When both departments view different realities, they cannot form a shared picture of the market!
In many companies, marketing and sales work in the same market but use different data. Marketing focuses on reach, campaigns, website visits, and target audiences. Sales focuses on accounts, contacts, opportunities, and specific conversations. Both departments strive to better understand customers, but they do so from different perspectives and often using different systems.
As long as demand was clearly visible and customers moved through a funnel in a relatively linear fashion, this model could work. Marketing generated awareness and leads, and sales took over the contacts deemed sufficiently qualified. But the sooner companies want to identify needs, the more this separation reaches its limits.
Commercial intelligence is changing the landscape. When companies are evaluated based on the products they manufacture, the technologies they use, the changes currently taking place, and the opportunity signals that indicate an emerging need, this information becomes relevant to more than just the sales team. At the same time, it forms the basis for determining which companies marketing should target, which topics become relevant, and when a company transitions from a general target audience to a specific account with higher priority.
This creates a new shared data foundation.
The current separation between marketing and sales is historically understandable. Marketing traditionally works with markets and target audiences, while sales works with individual customers. Data models have evolved accordingly.
Marketing wants to know what topics interest people, what content works, and which campaigns generate demand. Sales is more interested in which company is behind a contact, which individuals are involved in the decision-making process, and whether this can lead to a concrete opportunity.
The problem arises when these two perspectives aren’t connected.
For example, a company might regularly read technical content without the sales team knowing that a location expansion was announced at the same time. Conversely, a sales representative might know that an account is strategically highly interesting, while marketing continues to treat it like any other company within a broad target audience.
Both sides have information, but no shared interpretation.
As a result, context is lost.
Let’s imagine a provider of automation technology. A medium-sized manufacturing company repeatedly views content on automated assembly. For marketing, this is initially a sign of engagement. Perhaps a lead score increases, or perhaps the visitor is assigned to a specific campaign.
Commercial Intelligence adds a crucial layer.
If the same company is simultaneously expanding its production capacity, seeking new manufacturing employees, and has announced a new product line, the website visit takes on a completely different meaning.
It is then no longer just a digital interaction.
It aligns with a trend that is already evident within the company.
Conversely, a single content view at a company without any relevant changes is hardly meaningful.
This is precisely why marketing and sales need the same contextual data. Only when digital interactions are linked to company developments, need profiles, and opportunity signals does a reliable picture emerge of whether something relevant is actually happening.
This shared data foundation is transforming the role of marketing.
Traditional B2B campaigns often rely on relatively stable target audience criteria. Companies are selected based on industry, size, region, or function, and individuals within these segments are then provided with relevant content.
Commercial Intelligence enables a more dynamic approach.
When it becomes apparent that a company is expanding its production, establishing a new business unit, or strengthening specific technical capabilities, marketing can deliver content that aligns precisely with this development. An account then receives special attention not because it has belonged to a particular industry for years, but because its current situation has changed.
This brings marketing closer to real business needs.
For example, a manufacturer of measurement technology could do more than simply send the same messages to all companies in a specific industry. It could prioritize those accounts where new production processes, higher quality requirements, or new product lines indicate changing measurement needs.
The campaign then no longer simply follows the target audience.
It follows the context.
Sales uses the same information differently.
While marketing must decide which content and topics might be relevant to a specific account, sales must assess whether the time is right for a direct conversation.
These two decisions are closely linked.
If Marketing recognizes that a company is currently investing in a new technology, Sales should also be able to see this information. If Sales learns during a conversation that a specific project won’t become relevant for another twelve months, Marketing should not target that account with generic campaigns in the meantime, but rather take the existing context into account.
This is precisely where collaboration breaks down in many companies.
Information is collected in different systems and evaluated differently. Marketing doesn’t know what Sales has learned during a conversation. Sales doesn’t see which topics the account has already researched or what external changes are currently taking place.
As a result, there is no shared view of the customer.
Commercial Intelligence can create a unifying layer here.
When Marketing and Sales use the same company data, the concept of a funnel also changes.
A company doesn’t have to become a lead through a marketing campaign before it becomes relevant to Sales. Perhaps Commercial Intelligence recognizes even earlier that an interesting situation is developing there. Marketing can then support this account with targeted, relevant content, while Sales monitors whether additional signals emerge.
Conversely, a company may already be consuming a significant amount of content without its current situation indicating a specific need. In this case, marketing can continue to develop the relationship without sending sales into an active sales process too early.
This results less in a linear funnel and more in a shared commercial space.
Within this space, companies are constantly evolving. Some develop a stronger fit, others show initial opportunity signals, and still others become more relevant through specific interactions.
Marketing and sales are not observing two separate worlds.
They view the same accounts from different perspectives.
Traditionally, the handoff of a lead has been one of the most critical points in the B2B process.
At some point, Marketing decides that a contact is sufficiently qualified and passes it on to Sales. Sales then verifies whether the contact is actually relevant. This is precisely where tensions often arise, because both departments define “quality” differently.
