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How to use market estimates to structure a tech field

Let’s say you want to analyze the technology and business of ‘smart city’. ‘Smart city’ is huge and includes many different technologies. How can you get a first useful approximation to such a vast topic area? Here we describe how you can use market estimates to address such questions.

Related: What makes a good innovation analyst?

‘Smart city’ market estimates from Mergeflow

First, we simply searched for “smart city” OR “smart cities” in Mergeflow. Then, we looked at what Mergeflow’s Market Data tool extracted from worldwide “smart city” news, press releases, portals, and so on.

For our search, we got more than 450 smart-city-related market estimates from Mergeflow. In a first overview, Mergeflow displays these data as follows (click to enlarge):

Market estimates related to "smart city", extracted by Mergeflow. For some market segments, there are multiple estimates that differ from each other.
Market estimates related to “smart city”, extracted by Mergeflow. For some market segments, there are several estimates that differ from each other.

The scatterplot in the upper left shows the distribution of “smart city” market estimates. To the right is a tag cloud showing most relevant companies. Below are the largest and fastest-growing markets in the context of “smart city”. Notice that for some market segments, such as IoT, there are several estimates that differ from each other.

Related: How to get better search results for tech discovery

The largest markets

We then looked at market estimates that are related to ‘smart city’ but that are not explicitly on ‘smart city’. We call these estimates context markets. Here are the context markets with the largest estimated size (click on the image to see a larger version):

The market estimates in the context of "smart city" with the biggest estimated size.
The market estimates in the context of “smart city” with the biggest estimated size.

Look at the market segment names. From these segments, you quickly get a sense of the overall topic structure. For example, there are infrastructure technologies such as “IoT” or “wireless internet services”. Then there are applications such as “public safety and security”, and tech fields such as “manufacturing” or “automotive IoT”.

By the way, sometimes it is better not to look at the largest markets, but at the smallest ones. While this might be counter-intuitive, particularly for bringing a new technology to market, niche markets are often better. We explore this in another article, “How to find niche markets for new technologies”.

The fastest-growing markets

Next, we looked at the markets with the biggest estimated growth rates (CAGR).

Here are the top results (click to enlarge):

The fastest-growing (according to estimates) market segments in the context of "smart city".
The fastest-growing (according to estimates) market segments in the context of “smart city”.

It seems that the fast-growing markets are mostly infrastructure. For example, “5G”, “blockchain IoT”, various network technologies, and “fog computing” (also called “edge computing“) could all be considered infrastructure. Again, if there are estimates for the same market segment but with different numbers, we show them all.

Companies

The third type of information that you can use for tech field segmentation, and that Mergeflow extracts, are companies. Here is a companies tag cloud for our smart city markets search:

Companies mentioned in market estimates for the "smart city" context.
Companies mentioned in market estimates for the “smart city” context.

You can now also zoom in on individual companies, to see the markets in which they operate. When we do this for Cree, for example, we see that they operate in lighting markets:

Cree operates in lighting-related markets.
Cree operates in lighting-related markets.

So you can see that companies often provide another way of structuring a tech field.

This article was written by:

Florian Wolf

Florian Wolf

Florian is founder and CEO at Mergeflow, where he is responsible for company strategy and analytics development at Mergeflow. Previously, Florian developed analytics software for risk management at institutional investors. He also worked as a Research Associate in Computer Science and Genetics at the University of Cambridge. Florian has a PhD in Cognitive Sciences from MIT.