Almost Timely News, January 29, 2023: Warrior Nun Algorithm to Action, Free LinkedIn Course

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Almost Timely News

Get This: Yet Another New FREE Course

Hi. It’s kind of rough out there with new headlines every day announcing tens of thousands of layoffs. To help a little, I put together a new edition of the Trust Insights Power Up Your LinkedIn course, totally for free.

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What makes this course different? Here’s the thing about LinkedIn. Unlike other social networks, LinkedIn’s engineers regularly publish very technical papers about exactly how LinkedIn works. I read the papers, put all the clues together about the different algorithms that make LinkedIn work, and then create advice based on those technical clues. So I’m a lot more confident in suggestions about what works on LinkedIn because of that firsthand information than other social networks.

If you find it valuable, please share it with anyone who might need help tuning up their LinkedIn efforts for things like job hunting.

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Almost Timely News: Warrior Nun Algorithm to AI, Free LinkedIn Course (2023-01-29)

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What’s On My Mind: From Algorithm to Action, Part 1

I’ll be talking about this in more depth on the Trust Insights livestream this coming Thursday, but I want to give you (and my Save Warrior Nun friends) some insight about how to think about deconstructing an algorithm and turning that insight into action.

What’s this all about? There are a lot of signals and measures that the entertainment industry uses to determine what’s worth paying attention to, and one of those metrics is our friends at Parrot Analytics. The sign of a good analytics company is one that discloses how it measures things, and Parrot Analytics has published detailed documentation – and math – on how their Demand Expressions metric works in their DEMAND360 platform. I applaud them for what they’ve published.

Why do the Save Warrior Nun folks care about their Demand Expressions metric? For a show like Warrior Nun, as part of the campaign to save the show, it needs to still show strong demand among its audience. So the organizers asked the logical question – how do metrics like Parrot Analytics’ Demand Expressions work, and what can we do to improve our standing?

Let’s take a look at what that formula looks like, because it’s instructional for how we need to deconstruct an algorithm to fulfill our purpose, improving our standing with that algorithm. This is what’s published on their website, and trigger warning for math and a lot of it.

Parrot Analytics Demand Expression

Hello calculus, my old friend. What does this show? Fundamentally, this is a summation formula. Let’s take it apart. First, we have P, the population of a market. In this case, markets are geographic, like the USA or France or Brazil.

Second, we have a weighting, the w variable. This weighting is explained as the types of actions an audience can take around content, from passive impressions at the lowest level of demand (it’s super easy to passively consume content) all the way to creative participation, which is the highest indicator of demand because it’s tough to motivate people to MAKE stuff in support of their favorite content.

Third, we have a metric, a numeric value of the expression. This would be things like 200 YouTube views or 1 piece of content created or 500 retweets.

Finally, we have a summation function that summarizes and aggregates each platform’s weighting and numeric value. Mathematically, you’re taking the population times the summation of the weighted activities of that population in every market.

With me so far? We deconstruct their formula, their algorithm, into its components. Now, our next step is to look at each of those expressions, which they detail in a separate chart:

Demand expressions chart

We see they have four buckets of expression types – social media, public video platforms (they call them social video), research, and free streaming (which is polite for piracy). Those are the kinds of platforms they monitor, and they monitor for 9 different kinds of engagement, from highest demand (aka the most effort on the part of the audience) to lowest demand (least effort):

  1. Creative participation – aka making stuff
  2. Active consumption – going out and getting the content
  3. Deep research – actively leaving ratings and reviews
  4. Social encouragement – if I had to guess, things like mentions, reshares, reblog, quote tweets, etc.
  5. Public posting – putting up basic, simple content like a Tumblr post or a tweet
  6. Expressing an opinion – lower effort things like voting or leaving comments
  7. Subscribing to updates – I’d wager this is followers on all the different platforms
  8. Indicating interest – probably mentions of a given media property
  9. Passive impressions – I’d guess just raw exposure/reach numbers

Parrot Analytics hasn’t given specific details about which bucket contains which activities, but if you’ve been doing digital marketing for a while, it’s not hard to figure out what should go where.

Now, I would guess on the back end, they probably do some weighted averages, centering, and scaling to normalize the inputs, and then they run their formula. I’d bet there’s at least a bit of regression analysis going on behind the scenes to assign what exactly the weights are in their DemandRank. It’s almost certainly not linear, meaning something at the bottom of the chart is 1 point versus something at the top of the chart is 9 points. It’s probably not exponential, either, and I’d guess it’s determined by something like gradient boosting, where the values can fluctuate over time based on the input channels. After all, with recent management changes at certain social networks, you’d want to have dynamic re-weighting baked into your algorithm.

Here’s the thing. We don’t know what’s inside the black box, but we don’t have to know it. What we need to know are the inputs and their weights, and we more or less have that.

