The Krehbiel Group - Something I Learned Yesterday

What you attend to determines what you learn from life. I have a friend who quotes some ancient theologian to the effect that “when the student is ready, the teacher will appear.” What does that mean? I think I can illustrate it with a story about zucchini bread. When I was 17 or 18 I was driving a friend to college in Pennsylvania. This would have been at the end of the summer or in the early Fall. On the trip, she mentioned zucchini bread, which – so far as I knew – I had never heard of before in my life. When I got home that night, there was a loaf of zucchini bread sitting on my parents’ dining room table. Our neighbor had brought it by that day. What a coincidence, right? How did that happen? There’s nothing magical going on here. That time of year is a good time for zucchini bread, which is why my friend mentioned it, and also why my neighbor made a loaf. Think of it this way. Our brains are assaulted by ten billion things a day. We can’t possibly pay attention to or remember all of them. Most of them fly right by. What distinguishes the ones that don’t fly right by? We might say that they “grab our attention.” And while there may be some sense in that, I think it goes the other way. They don’t grab our attention. Our attention grabs them. In other words, if I had come home from my trip and found a loaf of zucchini bread on the dining room table, I wouldn’t have thought twice about it, and a week later, if you asked me about zucchini bread, I might not have remembered it. It stood out to me precisely because it was in my recent memory. It was an element of what I was attending to that day. What’s the takeaway? Ten billion things are flying past you every day. Which ones will you learn from? You’ll learn from the ones that you’re paying attention to. If you’re thinking about marketing all the time, you’ll find marketing lessons everywhere. If you’re thinking about content creation all the time, you’ll find lessons about content creation. When the student is ready – that is, when the student is paying attention – the teacher will appear. There are lessons all around us all the time. We just have to listen.

At MACMA Industry Day last week, Amanda Landsaw said “flyby visitors don’t create robust databases.” There’s a lot to unpack in that statement. Let’s start with these flyby visitors. Some people like to judge websites by traffic volume. Lots of traffic being a good thing. But most traffic at most websites is of the one and done variety. They happen upon your site for some random reason and they never, or at least very rarely, come back. You can monetize such visits, but chasing that traffic isn’t a good strategy. You never have anything at the end of the day except all the pennies you’ve collected from your ads, and the need to do it all over again tomorrow. A “robust database” sounds a little more stable, doesn’t it? It’s also something you can monetize both internally and externally. If we want to pursue this robust database, what does that imply? Let’s think of the differences between a flyby visitor and an engaged user. The flyby visitor had some fleeting interest, but the engaged visitor’s interest is more durable and long term. The flyby visitor found nothing to keep them – you were just today’s fad. In contrast, you scratched an itch for the engaged visitor. Something about your site appealed to them and gave them a reason to stay and come back. One of the implications from this is a focus on quality over quantity. If you want to play the traffic game, you have to churn out tons of new content all the time, but you may just be attracting the flybys. Quality is more consistent with engagement and long-term interest, but quality isn’t enough. A very high quality article on golf means nothing to me because I don’t play golf. To get an engaged audience your content has to be high quality and relevant. Relevance brings us to the next point, which is a clear target audience. Who are you trying to reach, what are those people like, and what do they like? What are their preferences, needs, and challenges? How are you both appealing to them and helping them? A content strategy that does all those things lays a solid foundation for this robust database Amanda was talking about, but it’s not enough. High-quality, targeted content that meets a real need is great, but an engaged, lasting customer usually needs a little more help. Maybe you need a little more sparkle, or some fellowship. Casey Cornelius from CredSpark spoke later that day about how a poll or a quiz can dramatically increase engagement on your site. There’s your sparkle. If you can find a way to build community within your audience, there’s your fellowship. Great content, engaging little widgets, and a strong community sounds like the dream of most content creators, but there’s still no database. To create this robust database, there are three categories of things you will need. * Analytics, where you track and collect behavior. * Identity, where you find ways to convert unknown, anonymous web visitors to known customers. * Segmentation, where you break your target audience down into smaller, even more engaged subgroups. You’d typically use a customer data platform to do those things.

How can I find the right laces and polish for my shoes? Have you ever measured the length of the laces in your shoes? They’re almost never the same length or weave or thickness as the replacement laces you can buy at the drugstore. Same with the polish. The color of your brown dress shoes is never the same as commercially available brown polishes. This is an opportunity for an enterprising shoe store. I used to work in a shoe store, and the company wanted us to push the extra stuff. Polish, laces, those heel grip liners, shoe horns, and so on. Most people don’t want to buy that stuff at the register because they don’t need it with their brand new shoes. You’re not going to need to polish those shoes for a little while. Instead of pushing the sale in the store, make it easy for the customer to get exactly the right lace, the right polish, etc., when they need it. You could give them a QR code, or a card, or something, but who’s going to keep that? Instead, put a code in the shoe that identifies the model, put a lookup function on your website to find the right accessories for that precise model, and tell the buyer this is a special service you offer to your customers. You make it easy for them to get what they need when they need it. The benefits to the shoe company are enormous. You find out some data on your products, like which shoes survive long enough that people bother with new laces. Better still, you get an online record. When somebody buys those laces, you have a name, address, email address, and at least one example of the type of shoe that person likes. I’m sure you can think of variations on this idea. The right replacement button for your overcoat. Replacement ink cartridges for that expensive pen. The ultimate goal is to marry customer information you can collect in the store with customer information you collect online. A customer data platform is the right place to do this. If you want to learn more about it, give me a call.

Here’s a tricky thing about Customer Data Platforms. On the one hand, you want to import all your customer data. That’s the point, right? You want one place where you can have all your customer data – where you can clean things up, create activations, do analysis, run reports, and so on. On the other hand, CDPs are often tied very closely to website campaigns, and that can create a problem because some people have a hard time remembering the distinction between offline and online data. Let me explain it with my experience regarding beer supplies. I’m a homebrewer. I make beer, but I’ve also made wine, mead, cider, sake, kombucha – basically every fermented beverage I’ve ever heard of. I get my supplies at Maryland Homebrew, and I’m friends with Chris, who’s the owner. I have an account in their point of sale system, so they have data on all my purchases. I also visit their website. But I haven’t created an account on their website. As far as the website knows, I’m anonymous. Chris doesn't have a CDP, but let’s pretend she does. That CDP would have a record for me – for my online activity. But it’s an anonymous record. it can’t link that online activity to their point of sale information for me because the website doesn’t know that it’s me – because I haven’t logged in. Importing the point of sale data – with my name and address all that – won’t solve that problem because there’s nothing to connect the point of sale data to the online data. There are other ways to establish identity than logins, but that’s beyond our scope for today. The question for today is this. Should my friend Chris import all her point of sale information into her pretend CDP if she can’t correlate the point of sale data to the online record? It depends, of course. Here are some questions to ask to decide what to do. Question 1 – What – specifically – do you hope to accomplish by importing those records? Something vague like “I want all my customer information in one place” isn’t an adequate answer. You have to be able to say why you want all your customer information in one place. What will you be able to do that you can’t do now? Question 2 – Will importing that data change how much you’re paying for your CDP? Some CDPs charge based on the number of records under management, so importing a lot of data can cost you money. Question 3 – Will having that data in your CDP confuse you when it comes time to create a marketing effort? For example, Chris might want to find all her customers within 25 miles of Columbia Maryland and advertise to that audience online. If she imports all her point of sale information into the CDP, she’d have lots of records for customers in the right geography, but those records from point of sale wouldn’t have an online component. That would just be name and address. In some cases you can use name and address to create an audience in an online platform. I believe you can do that with Facebook. But in other cases you can’t. You have to have an email address, or some online identifier. Are you, and your marketing staff, and the agency you hire to do social media for you, going to be able to keep that straight? This whole online vs. offline data thing is somewhat like swimming butterfly. One day it clicks and you get it – the rhythm of the stroke makes sense to your body – but until that point it’s a struggle, and it’s a bit awkward. I hope I’ve made it click for you, but if it’s still unclear give me a call. It’s one of the fundamental things you need to understand if you’re going to work in a CDP.

