics → Knowledge → Next Brief

Here, the human role shifts from operator to architect and oversight. Instead of checking each output, the focus is on defining rules and guardrails that make results reliable. It’s clear that “AI helps the organization improve its email marketing over time” is a better outcome than “AI generates content for us.”

How does it work in practice?

Here’s how companies at different levels of AI use can approach the same campaign:

 

	Level 1 (Copywriter)	Level 2 (Orchestrator)	Level 3 (Strategist)
Planning	A marketer writes a brief; AI assists with idea generation.	AI generates a semantic brief with sections, roles, and variants from a campaign type.	The system picks a campaign type and brief based on the goal and previous results.
Content creation	AI generates copy from prompts.	AI fills governed slots within your tone of voice; variant swap enables A/B testing.	AI personalizes content per segment using insights from the EKB.
Assembling an email	Manual email building.	Automatic composition: brief → semantic model → HTML	Fully automated, with interactive widgets selected by engagement data.
QA	Human review.	Governed rules catch most issues; humans handle final reviews.	Automated validation against the brief contract and historical benchmarks.
Learning	Results stay in reports.	Results documented structurally.	Feedback loop auto-updates EKB and variant scoring.

Final thoughts: How to start shifting

It’s important to understand where your team is now. Even if you see yourself at Level 1, that’s perfectly fine. It’s not like you need a platform overhaul. Instead, take three steps:

1. Ensure ‘agentic readiness’ in the organization. Involve the wider organization in the agentic workflow and define and clarify roles and responsibilities across the process from ideation to launch and measurement. Define the roles for both humans and AI across the development and delivery process.
2. Define your brand semantically. Create a profile covering your brand, tone of voice, and design tokens to “feed” AI.
3. Move from templates to component systems. Build governed semantic components with controlled variants.
4. Gather experiment data. Even if you don’t have a feedback loop yet, document A/B testing results with hypotheses and results to build the knowledge base for Level 3.

When you treat AI as a junior copywriter, you can save time on individual tasks. But building the semantic infrastructure for AI to orchestrate will change how email marketing operates. The technology is ready. Is your architecture?

This is an article by EMAS 2026 Gold Sponsor HUMAI/Stripo.

Author:

Dmytro Kudrenko

Founder and CEO of Stripo

How email marketers automate smarter: 8 proven campaign types, 7 real-world examples, and practical steps to build your own strategy.

Email marketing automation is one of the most effective ways to reach your customers at the right moment and AI has made it faster and more accessible than ever. Where you once needed a developer or data analyst to set up a complex trigger flow, today most platforms let marketers do it themselves. Tooling has caught up with ambition.

Email remains the king of digital marketing channels and for good reason. With 4.5 billion users worldwide, projected to reach 4.89 billion by 2027, it dwarfs every other channel, including Facebook, WhatsApp, and Instagram.

What does modern automation look like in practice? Instead of a monthly newsletter to your full list, you send a follow-up only to people who clicked on a specific product. Instead of a manual re-engagement campaign once a quarter, a workflow fires automatically after 45 days of silence. Instead of guessing what to recommend, your platform pulls from purchase history and does it for you.

This guide walks through the strategies other marketers in your field are using, the real-world examples behind them, and the practical steps to build your own approach, without the jargon.

8 Email automation strategies used by marketers like you

These eight campaign types come up again and again when you talk to email marketers, CRM specialists, and digital marketers who are getting consistent results. They are not exotic. They work because they connect the right message to a specific moment in the customer journey.

1. Welcome Series When someone signs up, they are at peak interest. A three-part welcome series — sent over the first week — consistently outperforms a single welcome email. The first email confirms the signup and sets expectations. The second introduces your best-selling products or most useful content. The third responds to what they did (or did not do) with emails one and two.
2. Abandoned Cart More than 70% of online shopping carts are abandoned. A three-step reminder flow — at 1 hour, 24 hours, and 1 week after abandonment — recovers a meaningful share of that. Including the specific product, a review or two, and a time-limited incentive in the final email tends to perform best.
3. Browse Abandonment Someone looked at a product page but did not add to cart. An automated email 2 to 4 hours later, showing what they browsed alongside similar items, captures intent that would otherwise disappear.
4. Re-engagement Subscribers who have gone quiet for 30, 60, or 90 days need a different approach than active customers. A short sequence with a clear value proposition — and an explicit option to unsubscribe — tends to either win them back or clean your list. Both outcomes are good.
5. Post-Purchase Follow-up The relationship does not end at checkout. Order confirmation, shipping update, delivery notification, and a review request 7 to 14 days later form a simple sequence that builds trust and generates social proof you can use elsewhere.
6. Birthday and Anniversary Emails A personal message with an exclusive discount on a customer’s birthday generates nearly three times the open rate of a standard campaign. These emails are easy to set up once and run indefinitely.
7. VIP and Loyalty Programs Customers who hit a spending threshold get an invite to an exclusive tier: early access to sales, a dedicated contact, or members-only content. These workflows increase customer lifetime value by rewarding your best customers in ways that feel personal, not generic.
8. Cross-sell and Upsell Sequences Seven to thirty days after a purchase, an automated email suggesting a complementary product or an upgrade performs significantly better than a cold promotional email. The purchase history tells you what to recommend — you just need the workflow to send it.

7 Real-World examples of email marketing automation

The strategies above are only useful if you can picture how other teams have actually put them to work. Here are seven concrete examples.

Welcome series with a conditional branch. An online retailer sends a two-part welcome. If the subscriber clicks through to a product category in email one, email two surfaces bestsellers from that category. If they do not click, email two sends a broader bestseller list instead. One workflow, two paths, much better relevance.

Abandoned cart with a hard deadline. A home goods brand sends three cart reminders. The third email — sent one week after abandonment — includes a 10% discount valid for 48 hours. That final email drives the majority of recovered revenue in the sequence.

Browse abandonment with social proof. A fashion retailer triggers an email after someone views a product without adding to cart. The email shows the viewed item, how many people bought it this week, and two similar options. Conversion rates on this campaign are consistently higher than on standard promotional emails.

Re-engagement with a clear choice. A subscription brand sends a three-email win-back sequence after 60 days of inactivity. The final email is blunt: “Do you still want to hear from us?” with two buttons — yes or no. The people who click yes become significantly more engaged in the months that follow.

Post-purchase review request. A beauty brand waits 10 days after delivery, then sends a short email asking for a review. The email includes a photo of the purchased product and takes the customer directly to the review form. Review volume increased by over 60% after automating this step.

Birthday email with a personal touch. A specialty food retailer sends a birthday email with a 15% discount and a message from the founder. Open rates on this email are nearly three times their list average. The discount redemption rate is twice that of standard promotions.

Cross-sell based on purchase history. A sports equipment retailer automatically sends a cross-sell email 14 days after purchase, recommending accessories that fit what the customer bought. The email is generated dynamically based on product category. It contributes around 18% more revenue per recipient compared to generic promotional emails.

