From a Practical Sales Problem to Commercial Intelligence
This series began with a simple observation: Today’s B2B sales teams have more data, more software, and more ways to reach out to prospects than ever before. Yet many companies still struggle to reliably determine which companies their sales teams should be targeting right now—and why.
It was precisely this contradiction that led to the creation of bloo.research.
The starting point wasn’t the idea of developing yet another company database. Nor was it about replacing sales teams with artificial intelligence or providing them with even larger lead lists. The problem was much more fundamental: Companies often have a surprisingly precise understanding of why certain customers need their products, but they are barely able to systematically apply that knowledge to an entire market.
An experienced sales representative can often tell within a few minutes whether a company might be a good fit. They look at the products, understand the technologies used, recognize typical production processes, and can assess which technical or economic situations align with their own offering. This knowledge is valuable, but it often remains tied to individual people and is difficult to apply to thousands of companies.
This is precisely where the crucial question arose: What if we could not only document this sales knowledge but also translate it into a systematic search logic?
This question initially led to the development of bloo.research and ultimately to the more comprehensive conceptual framework of Commercial Intelligence.
Anyone looking for new customers today has access to numerous tools. Business databases provide firmographic data and contact information, LinkedIn taps into professional networks, and Google provides access to virtually any publicly available company information. In addition, there are industry portals, trade publications, commercial registries, job boards, and specialized data providers.
As a result, sales teams are increasingly less likely to suffer from a fundamental lack of information. The opposite is more often the case. There is so much data that the real challenge lies in figuring out which of it is actually relevant to one’s own business.
A company may belong to the right industry and be the right size, yet still have no meaningful need for your offering. Another company might fall outside the traditional target audience definition but have exactly the products, processes, or technical requirements for which a solution was developed.
Traditional filters provide only limited answers to these differences.
This became particularly clear with specialized B2B offerings in the industrial sector. There, the description “mechanical engineering, 200 to 500 employees, Germany” is often barely sufficient to assess the quality of a potential customer. It may be more interesting to consider which products are manufactured, how they are produced, which technologies are used, and what requirements arise from these factors.
This shifted the focus of the question.
We no longer wanted to simply identify which companies belonged to a target group. We wanted to understand why a particular company might need our offering.
This change had greater consequences than initially expected.
Since the industry is no longer the starting point, we need to describe more precisely what creates demand. The expertise of skilled sales representatives therefore had to be translated into a format that a system could process.
A supplier of industrial measurement technology, for example, does not simply search for manufacturing companies. Instead, it identifies the specific manufacturing situations in which particular measurement requirements arise. An automation provider does not merely define company sizes but also considers which processes can typically be automated and under what conditions the economic pressure to do so increases. A component manufacturer examines the products of its potential customers and asks in which technical applications its component could actually be used effectively.
Thus, the traditional Ideal Customer Profile has gradually evolved into a Need Profile.
The Need Profile does not merely describe who a customer is. It describes the conditions under which an offer can become relevant.
It is precisely this shift that became a central component of bloo.research. The software was not intended merely to make database filtering more convenient, but to reflect a company’s understanding of what constitutes a truly valuable customer.
This fundamentally changes the search process. Instead of generating a list of companies and then reviewing them manually, the expert review can become an integral part of the search itself.
Without the development of modern AI, this concept would have been only limitedly scalable.
Traditional databases excel at processing structured information. If industry, location, revenue, or number of employees are available in a data field, companies can be reliably filtered by those criteria. It becomes more difficult with information that appears primarily in continuous text, product descriptions, technical documents, or other unstructured sources.
Yet that is precisely where a large portion of the information that is particularly relevant to a need profile is found.
Companies describe their products and applications on their websites. Technical documentation provides insights into processes and technologies. Job postings reveal the skills being developed. Press releases document changes, investments, and new markets. Often, the truly interesting picture only emerges when several of these sources of information are considered together.
Generative AI has made it possible to analyze such content on an entirely different scale. A system can not only search for a specific word but also attempt to understand what a company actually manufactures, what technical relationships exist, and whether this information aligns with a previously defined need.
This has made scalable a process that was previously primarily a manual task performed by experienced employees.
The key idea behind bloo.research was therefore never that AI would suddenly know more about a market than a skilled sales professional. The idea was to combine the expertise of the sales team with the scalability of AI.
This gave rise to a different approach to company research.
Traditional research might yield a hundred companies from a specific industry. Then the real work begins. Someone has to visit the websites, review product portfolios, and assess whether the companies truly align with the opportunity.
bloo.research was designed to change this process.
First, the system is provided with as precise a description as possible of what is being sought. Products, applications, production processes, technologies, or other technical characteristics may play a role here. Subsequently, companies are not only identified but also evaluated based on these criteria.
The result should therefore not simply be: “Here are a hundred companies.”
A far more interesting statement would be: “Here are companies for which we’ve found clear reasons why they might be a good fit for your needs.”