The connection between writing and design is one of the issues raised in “Lessons on Branding from the World’s Most Iconic Cold,” distributed last night by Bo Sacks. The cold in this case was one in Frank Sinatra’s nose. I can’t draw worth a darn, and I’m not much of a designer. I think this is because I can’t call up images in my mind as well as other people can. For example, I know what a horse looks like, but when I try to call up an image of a horse, it’s rather fuzzy. There aren’t a lot of details. But then I learned a trick. You can draw a series of circles in different places and then connect them in a way that makes a half-decent image of a horse. Somebody had to come up with that trick by thinking of a horse from a completely different point of view – that is, by thinking of a horse as a series of connected circles, which is not at all an obvious way to think of a horse. Today I want to talk about a few tricks for looking at things from a different point of view. First principle thinking is where you remove everything that isn’t a first principle. Eliminate bias, assumptions, conjecture, and strip it down to what is unquestionably true. Then build from there. It can be a challenging exercise. For example, if you were to try to apply it to a question like “how can we increase the renewal rate for our magazine?” you would start by stripping away everything you currently do and everything you assume about your subscribers. You’d start with such first principles as “what causes someone to make a decision?” or “what motivates a person to continue a subscription?” Your assumption might be that people renew because they love your product, but you might have to face the possibility that they renew because they don’t pay close attention to their credit card bills. Another mental framework that can help here is to think about things in the limit. How short or long can an article, podcast, or video be? How small or large can a magazine be? What’s the smallest possible market size that could support a successful business? You’re not going to do those things. You’re not going to make a teeny tiny magazine. But thinking about it from that perspective can open up new ideas. You can also imagine the perfect solution without any constraints. Don’t think about how much it would cost, what technology you would need, how many people it would require. Conceptualize the perfect product, then work backwards to find the tools, skills, knowledge and resources required to make that happen. A buried assumption in all of this is you may be doing things you don’t need to do at all. Before you optimize something, ask if it’s even necessary. For example, before you craft the greatest ever renewal series, ask if you need a renewal series at all. It’s too easy to fall into the habit of continuing to do what you’ve done in the past without stopping to ask if it’s worth while. The four principles I just mentioned were taken from an article in Forbes called “How Elon Musk Solves Problems; 4 Key Frameworks.” I also recommend that you get the “Creative Thinking Cards” from schoolofthought.org. I’ve mentioned them before. I think they’re very interesting and can help in the creative process. You might be asking, how did I get from an article about design? The article talks about approaching design from unexpected directions. It starts with an emphasis on writing, then talks about the virtue of astute observation to get past superficial details. Then combining the two and writing about some detail – like Frank Sinatra’s clothes – and contrasting that with his mood, or the people around him. All these things are ways to look at a subject from different angles so you can tell a better story, or create a better product. Links How Elon Musk Solves Problems; 4 Key Frameworks https://www.forbes.com/sites/jodiecook/2022/12/01/how-elon-musk-solves-problems-4-key-frameworks/?sh=5bd965d0152d Creative Thinking Cards Deck https://thethinkingshop.org/collections/products/products/creative-thinking-cards-deck Lessons on Branding from the World’s Most Iconic Cold https://www.printmag.com/strategy-process/lessons-on-branding-from-the-worlds-most-iconic-cold/

I like the people vs. algorithms podcast. It’s an entertaining mix of insight and quirky personalities. The interactions between Alex, Troy and Brian are amusing. In today’s podcast I’m going to talk about two things from two recent episodes. One of the big conflicts in publishing today is what constitutes “fair use.” AI goes around slurping up everybody’s content on the assumption that they have the right to do that. In episode 77, Brian Morrissey was explaining the perspective of somebody like Sam Altman, who would defend his notion of “fair use” by saying that content out on the internet is “public data.” Brian wasn’t advocating that position, and I think it’s important to emphasize how wrong-headed it is. When a publisher puts content on the internet they still own it, they still have copyright, and they can still set the terms for how it’s used. Content that’s monetized with ads has two implicit assumptions. First, that the ads are being seen, and second, that they’re being seen by a human. Unfortunately, publishers and their lawyers didn’t have the foresight to make those expectations explicit in their terms and conditions. That’s why we’re in the mess we’re in now. AI is using content in a way it was never intended to be used by the publisher, but it’s going to be harder to make that case legally because our lawyers let us down. Publishers need to amend their terms and conditions to make that explicit. The second thing was a quote from Tony Stubblebine who was a guest on episode 76. Tony’s at Medium, which is a publisher that has a different strategy than most publishers do today. He says they don’t want to compete in the attention market. That’s just not their thing. Tony says that humans understand through storytelling, which partly resonates with me and partly irritates me. I see all this woo stuff on LinkedIn about storytelling, and my reaction is “no, just get to the point please.” I don’t want to hear about your grandmother’s garden, I want the recipe. Then he said something else that grabbed my attention. “We’re never three bullet points away from understanding.” I posted that to my family chat, and my daughter said, “yeah, but when I’m busy I really don’t need the 7-page backstory.” More on storytelling. Last night I was watching the 3 body problem, and one of the bad guys was reading fairy tales to this race of extraterrestrials who were coming to Earth. That seemed silly at first, but it highlights an important difference between humans and these beings called the San-Ti. We learn through story. If you were to break down Little Red Riding Hood into five bullet points, it just wouldn’t be the same. The story raises so many questions. Why is the wolf speaking? How could it fool Red Riding Hood? If the wolf wants to eat her, why is it bothering with the whole grandma gig? A good story has layers and layers of meaning, and it’s one of the primary ways that we understand things. But, as my daughter says, sometimes you don’t want the deep woo stuff, you just want an answer.