How to build your own automation strategy

Starting with automation does not require a full platform overhaul. Most teams that do it well start small, prove value, and expand. Here is how.

Start with one clear goal. Are you trying to recover abandoned carts? Reduce churn? Improve post-purchase loyalty? Pick one. It will determine which campaign you build first and what success looks like.

Map out the trigger and the response. Every automation starts with a specific event: a signup, a purchase, a product view, 30 days of silence. Define that trigger, then define the one action you want the customer to take as a result. Keep it simple.

Write for one person, not a segment. The best automated emails feel like they were written for the individual reading them. Use the customer’s name, reference what they actually bought or viewed, and make the next step obvious.

Set up the workflow, then test it. Run the trigger yourself before it goes live. Check the email on mobile. Confirm the link goes to the right page. A/B test subject lines once you have enough volume. Small improvements compound quickly.

Review and adjust every quarter. Automation is not set-and-forget. Open rates shift, customer behavior changes, and offers that worked last year may not work today. Schedule a quarterly review of your top three flows.

What to look for in an email automation tool

Not all platforms are built the same. When evaluating tools, focus on what actually determines whether your campaigns succeed.

• Behavioral triggers that work in real time. If the platform can only segment by static attributes, you will always be behind the customer.
• Native connection to your customer and product data. The more your email tool knows about purchase history, browsing behavior, and lifecycle stage, the more relevant your automation can be.
• Visual workflow builder. You should be able to build and adjust flows without involving a developer for every change.
• A/B testing built into the automation layer. Not just for subject lines — for content, timing, and offers too.
• Reporting that connects email activity to revenue. Open rates matter, but what you really want to know is what the campaign is worth.

When customer data, product catalog, and automation live in the same environment, AI can do more than suggest a subject line: it can predict churn risk, personalize send times down to the individual, and surface recommendations grounded in real behavior.

The real opportunity isn’t AI that writes emails for you — it’s AI that thinks alongside you, across every touchpoint in the customer journey. Loomi is built for exactly that.

This is an article by EMAS 2026 Gold Sponsor Bloomreach.

Curious how to put this into practice? Visit EMAS 2026 on June 11 at Circa Amsterdam. Be inspired by international speakers and discover how to take your email marketing to the next level. Tickets and programme: shop.emas.nu

Author:

Carl Bleich

Content Marketing Manager at Bloomreach

Let’s start with a simple premise: What if the future of email marketing isn’t about sending more emails, but about sending fewer, better ones? In a world of cluttered inboxes, the real challenge isn’t just getting your email opened, it’s getting it noticed at all. And here’s where the value of AI comes in, not as a mere tool, but as a transformative force that could make email marketing as personal as a handwritten letter, yet as efficient as a Swiss watch.

Let’s dig in.

The rise of AI in email marketing

AI technologies are transforming how brands interact with their customers and reshaping how emails are delivered and consumed. They help brands sort through the chaos of data to serve up customer insights on a silver platter.

But it’s not just about personalization; it’s about creating a bespoke experience for each customer. AI can analyze vast amounts of data to understand customer behavior, predict future actions, and tailor messages to individual preferences.

Trend one: AI-driven personalization

Picture this: Instead of sending out a generic email blast that screams “Dear Valued Customer”, AI allows you to craft messages that resonate on a deeply personal level. By analyzing a customer’s browsing history, purchase behavior, and interaction with previous emails, AI can help you send the right message at the right time. This level of personalization can significantly increase engagement rates and drive conversions, taking the already impressive ROI of email to new heights.

Moreover, AI-driven personalization allows for dynamic content adaptation. This means the content of an email can change in real time based on the recipient’s current context, such as location, time of day, or device used. It’s the digital equivalent of a chameleon, adapting to its surroundings to ensure relevance and timeliness—again increasing the likelihood of engagement.

Trend two: AI as an intermediary

As AI becomes more integrated into email platforms, it’s beginning to act as an intermediary between brands and consumers, converting your marketing jargon into something that actually makes sense to the average person. For instance, Google’s Gemini in Gmail uses AI to summarize emails and highlight important messages. This means that marketers must focus on crafting clear, concise, and compelling content that AI systems can easily interpret and prioritize if they want to break through.

This shift requires marketers to think beyond traditional metrics like open rates and clicks, and consider how AI algorithms evaluate and rank their content. After all, the key to impressing any recipient (human or AI) is to understand what they’re looking for and deliver it in abundance.

Emails should not only be technically sound but also contextually relevant, which involves using natural language processing (NLP) techniques to craft messages that are easily digestible by AI systems. Additionally, marketers should focus on creating content that is visually appealing and structured in a way that highlights key information, making it easier for AI to extract and present the most important elements to the user.

Designing emails for AI and human audiences

In this brave new world of technological advancement, emails must appeal to both human readers and AI algorithms—striking a balance between engaging storytelling and algorithmic precision. This split focus ensures brands not only capture the attention of audiences, but also align with the technical requirements of AI systems.

Here are some strategies to help achieve this balance:

1. Clarity and intent: Ensure that your emails clearly communicate their purpose. Use straightforward language and a strong call to action to make it easy for AI to understand the message’s intent. Avoid jargon and overly complex sentences that might confuse AI systems. Instead, focus on delivering a clear and concise message that leaves no room for misinterpretation.
2. Quality over quantity: Focus on sending fewer, more relevant emails. High-quality content that aligns with user behavior and expectations provides stronger signals for AI systems, improving message prioritization. This approach not only enhances deliverability but also fosters a more meaningful connection with your audience. By prioritizing quality over quantity, marketers can build trust and credibility, encouraging recipients to engage more deeply with their content.
3. Engagement signals: Encourage interactions that signal engagement to AI systems. This includes clicks, replies, and other actions that indicate the recipient finds value in the content. These interactions serve as feedback loops that inform AI algorithms about the effectiveness of your messaging strategy. By analyzing these signals, marketers can refine their approach and optimize future campaigns for better results.

The importance of email deliverability

Deliverability is increasingly tied to relevance, and the rise of AI is accelerating that shift. Mailbox providers are getting better at filtering out noise, which means brands can no longer rely on volume to drive results—in fact, sending a higher volume of emails often hurts results more than it helps.

Instead, AI enables marketers to align with evolving deliverability standards by identifying the right audience, optimizing send times, and tailoring content to individual engagement patterns. This leads to healthier sender reputations, stronger inbox placement, and fewer messages being flagged as spam. In this new landscape, deliverability isn’t just a technical hurdle—it’s a direct reflection of how well you understand and respect your audience.

At the same time, mailbox providers themselves are rapidly evolving, becoming far more sophisticated in how they evaluate incoming messages. Algorithms now go well beyond basic spam signals, incorporating engagement data, user behavior, and even inferred intent to decide whether an email reaches the inbox, promotions tab, or gets filtered out entirely.

