INBOX INSIGHTS, June 23, 2021: Data Quality, Natural Language Generation, Instagram Brand Data - Trust Insights Marketing Analytics Consulting

Data Quality, Natural Language Generation, Instagram Brand Data

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Data Quality Requires Subject Matter Expertise

As I was preparing this week’s Data Diaries, I looked at the latest batch of brands that Facebook provides in our Crowdtangle software. I noticed something concerning:

What we see in the data are personal brands mixed in with corporate brands. The intent of looking at brands on Instagram is to look primarily at corporate brands, brands where the entity is a corporation, a company, an organization as opposed to an individual human being. Individual people we bucket under influencers. While we could argue semantics about the power of a personal brand – Celine Dion, for example, is both a human being and a brand – for the purposes of understanding how brands are posting content on social media, I prefer to put personal brands under influencers.

The exception would be where a personal brand isn’t really the person at all. Steve Madden, for example, is a fashion brand that manufactures footwear. The person, Steve Madden, isn’t much involved with Steve Madden the brand. The same is true of Estee Lauder. The real Estee Lauder is deceased, but the corporation maintained her name as the official brand.

So I took an hour to prune through the 4,000+ brands in our collection and removed about 500 personal brands. Now, did Facebook do something wrong? No. They lumped personal brands in with corporate brands because from their point of view, they’re functionally identical. Their classification system is different than ours because they have different intent.

But this speaks to the vital importance of subject matter expertise when it comes to data quality. If we want our data to serve the purposes we intend, we must ensure that it meets our standards. In this case, the data did not meet our standards – but the average person might not disambiguate between a personal brand and a corporate brand. It’s not something I’d want to outsource per se, and definitely something that’s not easily automated. Why? Because this kind of classification requires a lot of judgement, human judgement that requires more nuance than the average classification model would have with such a small dataset.

So what’s the point? Don’t blindly trust third party data, even when it comes from credible sources. Take the time to inspect it, to see what’s inside the box, and determine how well it aligns with your intended use. This applies to any kind of marketing data from a third party, from your Google Analytics instance to social media data to your direct mail list. You might be surprised what level of data quality you’re getting – and how much work it will take to clean up.

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In this week’s In-Ear Insights, Katie and Chris talk about the newest advances in natural language generation and walk through an example of what’s available now for creating content with the assistance of AI. Watch the demonstration, listen to the implications for marketers, and start formulating your AI-based content marketing strategy. Tune in to find out how!

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Data Diaries - Interesting Data We Found

In this week’s Data Diaries, we look at our newly-revised and appended Brands list on Instagram to see how unpaid brand content has performed in terms of engagement, year to date.

We see after a relatively stable springtime, where brand performance remained relatively static (around 0.275% engagement) performance has begun to decline again in May, dipping below 0.25% engagement. Performance was highest in January; peak brand performance averaged 0.371% in the first few days of the new year. Performance slipped the most in June, when it dipped as low as 0.222%. For context, 0.222% is engagement of about 1 in every 450 followers, while 0.371% is engagement of about 1 in every 270 followers. That’s a change of -40%.

One interesting tidbit that is in no way conclusive but merely coincidental for now: brand performance began to decline again right after the iOS 14.5 release on April 26. Since this is all unpaid content, there’s no reason to believe the ad targeting changes impacted unpaid content performance, but it’s possible that the negative press (and in-your-face popup when users launched Instagram, begging people to permit ad tracking) had a minor negative effect.

What’s the key takeaway? Unpaid Instagram performance for brands continues to slip, so if Instagram is key to your marketing strategy, you’ll likely need to add more paid efforts to maintain performance and keep attention on the service.

Methodology: Trust Insights used data Facebook’s Crowdtangle software to extract 1,190,519 Instagram posts from 4,715 brands. Brands were provided by Facebook, then cleaned and augmented by Trust Insights. Brands are defined as corporate Instagram accounts and excludes personal brands. The timeframe of the data is January 1, 2021 – June 20, 2021. The date of study is June 23, 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.

Additional disclosure: You’ll note that even after our removal of the personal brands, the charts above didn’t change substantially; overall, personal brands boosted the aggregate brand engagement by about 0.1% – not a huge amount.

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