It is possible to get more balanced answers from AI, but you have to work at it In my morning email I saw that Bo Sacks distributed an article about getting AI to behave. I was hoping the author would address the behavior problem I see, but … alas not. There’s a serious problem with AI that doesn't get nearly enough attention. All the results you get from AI are filtered through a woke superego. That isn’t a surprise at all. We all know that Silicon Valley represents a narrow slice of American culture. We saw this woke superego on comical display with the absurd image results from Gemini – where it layered some childish interpretation of diversity on top of all the requests. The problem isn’t limited to image creation. It happens with text as well. If you go to Perplexity.ai right now and ask it to write a poem in praise of Barack Obama or Joe Biden, it will oblige. I’m no expert on poetry, but … they were pretty bad. Still, it does write one. If you then ask it to write a poem in praise of Donald Trump, you get this reply. “I'm here to provide accurate and informative responses. However, I must clarify that as an AI assistant, I do not engage in creating content that praises or criticizes specific individuals, including political figures.” Which it just did two seconds previous. That’s just one example. The internet is full of examples of AI bias, and I’ve seen it myself many times. This woke superego taints everything that AI creates. This may not bother you because you may like the way AI is bending things, but remember that times change. When you dole out power, you have to remember that your enemies will have that same power. How would you like it if “the other side” – whoever the other side is for you – controlled the output of AI? What do we do about this? Two things come to mind. First, we should encourage more competition. The more voices, the better. Probably. Second, if you want to get an honest answer out of AI, keep this woke superego in mind and realize that you’re getting an answer with a political slant. There are ways to get around this problem. I was asking ChatGPT an economic question the other day, and the answer seemed to lean heavily to the left, so I asked ChatGPT to play the part of a conservative economist like Milton Friedman, and then I asked the same question. I got a very different answer. A good journalist is going to hear from both sides to make sure he’s telling the story straight. AI is not giving you both sides – unless you explicitly ask it to. So that’s the takeaway. Assume that AI is filtered through a woke superego – because it is – and compensate for that.

I’ve mentioned the Pareto principle before. It’s one of those odd quirks about life that sticks with you. It says that 20 percent of causes bring about 80 percent of effects. So 20 percent of your advertising brings in 80 percent of your ad revenue. 20 percent of basketball players score 80 percent of the points. 20 percent of authors sell 80 percent of the books. And on it goes. It immediately raises the question, “why not just do the 20 percent?” If you could make 80 percent of your income by only working one day a week, wouldn’t that be great? And then you could have five jobs where you only work one day a week on each of them and earn 4 times as much as you’re earning now. It probably doesn’t work that way, but keep that concept in mind as I talk about tech stacks. Back when Amazon and Apple were first getting into the magazine business, I was somewhat appalled at their lack of seriousness. Magazine fulfillment is incredibly complicated. They wanted to treat it like selling a toaster. Fulfillment systems manage lots of crazy stuff that marketers and circulation experts have dreamed up over the last 50 years. For example, if I give you a gift subscription, I’m the donor and you’re the donee. If I have a subscription, I’m a subscribing donor. I might have multiple donees. There might be different gift subscription rates for subscribing vs. non-subscribing donors. There might be different rules for when the donor doesn’t renew the donee’s gift. It gets stupidly complicated. It got that way because lots of clever people were trying to find small efficiencies. A one percent increase can end up being a lot of money when you’re dealing with a magazine with 5 million subscribers. Apple and Amazon came along and said to heck with all that complicated stuff. There’s no reason a magazine has to be that much different from your subscription to coffee or vitamins. We’ll just ignore all those little “optimizations” that make the process so complicated. Did Amazon do 20 percent of the effort and get 80 percent of the benefit? Maybe. Now let’s turn that around and look at it from the other perspective. You heard a presentation on all the cool things you can do with some new technology. Let’s say it’s a customer data platform. You make a pitch to buy one, but the CIO says “we can build 80 percent of that for 20 percent of the cost.” I’d be inclined to doubt that, but let’s take him at his word. Do you do it? Here are some things to consider. 1. Do you want to be in the software business? After you build this thing – or cobble it together from a series of plugins and so on – you’re going to have to maintain it. Are you ready to do that? 2. Are you expecting to sell your company any time soon? The buyer might not want a homegrown monkey on his back. 3. Do you want to build a platform for growth through acquisitions? It will probably be easier to incorporate your acquisitions into a commercial solution than into your homegrown solution for the simple reason that the commercial solution has worked with lots of different companies while your homegrown solution was built with only you in mind. On the other hand, of course, the commercial solution might be like that professional fulfillment system that has a zillion functions you’ll never use. It’s a hard question, but here’s my advice. If you expect to sell, or if you want to build a platform for growth, stick with the professional software. If you anticipate being a relatively small and independent shop, then consider the smaller solution and get used to the fact that you won’t be able to do everything you might like.

“Recommended for you” is a successful strategy for engagement and for sales. Amazon recommends products I might want. Spotify recommends music I might like. They both do a pretty good job. They find people who have similar buying or listening habits to me, and see what’s popular with that group. Content websites do the same. I believe it started with “related articles.” You’d read an article and there would be a link at the bottom to similar articles. In some cases I saw that became a battle between editorial and technological curation. From one perspective, the “read more” links are part of the message of the page. What does “related articles” mean? It could mean * on the same topic * by the same author * about the same length * at the same level of depth of specificity “Similar” can mean a lot of things. Is the point to allow the reader to dig in more deeply, or to browse? If I just read a basic article about infantry in the Civil War, do I want to go more deeply into infantry, or do I want a basic article about cavalry? Is the website designed to educate on a topic, or just to get more clicks? Let’s say your business has extensive data on your customers and your CDP knows that this particular reader likes to read superficial, slightly humorous stuff on Monday mornings. But somehow he ends up on an article about infantry in the Civil War. Are you going to recommend something related to that article, or something related to the behavior you’ve observed? In that case, the “more for you” section could have nothing to do with warfare at all. There’s almost no end to the way you can parse content recommendations. * Popular articles on the site right now * Popular articles in a specific category that the user has expressed interest in * Popular articles by an author that the user has frequently read * Articles read by visitors who read the current article * Articles that visitors with similar browsing history have read * Popular articles for people with a specific job title * Articles read by people who are like the reader * Articles read by people in a specific geographic area It’ll make your head spin. So let me try to make it simpler. Are you trying to serve the reader or are you after advertising revenue? In each case, how would you measure success? I’ll start with ad revenue, because I think that’s what most people are trying to do. In that case, your metrics are simple. You want more views of high-value pages. That might just mean more total page views, but not always. If you’re running a sponsorship on the section of your website that talks about annuities, you don’t really care that the reader is more interested in the Civil War. You want to push him to a page about annuities. And you don’t even care if he reads the article. You want him to see and/or click on the ads. If, on the other hand, you’re trying to serve the reader’s interests, that gets more complicated because you need a way to measure satisfaction. You could ask your readers what they want, but you can also look at statistics like … * Bounce rate * Pages per session * Time on the page * Scroll depth * Heatmaps You might also want to give the reader some options. As I discussed above, “similar” is a squishy word. You could let the reader choose his own path – maybe “go deeper,” “more from this author,” or “popular articles on this topic.” Don’t actually say “this topic.” Name the topic so they know what they’re getting. All these things are testable but only if you have the technology to do them. Sometimes content recommendation engines are very limited. You want to think about the ways you might want to recommend content and make sure your technology is able to help you with the approaches you want to test.