In addition, features like priority inboxing and automated categorization mean that even “delivered” emails aren’t guaranteed visibility. This puts added pressure on marketers to consistently earn engagement, not just avoid complaints. As these systems continue to advance, the gap will widen between senders who rely on outdated batch-and-blast tactics and those who embrace data-driven, AI-powered strategies designed to work with mailbox provider logic rather than against it.

For more information on how to enhance email deliverability, check out our guide.

Final thoughts

The integration of AI into email marketing presents fresh opportunities for marketers to enhance their strategies and maintain effective communication with their audiences. However, the key lies in balancing the needs of AI systems with the expectations of human recipients, ensuring that every email delivers value and fosters engagement.

Ultimately, the future of email marketing is bright, but it requires a strategic approach that embraces technological advancements and prioritizes deliverability. By staying informed and agile, marketers can navigate this evolving landscape and continue to drive meaningful connections with their customers.

This is an article by EMAS 2026 Gold Sponsor Braze.

Curious how to put this into practice? Visit EMAS 2026 on June 11 at Circa Amsterdam. Be inspired by international speakers and discover how to take your email marketing to the next level. Tickets and programme: emas.nu

Author:

Kelly Hogan

Senior Director, Global Email Deliverability and Compliance at Braze

Your biggest competitor in the inbox is no longer the brand below your email. It is your customer’s AI assistant. Apple Intelligence summaries are part of the default Mail experience on supported iOS devices. Gmail’s Gemini pre-reads messages and offers its own one-line digest. Outlook Copilot does the same, and agent-first inboxes like Superhuman AI and Shortwave are following. The first reader of your campaign is increasingly a model, not a human. Marketing teams now write for two readers at once: the human who decides whether to open and click, and the AI assistant that decides what the human sees first. Search has been through a similar adaptation through GEO and AEO. Email is following the same path, towards a balance between human and model reading.

In this article we look at what is actually changing in the inbox, what the parallel to search means in practice for marketing teams in the Netherlands, and how to write an email that holds up under both human and machine reading. We close with five principles and a note on where rich, themed campaigns still belong.

The agent layer is here

The change has happened quickly. Apple Intelligence email summaries have been available since iOS 18.1 and now run automatically on supported devices. Gmail’s Gemini-powered summary cards appear above messages the algorithm decides are long or complex enough to compress, and Google is adding AI to more parts of Gmail, from prioritization to reply drafting. Outlook Copilot is mainstream in enterprise. Third-party clients like Superhuman AI, Shortwave, Notion Mail and Arc Mail go further: the agent ranks, archives, replies and escalates without the user reading the original at all.

The dentsu 2026 CMO Navigator captures the broader picture. Nine in ten CMOs report that emerging AI capabilities are already reshaping their strategy, and the report calls this the “Algorithmic Era” (source). Email is one of the next places where AI will become visible to consumers, and where the impact for marketers is most immediate.

What changes when the first reader is a model

When a model reads first, three things change. Each has direct implications for how marketing email is briefed and written.

Subject line and preheader become decisive (again). In the early days of email marketing, subject line and preheader were treated as the most important 100 characters in a campaign. Dynamic preview rendering and image-led emails reduced their relative weight. Today, AI summarizers use the subject and preheader as primary signals for what to highlight, and how to summarize. Witty subject lines can still drive opens with human readers, but if they obscure the key message they will hurt summarization accuracy.

Structure beats flowing text. Hierarchical, scannable content gives AI summarizers more reliable signals. According to Attentive’s analysis of Gmail’s Gemini behavior, the first 100 to 200 characters of body copy drive the summary (source). The implication: place the value at the top and structure the rest. Long-form storytelling will be compressed to its core claim. If the core claim is buried, the model identifies a different one.

The proposition has to be extractable. If your offer cannot be stated in one sentence, the model will produce its own version. That version may not match your intended takeaway. Preheaders that contradict subject lines, headlines that compete with each other, and relevant info sitting in the bottom half all fail this test. Write the one-line summary you want first, then check that the email actually leads with it.

Lessons from search, lessons from commerce

Search has already been through this transition. SEO became GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) once LLMs entered search. dentsu has commercialized GEO as a service and has published on the Algorithmic Era of search, with new KPIs like True AI Share of Voice and a GenAI Visibility Index (source).

Email is going through the same transition. AI summarizes rather than ranks, but the marketer’s job is the same as in search: write content that is readable for the human recipient and for the AI that prepares what the recipient sees. Some practitioners call this Email Engine Optimization or agent-readable email. The label matters less than the underlying change.

Commerce shows what this looks like when it goes further. When Merkle tested AI shopping agents, the agents recommended products without visiting any brand websites. The agent chose where to source from, and brand sites were only one option; the recommendation came from what the LLM already knew (source). The brand had little role in the decision. Email works differently: the AI is given one specific email and asked to summarize it, so the brand’s words are the only input. But the principle is the same. Content not written for both readers will be summarized in ways the brand cannot predict.

What good looks like: five principles

These five principles apply to almost any campaign and are part of our standard processes.

1. Lead with the substance, not the warm-up. The first 100 to 200 characters of body copy now decide how the model summarizes the email. “Hi, we hope you are well. We have something exciting to share…” trains the AI to produce a summary about pleasantries. Open with the value, the offer, the deadline, or the news. Approach the opening line as the input to a summary, not as the introduction to a letter.
2. Structure first, flowing stories second. Use clear sections, descriptive headings, short paragraphs and explicit markers (“Offer ends Friday at 23:59”, “What’s included:” followed by a bulleted list). AI systems parse structured content well and extract meaning from long paragraphs less reliably. The closer your email looks to a well-marked-up document, the more reliably the model surfaces what you intended.
3. Make the proposition extractable. If a model could only return one sentence about your campaign, what would you want it to say? If you cannot answer that question quickly, neither your readers nor their AI assistants will. Test extractability before sending: write the desired one-line summary first, then check that the email actually leads with it. Or use your LLM to do so.
4. Subject and preheader are content, not packaging. Brief them as carefully as the body. Avoid treating the subject as a teaser and the preheader as the punchline. AI summaries are more accurate when subject, preheader and opening line align. What feels repetitive to a human reader can help the model identify the correct takeaway.
5. Test the summary before you send. Before approving any campaign, run it through Apple Intelligence, Gmail’s summary card and Outlook Copilot. If the summary the model generates is not the takeaway you intended, the email is not ready to send, regardless of the visuals. It is a quick step that few email marketeers have built into their workflow. We have built it into ours: agent-readability checks are part of the standard QA we now apply across client campaigns.

We apply this thinking to our own work for clients, where AI-readability is no longer a side experiment but a core part of how we build campaigns. These principles describe what Merkle calls content optimized for human engagement and machine comprehension. The wording comes from a different channel (commerce), but the underlying logic applies to email as well.

Where rich, themed campaigns still belong

These principles do not call for every email to become a plain-text functional message. Brand storytelling, re-engagement, visual product discovery and loyalty narratives still benefit from rich design, dynamic content and creativity. These formats still work, provided the principles above are applied to them.

