Marketing Automation in B2B: What Is Realistic and What Is Not

Marketing automation refers to the automated nurturing and engagement of contacts across email, CRM (Customer Relationship Management system, the software for managing customer relationships and contacts) and other channels. Marketing leadership often expects it to deliver, above all, more and better-qualified leads, and quickly. A recent survey of B2B marketing professionals shows that the software rarely meets this expectation on its own.

In short: marketing automation mostly keeps its promise, but rarely in full. In a survey of 207 B2B marketing professionals by Act-On Software and Ascend2 (as of March 2025), 96 percent said their marketing automation was at least partially successful. Only 34 percent described their results as very successful or best-in-class. Between these two figures sits the real expectation gap: almost everyone sees a benefit, but only a third sees the benefit they had pictured at the time of purchase. 51 percent named the lack of an overarching automation strategy as their biggest challenge. 44 percent named the quality of their own data. This matches the experience from digital commerce projects: an automation platform can only implement what already exists in terms of goals, segments and data foundation. Without that, the best software mainly produces faster mistakes.

What a B2B Marketing Automation Survey Reveals

The survey by Act-On Software and Ascend2 is one of the few recent studies that asks B2B marketing professionals directly about their actual experience with marketing automation, not just their general agreement with the principle. 207 professionals took part. The result is more nuanced than a simple success rate: 96 percent say their efforts were at least partially successful. That is a high number, but one that includes almost any outcome better than doing nothing. Only a look at the top category shows how much of that actually holds up to what marketing leadership had originally set out to achieve: just 34 percent.

Even so, 75 percent of respondents plan to increase their marketing automation budget in 2025. This shows that companies distinguish between the general benefit of automation and the quality they actually achieve, and keep investing despite the gap. Whether marketing automation is worthwhile in principle is therefore already answered. The real question for marketing leadership is how to move their own results from the broad majority into the top category.

Why Lead Scoring Takes Time

Part of the expectation gap arises at a single point: lead scoring, the automated assessment of contacts according to their assumed readiness to buy. A score is only as reliable as the closed cases it has learned from. With long B2B sales cycles and a manageable number of customers, this reference pool builds up more slowly than for a shop with new orders every day. We see this regularly in Brevo projects. A newly set-up score only produces a usable picture once the first customer cases have actually closed, not already at rollout. That does not make lead scoring useless. It makes the score a provisional estimate that should be checked regularly against actual closed deals, rather than sitting unchanged on a dashboard.

The same question of timing applies to measuring success overall. The survey names lead qualification and relieving the sales team as the most common uses, not primarily a short-term conversion rate. Anyone who measures an automation campaign by its close rate after just a few weeks is measuring at a point when, in many B2B cycles, the purchase decision has not yet been made at all.

From Action to Point Value: How a Lead-Scoring Model Takes Shape

The survey makes the expectation gap around lead scoring visible. What happens technically behind it remains open there. At its core, a score is nothing more than a numeric field on the contact that rises automatically for certain actions. Brevo, one of the platforms we work with, maps this through its own point attribute on the contact. Within an automation, this attribute counts up for defined actions, such as opening an email, clicking a link or visiting a particular page.

How many points a given action is worth is decided by the company itself, not the software. That decision belongs in the preparation phase, not in the first week after rollout, and it cannot be delegated to IT. Sales and marketing have to agree on which actions actually point to genuine buying interest and which only show curiosity. An opened newsletter email is a weak signal, a repeated visit to the pricing page a strong one. Only once a contact reaches a set point total does the automation move it into a separate list of qualified contacts or automatically assign it to a salesperson for follow-up. This last step decides whether lead scoring actually relieves the sales team or just produces an extra number on a dashboard. Without a fixed rule for what happens once the threshold is reached, the score has no consequence.

A score that measures behaviour alone has a weakness. It does not distinguish between a good-fit contact and a poor-fit one. That is why workable models combine two layers. The behaviour layer counts what a contact does. The fit layer assesses how well that contact matches the target customer profile, for example by industry, company size or the person's role in the purchasing process. A visit to the pricing page by an IT lead within the target segment is a different case from the same click by a contact outside the target group. Without the fit layer, the automation counts both the same way, and the score ends up measuring only interest, not suitability.