Compare that to something like SEO. Every digital marketer who’s been around for a while knows that Google’s algorithms are exceptionally opaque. We do know network graphing is a part of it and has been since the very beginning. We have a sense of how crawling and indexing work. We know there are some manual weights, and we have the Search Quality Rating Guidelines to provide oblique hints at their data – but we fundamentally don’t know the inputs and certainly don’t know the weights, so we can’t make actionable decisions.

The last question is, what are the specific inputs in Parrot’s formula? While they don’t disclose it in the technical guide, it is helpfully right on the front page of their website.

  • Social media: Instagram, Facebook, Tumblr, and Twitter
  • Free streaming: Popcorn Time, Kodi, BitTorrent, and The Pirate Bay
  • Social video: Facebook Video, YouTube, DailyMotion, Vimeo
  • Research: IMDb, Google, Rotten Tomatoes, Wikipedia

Great. So now we understand the weights, we understand the actions available to us on the channels, and we understand the channels. How do we transform this into action? We have to know what resources are available to us.

Catch up on this past week’s Trust Insights livestream on mobilizing a community, if you haven’t had the chance. One of the topics we talked about was how a community behind your efforts makes your marketing much, much easier. In the case of the Save Warrior Nun community, this is a community in the tens of thousands strong. However, compare that to a community like fans of Stranger Things or Game of Thrones whose fanbases are in the millions.

In this case, your fanbase is your major resource limitation. So now we look at the list of demand expressions and ask ourselves, given the comparatively small fan base, what should we pursue?

Look again at the math formula. Population times the summation of weighted expressions times metrics. If you have a super large community, you can clock millions of low weight expressions pretty easily. When Game of Thrones releases a new episode, it’s low effort to get a million people to tweet about it. But suppose instead of 50 million fans, you have 50 thousand fans?

That’s where the weighting comes in and why understanding the math is so important. If you have a smaller fanbase that’s hardcore and dedicated, you can and should pursue higher weighted items because the extra weighting from the difficulty offsets your small numbers. Suppose you’ve got 50 million Game of Thrones fans, but most of them are couch potatoes. How many are going to engage in creative participation, making fan edits, music videos, etc.? Comparatively few in a very large population, maybe one one hundredth of a percent. Now suppose you have a smaller, dedicated fan base like the Warrior Nun folks. Could you get the same NUMBER of people, say, 500, to make videos? If the fanbase is 100 times more dedicated, then the answer is yes – and thus you can make a bigger splash because of the math behind the algorithm. You can take a hardy band of adventurers and with the math on your side overcome a massive army of couch potatoes.

So what, in this case, should we do now that we know the algorithm, we know the math, we know the weightings? This is where we have to get smart with content repurposing. Suppose a fan creates a single video, a music video montage of their favorite scenes. For that fan to post it on Twitter is easy – but how much additional work is it to also post it to Facebook Video, YouTube, DailyMotion, and Vimeo? Comparatively easy. The hard part is done.

Suppose we have an audio recording of a fan meetup. What would it take to re-imagine that content? Very little – this is the Trust Insights Transmedia Content Marketing Framework at work. We take that audio, put it through a piece of software like Headliner, and now we have video. That video gets posted, and now we’re satisfying the creative participation part of the algorithm with content we’ve already created.

Suppose we have some fanfiction written by a fan. We take that text, have it read aloud by someone, and feed that through Headliner. Now we have audio, possibly for a podcast, video for the social video sites, and a transcript for blogs that can be read by Google.

This is how we as marketers can dramatically multiply our impact, simply by reimagining content in a variety of formats and being more places at once. We don’t have to create net new content everywhere. We just have to satisfy the weighted inputs of algorithms and provide them what they’re looking for – so as long as we obtain that information, we can transform an algorithm into action.

In our upcoming livestream, we’ll be talking about Twitter and LinkedIn’s algorithms, but this advice applies to any algorithm that you can find info about – and hence my repeated kudos to Parrot Analytics for being bold enough to post theirs online so we know how to optimize our marketing efforts.

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ICYMI: In Case You Missed it

Besides the newly-refreshed Google Analytics 4 course I’m relentlessly promoting (sorry not sorry), I would recommend the livestream we did this week on activating marketing and public relations with community. It’s amazing how well it works when you get it right.

Skill Up With Classes

These are just a few of the classes I have available over at the Trust Insights website that you can take.



Get Back to Work

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Free Book! Almost Timely 2022 Essays

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What I’m Reading: Your Stuff

Let’s look at the most interesting content from around the web on topics you care about, some of which you might have even written.

Social Media Marketing

Media and Content

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Tools, Machine Learning, and AI

Analytics, Stats, and Data Science

All Things IBM

Dealer’s Choice : Random Stuff

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How to Stay in Touch

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Events I’ll Be At

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Required Disclosures

Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.

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My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.

Thank You

Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.

See you next week,

Christopher S. Penn

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Christopher S. Penn is one of the world’s leading experts on AI in marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers: An Introduction and Primer. Christopher Penn is an AI keynote speaker around the world.


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