The constraint is that visual richness needs to be balanced with a textual and structural layer the AI can read. A visually heavy email that lacks a clear textual lead-in, descriptive alt text and a single extractable message will summarize badly. A heavily visual email with those elements in place can still summarize well, and still serve the human reader who scrolls.

Some email types are more exposed to AI summarization than others. Long, narrative-led brand emails are most affected, because the elements that AI removes are often the ones that carry the message. Transactional confirmations are largely unaffected. Promotional emails sit between the two: they perform worse when the offer is buried under creative, and better when the offer is clearly visible alongside the visuals. For each campaign type, decide whether the AI summary is decisive or supporting, and brief accordingly.

Two short cases

1: What the public market shows

Two patterns are visible in the public newsletter market.

The first is the daily-brief format used by senders like Morning Brew: a tight summary at the top, deep links below. This format works well for AI summarization, since the summary card and the email’s own intro tend to say the same thing.

The second is visible in the major Dutch news brands. NRC, de Volkskrant and FD have all moved towards plain, factual subject lines and preheaders that summarize the story directly rather than tease it. Compared to two years ago, the visual density of these emails is lower, and the textual hierarchy is sharper. These programs are adapting in advance of broader AI summarization in mainstream consumer mail clients.

2: A small experiment any team can run this week

Take ten of your most recent campaigns and do three things with each:

1. Forward each to a Gmail address running Gemini summaries and screenshot the summary card.
2. Open each on an iOS device with Apple Intelligence enabled and screenshot the summary.
3. Paste the HTML into Outlook Copilot and ask for a one-sentence summary.

Then compare the summaries to your campaign brief. Count how many of the ten match the intended takeaway. In a first test, most teams find that fewer than half of their emails produce the summary they intended. Seeing your own emails summarized by three different models makes the principles above concrete. The gap between intended and actual summary tells you whether a campaign is optimized for the agent layer in email.

What this means for our processes

For marketing teams, the implications are structural rather than only stylistic. Email has joined search and product content as a channel that must serve two readers at once: the human recipient and the agent that determines what the recipient sees. Treating this purely as a copywriting question would underestimate the change. This way of writing needs to be built into briefing templates, QA checklists and approval steps, not left to individual copywriters.

Key takeaways

• The first reader of a marketing email is increasingly a model, not a human.
• Subject line, preheader and the first 100 to 200 characters of body copy drive the summary.
• Structure, an extractable core message, and alignment between subject, preheader and opening line support both human and machine reading.
• Rich, themed campaigns still work, provided they include a clear textual layer the AI can parse.
• Add a step to test the AI summary before sending.

When the first reader is a model, write for both.

This is an article by EMAS 2026 Gold Sponsor Merkle.

Curious how to put this into practice? Visit EMAS 2026 on June 11 at Circa Amsterdam. Be inspired by international speakers and discover how to take your email marketing to the next level. Tickets and programme: shop.emas.nu

This article was written by Joost van Dam, Senior Strategist at Merkle Nederland.

Email remains one of marketing’s most trusted and effective channels. Yet new large-scale research reveals a striking gap between how strongly organisations believe in email and how rigorously they prove its business impact. The challenge for 2026 is no longer whether email works, but whether marketers can clearly demonstrate why it works.

Email ROI is no longer in question, but measurement still is

At Sinch Mailjet, this tension is clearly reflected in the findings from the Sinch Mailgun Email Impact Report, based on a global survey of more than 1,200 email senders and real sending data from over 400 billion emails.

The data shows that fewer than half of senders can accurately measure the ROI of their promotional emails, despite consistently strong returns. This creates a growing problem: mailbox providers increasingly reward genuine user engagement. In other words, you cannot optimise deliverability without understanding performance, and you cannot understand performance without measuring it.

The organisations that move from “a channel we believe in” to “a channel with proven business impact” will be better positioned to justify budgets, defend programmes and systematically improve results.

What the numbers show

Let’s start with the good news. Among senders who do measure email ROI, the results are compelling:

• More than 60% report at least €10 return for every €1 invested
• Around 13–14% achieve returns above 40:1
• Transactional email delivers similarly strong results

 “Making 10 euros for every 1 euro invested in a communication channel is genuinely exceptional, and the majority of senders measuring their returns are already there.” – Sinch Mailgun Email Impact Report

But those figures only cover senders who measure. Only 46% say they can measure the ROI of promotional email. For transactional email, that drops to 43%. The majority of professional email senders do not have a clear financial picture of what their email programme returns.

That is not a niche problem. When 23% of respondents cite difficulty proving email’s ROI as a top barrier to investment, and 20% struggle to get leadership buy-in, the root cause is almost always the same: the data is not there to make the case.

How to Tackle Email ROI: From belief to evidence

Closing the measurement gap does not require a complete operational overhaul. It starts with a few deliberate improvements in how email performance is tracked and reported.

Start with the right formula
The baseline calculation is straightforward. Take the total revenue attributed to email, subtract total email costs, divide by those costs, and multiply by 100. The result is your ROI percentage. A programme returning 200,000 euros against 10,000 euros in costs delivers a 1,900% ROI or roughly 19 euros per euro spent.

The challenge is making that calculation honest. Revenue attribution is rarely clean. Digital customer journeys involve multiple touchpoints, and email often contributes at more than one stage. If your organisation uses last-click attribution, email will be under-credited. If it uses first-click, the picture will be different again.

The most defensible model for email is time-decay attribution, which assigns greater weight to touchpoints closer to conversion. This reflects email’s tendency to appear throughout the customer journey without over-crediting any single interaction.

Account for what transactional email actually does
Transactional email: order confirmations, shipping updates, password resets and fraud alerts, does not drive revenue directly. But it protects it.

A failed password reset email blocks a user from completing a purchase. An undelivered payment reminder increases the risk of churn. A missing shipping update generates a support ticket. Each of these represents a measurable cost, and each avoided failure has a quantifiable value.

The numbers add faster than most teams expect. If just 1% of your 10,000 daily password reset emails fail to deliver, and that prevents 50 purchases at an average order value of €50, you are losing €2,500 every single day — from one message type, at a failure rate most organisations would consider acceptable. That is €75,000 a month quietly leaving through a door nobody is watching.

Organisations that want an honest view of transactional email ROI need to work across departments: support, finance, product. The data exists, but it rarely sits in one place. The effort is worth it. Among those who do measure transactional email ROI, 62% report returns above 10:1.

Move beyond open rates
Open and click rates remain useful engagement indicators, but they are not enough to demonstrate business impact.

Metrics such as:
• Revenue per email
• Revenue per campaign
• Total email-driven revenue
• Customer retention impact

provide a much stronger foundation for proving value internally.

At the same time, deliverability metrics such as bounce rates, inbox placement and spam complaints have become critical indicators of sender reputation and long-term performance.