This shifts the value of the research from quantity to rationale.
For the sales team, this means they no longer have to spend most of their time retroactively qualifying long lists. They can focus their attention more on the companies where the research has already revealed a plausible connection between the customer and the offering.
This very idea has been a recurring theme throughout the entire Commercial Intelligence series: less scope for searching, more context, and better reasons for a sales decision.
While working on this search logic, however, it became clear that even a very good fit does not fully solve the actual sales problem.
A company may be a perfect technical fit for your offering and yet still not make a purchase for years.
The machinery is functioning properly. Production is running at full capacity but remains stable. Supplier relationships are well-established, and there is no sufficient economic reason for change.
Another company with a nearly identical fit, however, finds itself in a completely different situation. It has won a major contract, is expanding production, and is simultaneously trying to recruit additional skilled workers. Suddenly, a technology that was only of general interest yesterday can become a concrete business necessity.
This is precisely where the next important step in development took place.
It’s not enough to understand who is a good fit.
We also need to understand what is changing.
This is how the process gradually evolved from simply identifying target customers to demand intelligence. Company announcements, new products, hiring, location developments, and other changes did not simply become additional data points. They were considered in the context of the previously defined demand situation.
Only then does a suitable company potentially become an emerging opportunity.
At this point, it became clear that we were actually working on a larger concept.
If companies can be identified based on products and processes, we need customer intelligence. When changes point to potential needs, we’re dealing with demand intelligence. As soon as we also want to understand how an individual account is structured, which competitors or alternatives exist, and which market developments influence the decision, additional information perspectives come into play.
However, this information only has real value if it collectively leads to a better decision.
This is precisely what gave rise to the Commercial Intelligence model.
Market Intelligence helps to understand the market environment. Customer Intelligence identifies which companies are fundamentally a good fit for a company’s offerings. Demand Intelligence examines changes that could necessitate action. Competitive Intelligence takes into account competitors, alternatives, and existing solutions, while Account Intelligence delves deeper into the specific context of an individual company.
Opportunity Intelligence ultimately emerges from the combination of this information. It seeks to determine whether the big picture reveals a situation that warrants special attention from the sales team today.
bloo.research is therefore not the definition of Commercial Intelligence.
It is a tool that emerged from precisely this conceptual model and seeks to make parts of it operationally usable.
This distinction is particularly important because, in sales, AI is often equated with automating the initial approach.
If a technology can analyze thousands of companies, the next thought seems obvious: Then thousands of personalized emails can also be generated automatically.
Technically, this is possible.
Strategically, however, it quickly leads back to the same old logic: More leads generate more messages, and more messages are supposed to produce more opportunities.
The approach behind bloo.research takes a different direction.
If AI makes research more efficient, the sales team shouldn’t necessarily reach out to more people. It should reach out to fewer irrelevant companies.
That’s a crucial difference.
A sales team gains little by sending 10,000 AI-personalized messages instead of 1,000 generic ones. It is far more valuable for the team to identify, among thousands of companies, those with a clear professional fit and a plausible reason to engage.
Commercial intelligence should therefore not be used to simply increase the volume of sales outreach.
It is intended to increase its relevance.
This also brings us full circle to the question of whether AI will replace sales.
bloo.research wasn’t founded on the idea that AI would one day independently manage entire customer relationships. Above all, the technology takes on tasks that machines can scale very well: searching through large amounts of information, identifying correlations, comparing companies, and monitoring relevant changes.
After that, a different kind of work begins.
A human must decide whether the hypothesis is plausible. They must understand which question in the conversation might actually be relevant and how the customer assesses their own situation. Through this exchange, entirely new information can emerge that would never have been apparent from the outside.
That is exactly what we saw in the previous post using the example of the automation opportunity. The analysis revealed that an interesting production situation was developing. However, it was only during the conversation that it became clear that the real bottleneck lay not primarily in assembly, but in the testing process.
Commercial intelligence should therefore not replace the conversation.
Instead, it is meant to ensure that more conversations take place for a good reason.
Perhaps that is where the greater value lies in the long run.
In many medium-sized companies, individual sales representatives possess an enormous understanding of the market. They know which products are suitable for specific applications, which types of customers frequently encounter problems, and what small changes indicate that a project might be in the works.
However, this knowledge is difficult to scale.
When an employee leaves the company, some of that knowledge is lost. It takes new employees years to recognize the same patterns. Different salespeople can assess the same market in completely different ways.
A commercial intelligence system can help gradually make this knowledge explicit.
Need Profiles describe why companies are of interest. Opportunity Signals document which changes may be relevant, and the results of real sales conversations show which hypotheses actually worked.
This creates a learning cycle.
The sales team’s knowledge improves the system, and the system makes this knowledge available to other employees. New experiences, in turn, feed back into the system.
In this way, individual market knowledge can increasingly develop into an organizational commercial intelligence capability.
That is precisely why we no longer view commercial intelligence as merely a technological issue.
