If you want to know whether a company could be a good customer, you usually look at the company itself.
What industry? How big? Where is it located? How many employees? What is its revenue?
Yet one of the most interesting sources of information is usually right in front of our eyes: the product portfolio.
That’s because every product a company sells tells a second story. It reveals which materials need to be processed, what components might be included, what technologies are required, what manufacturing processes are necessary, and what services a company will likely need to outsource.
For example, a company that manufactures heat pumps doesn’t just need “suppliers.” Behind the finished product lie housings, electronics, sensors, valves, heat exchangers, insulation materials, fastening technology, software, and numerous other components and services.
A manufacturer of industrial packaging systems, on the other hand, requires entirely different things. Drives, sensors, control systems, stainless steel components, conveyor technology, pneumatics, safety technology, or possibly thermal components may all be parts of its machines.
The product at the end of the value chain therefore reveals a surprising amount about what might be needed at the beginning of the chain.
And this is precisely what gives rise to a very powerful method for finding new customers.
Let’s say your company manufactures a specialized technical component for machines and systems.
The classic sales question is often:
“Which industries buy our component?”
This might lead to a target group such as mechanical engineering, plant engineering, or food processing machinery. Next, companies are researched, contacts are identified, and the sales pitch begins.
This approach generally works.
But the industry itself only answers part of the actual question.
After all, not every mechanical engineering company needs the same components. A manufacturer of machine tools has a different technical architecture than a producer of filling systems. A manufacturer of conveyor technology has different requirements than a supplier of industrial drying systems.
The key factor is therefore often not which industry the potential customer belongs to, but rather what products that customer manufactures.
The bloola cluster for product-based company research therefore distinguishes between three search approaches: Companies can be researched based on the products they manufacture, the products or systems they use, and the products they might potentially need based on their own applications. The third perspective is particularly interesting for sales. (Find Companies by Product)
This turns the traditional search logic on its head.
We no longer ask exclusively:
“Who could buy our product?”
But rather:
“In which products from other companies could our offering play a role at all?”
From our own product to the customer’s product
Let’s imagine a manufacturer of industrial gas burners for low-temperature process heat.
A traditional search might start with industries where heat is needed. The food industry, the chemical industry, the pharmaceutical industry, or plant engineering would be obvious candidates.
The problem: These industries are huge.
The much more interesting question, therefore, is in which specific products or systems the gas burner’s function might be needed.
Perhaps they are drying systems.
Perhaps industrial furnaces.
Perhaps facilities for producing ready-to-eat meals.
Perhaps production lines where products are heated, cooked, or kept warm.
This completely changes the perspective.
Suddenly, the sales market is no longer made up of “mechanical engineering companies in Europe.” It consists of companies that manufacture very specific machines or production lines.
This is precisely the idea described in the cluster article using food-processing equipment as an example: Instead of searching broadly for food machinery manufacturers, you can conduct targeted research to identify which companies develop production lines for convenience foods and whether those lines involve thermal processes such as heating, drying, cooking, or keeping food warm. This results not only in a list of companies but also in a well-founded hypothesis as to why your own component might be relevant there. (Finding companies by product)
That’s a significant difference.
A finished product almost never consists of just a single technological expertise.
Let’s take an industrial packaging machine as an example.
From the outside, we initially see a machine.
If we look more closely, we might see stainless steel structures, servo drives, control systems, sensors, pneumatics, conveyor technology, safety devices, camera systems, seals, cables, control cabinets, and software.
Thus, the machine manufacturer’s product portfolio indirectly reflects part of its potential procurement profile.
The same principle applies to completely different products.
A manufacturer of battery systems may require cells, power electronics, cooling systems, housings, connection technology, and battery management.
A manufacturer of laboratory equipment may require precision mechanics, electronics, pumps, sensors, displays, and software.
A manufacturer of high-quality furniture requires specific materials, hardware, finishes, fasteners, or machining processes.
This does not mean that we can know with absolute certainty, based on a product description, which components are actually used.
But we can begin to establish a technical connection between our offering and a potential customer’s product.
A hypothesis about the customer’s needs emerges from the general target audience.
A traditional company database works exceptionally well with pre-structured information.
If we’re looking for mechanical engineering companies in Germany with 100 to 500 employees, it can answer that question very efficiently.
It gets more difficult with a question like:
“Which European manufacturers build systems that might require a thermal process between 80 and 250 degrees?”
There’s usually no predefined data field for this.
Instead, the information is scattered across product pages, technical data sheets, catalogs, application descriptions, reference projects, or press releases.
This is precisely where one of the major changes brought about by AI-powered research lies.
AI doesn’t just have to filter based on an existing characteristic. It can analyze unstructured information and attempt to establish technical connections between products. The cluster article, for example, describes the situation of a company that offers complete production lines for frozen pizza. Further questions can be derived from this information: What production steps are part of such a line? What machines are required? What technologies and components might be involved? (Find Companies by Technology)
The key point here is not that AI suddenly knows everything.
What matters is that we can ask new questions.
This new type of research therefore also changes the quality of the results.
A traditional list says:
“Here are 500 mechanical engineering companies.”