Deliverability and engagement are now inseparable
Mailbox providers like Gmail, Yahoo and Outlook increasingly use engagement

There is a direct connection between measurement and deliverability that is easy to miss. Mailbox providers like Gmail, Yahoo, and Outlook use engagement signals to determine where emails land. If your programme is generating low opens, high bounces, or elevated spam complaints, your inbox placement will suffer, regardless of how technically sound your sending infrastructure is.

That means deliverability cannot be optimised in isolation. You need to understand which segments are engaging, which content drives action, and which lists are stale. All of that requires measurement. The programmes struggling most with deliverability are often those flying blind on performance.

Among the top performers in the benchmark data, the pattern is consistent. Air Freight and Logistics achieved a 99.25% delivery rate with a 0.01% bounce rate. Capital Markets recorded 98.13% delivery with low unsubscribe volumes. These are not just good infrastructure numbers. They reflect sender behavior: clean lists, relevant content, engaged audiences.

Where email investment is heading in 2026

The forward-looking signals in the report point to a channel that is not only stable but attracting renewed investment, particularly from organisations willing to treat email as a discipline rather than a default.

Budgets remain strong
31% of organisations plan to increase their email budgets in 2026, while only 7% expect reductions. The remaining 48% plan to maintain current investment. Taken together, that means roughly 79% of organizations are treating email as a stable or growing line item.

That is a meaningful signal. In a period when marketing budgets are under scrutiny, email is holding its ground and then some. The 78% of respondents who describe email as very or extremely important to organizational success are not speaking in abstractions. They are describing a channel that, when it fails, is felt immediately across the business.

AI is accelerating email performance
AI adoption in email marketing is rapidly increasing:
• 79% of senders already use or plan to use AI
• The most common applications include copy generation and subject line optimisation
• More advanced use cases include predictive analytics, segmentation and send-time optimisation

Importantly, organisations already using AI report significantly stronger programme performance compared to those that are not.

54% of senders who have implemented AI say their email programmes improved moderately or significantly in 2025, compared to 37% among those not yet using AI.

AI is not a shortcut to success. But it is becoming a major accelerator for teams with strong foundations in measurement and deliverability.

Authentication standards are becoming essential
The technical environment around email is tightening, and that is broadly good news for senders who have their house in order.

DMARC adoption has risen from 43% in 2023 to 61% today, driven partly by enforcement requirements from Google, Yahoo, and Microsoft. More than half of those using DMARC have now moved to quarantine or reject policies, meaning they are actively blocking emails that fail authentication rather than simply monitoring the situation.

BIMI adoption has jumped from under 10% in previous years to 22% of all senders. Seventy-six percent of BIMI users report tangible benefits after launching inbox logos, including higher open rates and improved brand recall. For European senders operating across multiple markets, these signals matter: inbox trust is increasingly earned through authentication, not assumed.

The accountability gap is the next frontier

The data from the Email Impact Report makes one thing clear: email’s performance is not the problem. The problem is the gap between what email delivers and what organisations can document, defend, and build on.

Senders who close that gap — by investing in measurement, attribution, deliverability monitoring, and authentication — are the ones who will be able to act on what AI, automation and securing real deliverability by high engagement make possible. They will also be the ones with the evidence to protect and grow their email programmes when budgets come under pressure.

The 2026 shift in email is not primarily a technology shift. It is an accountability shift. The channel has always performed. Now it needs to be able to prove it.

“Email does not need to prove it can deliver results — it already has. What it needs now is the measurement infrastructure to prove it every single time, to every stakeholder who controls the budget.”

This is an article by EMAS 2026 Gold Sponsor Sinch.

Curious how to put this into practice? Visit EMAS 2026 on June 11 at Circa Amsterdam. Be inspired by international speakers and discover how to take your email marketing to the next level. Tickets and program.

Author
David Jordine, Senior Technical Account Manager at Sinch Mailjet

Like many cultural venues and entertainment organisations, theatres, museums, and music halls often sit on a wealth of customer data – ticketing, website analytics, email engagement, surveys – but much of it lives in separate systems. This fragmentation makes it hard to see the full picture of each visitor, and harder still to deliver truly relevant recommendations.

To solve this, organisations in the cultural sector are increasingly implementing a Customer Data Platform (CDP). A CDP unifies data from all sources into continuously updated customer profiles, enabling marketing and audience teams to act on accurate, timely insights across channels.

This use case provides an example of how a cultural organisation in the culture & entertainment sector is applying a Customer Data Platform (CDP) to overcome these challenges and unlock new value from their data.

Why organisations are investing in a CDP

A CDP journey in the culture & entertainment sector  often built on two flagship goals:

1. Automated event recommendations (Next Event To Attend – NETA)
   A personalised recommendation engine that prioritises behaviour across channels over pure purchase history, factoring in genre and artist affinity.
2. Loyalty segmentation
   A flexible score model that allows organisations to identify risks (declining engagement) and opportunities (growth potential) within each loyalty band , and act accordingly.

Goal 1: NETA – behaviour over transactions

The NETA model assigns weight to a range of behavioural and transactional signals:

Notably:

• Email clicks are tracked as intent signals even when the booking happens externally (e.g., Tixly).
• Survey and feedback data feed into recommendations to reflect offline and qualitative insights.
• Artist affinity can outweigh genre, so a fan of a particular performer gets more relevant suggestions.

Goal 2: Loyalty segmentation

The score model uses audience-friendly terminology common in the cultural sector:

 

Each tier is split into Budget (low spend) and Premium (high spend). The model also accounts for non-purchase audiences like newsletter subscribers, website visitors, and social followers.

The impact organisations can expect (and how to measure it)

Success goes beyond selling tickets. Organisations typically measure impact by:

• Higher conversions on personalised event picks.
• Moving audiences into higher loyalty tiers.
• Cleaner marketing lists with fewer irrelevant recommendations.
• Tailored campaigns for special groups like Podiumpas holders.

While the project is still in development and results are yet to be measured, projections based on typical theatre patterns show the potential:

• Abandoned search: If 1,000 people search for shows each week and 200 leave without booking, recovering just 5% at €100 per booking could mean ~€52,000 extra revenue/year.
• Next Best Offer: Improving occupancy in a 600-seat hall by 2% could add ~€240,000 in annual ticket sales before costs.
• ROMI outlook: With an estimated year-one investment of €25,000, the model suggests the CDP could more than pay for itself in the first year, with returns increasing as more data points and use cases go live.

Implementation approach

A phased approach helps avoid “big bang” delays and delivers value early:

1. Use-case development – Define NETA and loyalty segmentation as first priorities.
2. Discovery workshop – Map data sources, segment definitions, KPIs, and constraints.
3. Internal preparation – Translate requirements into data sources, dependencies, and “good enough” thresholds.
4. Design & plan – Agree on timelines and responsibilities; connect priority sources and unify identities; activate initial campaigns.

The takeaway

This use case shows how even a single cultural organisation can turn scattered data into a powerful tool for personalisation and loyalty. By starting with clear, high-impact use cases and phasing the rollout, the groundwork is laid for both creative and commercial wins—proof that with the right approach, even “messy” data can become a valuable asset.

