How websites, job postings, press releases, and product data provide insights into demand
Companies rarely post the following on their websites:
“We have a problem and are looking for a new solution within the next six months.”
Nevertheless, they publish information every day about what’s on their minds.
They describe new products, recruit employees, expand production capacity, publish technical data, discuss new markets, or report on investments. Sometimes they announce a new production facility, sometimes they suddenly start looking for several automation engineers, and sometimes only a technical detail in a new product changes.
None of this information, taken on its own , necessarily indicates a specific need. Taken together, however, it can begin to tell a story.
That is precisely why modern corporate research is not just about collecting data. It must learn to understand a company’s language. After all, a need is rarely published as such. Itmust be inferred from the context.
One of the most common mistakes in customer research arises when providers search using their own terminology.
A manufacturer of automation solutions searches for companies that “need automation.” A provider of tool monitoring searches for “tool monitoring.” A manufacturer of process heating solutions searches for “industrial process heating.” At first glance, this sounds logical.
The problem is simply this: Potential customers may not use these terms at all.
A manufacturing company might not explicitly state that it needs an automation solution. Instead, it might mention increasing production volumes, staff shortages, new production lines, or the introduction of three-shift operations. A machine builder doesn’t state that it needs a specific sensor. Instead, it describes a new machine, higher precision, additional monitoring functions, or a new safety requirement. A food producer doesn’t announce that it needs process heat. It might talk about a new cooking line, expanded production capacity, or a new convenience product.
The need, then, does not lie in the term used. It lies in the situation. That is precisely why, in modern sales, we must learn not to search for companies based on whether they speak our language. We must understand how they themselves talk about their reality.
Let’s take CNC manufacturing as an example. One company explicitly mentions “5-axis CNC milling” on its website. Another refers to “high-precision machining.” A third simply states that it manufactures complex components with high geometric requirements. A fourthfeatures job postings for CNC millers and CAM programmers on its careers page. And a fifth publishes a press release about its investment in a new machining center.
All five companies may have very similar technical capabilities. However, anyone searching exclusively for the exact term “5-axis CNC milling” will find only some of them.
That is the real problem with traditional keyword research.
It looks for words.
Commercial Intelligence tries to understand meaning.
The question is no longer just:
“Does the term appear?”
But rather:
“Which statements describe the same business or technical situation?”
This semantic level is crucial if we want to identify needs beyond those that are explicitly stated.
A company’s website is therefore much more than just a digital business card.
It is often the most comprehensive public description of what a company does, which customers it serves, what products it manufactures, and which capabilities it considers particularly important.
For example, a mechanical engineering company describes its systems, applications, and technologies there. A contract manufacturer showcases its machinery and production processes. A manufacturer of technical components lists materials, dimensions, certifications, and areas of application.
What’s interesting here isn’t just what’s mentioned; the way the information is presented can also provide clues.
Is a particular process emphasized especially strongly? Is there a separate page dedicated to automation? Are new applications described? Do new markets or product categories suddenly appear? Are production capacities or technological capabilities presented in greater detail than before?
A website thus does more than just describe a company’s current state; it often reveals which topics the company considers strategically important. This provides valuable context for commercial intelligence.
Product pages and technical data sheets become even more interesting. This is because products force companies to be specific. Materials, temperature ranges, tolerances, performance values, certifications, sizes, or environmental conditions cannot be described arbitrarily; they describe real technical requirements.
Let’s imagine a manufacturer introducing a new generation of products designed for higher operating temperatures. For many readers, this is simply a technical improvement. For a supplier of seals, materials, cooling systems, or sensors, however, the same information can have a completely different meaning. The higher temperature may alter the requirements for components and materials. The product itself thus becomes an indication of potential changes in the supply chain. This is precisely why, in the previous post, we saw that products can reveal their potential supplier relationships.
Now another layer is added:
The language of product data reveals the requirements underlying these relationships.
Job postings are particularly interesting. That’s because companies usually don’t hire employees out of theoretical interest. They hire them because something needs to be done. A single job posting should never be overinterpreted. Filling a vacancy is different from establishing a new department.
However, when several job openings appear at the same time, patterns may emerge.
One company is suddenly looking for PLC programmers, commissioning engineers, and automation technicians. Another is seeking several employees for a new production line that’s being set up. A third is establishing a division for additive manufacturing and is looking for design engineers, process engineers, and production staff.
This may provide us with clues about technologies, processes, and changes that aren’t yet visible on the company’s regular website.
The language used in the job posting is particularly valuable because it is often very specific.
The job posting therefore does not necessarily describe a future investment project. But it can reveal which skills a company is currently seeking to develop or strengthen. And from Demand Discovery’s perspective, this shift is highly interesting.
Websites often show what a company is. Press releases more often show what is changing.
For commercial intelligence, they are potential signals of change. However, their significance only becomes clear through context. The news that “a company is opening a new plant” alone says little about whether this will create a sales opportunity for us.
But if we know which products are to be manufactured there, what processes are required, and what technical solutions we offer, the meaning changes.
Then we can ask:
What will this company likely need to build, expand, or procure as a result of this change?
This turns a piece of news into a hypothesis about a need.
That’s why phrases that, at first glance, seem to have nothing to do with purchasing or investments are particularly valuable.
For example, one company writes:
“Due to sharply rising demand, we are expanding our production capacity.”
It does not say:
“We need new machines.”
Nevertheless, the next question suggests itself. How should this capacity be expanded?
Or a manufacturer announces:
“With the new product generation, we’re doubling performance while maintaining the same installation space.”