A product-based search should ideally say:
“This company is of interest because it develops equipment for the production of ready-to-eat meals, which involves several thermal processing steps. Therefore, there could be a technical application for our component.”
The second result is not yet a confirmed sales opportunity.
But it has something the first result doesn’t:
a reason.
And it is precisely this reason that is crucial for modern sales.
The sales team receives not just a company name, but a hypothesis that they can test, prioritize, and use to approach the prospect.
Commercial intelligence, therefore, doesn’t start with a company’s address, but with the business context.
This method becomes particularly interesting when it reveals companies outside the existing target group.
Let’s take the example of a manufacturer of a specialized high-performance plastic.
Perhaps it currently sells primarily to the automotive industry.
Its traditional market definition might therefore be:
Automotive suppliers in Europe.
But why is the plastic actually purchased?
Perhaps because of its temperature resistance.
Perhaps because it is particularly lightweight.
Perhaps because of its chemical resistance.
Perhaps because of its electrical insulation properties.
Once we consider these functional properties, entirely different products can become interesting.
Medical devices.
Electronic assemblies.
Laboratory equipment.
Battery systems.
Machine components.
Pumps.
Valves.
Or products from industries that have not been a sales focus at all until now.
As a result, the market is no longer defined exclusively by industries.
It is defined by applications.
This is precisely what gives rise to the “commercial space” once again: all companies whose products or applications have a clear connection to our own offerings.
And this market can be significantly larger than the industry in which we have been searching so far.
The title of this article is deliberately provocative.
Of course, a product page doesn’t usually say:
“These five suppliers provide us with these ten components.”
What products actually reveal is something else.
They reveal their potential supply chain structure.
If we understand how a product is constructed, what functions it must fulfill, and what processes are necessary for its manufacture, we can deduce what types of materials, components, technologies, or services might be relevant.
This is a demand hypothesis.
And this is precisely where we must clearly distinguish between information and inference.
If a company manufactures packaging machines for ready-to-eat meals, it follows plausibly that certain thermal components could be relevant. However, it would be wrong to immediately conclude from this that this company needs our specific component right now. The cluster article also explicitly draws this line: A plausible technical conclusion should be labeled as such and, whenever possible, made verifiable through sources and justifications. (Find Companies by Product)
This distinction is not a weakness of the model.
It is its strength.
After all, Commercial Intelligence should not claim to know more than can be publicly substantiated.
It is intended to reduce uncertainty in a structured way.
This brings us back to the same limitation we discussed in the previous post on manufacturing processes.
A company may be a perfect fit for our offering and still not make a purchase.
Perhaps it manufactures the very machine in which our component could be used. Or perhaps it has been using an established supplier for years.
Perhaps the technical architecture has just been redesigned and locked in for the next five years.
Perhaps there’s no pressure to change at all.
Perhaps our technology is compatible but not economically viable.
That’s why we need to distinguish between technical compatibility and current needs.
The product portfolio can indicate that a company is fundamentally relevant. If, in addition, a new product generation is announced, a new plant is being built, a production line is being expanded, development engineers with the right skills are being sought, or a new market is being tapped, the situation changes.
Now a demand signal is added to the product fit.
And when fit, change, and timing align, a high-fit account begins to turn into a real opportunity. This exact sequence is also described by the cluster ranging from technical or compatibility fit through demand signals to opportunity. (Find Companies by Product)
For many B2B companies, this represents a far greater strategic opportunity than just a new search function.
You can rethink your own market model.
Instead of saying, for example:
“We sell to machine builders,”
a company could say in the future:
“We sell to manufacturers of machines whose products require specific thermal processes.”
Or instead of:
“Our customers are in the medical technology sector,”
the more precise description would be:
“Our customers develop products that require high-precision, biocompatible plastic components.”
Or instead of:
“We supply the automotive industry.”
it should be:
“We supply manufacturers of products that require high temperature resistance, low weight, and electrical insulation all at the same time.”
In this way, a company no longer describes its market based on the identity of its customers.
It describes it based on the reason why these customers might buy.
And that is a fundamental difference.
In traditional sales, we often ask:
“Which companies buy products like ours?”
A more interesting question might be:
“Which products from other companies can even be manufactured effectively without solutions like ours?”
Those who can answer this question begin to view their markets from a completely different perspective.
A company’s product portfolio then becomes a roadmap of potential needs. It reveals which functions must be fulfilled, which components might be necessary, and which technical interdependencies exist.
As a result, target customers are no longer derived solely from industry lists.
They emerge from clear relationships between what we offer and what other companies produce.
Perhaps, therefore, the next time we search for new customers, we shouldn’t start by asking:
“Who belongs to our target group?”
But rather:
“Which product from another company actually holds our next order?”
Product portfolios show us what a company manufactures and which components or services might be needed as a result.
However, two manufacturers of the same product can have completely different technical setups.
The next step, therefore, is to take a closer look at the machines, equipment, software solutions, and technical systems a company uses.
After all, sometimes it’s not the product itself, but the technology used, that reveals whether a company is truly a good fit for our offering.
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 competitors are up to next at
before they do.
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