Author
Deployteq
Sponsor of EMAS2025

How to keep personalization and email marketing running while migrating to a new data platform? That was the central question for Harman International, the company behind Harman Kardon and JBL. Harman International recently transitioned from an advanced platform to a compact tool that combines a CDP and an ESP in one solution. In his presentation at EMAS 2025, Rudi Claassen, CRM and Email Marketing Manager at Harman international, will share insights on personalization: how to use data enrichment and apply it in campaigns. Navigating the complexity of managing multiple brands, markets and languages is something Harman International deals with daily. 

From cars to film theaters

Harman International is best known to the general public for its portable speakers and soundbars. But as explains, that’s just the tip of the iceberg: “We are huge in the automotive industry, for example.” The company supplies audio systems for brands like BMW, Volvo, and Toyota.

What many people don’t know is that Harman International is also a significant player in the professional audio market. The company supplies sound systems to large stadiums and concert halls around the globe. “And,” says Rudi, “more than half of the cinemas worldwide are equipped with JBL speakers.”.

CRM in an international context

A well-configured customer relationship management (CRM) system is essential for Harman International. Managing customer data across different markets and languages brings complex challenges. “The biggest challenge lies in scalability and personalization without losing the human touch or the right tone of voice,” says Rudi.

“The transition from an advanced platform to the simpler yet powerful platform Simon Data revealed the importance of content structures that are flexible enough to accommodate local nuances while still allowing central control”, Rudi explains. Harman International developed a JSON code together with Springbok Agency to work in an efficient way updating live content. This code proved to be a flexible alternative to previous rigid data extensions.

The use of customizable content blocks

Harman International builds emails using content blocks that can be customized by message or region. Each content block can be unique, requiring flexible templates based on complex HTML code. This led to an email architecture that allows dynamic adjustments by country, brand, and language. In other words: a fully automated and personalized system within an extremely complex international marketing landscape.

Rudi’s team works with clear frameworks that form the basis of their email templates. He explains: “We try to create a sense of familiarity for our email recipients so that they can clearly recognize: this is a new product launch, this is branding, this is promotion-driven or an event campaign.” By using recognizable templates, each campaign has a consistent format, while still leaving room for creativity and content relevance per brand and region.

Brand identity plays a key role in designing email campaigns across multiple brands and audiences. Whether it’s the energetic style of JBL or the elegant feel of Harman Kardon, every email must reinforce the brand. “For us, brand identity is the common thread in everything we do,” says Rudi. “With dynamic content blocks, that consistency is maintained.”

Combining technology with creativity: the power of simplicity

Scaling personalization across regions requires not only technical improvements, but also creative flexibility. From campaigns to events, everything needs to be visually and substantively appealing. “Creativity and technology must go hand in hand,” Rudi emphasizes.

The challenge lies in translating creative concepts into local executions. “One of the biggest eye-openers in that regard, is the power of simplicity,” says Rudi. Where the team previously used an extensive enterprise solution with countless cloud modules, the new, more compact yet powerful tool with the advantage of all-in-one functionalities forced them to return to the basics. “We had to rethink what truly adds value to the customer,” Rudi explains. Recognizability for the recipient became key.

Smart technology requires smart data

If he could redesign the global CRM tech stack from scratch, Rudi would advocate building an architecture from day one that can seamlessly switch between brands, markets, and languages. That’s the only way to achieve the agility needed in an international, multi-brand environment. “Scalability and modularity are not luxuries; they are essential,” Rudi stresses.

But technology alone is not enough. Power lies in the combination of infrastructure and data quality. “The data you put into the tech stack is equally important. Without reliable, well-structured data, campaigns remain generic no matter how smart your technology is.” Rudi therefore advocates investing in data quality, not just in availability but especially in usability for scaled personalization.

A look behind the scenes

During his presentation at EMAS 2025, Rudi will offer realistic and applicable insights into what a migration project truly entails. “What I hope to share is a behind-the-scenes look at a migration that was more than just a technical switch,” he says. “We’ll show how we overcame both technical and creative obstacles along the way.”

So don’t expect merely marketing buzzwords, but rather practical insights: how to enrich data, how to use it for personalized campaigns, and how the right architecture lays a scalable, flexible foundation. As Rudi summarizes: “a story that is immediately applicable: pragmatic, realistic, and built from real-world experience.”

Email marketing is no longer a matter of simply “sending and hoping for the best.” The rules are changing, the requirements are becoming stricter, and anyone who doesn’t have their technology or data in order will notice it immediately: lower open rates, higher bounce rates, or worse — the spam folder. But what causes this? Are spam words really a risk? Is DMARC mandatory?

Within the DDMA Email Committee, we decided to put it to the test. Not with theory or slides, but by tackling a practical case to answer the question: Which factors have the most impact on email deliverability?

The assignment: help a struggling email team

We gave ourselves the following scenario:

A company sends 400,000 emails every week. But… they are experiencing deliverability issues. More and more emails are ending up in spam or are not being delivered at all. The question for the committee members: which factors are most likely having a negative impact?

In front of us were 14 cards with possible causes, from technical settings to copywriting choices. Together, we ranked them from “most impact” to “least impact.”

This exercise provided sharp insights and valuable discussions, ultimately resulting in a practical top 14 that serves as a handy checklist.

Top of the list: the must-haves

We quickly agreed on a shared number one: SPF and DKIM. You must have these properly configured; otherwise, your emails simply won’t get delivered. Due to the high sending volume, DMARC came in second place, followed by spam traps.

1. SPF and DKIM
   Without these two, your emails are at a disadvantage. SPF (Sender Policy Framework) specifies which servers are allowed to send emails on behalf of your domain. DKIM (DomainKeys Identified Mail) adds a digital signature to your emails. Both are essential for email authentication. Missing one? You risk landing directly in spam.
2. DMARC
   DMARC (Domain-based Message Authentication, Reporting & Conformance) builds on SPF and DKIM. It tells the receiving server what to do if authentication fails: block, quarantine, or still deliver the message. Without DMARC, you’re vulnerable to spoofing and have less control over your sender reputation.
3. Spam traps
   Spam traps are not real people’s addresses but addresses set up specifically to catch spammers. If you send emails to these, it signals that your list is not clean. ISPs use this to penalize senders — and rightfully so.

Middle tier: behavior and list hygiene

1. High bounce rate
   Many bounces (emails that return because an address no longer exists) damage your reputation. If your emails often don’t get delivered, providers start to doubt your reliability.
2. Inconsistent sending patterns
   A few weeks of nothing, then suddenly 400,000 emails in one day? Inbox providers like predictability. Irregular patterns can signal that something is wrong.
3. Outdated send list
   Still emailing people who haven’t opened anything in years? That’s asking for trouble. Inactive contacts mean low engagement and hurt your reputation.
4. Low interaction, few clicks
   Engagement is an important factor in reputation. The more people open and click on your emails, the better. Low interaction? Then inbox providers assume your email is unwanted.

