About this series of articles
This is the second in a series of seven articles. In the first article, I talked about how you can use venture capital news to gain technology, innovation, and business insights. In this article, I’ll describe for market analyses:
- What kinds of data I mean, and where to find them
- The insights you can expect to find in market analyses
- What you probably won’t find in market analyses
- Strategies for searching so you get useful insights
What I mean by “market analyses”, and where to find them
By “market analyses”, I don’t mean studies that span hundreds of pages and go into a lot of detail. Rather, I mean articles that give you some basic data on a market. These data include market growth and size estimates, but also relevant applications, companies, and technologies in that market. And it is often these latter data that are most useful, as I will describe below.
You can find market analysis articles in news portals, such as this one, or in press releases. Company websites also have them sometimes.
What you can do with market analyses
Many people think “market sizes” and “market growth rates” when they hear “market analysis”. But most people also know that you have to take these numbers with a grain of salt. In most cases, you simply don’t know exactly what assumptions went into these numbers. Therefore it is important to check their plausibility.
Related: How to check the plausibility of a market estimate
But besides market size and growth rate numbers, market analyses can help you map out a technology or business area, find applications of a technology, and find out in what markets a company is active. Let’s look at these three use cases in turn.
Mapping out a technology or business area
Let’s say you want to better understand and map out the structure of a multifaceted technology or business area. “Smart city”, for example, or “packaging”, or “digital industry”. What technologies play a role there? What applications? And what companies?
You could do this manually…
You can use market analyses to address such questions. Here is the process for doing this manually, using “smart city” as an example:
- Collect news, press releases, and other content on “smart city”.
- Only keep those contents that include a market analysis. That is, only keep contents that mention some market segment, as well as a size and growth estimate for that market segment.
- Extract the data from your remaining contents, and organize these data:
- Extract all “smart city” market size and growth rate (= CAGR) estimates.
- Normalize the data. That is, make sure that all size and CAGR estimates refer to the same time frame. Use linear interpolation to time-frame-normalize the size and growth curves.
- Put the data into a scatterplot, where x = size and y = CAGR. This gives you an idea of analyst consensus regarding size and growth estimates. If all the dots are lumped together, analyst consensus is high. If the dots are all over the place, analyst consensus is low.
- Get the names of all companies mentioned in your contents. For each company, count how often it appears. More frequently mentioned companies are seen as “relevant” by more analysts.
- Extract all other market estimates, that is, the ones not on “smart city” but on related topics (“edge computing” or “smart sensors”, for instance). We call these “context markets” (= not what you’re explicitly searching for, but relevant in your context).
- Normalize the context market data. This way you can see the biggest and the fastest-growing context markets.
Ugh.
…or you could use algorithms
Fortunately there is an easier way:
- Get a Mergeflow subscription.
- Search for “smart city” in Mergeflow.
- Explore the results.
The entire manual process that I described above is automated by Mergeflow. This saves you several days of work–for every single search. And because the process is now so much faster, you are agile and can iterate. For example, after a first very general search, you can zoom in on various facets of the results you get.
Related: How Mergeflow extracts market data from texts
An example: Mapping out “smart city”
Technology or business areas can be hard to scope out. They often consist of many subfields, including various technologies, applications, materials, and other things. But you can use market analyses to at least get a first useful approximation of the structure of a tech or business field.
Let’s see how this works, using “smart city” as our example topic. Here is a first high-level overview of “smart city” market data in Mergeflow (click on the image to enlarge):
The scatterplot in the upper left shows the distribution of market estimates that pertain directly to our search query, “smart city”. Each dot represents one market estimate. As you can see, the estimates vary considerably.
The upper right quadrant shows you most relevant companies in smart city. Bigger font means that a company appears across more market estimates (= is seen as relevant by more analysts).
