Case Study: Using Influencer Identification Data

Case Study: Using Influencer Identification Data to Build Audience

One of the challenges of marketing data is putting it to use. It’s fine to run reports and provide analysis, but if you don’t make decisions with your data, if you don’t change what you’re doing, then the data is of limited value. What does it look like when we put data to immediate, practical use? Does data-driven marketing really work?

This past week, the team at Content Marketing Institute asked me to participate as a guest on their weekly #CMWorld Twitter chat. When I looked at the engagement numbers for 2021, it didn’t look great:

Previous CMWorld Engagement

Now, to be clear, this is no fault of CMI; this is just normal social media decline. All social networks over time tend to degrade in performance if you’re not constantly testing things to see what works and challenging the algorithms.

I knew we needed to bring in new blood; the canary in the coal mine for declining engagement was the number of distinct participants, the second to bottom line in the chart. It’s great – and important – to have your regulars, the people you can always count on to be a part of the show. But scheduling is tough. Customer work comes up.

So the question I asked was, how can we identify new people who could bring in new audiences? We look to influencer identification! Using data that Trust Insights collected over the last 6 months on popular marketing Twitter chats, we constructed a network graph of the people most influential in those chats:

Network graph

There are three things to look at; the Semrushchat crowd is the magenta group. The CMWorld chat group is the blue group. And in the middle are folks who are active in both communities. That’s who we needed – ambassadors, bridges from one community to another, people who could attract new audiences.

So once we identified who those folks were, I tweeted about the chat, used that image, and tagged the top 20 folks in a couple of tweets, 4 hours before and 2 minutes before the Twitter chat:


What happened? Did we successfully attract new audience?

Twitter chat results

We did. We had the third-highest engagement of Twitter chats year to date for CMWorld’s Twitter chat. We had the fourth-highest number of unique participants. But critically, the last line on the graph shows that we had the total highest addressable audience all year (the sum of all the followers of participants).

This is the indicator that we got new people into the chat; bigger addressable audiences means some of that purple and green cloud made it into the CMWorld chat. It was the regulars PLUS the new folks, and that’s what drove the increases.

Now, did the topic of analytics have an influence? Perhaps. But the CMWorld chat has had so many great topics that I would cast doubt the topic alone had much to do with it. A great topic will definitely bring back your regulars, but given how hard it is to discover new things on social media, choosing a great topic is the equivalent of “build it and they will come” – a relatively poor strategy.

Thus, making use of audience analytics, of influencer identification data, makes for a fairly compelling case if you want to grow your audience. If you’re running a virtual event, this would be one way of growing your audience, especially if you use data from other virtual events in your industry. These techniques aren’t limited to just Twitter, either; any data where you have identifiable entities talking to and about each other is ripe for analysis, and then leveraging that analysis to grow your audience.

Here’s the takeaway: put your data to work! Don’t analyze just for the sake of analysis – do something with your data, make decisions with it, take action and use your data to grow whatever campaigns you’re working on.

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Trust Insights ( is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.

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