Want to learn more about these kinds of technical and strategic challenges? Attend the Email Marketing Automation Summit (EMAS) on June 27! A day full of insights on deliverability, marketing automation, AI, and more. Tickets: emas.nu

Bottom of the list: the refinements

1. Shared IP
   A shared IP address means your reputation can be affected by the behavior of other senders using the same IP. This doesn’t have to be a problem, but it does make you dependent on others.
2. URL shorteners
   Shortened links are sometimes used in phishing and spam. Some filters see this as suspicious. It’s better to use full, recognizable URLs.
3. Errors in HTML
   Poor or sloppy HTML code can cause your email to display incorrectly or even get blocked. Always test thoroughly before sending.
4. Missing plain text version
   Sending only an HTML version without a plain text alternative? Some systems see that as incomplete. Adding a simple text version is easy and prevents problems.
5. Low text-to-image ratio
   An email consisting mainly of images and little text is more likely to be flagged as spam. A healthy balance between visuals and text is important.
6. Spam words like “FREE”
   Long seen as a major red flag for spam filters, but now less important than previously thought. Still, filling your email with exaggerated words and exclamation marks definitely doesn’t help.

What this exercise taught us

The strength of this session lay not only in ranking the cards but especially in the discussion. By working as a team to analyze a concrete case, we quickly arrived at valuable insights:

• Many problems stem from a weak technical foundation. Start there.
• Behavior and list quality are the next logical steps. Send regularly, clean your list, and send only relevant content.
• Refinements like spam words and image ratios only make sense once the basics are in place.

In short: Deliverability is a chain. And like any chain, it’s only as strong as its weakest link.

Summary

Want to be sure your emails keep reaching the inbox? Start with SPF, DKIM, and DMARC. Then work on your sending rhythm and list quality. And always keep monitoring, because today’s rules could change tomorrow.

Curious how to put this into practice? Visit EMAS 2025 on June 27 at Circa Amsterdam. Be inspired by international speakers and discover how to take your email marketing to the next level. Tickets and program: shop.emas.nu

Marketing automation offers wonderful opportunities to make marketing, sales and service work better together. But in practice, it still often proves difficult to get systems to really land well in an organization.

Common bottlenecks?
Data scattered across different systems. Teams working on their own islands. Marketing and sales that evaluate leads differently. And platforms that are set up primarily from technology, not daily processes.

At Bikkelhart (part of Conclusion), we believe that marketing automation only works when the foundation is right. We’ll show you how we built that foundation together with organizations like Conclusion and Hiscox – and which steps are involved.

The approach: building a solid foundation

1. Discovery: first understand, then furnish

Before we configure anything, we map out the situation:

• What does the customer journey look like?
• What products and propositions are there?
• What is the existing system landscape?
• How do the marketing, sales and service teams now work together?

Based on this, we determine which processes are important to support first, which links are needed and where quick wins lie.

The first insights often arise during this phase. For example, that teams manage the same customer data in three different places. Or that the sales pipeline is not kept up to date anywhere.

2. Configuration: getting the basics right

Then we set up HubSpot to fit the organization:

• Basic settings per team
• Integrations with tools such as Google Analytics, LinkedIn Ads and CRM systems
• Migration and cleanup of existing data
• Training and documentation for users

In many cases, we use AI to accelerate data cleaning and duplicate checking. This not only makes the base cleaner, but also future-proof.

By paying immediate attention to data quality, you reduce the risk of score models becoming unreliable or dashboards giving false insights.

3. Adoption: making sure it works in practice

Good configuration is important, but only when people use the system on a daily basis does real value emerge. “Shit in, is shit out.” If people can’t work well with the system, data quality deteriorates and your analyses don’t add up.

Therefore, we provide intensive guidance for adoption. Consider:

• Practical explanation of new ways of working
• Relevant dashboards that align with targets and consultation structures
• Alignment between marketing and sales on lead definitions

We often see that a few small choices here make all the difference. For example, by discussing the pipeline from HubSpot by default in the sales meeting, or by making clear agreements about enriching lead data.

4. Accelerate: optimize based on data

Once the foundation is in place and teams are actively working in HubSpot, the focus shifts to growth.

Together we analyze where opportunities for improvement lie:

• Which phases in the funnel have the highest failure rates?
• What content leads to quality leads?
• Where is the delay in follow-up?

Based on insights from CRM data, visit reports and customer feedback, we continuously adjust processes, campaigns and sales materials. AI helps to recognize patterns faster, but choices are always made together with people who know the context.

Cases: proven in practice

Conclusion: 42 companies, one central structure

The 42 Conclusion companies each individually work from the passion for their own specialty, but with the drive to realize better solutions together. This unique approach offers clients the advantage of a complete service provider, combined with far-reaching expertise in specific sub-topics.

The challenge lies in the sales approach, and that’s where HubSpot comes in. The question was: how do you maintain the diversity but create general overview and even more collaboration/cross sell?

We set up one HubSpot environment in which:

• Sales processes are standardized where necessary
• Labels continue to work flexibly within their own accounts
• Insights about networks, pipelines, and customer relationships can be shared

The biggest win? Sales staff now all work from the same system, with uniform data and a shared understanding of opportunities and relationships.

Hiscox: marketing, sales and service connected in one platform

At Hiscox, another challenge was at play: lead follow-up, sales activities and service inquiries were handled in different systems.

By bringing marketing, sales and service together in HubSpot:

• Can transfer marketing leads directly with the right context
• Scores sales leads based on behavior and interactions
• Every customer request from service is tracked and used to improve campaigns

With short feedback loops between teams, the connection to the customer journey has become much stronger.

The success formula of Bikkelhart

What makes this approach different from others? It’s in the combination of people and technology. We believe in:

• AI as an accelerator: For data cleansing, lead scoring, behavioral analysis and real-time insights.
• Human in the loop: Experts who understand processes and make sure technology is really used.
• Central data as a source of truth: No fragmentation, but one truth for all.

We do not just build systems, but create real transformations.

This article was written by Ralph Kaan, Growth & Content Director at Bikkelhart. Bikkelhart is sponsor of the 2025 DDMA EMAS (Email Marketing Automation Summit).

Customer expectations have significantly evolved. Relying solely on email for customer communication is no longer enough. Modern consumers expect personalized experiences across multiple touchpoints, making it necessary for businesses to adopt a holistic, omnichannel approach to customer engagement. This shift creates both challenges and opportunities for marketers and customer experience professionals.

This article explores how businesses can move beyond email to create cohesive, engaging experiences that truly resonate with their audience. We’ll examine the key components of a successful omnichannel strategy and discuss how to implement effective technologies to support your efforts.

Whether you’re new to omnichannel marketing or looking to refine your existing strategies, this guide will equip you with practical insights to enhance your customer engagement in an increasingly complex digital ecosystem.

Understanding the omnichannel approach

To effectively craft holistic customer journeys, it’s important to grasp the concept of an omnichannel approach and how it differs from traditional multichannel strategies.