Then there are markets in the context of our “smart city” search. Remember we (or Mergeflow) collected news, press releases, etc. on “smart city”, and then extracted all market estimates from these contents. And not all of these market estimates are explicitly on the smart city market. For example, a press release might discuss the “Low Power Wide Area Network (LPWAN) market”, and mention that LPWAN could provide connectivity in smart cities. These are the context markets in the lower half of the screenshot above. And these context markets help you map out your search area, “smart city” in our case.
Notice that some context markets are mentioned more than once. For example, there are several estimates for “internet of things” or “IoT”. Mergeflow only groups estimates together if their numbers (size and growth rate estimates) match. This is not the case here, so they are listed separately. And this tells you that analysts differ in their market size and CAGR estimations of a topic (“IoT” in this case).
Related: How to use market estimates to structure a tech field
Find applications and markets for a technology
Let’s look at another example that shows you how you can use context markets. Let’s say you want to find applications and markets for “quantum dots”. Quantum dots are nanometer-size semiconductors with optical and electronic properties that make them interesting for a range of applications.
Context markets help you find these applications. Below is a screenshot of a Mergeflow search for quantum dots (quantum dots are part of a larger group of emerging technologies that Mergeflow tracks across its contents). Notice that some market segments, e.g. “display”, appear more than once because the estimates differ.
When you look at the markets in the context of quantum dots section, you can see that various types of displays are relevant, for instance. Now, Mergeflow doesn’t tell you that “displays are an application of quantum dots”. You have to draw this inference for yourself. But Mergeflow makes this a lot easier because it lets you access all the raw documents and data behind the charts shown above. When you do this, you can make such connections quite easily.
Related: How to find niche markets for new technologies
Find out in what markets a company is active (works best for big companies)
What does [insert-name-of-company-here] do?
Context markets help you address such questions. The idea is to find contents that mention the company, and that also mention some market segment. These context market segments tell you something about what the company does.
In Mergeflow, this is quite easy. Just search for the company and look at the context markets. Here is a screenshot of a search for Honeywell, for example:
There is no “Honeywell market”. So we don’t have a scatterplot of “direct” market estimates here (direct = directly pertaining to our search).
But you can see relevant companies (including Honeywell itself). These companies may be competitors, customers, or partners. Or a bit of everything, which is often the case with big corporations that have complex structures.
Then we have the context markets. These are markets where analysts see Honeywell play a role. Notice how these context markets almost look like a strategy recommendation. You can almost hear someone say, “nanosensors and 5G industrial IoT are growth markets in which we should invest”.
What you might not find in market analyses
Similar to venture investment news, market analyses are written with a broader audience in mind. This means that a market analysis is unlikely to be full of technological or scientific details. Conversely, if you search for highly specific technologies or scientific terms, you probably won’t find any market analyses.
The companies mentioned in market analyses tend to be bigger, or at least more established ones. But there are exceptions here.
Good search strategies for market analyses
This is similar to what I said about venture investment news as well. It’s usually good to use more-general terminology, and to think about how a smart but non-expert person would talk about your topic.
We often say this in other contexts as well, but it might be particularly true for market analyses: It’s better to start off broadly with your search. Then you can use the results–context markets in particular–as “inspiration” for where to zoom in.
Let me illustrate the “start off broadly” approach with another topic, CRISPR (the gene editing method). My broad “CRISPR” search gives me the following fastest-growing context markets (notice that “synthetic biology” is mentioned twice not because of the caps/non-caps spelling but because of the differences in CAGR estimates):
Apparently AI, or artificial intelligence, is the fastest-growing market in the context of CRISPR. We can now use this to zoom in, on “CRISPR” AND “artificial intelligence” (we added “machine learning” and “deep learning” to our query as well, basically as “synonyms” for AI). And now we can check out the data extracted from venture capital news, for example (click on the screenshot to enlarge):
The most-recently-funded company in this case, Synthego, uses a combination of CRISPR and machine learning for drug discovery, for example.
This iterative approach to searching is not only a lot faster than planning too much in advance. It also increases your chances of finding companies, technologies, and applications that are relevant but that you didn’t even know might exist.
Related: How to get better search results for tech discovery