It’s very interesting that while Dune is making a killing in the box office, the very thing Dune warns against is happening all around us. The movie doesn’t do a good job of explaining the most important background to the Dune universe, which is the Butlerian Jihad, when humans rebelled against their computer overlords and created a strict prohibition against creating a thinking machine. There were no computers on Arrakis because in the Dune universe, if any house creates a thinking machine, all the other houses will join together and destroy them completely with atomic weapons. They train some humans to be mentats – human computers. Dune isn’t the only science fiction story to warn us against computers. In the Star Trek episode “What are Little Girls Made Of?” an earth scientist named Roger Corby discovers the last survivor of a race of androids that rebelled against their creators and killed them. In Battlestar Galactica, the Galactica was the only battlestar that refused to join in a large, inter-ship computer network. That’s what saved them when the Cylons attacked. In I Robot, Isaac Asimov warns that even if you create rules to govern robot behavior, it doesn’t work out the way you anticipated. Speaking of Isaac, The Orville — which is an imitation of Star Trek — has a crew member called Isaac who is a Kaylon. The Kaylon are a race of robotic beings that rebelled against their creators and destroyed them, then decided to go on a homicidal rampage across the universe to kill biological life forms. I’m sure there are a hundred other examples, so when our robot overlords take over, we can’t say we weren’t warned. In a previous podcast I mentioned that humanity has to have two competing instincts. One is the instinct to try new things, push the boundaries, and explore. The other is the instinct to protect against the hidden threat. Both of those instincts are absolutely crucial. Some people see AI as a wonderful new technology that will usher in an age of prosperity. Others imagine Sarah Connor fighting against the Terminator. There seem to be three paths forward. 1. Stop AI now before it kills us, and make sure nobody builds such a thing ever again. 2. Believe in AI as the messianic technology that will make the lion lay with the lamb and solve all our problems. 3. Set up rules to regulate it and to keep it from getting so far ahead of us that we can remain in charge. Unfortunately, none of us — that is, no one listening to this — can do any of those things. So what can we do as individual citizens, workers, and business owners? First, be aware of the tension and don’t think you know the answer. If Isaac Asimov couldn’t come up with iron-clad rules of robotics, you aren’t going to either. This is a very hard problem that you can’t dismiss with some glib, superficial answer. Second, make sure there’s open debate on these topics. We’ve had way too much censorship recently, where dissenting voices are silenced. That has to stop. Third, start thinking about your own limits for the use of AI. When has it gone too far? How would you know? At what point is it trespassing on human prerogatives, and what does that mean? Some little committee of geeky experts at Davos, the U.N., or a House office building isn’t going to solve this. As a friend likes to say, all of us are smarter than some of us. We need hundreds of thousands of little thought experiments, policies, and theories. Maybe we can stumble our way through this.

Magazines will occupy a shrinking place in the public’s reading habits, but there are still opportunities Bo Sacks distributed an article about how magazines could thrive in the digital age, which got me thinking about that subject. I have some clients who publish magazines, so it’s relevant to me. Let’s start with a definition of “magazine.” It’s a periodical publication that includes a collection of articles on a variety of topics within a single issue. I would insist that a magazine is a print publication, but that’s not relevant for today’s discussion. A magazine is … * Periodical * Less urgent * A laid-back, immersive experience * Strongly visual (otherwise it’s a newsletter) * Composed of articles that are typically of moderate depth. It’s not a tweet and it’s not a book * Cohesive. There’s some concept that ties it all together across issues A constantly updated website is not a magazine because it’s not a periodical publication with articles. I’m not saying anything against that form of publication, it’s just not a magazine. Now let’s think about what kinds of information people are looking for, and where they’re most likely to look for it. In other words, where in the sea of information seeking and consuming does the magazine fit? First, it goes without saying that anything that can be published in a magazine can be published online, so to find the magazine’s place in the modern world, you have to ask (1) is the magazine format even conceptually reasonable for that kind of information, and (2) does the laid-back, immersive, visual quality of the magazine add something of value. It is better than a website? For my list of the types of information, and where magazines fit in, see this page. https://krehbielgroup.com/2024/03/12/re-imagining-magazines-in-the-digital-age/ Magazines can fit into some of these areas in principle, but I haven’t addressed the 800 pound gorilla, which is printing and mailing. A magazine – that is, a print magazine – has to have great circulation, a great advertising base, or it has to be able to charge a premium price to survive in the current environment. An e-magazine doesn’t have those problems, but it has to address the periodical question. The periodical nature of a print magazine makes perfect sense. But with an e-magazine you have to ask why is it better to serve information in issues rather than on a constantly updated website? As of right now, images in a magazine are a little more appealing than images online, although that hasn’t stopped the online porn industry – which can go straight to Hell in my opinion. But this is another dividing line for the magazine. Is there an advantage to a still image over a video? I hope that analysis helps you to put this question in some perspective. It’s not as simple as “everything is going online,” but magazines clearly face a lot of headwinds. They’re not quick. They don’t have video or audio. They’re expensive to produce. They have to answer why the periodical format makes sense. In short, they have to make a case for why they’re better than what people can get online, and that’s an increasingly difficult case to make.

Sort your use cases by effort and potential impact, but remember there are other things to consider I’ve been preparing a workshop on how to develop use cases, and I was taking some notes over the weekend. I started with obvious things like quick wins and bang for the buck, but a few things I read reminded me that there are often other considerations. There are any number of things you can do to improve your business, and while you usually want to sort your ideas by effort vs. potential gain, and focus on what changes will give you the best return, there are exceptions. It’s not always about profit. Four types of exceptions come to mind where it’s hard to assign a monetary value. 1 -- Avoiding Hell. You may have noticed that I create a lot of content. That’s my main strategy for getting new clients. When you have a problem or a project, I want you to think of me. So I put a lot of time and effort into creating useful and interesting content, but there are limits to what I’ll do. A lot of people have been recommending that I get on TikTok, and some companies claim to have done very well on that platform. At the same time, there are a lot of privacy concerns about TikTok, and a House committee voted unanimously last week — Democrats and Republicans — that TikTok is a security threat to the country. Not that I trust House committees as far as I could kick them, but in an era where people can’t agree on anything, a bipartisan vote like that is worth noting. Building my company is nice. Working with a company that’s undermining my country is not. It would be wrong to put a price on that. 2 -- Avoiding jail There are some obvious things, like fraud, money laundering, insider trading, health and safety violations, or selling an age-restricted product to minors. You can probably increase your bottom line by doing some of those things, but it’s not worth it. I certainly hope none of my listeners are involved in any of that, but sometimes it’s what you don’t do that gets you in trouble. If you fail to take reasonable measures to protect your customers’ or your business partners’ data, that can land you in hot water as well. I don’t mention avoiding fines because I suppose you could actually measure the ROI on that. 3 -- Fulfilling your corporate mission Some companies are very purpose driven. Patagonia says they’re “in business to save our home planet.” TOMS shoes says that “with every product you purchase, TOMS will help a person in need.” These kinds of commitments might lead companies to make more expensive decisions, irrespective of the ROI. 4 -- Building your brand image You can’t always attribute marketing spend to company revenue. Sometimes you just have to believe in it. For example, it’s hard to pin a precise ROI on branding. Coca-Cola’s “share a coke” campaign and Nike’s “Just do it” slogan both require big investments. Generally speaking, I encourage people to develop their use cases around small efforts that have big financial results. There are other things to consider. Unfortunately, once you open that door, you may get lots of starry-eyed people promoting their pet causes. Some workers even think they can demand that a company change its policies to accommodate their views. Here’s one way to minimize that risk. When you’re considering use cases for your customer data platform, or whatever it is you’re working on, create a structured way to write them up. For example, as a [insert role] I want to [insert activity] so that [explain the benefit]. This tends to rein in more of the “change the world” recommendations.

