Network analysis is a process of interrogating the relationships between different nodes in a network. Nodes can be any entity, such as people, influencers, organizations, words, or topics. Network analysis aims to identify patterns and discover relationships between these nodes. Marketers and analysts use various methods, including social network analysis, link, and path analysis.
Each method has its strengths and weaknesses, but they all share the goal of helping you understand the complex web of connections that make up a network. By understanding these connections, you can learn the behavior of individuals within the network, the topics that connect different communities, and the overall structure of the network itself.
How Can Marketers Use Network Analysis
Network analysis can be a valuable tool for marketers who want to understand topical-based communities, brand perception, or how campaign content is shared within a data set. By understanding these relationships, marketers can better target their marketing efforts and tailor their narratives to specific groups of customers.
For example, let’s say you’re a marketer for a clothing company. You might use network analysis to understand the relationships between different fashion influencers on social media. By understanding which influencers are connected, you can identify which ones are most likely to promote your products to their followers. In addition, network analysis often will uncover new influencers within a topical community.
Also, network analysis can help identify topics and themes of a large dataset of social media conversations. For example, when researching the term “digital transformation,” network analysis can help determine other themes mentioned alongside digital transformation topics. This can be valuable for marketers who want to know what their customers are talking about and use that data to inform their digital marketing strategy.
A few enterprise social listening platforms offer similar analyses, but these can be costly. For everyone else, there’s a tool called NodeXL Pro.
What is NodeXL?
NodeXL is an open-source software application that allows you to collect, analyze, and visualize data from social media networks. They have an easy-to-use interface, and users from all levels can use it to perform network analysis. You don’t have to be a data scientist to find success with NodeXL.
NodeXL is a tool created by the Social Media Research Foundation. The foundation is dedicated to fostering research on contemporary network society using their tool, which is used in classrooms and universities globally. They have several licensing options, but here are the basics:
- NodeXL Basic: Free
- NodeXL Student: $39 a year
- NodeXL Pro: $39 to $99 a month
NodeXL Gallery & Examples
Also, here’s a fascinating 12-min video of Marc Smith, the director of the Social Media Research Foundation, talking about the importance of crowds, citing a research study, “Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters” (Pew Research & Social Media Research Foundation.) The video and research are almost ten years old but still relevant today.
Below is a breakdown of Marc’s explanation of network analysis graphs. He also refers to them as clustering. At a high level, Marc talks about the structure and stages of network graphs:
- Hubs: When graphs are first formed, they start with hubs. These are the broadcasters, and they create content and audiences. The second stage is when the audience builds their audience, and the third stage is when a community is formed.
- Bridges: These are connectors and active participants in one community but are starting to branch out into other topical-based communities.
- Islands: They are proof that the community and content have visibility from others in the industry.
Is NodeXL Pro a Social Listening Tool?
Not really. NodeXL is designed for network analysis, focusing on helping you understand the relationships between people, content, and meaning rather than what they’re saying or in which channels.
That said, NodeXL can still be a valuable tool for your martech stack, particularly if you want to track how information flows through a network. For example, you could use NodeXL to map the relationships between Twitter users talking about your brand, products, and services.
To do this, you first need to scrape Twitter for all the tweets related to a set of topics or hashtags. Then, you would use NodeXL to analyze the network of relationships between those tweets and the people who created them.
NodeXL is a valuable tool for understanding the hidden relationships between people, content, and meaning. While it’s not designed specifically for social listening, the data insights can still be used for that purpose.