She delivered a Thursday presentation entitled “Customizing generative AI applications for your business using your data.” The tool that Tanke uses for customization is Amazon Bedrock, which supports Retrieval-Augmented Generation, or RAG.
Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences. RAG extends the already powerful capabilities of LLMs to specific domains or an organization’s internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.
Because Amazon has obviously referred to my seven questions—OK, maybe they didn’t—the RAG page devotes time to the “why” question and the “benefits” question.
So what happens when you use LLMs WITHOUT retrieval-augmented generation?
You can think of the Large Language Model as an over-enthusiastic new employee who refuses to stay informed with current events but will always answer every question with absolute confidence.
How does RAG solve these problems? It “redirects the LLM to retrieve relevant information from authoritative, pre-determined knowledge sources.” RAG allows you to introduce more current information to the LLM which reduces cost, increases accuracy (and attributes sources), and supports better testing and improvements.
Years ago, Steve Martin had a routine in which he encouraged his audience to say, in unison, that they promise to be different and they promise to be unique.
No, repeating the canned phrase about standing out from the crowd does NOT make you stand out from the crowd.
But wait. It gets worse.
The authenticity bot
When I reshared Rodriguez’s post, I wanted to illustrate it with an image that showed how many people use the phrase “stand out from the crowd.”
But while I couldn’t get that exact number on my smartphone search (a subsequent laptop search revealed 477 million search results), I got something else: Google Gemini’s experimental generative AI response to the question, bereft of irony just like everything else we’ve encountered in this exercise.
You see, according to Gemini, one way to stand out from the crowd is to “be authentic.”
Yes, Google Gemini really said that.
Google search results, including generative AI results.
Now I don’t know about a bot telling me to “be authentic.”
Rodriguez addresses “how” and “why”
Going back to Taylor “Taz” Rodriguez’s post, he had a better suggestion for marketers. Instead of using canned phrases, we should instead create original answers to these two questions:
HOW do you help your clients stand apart from the competition?
WHY have your past & current clientele chosen to work with you?
BOTH questions are important, both need to be addressed, and it really doesn’t matter which one you address first.
In fact, there are some very good reasons to start with the “how” question in this case. It’s wonderful for the marketer to focus on the question of how they stand apart from the competition.
Apologies in advance, but if you’re NOT interested in fingerprints, you’ll want to skip over this Bredemarket identity/biometrics post, my THIRD one about fingerprint uniqueness and/or similarity or whatever because the difference between uniqueness and similarity really isn’t important, is it?
Yes, one more post about the study whose principal author was Gabe Guo, the self-styled “inventor of cross-fingerprint recognition.”
I also wrote something only on LinkedIn (and Facebook) that cited a CNN article that quoted Christophe Champod and Simon Cole. (Interestingly enough, my last post on Cole concerned how words matter, which is appropriate in this discussion.) Unfortunately, the person who wrote the CNN headline (“Are fingerprints unique? Not really, AI-based study finds”) didn’t pay attention to a word that Champod and Simon Cole said.
But don’t miss this
Well, two other people have weighed in on the paper: Glenn Langenburg and Eric Ray, co-presenters on the Double Loop Podcast. (“Double loop” is a fingerprint thing.)
So who are Langenburg and Ray? You can read their full biographies here, but both of them are certified latent print examiners. This certification, administered by the International Association for Identification, is designed to ensure that the certified person is knowledgeable about both latent (crime scene) fingerprints and known fingerprints, and how to determine whether or not two prints come from the same person. If someone is going to testify in court about fingerprint comparison, this certification is recognized as a way to designate someone as an expert on the subject, as opposed to a college undergraduate. (As of today, the list of IAI certified latent print examiners as of December 2023 can be found here in PDF form.)
Podcast episode 264 dives into the Columbia study in detail, including what the study said, what it didn’t say, and what the publicity for the study said that doesn’t match the study.