Marketing may classify a lead as high-quality because the contact has engaged with multiple pieces of content. Sales, however, determines during a conversation that there is neither a need nor a budget nor a suitable timing.
The problem often lies less in the performance of either department than in differing information models.
If, on the other hand, both sides work with the same information regarding company fit, current changes, and potential needs, the handoff changes.
An account is then evaluated based on more than just activity; its overall situation is factored into the prioritization.
This also means that a company can still be highly interesting—even without filling out a form or generating a traditional inbound lead—if multiple external signals point to a relevant change.
This is precisely how commercial intelligence partially breaks away from the traditional notion that demand must first become visible through a marketing interaction.
A shared database changes not only how target audiences are managed but also the content itself.
Much B2B content today is created based on a relatively general idea of which topics might interest a target audience. This often leads to broad content about efficiency, digitalization, the skills shortage, or innovation.
If, on the other hand, marketing recognizes which situations actually lead to opportunities, content can become much more specific.
Let’s take another example of a company that sells automation solutions. If it turns out that successful projects often arise where production growth and labor shortages occur simultaneously, this isn’t just an insight for sales.
It’s also a powerful topic for marketing.
Content can explain when growth begins to overwhelm existing production models, what symptoms typically arise, and what options companies have before a bottleneck becomes critical.
This allows marketing to address the same issues that sales will later explore in more depth during a conversation.
That is precisely what creates consistency.
The customer doesn’t first receive a general marketing message and then a completely different sales approach. Both departments work with the same understanding of the customer’s situation.
However, shared data does not mean that information flows only from marketing to sales.
The more important learning process can actually occur in the opposite direction.
Through conversations, sales learns which hypotheses were correct, which signals were misinterpreted, and which topics actually concern customers. This knowledge is enormously valuable to marketing.
For example, if many companies are planning to expand their facilities but investments in automation won’t become relevant until much later, this changes how that signal is evaluated. Conversely, if a specific combination of new product lines and quality requirements regularly leads to concrete projects, this insight should influence both targeting and content strategy.
This creates a shared learning cycle.
Commercial Intelligence generates hypotheses about the market. Marketing observes how accounts react to these topics. Sales tests the hypotheses through conversations. The insights gained from this process feed back into need profiles, opportunity signals, and future campaigns.
In this way, two separate departments increasingly become a shared commercial learning system.
The more closely marketing and sales work with shared company data, the less meaningful a purely contact-based qualification becomes.
A single person can consume a great deal of content without their company currently being ready to buy. Conversely, an account may be on the verge of an important investment decision, even though no one there has ever filled out a form.
The central focus of evaluation is therefore increasingly shifting from the contact to the company and from the activity to the situation.
This does not mean that engagement data is disappearing. It remains valuable because it can show which topics are receiving attention within an account.
But its significance only emerges in the context of other information.
Commercial intent is not simply the sum of clicks.
It emerges when company fit, current developments, and actual interest come together to form a consistent picture.
This is precisely the picture that marketing and sales should see together.
Ultimately, therefore, it’s about more than just technical integration.
Companies can seamlessly integrate CRM, marketing automation, and analytics and still have different ideas about what constitutes a good account.
The real integration begins earlier.
Marketing and sales need a shared understanding of which customers are a good fit for the offering, which situations generate demand, and which changes indicate increasing relevance.
The Need Profile thus becomes a shared language.
Opportunity signals become a shared framework for observation.
And commercial intelligence becomes the shared information layer on which both departments base their decisions.
Marketing decides where to focus attention.
Sales decides where this should lead to a direct conversation.
However, both decisions are based on the same view of the market.
In the future, marketing and sales will need the same data because the line between generating demand and identifying needs is becoming increasingly blurred.
A company can become relevant before it responds to a campaign. At the same time, digital interest can take on a completely different meaning when changes within the company become apparent in parallel.
Commercial Intelligence bridges these perspectives.
Marketing no longer sees only who is consuming content, but also why a company might be of interest at that moment. Sales no longer sees just an account and an opportunity, but also the topics and developments that preceded this interest.
This creates a shared commercial view of the market.
The crucial question is no longer whether a lead belongs to marketing or sales.
What matters is how relevant a company is at that moment and what next step fits its current situation.
And this is precisely where the next question arises.
If, in the future, we no longer manage sales primarily based on the volume of leads generated and activities conducted, but rather on how well relevant companies are identified and prioritized, we may also need a different metric.
Many sales organizations still measure activity above all else. How many leads were generated, how many contacts were reached out to, how many appointments were scheduled, and how many opportunities were added to the pipeline?
These metrics remain important. But they only tell part of the story.
After all, a sales team can be very active and still be chasing the wrong companies. At the same time, another team can work with significantly fewer contacts and still be more successful because it focuses its attention early on those accounts where actual need arises.
This raises a fundamental question:
Shouldn’t we measure sales in the future not only by how much it does, but also by how well it identifies commercial relevance?
That’s exactly what the next article is about:
CI-032: The New KPI in Sales.
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