INBOX INSIGHTS, April 28, 2021: Influencer Data in Action, Process Change Management, Facebook Brand Engagement - Trust Insights Marketing Analytics Consulting

Influencer Data in Action, Process Change Management, Facebook Brand Engagement

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Case Study: Using Influencer Identification Data

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:

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:

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?

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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In week’s episode of In-Ear Insights, Katie and Chris talk shop on change management when it comes to process management. How do we handle the creation and adoption of new processes, especially when they’re big changes? Tune in!

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Last week on So What? The Marketing Analytics and Insights Live Show, we looked at how to construct a basic Google Analytics 4 attribution model, since the feature doesn’t currently exist for the most part.

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This Thursday at 1 PM Eastern, we’re going to look at marketing forecasting with AI and how to make use of a content marketing forecast. Join us on our YouTube channel!

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Data Diaries - Interesting Data We Found

This week, we look at Facebook’s engagement rate for brands on unpaid content. The big question everyone’s had with the updates to iOS 14.5 is, will Facebook accommodate brands by boosting engagement as a way to keep the ad dollars flowing?

The quick answer, of course, is that it’s too early to tell. That said, what we see isn’t encouraging:

Indeed, brand engagement has gone from a median of 0.0175% at the beginning of 2021 to a low median of 0.008%, a 54.3% decline. To put in more concrete terms, a 0.008% engagement rate is the same as saying 1 out of every 12,500 Facebook brand fans engages with brand content. That’s a stunning number. If you were to stand outside your office, on the sidewalk, and engage with 2 people – even just handing them a brochure – chances are you’d be beating your Facebook unpaid content engagement.

This is why Facebook is so panicked about the iOS 14.5 tracking changes, and why it lobbied so hard against them; Facebook knows that brand engagement of unpaid content is in the toilet, largely of their own doing. They’ve suppressed brand reach so heavily over the past 5 years that you’d literally get better engagement from flyers hanging in restrooms with those little tear-off strips of paper at the bottom, all in an effort to force brands to pay. The iOS privacy changes (along with other changes like more restrictions on third-party cookies) mean that brands will see less accurate data and lower performance from their paid ads – leaving many brands to rightfully question whether Facebook is worth the effort at all.

The key takeaway here is to check your own attribution models for Facebook – paid and unpaid – in software like Google Analytics very carefully over the next two weeks. Check in more frequently than you normally do, and see if these big-picture trends are impacting your efforts.

Methodology: Trust Insights used Facebook’s Crowdtangle software to extract 3,267,445 Facebook posts from 3,015 brand Pages. Brands were identified by both Facebook and Trust Insights. The timeframe of the data is January 1 – April 27, 2021. The date of study is April 28, 2021. Trust Insights is the sole sponsor of the study and neither gave nor received compensation for data used, beyond applicable service fees to software vendors, and declares no competing interests.

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