Eric and Glenn respond to the recent allegations that a computer science undergraduate at Columbia University, using Artificial Intelligence, has “proven that fingerprints aren’t unique” or at least…that’s how the media is mischaracterizing a new published paper by Guo, et al. The guys dissect the actual publication (“Unveiling intra-person fingerprint similarity via deep contrastive learning” in Science Advances, 2024 by Gabe Guo, et al.). They state very clearly what the paper actually does show, which is a far cry from the headlines and even public dissemination originating from Columbia University and the author. The guys talk about some of the important limitations of the study and how limited the application is to real forensic investigations. They then explore some of the media and social media outlets that have clearly misunderstood this paper and seem to have little understanding of forensic science. Finally, Eric and Glenn look at some quotes and comments from knowledgeable sources who also have recognized the flaws in the paper, the authors’ exaggerations, and lack of understanding of the value of their findings.
Yes, the episode is over an hour long, but if you want to hear a good discussion of the paper that goes beyond the headlines, I strongly recommend that you listen to it.
TL;DR
If you’re in a TL;DR frame of mind, I’ll just offer one tidbit: “uniqueness” and “similarity” are not identical. Frankly, they’re not even similar.
Let’s say that your identity/biometric firm has decided that silence ISN’T golden, and that perhaps your firm needs to talk about its products and services.
So you turn to your favorite generative AI tool to write something that will represent your company in front of everyone. What could go wrong?
Battling synthetic identities requires a multi-pronged approach. Layering advanced technology is key: robust identity verification using government-issued IDs and biometrics to confirm a person’s existence, data enrichment and validation from diverse sources to check for inconsistencies, and machine learning algorithms to identify suspicious patterns and red flags. Collaboration is crucial too, from financial institutions sharing watchlists to governments strengthening regulations and consumers practicing good cyber hygiene. Ultimately, vigilance and a layered defense are the best weapons against these ever-evolving digital phantoms.
From Google Bard.
Great. You’re done, and you saved a lot of money by NOT hiring an identity blog writing expert. The text makes a lot of important points, so I’m sure that your prospects will be inspired by it.
Bot-speak is not an optimal communication strategy either. Generated at craiyon.com.
Well…
…until your prospects ask what YOU do and how you are better than every other identity firm out there. If you’re the same as all the other “me too” solutions, then your prospects will just go with the lowest price provider.
So how do you go about intelligently writing about biometrics?
Intelligently writing about biometrics doesn’t only require some critical words such as “validation.”
Intelligently writing about biometrics doesn’t only require that you KNOW what those words mean, and that you’re conversant in basic biometric topics. (If you want to know five topics a biometric content marketing expert needs to understand, read my post on that subject.)
Intelligently writing about biometrics requires that you put all of this information together AND effectively communicate your message…
…including why your identity/biometrics firm is great and why all the other identity/biometric firms are NOT great.
If you’re doing this on your own, be sure to ask yourself a lot of questions so that you get started on the right track.
If you’re asking Bredemarket to help you create your identity/biometric content by intelligently writing about biometrics, I’ll take care of the questions.
Oh, and one more thing: if you noted my use of the word “no siree” earlier in this post, it was taken from the Talking Heads song “The Big Country.” Here’s an independent video of that song, especially recommended for people outside of North America who may not realize that the United States and Canada are…well, big countries.
As you may have seen elsewhere, I’ve been wondering whether the widely-publicized Columbia University study on the uniqueness of fingerprints isn’t any more than a simple “discovery” of fingerprint patterns, which we’ve known about for years. But to prove or refute my suspicions, I had to read the study first.
My initial exposure to the Columbia study
I’ve been meaning to delve into the minutiae of the Columbia University fingerprint study ever since I initially wrote about it last Thursday.
(And yes, that’s a joke. The so-called experts say that the word “delve” is a mark of AI-generated content. And “minutiae”…well, you know.)
Gabe Guo, who freely admits that he knows nothing about forensic science, is an idiot. For decades we have known that fingerprints ARE unique, and the original forensic journals were correct in not publishing this drivel.