Omnichannel vs. multichannel: Key distinctions

Both omnichannel and multichannel approaches involve using multiple channels to reach customers, but they differ significantly in execution:

• Multichannel: This approach uses various channels to communicate with customers, but these channels often operate independently. Each channel has its own strategy and goals, with minimal integration between them.
• Omnichannel: An omnichannel strategy creates a seamless, integrated experience across all channels. It recognizes that customers may start their journey on one channel and continue it on another, ensuring consistency and personalization throughout.

The tangible benefits of an omnichannel strategy

Implementing an omnichannel approach offers concrete advantages for both businesses and customers:

1. Enhanced customer experience: A consistent experience across all touchpoints increases customer satisfaction and fosters loyalty.
2. Improved data insights: An integrated approach enables better data collection and analysis, providing deeper insights into customer behavior and preferences.
3. Higher conversion rates: A seamless journey across channels can boost conversion rates, as customers can easily resume their journey regardless of the channel they’re using.
4. Optimized resource allocation: Understanding which channels are most effective for different stages of the customer journey allows for more efficient resource allocation.
5. Competitive edge: In a crowded marketplace, a superior omnichannel experience can significantly differentiate your brand.

Key components of a successful omnichannel strategy

Creating an effective omnichannel strategy requires careful planning and integration of several key elements. Let’s explore these components in detail:

Consistent brand messaging

A cohesive brand experience across all channels is fundamental to a successful omnichannel strategy. This means:

• Maintaining a consistent tone of voice
• Using uniform visual elements (logos, color schemes, typography)
• Ensuring that your brand values are reflected in every interaction

Consistency builds trust and reinforces brand recognition, making it easier for customers to engage with your brand regardless of the channel they choose.

Integrated customer data management

To deliver a truly personalized omnichannel experience, you need a unified view of your customer data. This involves:

• Implementing a robust Customer Data Platform (CDP)
• Integrating data from all touchpoints (website, mobile apps, in-store interactions, etc.)
• Ensuring real-time data synchronization across channels

Personalized interactions

With integrated data at your fingertips, you can create highly personalized experiences:

• Tailoring content based on customer preferences and behavior
• Offering product recommendations based on purchase history
• Sending targeted communications at the right time and through the right channel

Seamless channel transitions

An essential aspect of omnichannel marketing is allowing customers to switch between channels effortlessly:

• Enabling customers to start a process on one channel and complete it on another
• Ensuring that customer service representatives have access to full interaction history
• Creating a consistent user interface across digital platforms

Beyond email: Exploring complementary channels

While email remains a powerful tool, an effective omnichannel strategy incorporates various other channels to create a comprehensive customer experience. Let’s examine some key channels:

Mobile marketing

Mobile has become an integral part of the customer journey:

• SMS: For time-sensitive notifications and promotions
• Mobile apps: Offering personalized experiences and easy access to products/services
• Push notifications: Engaging users with relevant, timely information

Social media

Social platforms offer unique opportunities for engagement:

• Customer service: Responding to queries and complaints in real-time
• Content marketing: Sharing valuable, brand-relevant content
• Social commerce: Enabling purchases directly through social platforms

Personalized web content

Your website should adapt to each visitor:

• Dynamic content: Showing relevant products or content based on user behavior
• Personalized recommendations: Suggesting items based on browsing and purchase history
• Custom landing pages: Creating targeted pages for specific customer segments or campaigns

Advertising

Integrated advertising can reinforce your omnichannel efforts:

• Retargeting: Showing ads based on previous interactions with your brand
• Cross-channel campaigns: Ensuring consistency in messaging across different ad platforms
• Personalized ad content: Tailoring ad creative and copy to specific customer segments

By effectively integrating these channels, you can create a cohesive customer journey that goes far beyond traditional email marketing.

Implementing an omnichannel strategy

Moving from theory to practice, let’s explore how to effectively implement an omnichannel strategy in your organization.

Mapping the customer journey

Understanding your customers’ paths is essential for creating a seamless omnichannel experience:

• Identify key touchpoints: List all possible interactions a customer might have with your brand
• Analyze customer behavior: Use data to understand how customers move between channels
• Spot pain points: Identify areas where customers might face difficulties or drop off
• Create journey maps: Visualize typical customer journeys to guide your strategy

Selecting the right technologies

The right tech stack is fundamental for executing an omnichannel strategy:

• Customer Data Platform (CDP): To unify and manage customer data across channels
• Marketing automation tools: For orchestrating personalized communications
• CRM systems: To manage customer relationships and track interactions
• Analytics tools: To measure performance and gain insights

Training your teams

An omnichannel approach often requires a shift in organizational mindset:

• Cross-department collaboration: Encourage communication between marketing, sales, and customer service teams
• Skill development: Train staff on new technologies and customer-centric approaches
• Change management: Help teams understand the importance of an integrated approach

Measuring and optimizing performance

Continuous improvement is key to a successful omnichannel strategy:

• Define KPIs: Establish clear metrics for measuring success across channels
• Regular reporting: Set up dashboards to monitor performance in real-time
• A/B testing: Continuously test and refine your approach
• Customer feedback: Regularly collect and act on customer input

The future of omnichannel customer engagement

As technology evolves, so do the possibilities for omnichannel engagement. Here are some trends to watch:

Artificial Intelligence and Machine Learning

AI and ML are set to play a larger role in omnichannel strategies:

• Predictive analytics: Anticipating customer needs and behaviors
• Chatbots and virtual assistants: Providing 24/7 personalized support
• Dynamic content optimization: Automatically adjusting content based on real-time data

Voice and conversational interfaces

As voice-activated devices become more prevalent, they’re likely to become an important channel:

• Voice search optimization: Adapting content for voice queries
• Voice-activated purchasing: Enabling transactions through voice commands
• Conversational marketing: Creating more natural, dialogue-based interactions

Augmented and Virtual Reality

AR and VR technologies offer new ways to engage customers:

• Virtual try-ons: Allowing customers to test products virtually
• Immersive brand experiences: Creating engaging, memorable interactions
• Enhanced customer support: Using AR for visual, step-by-step guidance

Key Takeaways

Crafting holistic customer journeys across multiple channels is no longer a luxury—it’s a necessity in today’s competitive landscape. By moving beyond email and embracing a true omnichannel approach, businesses can create more engaging, personalized, and effective customer experiences.

Remember, the key to success lies in:

• Maintaining consistent brand messaging across all channels
• Integrating customer data for a unified view
• Personalizing interactions at every touchpoint
• Ensuring seamless transitions between channels
• Continuously measuring and optimizing your approach

As you embark on or refine your omnichannel journey, keep the customer at the center of your strategy. By doing so, you’ll not only meet but exceed their expectations, fostering loyalty and driving business growth in an increasingly complex digital world.

This article was written by Isabelle Henry, Head of Marketing at Actito. Actito is sponsor of the 2025 DDMA EMAS (Email Marketing Automation Summit).