It is a knowledge management system for growth.
Throughout this series, we have deliberately used our own term, even though many of its individual components already exist under other names.
Market intelligence isn’t new. Intent data isn’t new. Account research isn’t new, and predictive analytics has been around for years.
The problem, rather, is that this information often remains trapped in separate systems and organizational silos.
Marketing tracks digital interactions. Sales knows individual accounts. Strategy analyzes markets, and product management understands applications and technical requirements. Each department holds a piece of the puzzle.
Commercial Intelligence describes the level at which this information is consolidated to enable a commercial decision.
The key question is always:
Which company should we talk to right now, and why?
bloo.research was born out of an effort to answer this question in a practical and scalable way.
It’s not perfect.
But it’s significantly more systematic than relying on a combination of industry lists, Google searches, and chance.
That’s why another insight is also important.
The quality of bloo.research does not stem solely from the AI model used.
Two companies can use the same AI and obtain completely different search results because they define what to search for with varying degrees of precision.
If a company simply says it is looking for “mechanical engineers in Germany,” even a powerful AI will produce results that are only marginally better than those of a traditional database.
However, if you describe which products a potential customer manufactures, which processes are relevant, and under what conditions a need for your own offering might arise, the search changes.
That is why developing the search logic is an integral part of the process itself.
The customer’s knowledge is not replaced by AI. It becomes the starting point for AI.
This is precisely one of the most important principles behind bloo.research:
AI doesn’t know the market any better than the company does. But it can help apply the company’s knowledge to a market that has become too large for humans to handle on their own.
The next step, therefore, is already underway in the development we’ve described in recent articles.
Analyzing a company once is valuable.
It becomes even more valuable to continuously monitor relevant companies.
An account that seems uninteresting today may launch a new product line tomorrow. An existing high-fit account may suddenly expand its capacity or adopt new technologies. Other companies may change their markets or supply chains.
If these changes are continuously identified and evaluated in the context of the Need Profile, research evolves into a sustainable commercial intelligence system.
This once again transforms the role of bloo.research.
The original question was: How do we find companies whose products and processes truly align with our offerings?
The follow-up question is now:
How can we continuously identify those companies where a business situation is currently developing in which our offerings could become relevant?
It is precisely this transition from search to monitoring and from fit to opportunity that describes the evolution behind commercial intelligence.
bloo.research was born out of a very practical problem.
We were no longer willing to accept that highly qualified sales representatives spend a significant portion of their time searching Google results, company websites, and databases for potentially suitable customers.
At the same time, we didn’t want to simply produce even larger lists of leads.
The key idea was to translatea company’s understanding of why customers need its products into a systematic research logic and to scale this using the capabilities of modern AI.
Step by step, this evolved into a broader conceptual framework.
Target groups became “need profiles.” Static company lists became a “commercial space.” Company announcements became “opportunity signals” when, in the right context, they indicated a potential need. And by combining different information perspectives, “opportunity intelligence” emerged as the foundation for better sales decisions.
As a result, the original question about identifying better target customers evolved into a more comprehensive one:
How can companies systematically identify future business opportunities earlier?
This is precisely what Commercial Intelligence is all about.
And bloo.research was born out of an effort to make it possible to answer this question in a practical way.
This post concludes the introductory series on Commercial Intelligence.
We started with what initially seemed like a simple problem: Traditional B2B sales teams are finding it increasingly easy to identify companies and contacts, but they don’t automatically find the companies where a relevant need is currently arising.
From there, we gradually developed a different sales logic. We examined why products and production processes often reveal more about actual fit than mere industry classifications, how companies signal changes through their public communications, and why opportunity signals only gain meaning when placed in the right context. We examined how AI can scale research, why this does not diminish the role of sales but rather makes it more demanding, and why marketing and sales will need to share the same view of the market in the future.
Finally, based on these ideas, we developed a commercial intelligence process and an operating model, and demonstrated how an early signal can evolve into an emerging opportunity and, later, into a confirmed sales opportunity.
This brings us full circle back to the initial thesis of this series:
B2B sales is facing a fundamental paradigm shift. Growth is no longer determined by the ability to find as many leads as possible, but rather by the ability to identify relevant commercial needs earlier.
Commercial Intelligence is the effort to systematically build precisely this capability.
Not as a short-term AI trend.
But rather as a new way of understanding the market and conducting sales.
In addition to manual research, we’ve developed automated tools for you:
bloo.research— Find the right B2B companies in minutes
bloo.radar - Find out what your competition is going to do next
before they do it.
If you want to not only understand AI but also implement it in a structured way within your company, then:
👉 Learn more about our AI training:
https://bloo.school
👉 Learn more about our Smart Market Fit offerings:
https://bloola.com/smf - The Smart Market Fit Course
https://bloola.com/smf-system - The Smart Market Fit System for Businesses
👉 Or learn more about our consulting and automation solutions:
https://bloola.com