For the sales team, what ultimately counts is less the score itself than the list it produces. A score that never becomes visible, for instance because it only exists in the marketing tool and never appears in the sales team's view, changes nothing in day-to-day business. Handing contacts over to a visible, regularly reviewed list is far more than a technical detail. It decides whether the model has any effect at all.

What to Clarify Before Your First Automation Workflow

The 44 percent who name data quality as their biggest challenge describe a pattern that shows up regularly in digital commerce projects. Even a well-configured automation rule can only work with the data that is actually correctly maintained in the CRM, the ERP (Enterprise Resource Planning, the merchandise management system) or the shop. Missing consent, outdated contact fields or inconsistent segmentation carry over unchanged into every automation rule built on top of them.

Before rollout, it is worth taking a short but honest stock of the situation. Which segments are actually maintained, and which were only ever set up and never touched again? Which department is responsible for which data field when the CRM and the shop show different information? And which step in the planned workflow reflects a customer journey that can already be observed today, rather than a wish for one? For marketing automation projects, we use Brevo among other platforms, and we start at exactly this point before the first workflow is built.

Data Quality: Three Questions Before the First Click

Data quality cannot be answered with a simple yes or no. It breaks down into a series of individually checkable questions. Three of them decide whether an automation workflow can work sensibly with the data on hand at all.

• Completeness. What proportion of contacts carry the fields the planned workflow is meant to use, for instance industry, role or consent? A workflow that branches by industry, where a large share of contacts have no industry field at all, branches there incorrectly or not at all.
• Currency. When was a field last confirmed, not just created? A role recorded years ago is no longer a reliable basis once contact persons change within B2B organisations.
• Uniqueness. Does the same contact exist more than once, for instance because they signed up separately through a form and through an event? A duplicated contact collects points twice, or receives two conflicting welcome sequences at the same time.

Segmentation: Where to Draw the First Line

Segmentation means dividing contacts into groups that should be addressed differently, not setting up as many lists as possible. In practice, a single, clearly justified dividing line is enough for the first workflow. That could be by role, because procurement responds to different arguments than IT leadership. It could be by industry, because food and consumer goods manufacturers have different questions than industrial companies with complex product catalogues. Or it could be by funnel stage, meaning how far along a contact already is in the buying process. Adding more than one dividing line at once is only worthwhile once the first one has demonstrably worked. Otherwise the workflow branches faster than the effect of individual branches can still be evaluated.

Ownership: Who Maintains the Model After It Goes Live

A lead-scoring model and a welcome sequence are not finished once they go live. They need a clearly assigned owner. In practice, a single named person or a small team works well, responsible for three things: adjusting point values once an action turns out to be over- or under-weighted, the quality of the contact data in the fields the workflow relies on, and the decision on when a second workflow is added. Without this ownership, the automation becomes an orphan after rollout. It keeps running, but nobody adapts it to a changed reality anymore, for example a new product or a new target group.

The agreement on point values described above also belongs in this preparation phase, not in the week after rollout. If it is added later, the first workflow is already running on a model that nobody in the company has actually signed off on.

A Realistic First Use Case: The Welcome Sequence

For the first workflow, the shortest path from idea to evaluation matters more than the most sophisticated version. For most B2B companies, that is a welcome sequence: a short series of emails that starts automatically as soon as a contact is newly added to a list, for instance through a completed form or a lead magnet. Brevo, one of the platforms we work with, offers a prebuilt automation for exactly this point. A contact is added to one or more lists, and the automation starts, though only for contacts added after activation, not retroactively for the existing database.

Choosing email as the channel for this first workflow is no accident. Among respondents to the Act-On/Ascend2 study, email marketing is the most widely used automation function overall, at 57 percent, well ahead of other channels. A company working with marketing automation for the first time is best off testing its own data quality and its own scoring model on a channel its organisation already masters, before a second channel such as SMS or a push notification is added.

Technically, a workflow like this is built from a few recurring components: a trigger that starts the automation, wait periods between individual steps, and conditions that split the subsequent path according to behaviour. The following three weeks show how these pieces combine into a complete first sequence.