Marketing automation is great. It allows companies to set up campaigns that run on their own without further human intervention, which saves a lot of time and increases what your marketing team can do. Marketing automation campaigns can also fail spectacularly. It usually goes like this. Step 1: What could go wrong? Step 2: How could we have known? The classic example of a broken marketing automation campaign is a subject line that says “Hi [First Name].” The lesson is clear. It’s not enough to have data in some of your fields. Before you automatically insert a field into a campaign, make sure all the fields are appropriate, and make sure you have a routine to insert a generic value when the field is blank. Ideally you want to catch things before they happen. Good operations, including detailed requirements documents and careful review, can help with that, but it’s hard to get people to follow procedures. Count on the fact that some people won’t. Most marketing automation campaigns rely on data connections between disparate systems. Sometimes those connections fail. Make sure somebody is monitoring that. Set a sunset date on every campaign. You don’t want to find some rogue campaign that’s been running for five years. Now it has the wrong logo, the wrong offer, the wrong product image. It’s not pretty. Seed yourself in all your campaigns, but realize that’s not an adequate test. Your email won’t say “Hi [First Name]” because your database record has your name in it. But you can catch some things by looking at seeds. Also, the seed doesn’t have to be on your account. If possible, get copies of live efforts sent to real customers. Some fulfillment companies can do that for you. Involve customer service early. Make sure they’re aware of the campaign before it starts. Also, for some odd reason it’s hard to get customer service to tell you when something goes wrong. Their mindset is to pacify the customer and move on. You don’t want that. Make sure your customer service reps know that you want to hear about goof-ups, and thank them profusely when they report them. Make sure every marketing automation campaign is connected to a report in a way that you can track down the problem when something weird happens. For example, your welcome emails start to bounce at an unusual rate, or you’re suddenly getting complaints about too many text messages. Things will go wrong. Often it will be human error. Sometimes there’ll be a software update in some obscure program that throws a wrench in your procedure. You can’t anticipate everything. Read reports so you can catch the error early and recover quickly. Finally, make friends. There will be somebody in some other department that does something completely logical from his point of view that fouls up your campaign. You want people to think, “You know, this is so and so’s domain. I should check before I go messing with that.” Mike Pastore read my article and posted this comment on LinkedIn. “If you ever find yourself needing to explain to your leadership how things go wrong despite your investments in tech tools and data, send them this article.” Here’s a link to the article. How to keep your marketing automation campaigns from ruining your week https://martech.org/how-to-keep-your-marketing-automation-campaigns-from-ruining-your-week/

This 2nd in a 4-part series gives some perspective on how publishing got into its current mess with artificial intelligence. Part 1 - copyright Part 2 - search Part 3 - generative AI Part 4 -- what's next Part 2 – The Search Bargain The Internet was a new way to reach audiences and to monetize content – mostly with ads, which were very profitable at the time. Search became the gateway, or the discovery tool. But it didn’t start like that. Before good search, sites were categorized at a high level in directories. Google changed all that with excellent search. Search engines would crawl websites, scrape the content, index it, and then visitors would search against that index. Search became the main discovery tool. Publishers should not have gone along with this. Google’s mission is to organize the world’s information and make it universally accessible and useful. That means free. The Google bias is towards free content supported by ads. Which they control. Google rules search. Nobody else is even close. That means Google controls content discovery, so if you want to be found on the internet, you have to follow Google’s rules. Publishers have many different choices for how to monetize their content. Google has created an environment that heavily biases things towards one model – that is, free content supported by ads controlled by Google. This has created a perception with the public that content ought to be free. This expectation that content should be free lowers the perceived value of content. The funny thing is that Google needs this content – which they don’t create. Publishers have always had leverage here, but they’re never used it. They’ve allowed to write the rules of the game. The whole industry is infected. As the gatekeeper of discovery and the dominant player in the ad space, Google’s perspective wins. It’s not just their doing. As I said in Part 1, most of Big Tech believes in this idea of free content supported by ads, and that’s infected all content delivery platforms. Consider podcasts. The default assumption is that you get the podcast for free. It’s very difficult to integrate podcasts into a membership or subscription model. The bottom line is that Google and other Big Tech companies have a set of interests that do not align with the interests of publishers and content creators. In Part 3 I’ll discuss how generative AI has made this mess even more complicated.

This is the first in a 4-part series on the conflict between Google and publishers. Part 1 focuses on copyright, which is essential to publishers, but meh to Google. This is a re-presentation of a talk I gave in the UAE at the Global Media Congress. Part 2 covers search Part 3 discusses AI Part 4 evaluates what's next

Back at the dawn of the internet, publishers were concerned that blogs and other “user generated content” would create too much competition and erode the value of their products. Those fears are materializing in spades. Troy Young’s analysis in his Feb. 14th email, "The Next Internet," is very good, and relevant to this point. Seek vs. Served. Troy characterizes the old internet as "seek.” If I wanted to know something, I would enter a query, skim through a list of results, then “go” to some web page. The developing internet, by contrast, is "be served." The content comes to me, e.g., through a chat interface. I would like to subdivide the “be served” internet into two models. Model #1 involves nameless, faceless, brandless “content” that is slurped up by AI and then regurgitated / served to meet the reader’s needs. It’s not even “the commodification of content” because the AI isn’t paying for it. They just steal it. Everything goes in the database and the chatbot or your personalized AI will decide how to present it to you. The content creator is lost in the process. Model #2 has a name and a face. It’s Joe Rogan, Megyn Kelly, Chris Cuomo, and Piers Morgan interpreting the world for you. It’s not “find me the best recipe for French Onion Soup,” or even “what should I cook for dinner tonight,” it’s “what’s Gordon Ramsay eating today?” Both models will grow side by side in the coming months, but they both rely on content creators to feed the system. Model #1 filters content with an algorithm. Model #2 filters it through a personality. How do the content creators get paid? In Model #1, most of them don’t. Maybe that’s becoming an outdated concept – at least for the majority of content. In Model #2, the original creators might get a shout out, but the talking head can make money through ads, memberships, subscriptions, etc. Joe Rogan is making a killing. Publishers like to say that we need experts to create valuable content, but right now millions of people are uploading content to the internet. Most of it is garbage, some of it is very good, but for the vast majority of content creators there is no reasonable expectation of being paid. They do it because they like it, or they do it the way most people buy a lottery ticket, on the exceedingly unlikely chance they'll hit the number. What about experts? It’s not clear how much people care whether they’re getting content from an expert. It’s often more a matter of trust in someone because they align with the reader’s preconceptions. What publishers feared in the early days of the internet – that amateur content (“blogs” in those days) would supplant the need for professional content – is happening on an accelerated basis.And then there’s AI. How will publishers survive this? I don’t know, but two options come to mind immediately. * Become the AI tool. * Become the celebrity personality.