Here, too, it does not say:
“We need new materials, cooling systems, or manufacturing technologies.”
But a significantly higher power density can raise follow-up technical questions.
Or a company writes in a job posting:
“You will oversee the setup and commissioning of new automated production lines.”
The term “purchase intent” doesn’t appear anywhere. Nevertheless, this information is highly relevant for certain suppliers. Commercial intelligence, therefore, also means not just reading such statements literally.
We must ask:
What business implications could arise from this statement?
However, this quickly poses a risk. If we see a potential need in every news item, every job posting, and every new product, we’re not improving sales. We’re just creating more noise. That’s why we need a Need Profile.
The Need Profile doesn’t just describe which companies are a good fit for us. It describes which technical, operational, and business situations are relevant to what we offer.
For example, an automation provider might determine that the following are of particular interest:
Companies with highly manual production processes, rising production volumes, and a noticeable labor shortage.
An industrial cooling provider might focus on companies where new products create higher thermal demands. A manufacturer of precision components might be looking for companies whose products require tighter tolerances or more sophisticated materials.
Now information can be categorized. A job posting is not simply “a signal.” It is only relevant if it matches the Need Profile. This is precisely how unstructured information becomes commercial intelligence.
Not all public information carries the same weight. An official investment announcement from a company carries more weight than an inference drawn from a general product description. A specific job posting is more reliable than an outdated entry in a business directory. A current technical data sheet can be more precise than a press release that is several years old.
That is why evaluating the evidence is always part of modern corporate research.
This distinction becomes particularly important once AI analyzes large amounts of such information. This is because AI is very good at recognizing connections. However, it can also quickly draw an overly strong conclusion from a plausible connection if we do not set clear boundaries.
Commercial intelligence must therefore not mean:
“AI finds the truth.”
The more meaningful task is:
AI finds evidence, connects information, and makes it clear why a particular hypothesis arises from it.
The sales team then decides how robust and relevant this hypothesis is.
Let’s take a concrete example. A manufacturer of technical plastic parts updates its website and introduces a new product line for electric mobility. Shortly thereafter, severaljob postings for process mechanics and automation technicians appear.
A local business newspaper reports on an expansion of the production area. A product page suddenly lists significantly higher requirements for temperature resistance and component precision. Each piece of information on its own might seem relatively unspectacular. Together, however, they tell a story.
The company is entering a new product segment. Production is growing. New employees and skills are needed. Technical requirements are changing.
For suppliers of injection molding machines, automation, tooling, materials, quality inspection, or measurement technology, this can create a highly interesting situation.
We don’t yet know what project will actually materialize.
But we know much more than just:
“The company is part of the plastics industry.”
That is precisely where the difference lies.
Another interesting effect is that language sometimes reveals changes earlier than formal data.
Statistically, a company continues to be classified in the same industry. The number of employees and revenue may also show little change at first. However, new terms begin to appear on the website.
The language of the company begins to change before traditional business data even reflects this change.
This is particularly interesting for commercial intelligence. After all, the sales team isn’t just looking for the company that perfectly fits the existing ICP today. It wants to identify as early as possible which companies might become relevant tomorrow. Language can serve as an early indicator in this regard.
This is precisely where artificial intelligence demonstrates its true strength. Traditional search systems are excellent at finding specific terms. AI, on the other hand, can also attempt to understand whether different terms have the same meaning.
For example, it can recognize that “high-precision machining,” “precision machining,” and “complex CNC machining” can all describe similar manufacturing contexts.
Or that “production expansion,” “capacity increase,” and “new manufacturing facility” may all contribute to the same overarching topic.
This fundamentally changes how we conduct business research. We no longer need to know every term in advance. We can describe the situation we’re looking for. The search can thenidentify various linguistic expressions of that situation.
This is the transition from keyword search to context search.
And it is precisely this step that is crucial for demand discovery.
Nevertheless, we need to draw an important line. If we understand a company’s language, we can much better recognize what’s happening there. That doesn’t automatically mean an opportunity exists.
A new job posting could indicate growth, but it could also simply be a replacement hire.
A new product page may signal an innovation, but it could also simply be a marketing update.
An expansion of production can trigger new investments, but it isn’t necessarily related to our product offerings.
That is why we need to make a further distinction in the next step:
What information is merely of interest?
And which changes are so relevant that they might actually point to a future project?
That’s exactly where the systematic work with opportunity signals begins.
Companies reveal a surprising amount about their future needs.
They rarely do so in the language of sales.
They talk about new products, employees, production capacity, technologies, markets, and technical requirements.
Anyone who searches only for their own product name or for traditional purchasing terms will miss a large portion of this information.
The key skill in modern business research is therefore not just finding words, but understanding the connections between them.
The most important question is not:
“Has this company stated that it needs our solution?”
But rather:
“What does this company tell us about its current situation when we read through its various pieces of information together?”
Those who understand this language recognize changes sooner. And those who recognize changes sooner can identify needs before they turn into a traditional inquiry.
This brings us to one of the most important questions in the entire methodology.
When websites, job postings, product data, and press releases provide thousands of clues every day, we need to be able to distinguish which ones are truly relevant.
After all, not every change leads to a project.
And not every interesting signal indicates an intention to buy.
In the next post, CI-024: Opportunity Signals—Which Signals Indicate Upcoming Projects, we’ll therefore examine which types of changes are particularly interesting, how multiple signals can be combined, and when a single piece of information actually becomes an indication of an emerging sales opportunity.
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