What is Mergeflow Grid Search?

A Grid Search produces an interactive activity heatmap that lets you explore large numbers of topics, and how they interrelate. One set of topics defines the rows of the heatmap, another set of topics defines the columns. The values of a heatmap cell indicate how much overlap there is between any row and column topic. The grid visualizes these values by color intensity, so you can quickly eyeball many topics and their interrelations.

Grids (= the results of a Grid Search) are interactive. You can click on any heatmap cell in order to explore the sources (news, R&D, patents, investment events, etc.) behind that cell.

Grid Search gives you a shortcut that helps you quickly identify whether or not a topic--or a combination of topics--is worth your time.

Example topic combinations in a Grid Search

All kinds of topic combinations are possible, where one topic category forms the heatmap rows, and another topic category forms the heatmap columns. For example:

"Innovations" x "Materials" addresses questions such as "How do innovations in production (= rows) relate to various types of materials (= columns)?"

"Technologies" x "Business Units" addresses questions such as "Which machine learning technologies (= rows) fit into which of my business units (= columns)?"

"Materials" x "Companies" addresses questions such as "In which materials (= rows) do we see activities (R&D, investments, or patents, for instance) by which companies (= columns)?"

"Technologies" x "Markets & Applications" addresses questions such as "Which technologies (= rows) are relevant to which markets or applications (= columns)?"

"Emerging Topics" x "Products & Solutions" addresses questions such as "Which emerging technologies (= rows) are relevant to which products or solutions (= columns)?"

How to "read" a grid

Here is a simple example of what a grid could look like, and how you could use it to address various types of questions:


Our blog has several articles where we use grids to address various questions. For example:

Discovering strategies in additive manufacturing

Tackling climate change with machine learning

Clinical trials on nutrition, wearables, and lifestyle

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