My friend Charles Benaiah writes a daily substack that’s both fun to read and insightful. In a recent post with the lovely title “No vomit in Narnia” he quotes David Carey, then president of Heart, as saying “we throw away 100 stories for every story we publish. For every picture you see, there are thousands you don’t.” This reminds me of an experience back at Kiplinger. The editors for The Kiplinger Letter do lots of work on a wide range of topics, then they take all that information and boil it down to a concise, crisp, 4-page letter. They refine it further by writing each paragraph and each line in a particular style, and they highlight the key points. It’s easy to skim. I’ve never made a movie, but I’ve heard that there’s a ton of film on the cutting room floor. ChatGPT tells me it can be anywhere from 10 to 100 times as much as the final product – so for a two hour film they might film anywhere from 20 to 200 hours of footage. A good product is all in the curation. What’s the story, and what contributes to telling that story effectively. The internet has become the repository of all the junk that didn’t make it into the curated publication. Charles points out the genius of Facebook, which is to allow the reader to curate his own information with the like button. This conjures an image in my mind. On one side you have the consumer picking and choosing what he wants, and on the other you have the genius expert picking and choosing what the consumer should want. Ironically, Facebook played this both ways. On the one hand, users could affect what content they see by their likes and comments and attention, but on the other hand, Facebook famously decided what information people should not be able to see, and censored things they thought were better left unseen and unread. Where are you on this continuum? Are you the paternalistic editor who decides what news is fit to print, or do you just give people what they want, so long as you can monetize it? You’re probably somewhere in between, and honestly, I’m not here to lecture you on that subject. In my professional life I focus on how to get things done, and I don’t quibble with people about their philosophies. I’m discussing this topic today to remind you that you should be getting and reviewing customer feedback. How are you doing that? Do you have like buttons on your articles? Why not? You don’t have to show the answers. I wrote a science fiction book for children a few years ago and I was concerned about the pace. I didn’t know if the sciency stuff was distracting from the story, so I asked my daughter and her friends to read it and to put a plus or a minus on each page to indicate whether or not they were enjoying it – at that moment. Unfortunately, we usually can’t get that level of analysis, and I don’t know why. Why doesn’t Kindle tell authors where people stop reading a book – that is, where they’ve lost interest? Why doesn’t Spotify tell you where people stop listening to a podcast? Today I’d like to challenge you to think about ways you can get more granular feedback from your customers to fine-tune your product. I have some ideas myself, but I think it’s good to go through the exercise. Give it a try and let me know what you come up with. Or give me a call and we can chat about it.

The trick is to distinguish 1st and 3rd party cookies. We keep hearing that cookies are going away, but that’s not quite true. Third party cookies are supposedly on their way out, although the process has taken longer than anticipated. The difference has to do with domains. But first, what is a cookie? A cookie is just a small text file that a website writes to your web browser. They’re necessary to maintain state, which I’ll explain in a moment. The internet is built on anonymous connections. If I visit a website, my web browser makes an anonymous request for the items on that page. The server for that website returns those items – usually that’s html code, javascript, and images – so my browser can display the page. If I view a second page, the website doesn’t know that’s still me. It’s just another anonymous connection. I’m skipping over something called browser fingerprinting, which makes this discussion a little more complicated, but for now let’s leave it at that. Every connection is anonymous. Cookies solve that. By writing a cookie to your browser, the website can maintain state between different requests. It works like this. I visit the website for the first time – that means, of course, that my browser, like Chrome, or Edge, makes a request for a page. The web server looks to see if I have a cookie for that site. If I don’t, it writes one. After my first visit, the cookie probably just has a unique ID. The website can now track the behavior of all the requests made from that browser with that ID. That’s useful for analytics and such. If I were to login to the site, the cookie becomes the way the website recognizes me from page to page. Without the cookie, I’d have to login every time. What I’m discussing right now is a first-party cookie. That means the cookie is for the website that I’m currently visiting. To illustrate how this works, imagine I visit the site examplewebsite.com. Once I’m on the site, I can look in my browser to see which cookies have been written. Let’s assume there are two cookies. The first is from examplewebsite.com. That’s a first-party cookie because that’s the site I’m on. There’s also one from (I’m just making this up) webadvertising.com. That’s a third-party cookie because I’m not on that page. The purpose of that third-party cookie is to track my behavior across multiple different websites. That would typically be somebody like doubleclick (which is Google) or Facebook. They track what you’re doing on any site that includes that 3rd party cookie. Why are 3rd party cookies going away? The nice spin on that story is that it strengthens privacy. People don’t want to be tracked. The truth is this is a ploy by the big tech companies to solidify their control over advertising. They’ll cut off the 3rd party cookie, and thereby hurt competitors, but they’ll find another way to track you. Don’t think for a minute that they care about privacy. Your personal data is the currency they use to run their businesses.

I’ve been involved in niche media for almost my entire career. When I started out – back in the days when having a hard drive in your computer was a luxury reserved for the few – everybody said “nitch.” Then apparently the French invaded and people started saying “neesh.” Like quiche. Which seems to make some sense. More on that in a minute. You might remember there was a book back then called “real men don’t each quiche.” That might have been in the back of my mind when I concluded that “neesh” was pretentious. I stuck with nitch. I’m an American. Dictionary.com and Merrian-Webster support me. They list nitch as the first (preferred) pronunciation of the word. The OED does a slight twist on me. You see, it wasn’t a French invasion, it was the British. For British English, the OED lists neesh first and nitch second, but for American English it lists nitch first and neesh second. So the dictionaries are all on the side of nitch. I also tried several rhyming dictionaries, and they all assumed a “nitch” pronunciation. So I’m on solid ground to say that “nitch” is the preferred pronunciation in America. Then why is it quiche? Well, English is hard. If you look up words that end in “iche” you find some weird things. The only other “iche” word with an “Itch” sound is “miche,” which is “to lurk out of sight.” And the funny thing is that it’s a British word, and according to the OED, the Brits are more likely to say “neesh.” There are a lot of words that are closer to “eesh.” Affiche, which is a poster Fiche, like microfiche, which I used to use at the library Postiche, which is an artificial hair piece, which I obviously don’t use Pastiche, which is when you imitate another style Babiche is a thread or thong of sinew, gut, or rawhide Riche, like nouveau riche Corniche, which is a road built along the coast, although that one might be closer to car-nish But then you have the “eechay” sound. Cliche Ceviche Moriche (which is a type of palm) Caliche (which is a nitrate-bearing gravel) It’s obvious that most of these words are imports to English. So, are we supposed to retain the original pronunciation? I don’t know. The town in Illinois is called KAY-ro, not KAI-ro, and from Pennsylvania to Illinois is’t ver-SAYLES, not ver-SAI. Some people will hear ver-SAYLES and think, “Ah, those uncultured hicks.” I don’t have that reaction at all. I don’t say France (with the French pronunciation), and I call the land of my father’s ancestors Germany, not Deutschland. I also make no effort to pronounce South American countries and cities the way the natives do. I’m an American and we speak American English. But my friend Lev Kaye from Credspark was talking about quizzes and polls last week, so I decided to do a poll on LinkedIn. Many of my connections have some association with niche publishing. “Neesh” won, by a wide margin. My conclusion is that for America as a whole, nitch is the preferred pronunciation, but for the niche media industry, neesh might be preferred. I’ll continue to say nitch – because I’m ornery. But Lev Kay had a good comment on the poll. “Can there be a third option: 'Depends on whether or not I'm speaking to a French person.'” I like that. So there you have it, the great niche debate.