Week 1: Setting Expectations, Not Selling

The first email goes out immediately after a contact joins the list and has exactly one job: explaining what comes next. How often will the company be in touch over the coming weeks, what will the content cover, and how can the contact unsubscribe from the sequence at any time? A sales offer does not belong here yet. The contact has only just shown interest, not stated an intention to buy. This email generates the first, deliberately low-weighted scoring points. Opens and clicks show whether the address is actually in active use at all.

Week 2: Delivering Value, Watching Behaviour

The second email points to existing content that addresses the target group's problem concretely, for instance an article or a guide, rather than another announcement about the company itself. The click on this content is the most meaningful signal in the whole sequence, because it shows genuine interest in the content, not just habitual behaviour. This week is worth the first look at the fit layer from the previous section. Do the contact's role and industry actually match the target customer profile, or is the interest there but outside the actual target group?

Week 3: Splitting the Path, Not Treating Everyone the Same

By the third week at the latest, the sequence splits into two paths, depending on whether a contact engaged with the previous steps or not. Those who engaged receive a more concrete email referencing a specific service or a consultation, paired with a direct question about their own plans. Those who did not engage stay for now in a longer, less frequent cadence at a reduced rate. For a contact who leaves two emails unopened, more time or a different approach makes more sense than a third email on the same rhythm. If a contact reaches the previously set point threshold at this step, the automation automatically moves it into the list of qualified contacts that the sales team reviews regularly.

Three weeks here is a guideline, not a law of nature. What matters more than the exact number of steps is that each step has its own, predetermined job, and that the sequence eventually leads to a decision: handover to sales, or a step-down into slower, ongoing nurturing. A welcome sequence that simply ends after the third email, without any condition ever being checked, is an automation without a goal.

What You Can Realistically Expect

What is realistic is that marketing automation secures consistency across a long decision path: the same message, at the right time, without needing manual upkeep as the number of contacts grows. This matches the uses named in the survey: lead qualification, relieving the sales team, consistent communication across the entire customer journey. What is not realistic is assuming the software will deliver more and better leads by itself, without first clarifying what a qualified lead actually means at this particular company.

Marketing leadership that defines, before rollout, what is measured, how the score is derived, and who is responsible for which data field, starts closer to the 34 percent with the best results than to the rest of the satisfied majority. The software alone does not decide that.

How to Measure Success

The Act-On/Ascend2 figures describe how companies assess their marketing automation overall, not how an individual marketing lead can read that off concretely within their own organisation. That requires its own, more granular metrics, drawn directly from the welcome sequence and scoring model described above. A fixed, short rhythm for reviewing these figures, for instance weekly during the first two months, stops a problem from going unnoticed for weeks.

• Response time after handover. The time between reaching the scoring threshold and the first actual contact from the sales team. An automation that qualifies a contact cleanly but then leaves it sitting unworked in a list for days loses the head start it created.
• Score hit rate. The share of automatically qualified contacts that the sales team subsequently confirms as qualified themselves. This figure speaks directly to the maturation period of the score described above. While too few closed cases exist, a low hit rate is more a sign of a model that is still young than one that is wrong.
• Engagement trend across the sequence. The open and click rate of the first email compared with the third. A marked drop shows where the sequence is too long, too promotional, or designed for too many different contacts at once.
• Completeness of required fields. The share of contacts for whom the fields the workflow needs are actually filled in. This figure rarely changes from one week to the next, but still needs regular checking, because it quietly deteriorates as soon as new entry points, such as a new form or a new event, arrive without the same data capture.
• Close rate across the full path. Only meaningful over a longer period, but the metric that counts in the end. How many of the contacts who went through the welcome sequence have actually become customers after several months, compared with contacts outside the automation?

None of these metrics replaces the others. A good response time combined with a low score hit rate just means the sales team is reacting quickly to the wrong contacts. Anyone tracking only a single figure usually ends up watching whichever one currently looks good.

Our Process with Brevo

The sections above describe what should be clarified before an automation workflow and what a first workflow can look like. Companies that do not want to build this alone can also have the same path supported externally. For marketing automation projects, we use Brevo among other platforms, and in practice we follow a recurring process regardless of the specific company.

