INBOX INSIGHTS, April 21, 2021: Data Impartiality, Tech Change Management, SaaS Content Republishing

Data Impartiality, Tech Change Management, SaaS Content Republishing

Inbox Insights from Trust Insights

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Impartiality in Marketing Data

I’ve been reading through The McKinsey Way, Ethan Raisel’s 1999 business book about the eponymous consulting firm, and in one chapter, Raisel talks about how McKinsey never had any form of sales, just thought leadership marketing and recurring business. Their marketing angle was – and is still – getting a perspective on a client’s business that the client, for any number of reasons, was unable to garner for themselves.

That got me thinking about the unique selling proposition for Trust Insights has, and an angle that I hadn’t given much thought to. Yes, we use machine learning and data science to find answers. Yes, we work with organizational behavior and change management. But the angle I hadn’t paid much attention to until I really thought about it is simple:

Impartiality.

“Huh?” you may ask. What does that have to do with anything? Consider for a moment all the analytics data you work with. Facebook has its analytics, especially for advertisers. Google has its data, from Google Analytics to Google Ads to YouTube, and so on. Twitter has its data. Every marketing data source and software company has analytics and data; that’s more or less table minimum these days (unless your software really sucks).

But here’s the catch: every vendor has an incentive to make itself look good, especially when advertising dollars are involved. Facebook has a strong incentive to make Facebook ads look like they’re outperforming everything. They’ll tell you viewthrough conversions (conversions that happened outside of the clickstream but the person at some point viewed a Facebook ad) are through the roof, so that you’ll buy more ads from Facebook. Google Ads will tell you the same thing.

If you were to add up all the viewthrough conversions and partial credit conversions and so on, you might arrive at the same place one of my clients did recently – with ad systems telling them they sold 300% more than they actually do. They certainly would have liked to have sold 3x what they actually did, but all that told them was that ad system reporting was unreliable at best, dishonest at worst.

Reading Raisel’s book and reflecting on my client’s experience made me realize that’s something relatively unique we offer: we don’t have a horse in the race for reporting. We don’t have an incentive to inflate one number or penalize any particular data source to make another data source look great. We can be impartial, and in doing so not only bring in an outside perspective, but a neutral perspective.

The key takeaway here is to consider how impartial your various data sources are. Does the vendor sharing the data have a financial incentive to skew the data in their favor? If so, how bad is that skew, and what can you do to mitigate it?

Binge Watch and Listen

In week’s episode of In-Ear Insights, Katie and Chris talk shop on change management when it comes to technology. When a new piece of software is introduced, how do you handle the changes? Using the rollout of Google Analytics 4 as an example, what are the steps we must take to ensure as smooth a rollout of new technology as feasible? Tune in!

Watch/listen to this episode of In-Ear Insights here »

Last week on So What? The Marketing Analytics and Insights Live Show, we tackled how to think about competitive analysis and looked at a few examples of competitive digital marketing analytics..

Watch/listen to this episode of So What? here »

This Thursday at 1 PM Eastern, we’re going to look at how to construct a basic Google Analytics 4 attribution model, since the feature doesn’t currently exist. Join us on our YouTube channel!

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Data Diaries - Interesting Data We Found

In the past, we’ve focused on content republishing broadly, for all industries. This week, we wanted to dig into one specific niche for content republishing, SaaS software. After all, we would expect for any given niche in a market that content republishing would work differently, be less or more effective. We know from past experience that news sites and publications tend to republish frequently and see good benefit from. What about content around SaaS?

In an examination of 24,207 unique articles about SaaS in the last 21 days, we found the following. First, of those articles, a full 75.6% had been republished at least once, which is a startling statistic on its own. When we do matching to compare apples-to-apples republished versus original content performance, we see something stunning:

Content Republishing for the SaaS topic

What we see is that in the specific subtopic of SaaS, content republishing does substantially WORSE than original content. It attracts less traffic, has lower traffic values, and earns fewer shares and linking domains. That’s… rough. We also see that republished content seems to be following the advice of many SEO folks, that you should constantly be revising and updating your republished content, making it richer; the length of republished content is more than 5x longer than original content.

What’s happening here? There’s no easy explanation; in the original dataset, there’s a rich mix of differnt publishers and companies. There are even distributions for things like domain rating, so it’s not a case of one cohort of sites doing well and another cohort dragging things down, at least not obviously.

For now, the key takeaways are twofold. First, check your own niche or vertical to see how content republishing performs in your specific industry. Don’t accept generic advice that may draw data from sites that are nothing like yours.

Second, if you’re in the software business, check the performance of your own republished content. See if the above results are also typical for you, or if you’re an anomaly (in a good way) compared to the norm.

Methodology: Trust Insights used AHREFS SEO software to export 24,207 pieces of content about the keyword SaaS from its index. Content was restricted to the English language, filtering out any adult material, filtering out broken links, and excluding homepages. The timeframe of the data is April 1-21, 2021. The date of study is April 21, 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.

In Case You Missed It
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Weekly Wrapup

This is a roundup of the best content you and others have written and shared in the last week.

Data Science and AI

SEO, Google, and Paid Media

Social Media Marketing

Content Marketing

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Upcoming Events

Where can you find us in person?

  • HighEdWeb Analytics Summit, April 2021, virtual
  • ContentTech Summit, April 2021, virtual
  • ECommerce Marketing Summit, May 2021, virtual
  • AAF/Women in Analytics, May 13, 2021, virtual

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