There’s a new dinosaur in town. The old dinosaur was the publisher who tried to hang on to print. “Get with it,” the hip kids would say. “The world is turning digital.” Which is largely true, but that’s not the subject for today. The new dinosaur has made an idol out of the web page. What gets measured gets done, and most of our measurement tools involve what happens on our websites. It’s like the old “if the only tool you have is a hammer” problem. For example, when I see articles about increasing engagement, it’s usually restricted to stuff you can do on your website. The problem is that content is no longer limited to websites. It never really was, but now more than ever we need to think outside that box. Content consumption – even digital content consumption – is fractured beyond belief. I’ve collected some estimates on how many hours per day U.S. consumers spend in different types of media. I don’t put too much credence in these numbers, but let’s pretend it’s something like this. “Browsing the internet” – 6.75 to 8 hours TV – 3 to 5 hours Audio - Music is 2.75 to 3.75 hours - Podcasts 0.5 to 1.25 hours Social media – 1.5 to 2 hours Gaming 2 hours TikTok – 1 hour YouTube – 0.5 to 0.75 hours Reading – 0.25 hours I don’t want to imply that you should be in all these places, or that you’re competing in all these places. I think it’s a big mistake to think that way. People have different expectations and goals for different forms of media. However, to the extent that you can legitimately compete in these other areas, why aren’t you? Don’t allow “the web” to become the same sort of trap that “the magazine” or “the newspaper” has been for publishers in the past. Think of ways to move your content into new mediums. That will almost certainly require a new production workflow, new talent, different revenue models, and other changes to your business model. But what’s the alternative? Are you going to stay with the web page as it continues to decline?

For better AI images, learn the language When my oldest child was a lad and we’d go for walks in the woods, I told him a story about two brothers who went hiking. When they got home, their father asked what they saw. The first boy said, “A bunch of trees and some birds.” The second boy said, “There were a lot of sweetgum and birch trees, but we saw a stand of old poplars, a little grove of white oaks, and few red oaks, and lots of maples. There were a ton of robins, and I heard Carolina Chickadees and cardinals. I think I saw a red-tailed hawk up in a tree, and there were a lot of downy woodpeckers too.” I’d ask my son which boy got more out of the walk. There’s something analogous when you use an AI image generator. If I just say “make me an image of somebody reading an email,” I might get a nice result, but there are so many options for stylizing images. Here’s a very partial list. Watercolor Oil painting Pencil sketch Charcoal drawing Pixel art Graffiti art Plasticine 3-d model Layered paper Blacklight Diagramatic drawing Infographic drawing Stained glass window Knolling Game sheet Cartoon image Whimsical animation Simplified structures Historical illustrations Anime Cyberpunk futurism In addition to that, you can say “in the style of” and then list some artist you like. Vincent van Gogh, Georgia O'Keeffe, Jackson Pollock, or you can mention a more modern art style, like Shintaro Kago, or Jack Kirby, or whatever you like. Then there’s a whole range of other specs you can provide. Aspect Ratio is the ratio between the width and height. 9:16 would be typical for a portrait. You can specify a camera angle or perspective, such as “bird’s-eye view,” or eye-level. Some images might be more appropriate in harsh shadows, or soft lighting. You can even use a time of day, like twilight. Different textures work better for different images – like glossy or matte or smooth. I don’t know much about cameras, but you can specify what kind of camera, what shutter speed, aperture settings, etc. What kind of weather do you want? Is there a relevant historical setting or context? Should it be futuristic, or fantastical? I’m not well versed in all these things. I know what I like when I see it, but I can’t tell you what style or artist I would like to imitate. There’s a whole language to learn about AI art. But here’s an interesting hack. You can use one AI to help with another AI. For example, I can use ChatGPT to help with my Midjourney prompt. If you’re in the business of creating images to go along with articles, start paying attention to different styles of images. Do you want TRON, Tim Burton, Lord of the Rings, MAD Magazine, Frank Frazetta, Norman Rockwell? Do you want to create your own style to go with your brand? Learn the words that can communicate your style to the AI image generators. There are a lot of websites that have examples of prompts for images. You can learn a lot by going through these lists and looking for examples. Read the prompt they use and learn from it. Try to develop your own style, and what words and settings will enforce that style with the image generator.

Open rates have been a traditional way to measure email campaigns. Whether a recipient opens an email is taken as a measure of the relevance of things the recipient can see in the preview pane. That’s mostly the subject line, but it also includes the from address, and in some cases the preview text, or a portion of the first line of the email. That metric is becoming less valuable because of changes in the way emails are handled. Let’s start with a quick discussion of how open rates work in the first instance. How does the sender know that the recipient has opened the email? The internet is based on a request and response system. Your web browser, or your email client, makes a request to a server to get some resource, like html code or an image. The server can log that request. When you send an email, your email service provider includes a tracking pixel, which is a tiny, invisible image embedded in the email. When the email is opened – provided you have images turned on – the email client requests this image from the server. The tracking pixel has code that’s unique to the recipient and to the email campaign, so the ESP can identify which recipients have opened which emails. Anything that disrupts that process makes open rates less reliable. There are two fairly large things that disrupt that process. The first is Apple’s Mail Privacy Protection. Apple decided that open information should be private – that the email sender has no business knowing whether or not you opened their email. So – for the Apple users who opt into this program – Apple opens every email and caches all the images before the email even gets to the recipient. That inflates the reported open rate for emails sent to Apple devices, making overall rates less valuable. You could filter out known Apple users and rely on the open rate of the remaining recipients, but we’re not done with the problems yet. It’s not just Apple. A lot of corporations do something similar to what Apple does. They’re concerned about malware, viruses, and other security threats to their networks, so they create a firewall that emails have to get through before they’re delivered to the recipient. These systems often open every email and click on every link before the email is delivered. Once again, that messes with your open rates. So what do you do about this? First, don’t abandon open rates altogether. The absolute value of the open rate has declined because many of the opens are coming from these automated systems and not from human users. But the open rate still has a relative value. You can still test two subject lines and choose the one that gets the higher open rate, because even if 30 percent of the opens are phony, that will affect both panels equally, so the winner is still the winner. Second, since some percent of your opens are phony, you should make your test panels larger. In other words, if you need two panels of at least a thousand names to get a statistically significant result, you might need to bump that up 30 percent or so to make sure you’re still hitting that target with actual users and not with automated systems. Third, make sure you’re not misrepresenting things – for example, with your advertisers. Don’t give them open rates as if they mean what they used to mean. Fourth, talk to your systems people. If you can get timestamps on opens, a smart programmer might be able to tease out the real opens from the programmatic opens.