1. Stocktaking. Before any workflow configuration, we check which fields in the CRM or the connected shop are actually maintained, how many contacts are duplicated, and which consents are already documented. This phase corresponds exactly to the three data-quality questions described in the previous section.
2. Defining the scoring model together with sales. We moderate the conversation between marketing and sales in which the point values for individual actions and the fit criteria are worked out, rather than prescribing a model technically that sales then does not actually back.
3. Building the first workflow. We build the first, deliberately limited workflow in Brevo, usually a welcome sequence or a comparable nurturing sequence (a series of informative emails without an immediate sales goal), including the branch at the scoring threshold.
4. Test phase with evaluation. We evaluate the first weeks after launch using the metrics described above, before a second workflow is created. An automation programme that grows wider before the first workflow has demonstrably worked multiplies the corrections needed later.
5. Expansion. Only after that do we extend the automation set-up with further sequences, for instance for existing customers or for specific product interests, building on the same scoring model instead of a new model for every workflow.

After expansion, the maintenance described in the previous sections remains in place. We recommend checking the scoring model against actual closed deals at least quarterly, and recording ownership for this in writing from the start, not only once the person originally responsible leaves the project.

How long this process takes overall depends strongly on the starting condition of the data, not on the software. An already well-maintained CRM shortens the stocktaking noticeably compared with lists that have grown for years without ever being cleaned up. An average project duration across multiple customer projects cannot be derived from this in any credible way, which is why we deliberately give no blanket timeframe here.

Conclusion

The figures from the Act-On Software and Ascend2 survey show a gap, not a contradiction. Almost all respondents see a benefit in marketing automation, but only a third achieves the result they had pictured. The difference arises mostly before the software comes in, in strategy and in data quality. Anyone who clarifies, before rollout, what counts as a qualified lead, how the score is checked, and who is responsible for which data field, raises their chance of belonging to the minority with the best results. The practical path there runs through a single, limited first workflow with clearly defined metrics, not through as complete an automation programme as possible from day one.

Frequently Asked Questions About Marketing Automation in B2B

How can you tell whether marketing automation is actually working?

Not from the raw success rate alone. 96 percent of respondents to the Act-On/Ascend2 study consider their automation at least partially successful, only 34 percent very successful or best-in-class. More telling are your own metrics, such as the sales team's response time after handover and the score hit rate against actual closed deals.

How long does it take for a lead-scoring model to deliver reliable results?

A score learns from closed cases. With long B2B sales cycles and a manageable number of customers, that takes longer than for a shop with new orders every day. Until enough deals have closed, the score remains a provisional estimate that should be checked regularly against reality.

Which workflow is best suited for getting started?

An email welcome sequence, because it offers the shortest path from idea to evaluation, and because email marketing is, according to the same study, the most widely used automation function at 57 percent. A second channel or a second workflow is only worthwhile once the first has demonstrably worked.

What needs to be clarified before the first automation workflow?

Three things: the completeness, currency and uniqueness of the existing contact data, a single, clearly justified segmentation line, and a definition, agreed jointly with sales, of how many scoring points each action is worth.

What is the difference between behaviour scoring and fit scoring?

Behaviour scoring counts what a contact does, such as opening an email or visiting a page. Fit scoring assesses how well the contact matches the target customer profile, for instance by industry or role. A model that measures behaviour alone treats a good-fit and a poor-fit contact identically whenever their click behaviour matches.

How often should you review a lead-scoring model?

In our experience, at least quarterly, and in addition whenever a larger number of new cases has closed. A model that is set at rollout and never touched again goes out of date unnoticed.

Planning marketing automation in B2B?

Arcmedia has been supporting companies with digital commerce projects since 1995, from analysis through implementation to ongoing operations. Our experts are happy to advise you on your project.

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Sources

• Act-On Software and Ascend2, "The State of B2B Marketing Automation 2025" (GlobeNewswire press release, 6 March 2025)
• Brevo Help Center, official documentation on lead-scoring automations and scores
• Brevo Help Center, official documentation on the prebuilt welcome automation
• Arcmedia, Marketing Automation service page