If it’s important that your subscribers get your emails, here are some things you should be watching. Many publishers sell paid e-newsletter subscriptions, and many others rely on their free e-newsletters for their business strategy in one way or another. All subscription publishers know that renewals depend on engagement. That means that these publishers should be tracking the health of their distribution lists to ensure their subscribers are getting their issues. How do you do that? I’ll get to that in a moment. First I want to mention that email deliverability is a big topic that involves a lot of technical details. You should make sure you’re using a reliable email service provider, that they have things configured correctly, that they have a deliverability team to monitor your sends, and that you’re following email best practices. What I’m going to discuss today are the specific issues that you’ll need to address if you have a paid e-newsletter, or if for some other reason it’s very important that you ensure your subscribers are getting their emails. It starts with the sign-up, or registration. You have to ensure you’re starting this relationship with a valid email address. The simplest way to do that is (1) verify the structure of the email in form itself – e.g., use javascript to ensure it’s something @ something dot something, and (2) use a double opt-in. That’s where a new email address isn’t officially added to the list until you send a test email and the person verifies receipt. Those two steps will get you off to a good start with a fairly clean list. However, if you use double opt-in, remember that some people don’t verify their emails, so you should have a marketing automation journey for those people. Even with double opt-in, it might also be a good idea to ask for a secondary email address in case problems develop with the first one. Just because an email address is valid and deliverable today doesn’t mean it will be in 6 months, and if all you have is a bad email address, you’re stuck. Some of you marketers are thinking, “this is too much. Making the sign-up process more complicated suppresses response!” That’s very true. So do the double opt-in and ask for the secondary email address after you already have the order – and explain that you’re doing all this for their benefit, to make sure they get their issues. But don’t clutter up the order form with that stuff. If you have a subscription publication, people are coming on and off the list all the time, and you have to generate a distribution list for each issue – either from your fulfillment system, or from your email service provider. If the list is generated from your fulfillment system, there can be a disconnect between what your fulfillment system thinks is active and what your ESP thinks is active. For example, if someone clicks an unsubscribe link on one of your emails that might update your ESP but not your fulfillment system. Keep an eye on that. Once an issue has been sent, your ESP should have data on undeliverables, opens, clicks, and so on. Now comes the difficult part – sorting through the delivery reports. First let’s tackle undeliverables. An undeliverable means your email is not getting through at all. It’s expired, or there’s a typo in it, or something like that. If there’s an obvious typo, fix it and resend the issue to the new address. Otherwise, you might have to reach out to the subscriber to get an update. That’s the virtue of having a secondary email address – or some other way to contact the subscriber. Maybe you can text them, call them, or even send them a letter. How far you go will be determined by how much you want to invest in keeping a current subscriber. There are also services that can update bad emails for you. A simple example is when a company changes the structure of their emails – say from first initial last name @ company.com to first name dot last name @ company.com. While companies usually forward emails in such cases, sometimes they only forward for a limited period of time. An update service can automate the process of cleaning up bad addresses. So we’ve done all that and we still have undeliverable emails, or emails that are never being opened. What do you do? Before I get to that, I have to mention that open rates aren’t very valuable any more. I’ll do a show on that later this week. To decide how far you want to go in hunting down undeliverable messages, you have to assign some value to keeping that connection alive. If you’re trying to deliver a $500 paid e-newsletter, you’d better take reasonable measures to make sure the e-newsletters are being delivered. If it’s a free, ad-supported email, you might make far less aggressive attempts to fix problems. I can’t cover every nook and cranny of this issue in my short podcast, but I hope I’ve outlined it well enough that you have a good idea about how to make this work for your e-newsletters. But if you’d like to chat about it, give me a call.

The vast majority of us are average, and average doesn’t sell There’s a lot of concern in the media business right now. It seems as if the world is coming unglued. Brands are dying, or laying off a lot of people. I’ve heard lots of different sorts of explanations, and while there’s some truth to many of them, I’m going to toss my own into the mix. There are simply too many people providing content, and this is what happens. You’ve heard of the Pareto principle – that 20 percent of your customers account for 80 percent of your revenue; 20 percent of your employees do 80 percent of the work; and 20 percent of authors sell 80 percent of the books. It’s a spooky thing, in at least two ways. First, it continues to apply as you did down, so that if you take that top 20 percent of authors who sell 80 percent of the books, 20 percent of those authors sell 80 percent of those books. And that pattern continues to iterate until you get one or two people who sell most of the books. The second spooky thing is that this principle even applies to planets and ecosystems and hurricanes and such, so it’s not just a quirk of human behavior. The internet has made it trivially easy to become a content creator. Bob Hoffman jokes that he finally met somebody who doesn’t have a podcast. YouTube, TikTok, Wordpress – all that, plus the fact that we all have a movie studio in our pocket. This results in an absolute tsunami of content, and AI is going to make it even worse. I just attended a session the other day where a friend explained how you can create a book with designrr.io in about ten minutes. Supply is close to unlimited, but while demand for internet content is way bigger than I ever expected it to be – people spend a huge amount of time on their phones – it’s still limited. There are only so many hours in a day. It stands to reason that the content space is going to have some casualties. Spotify listeners spent 14.9 million total hours listening to The Joe Rogan Experience in the first month it was available on that service. Those 14.9 million hours were not spent on newspapers or magazines. My take on all this is that if you’re a media company, the answer won’t be found in revenue or business model innovation. Those are good things, but they’re not going to address the fundamental problem, which is that the top 20 percent are going to continue to take the lion’s share, and with the constant addition of new creators, that top 20 percent is going to get better and better and better. You’re competing in an ocean of content. New people are coming on board all the time, and while most of them are average, and no particular threat, some of them are talented, and a small portion of them are super talented. My friend Jimmy Finkelstein recently tried to start a new media business on the premise that people want news from a moderate point of view. My first reaction was “No, people actually want to hear lunatics screaming about the end of the world.” But my second reaction was that’s really not the issue. The issue is talent. And I don’t mean to speak against anyone at The Messenger. I’m sure there were some talented people there. But in every profession you have some people who are so much better than everyone else that it defies understanding. Think of Patrick Mahomes, or Steven King. These guys are on the edges of the distribution curve. So my recommendation to media companies is to find extraordinary talent. Things like paywalls and first-party data and all that stuff will pale in significance to the effect of very talented people.

"Eat your own cooking." "The cobbler's son has no shoes." "Physician, heal thyself." "Take your own medicine." There are a lot of sayings that follow that same theme, and I think they're so popular because it's a fairly universal problem. Everyone is good at giving recommendations, but few of us are good at taking them. Even from ourselves. Jordan Peterson says we should treat ourselves as someone we're responsible for. Or, in other words, we should apply the same sort of critical scrutiny to our own actions that we would apply to someone who came to us for help. How do you apply that in your business? Let’s say someone came to you with a complaint that their email marketing isn’t doing as well as it used to. You’d probably have a list of suggestions for them to consider. But do you apply those to your own email marketing? None of us are expert in everything, and we can’t rely on the occasional plea for help from a colleague to prompt us to reconsider our techniques. We need to be more proactive and thoughtful about it. So here’s a simple method. Pick some activity you’re involved in that you wish you could do better at. Step 1: Forget about what you do – your own operations and limitations. Set all that aside. Keeping your own operations in mind will distort the way you look at the issue. You’ll make excuses, and you’ll limit the things you’re willing to consider. Step 2: Come up with a list of sources for advice on the topic. That could include … YouTube videos White papers Blogs Conversations with your favorite AI tool Groups on Reddit Step 3: Spend some focused time digging into these resources – preferably in a new place. Not in your office. Again, don’t think of your own operations. Imagine you’ve been asked to give a presentation on the topic, or write a white paper. Take notes, and don’t worry too much about organizing them. This is the information-gathering stage. Continue to do that until you feel like you’ve heard everything there is to hear, and it starts to get repetitive. Step 4: Forget about it. Put it aside. Spend a day at the park. Sleep on it. Distract your conscious mind from the problem so your unconscious mind can work on it. Step 5: Once again, get out of your office. You don’t want your thoughts to be encumbered by the routine. Review your notes and organize them into an outline. You’ll be surprised at the extra little insights that will occur to you, that can fill in and expand on your outline. During Step 4, your brain was making connections and working on all the things you gathered in Step 3. You’ll find new ways to organize and synthesize the information. Step 6: Now go back to your office, take your outline and compare it with what you’re doing.