The brave new world of artificial intelligence is fundamentally disproving previously sacred assumptions, and anyone who resists these assumptions is denying scientific knowledge and should go back to their caves.
Well, let’s see what the study actually SAYS.
Until today, I hadn’t had a chance to read the study. But I wanted to do this, because a paragraph in the article that described the study got me thinking. I needed to see the study itself to confirm my suspicions.
“The AI was not using ‘minutiae,’ which are the branchings and endpoints in fingerprint ridges – the patterns used in traditional fingerprint comparison,” said Guo, who began the study as a first-year student at Columbia Engineering in 2021. “Instead, it was using something else, related to the angles and curvatures of the swirls and loops in the center of the fingerprint.”
I’ll preface this by saying that while I have worked with fingerprints for 29 years, I am nowhere near a forensic expert. I know enough to cause trouble.
But I know who the real forensic experts are, so I’m going to refer to a page on onin.com, the site created by Ed German. German, who is talented at explaining fingerprint concepts to lay people, created a page to explain “Level 1, 2 and 3 Details.” (It also explains ACE-V, for people interested in that term.)
Here are German’s quick explanations of Level 1, 2, and 3 detail. These are illustrated at the original page, but I’m just putting the textual definitions here.
“Level 1 includes the general ridge flow and pattern configuration. Level 1 detail is not sufficient for individualization, but can be used for exclusion. Level 1 detail may include information enabling orientation, core and delta location, and distinction of finger versus palm.”
“Level 2 detail includes formations, defined as a ridge ending, bifurcation, dot, or combinations thereof. The relationship of Level 2 detail enables individualization.”
“Level 3 detail includes all dimensional attributes of a ridge, such as ridge path deviation, width, shape, pores, edge contour, incipient ridges, breaks, creases, scars and other permanent details.”
We’re not going to get into Level 3 in this post. But if you look at German’s summary of Level 2, you’ll see that he is discussing the aforementioned MINUTIAE (which, according to German, “enables individualization”). And if you look at German’s summary of Level 1, he’s discussing RIDGE FLOW, or perhaps “the angles and curvatures of the swirls and loops in the center of the fingerprint” (which, according to German, “is not sufficient for individualization”).
I needed to read the original study to see what Guo actually said, and to determine if AI discovered something novel beyond what forensic scientists consider the information “in the center of the fingerprint.”
So let’s look at the study
I finally took the time to read the study, “Unveiling intra-person fingerprint similarity via deep contrastive learning,” as published in Science Advances on January 12. While there is a lot to read here, I’m going to skip to Guo et al’s description of the fingerprint comparison method used by AI. Central to this comparison is the concept of a “feature map.”
Figure 2A shows that all the feature maps exhibit a statistically significant ability to distinguish between pairs of distinct fingerprints from the same person and different people. However, some are clearly better than others. In general, the more fingerprint-like a feature map looks, the more strongly it shows the similarity. We highlight that the binarized images performed almost as well as the original images, meaning that the similarity is due mostly to inherent ridge patterns, rather than spurious characteristics (e.g., image brightness, image background noise, and pressure applied by the user when providing the sample). Furthermore, it is very interesting that ridge orientation maps perform almost as well as the binarized and original images—this suggests that most of the cross-finger similarity can actually be explained by ridge orientation.
(The implied reversal from the forensic order of things is interesting. Specifically, ridge orientation, which yields a bunch of rich data, is considered more authoritative than mere minutiae points, which are just teeny little dots that don’t look like a fingerprint. Forensic examiners consider the minutiae more authoritative than the ridge detail.)
Based upon the initial findings, Guo et al delved deeper. (Sorry, couldn’t help myself.) Specifically, they interrogated the feature maps.
We observe a trend in the filter visualizations going from the beginning to the end of the network: filters in earlier layers exhibit simpler ridge/minutia patterns, the middle layers show more complex multidirectional patterns, and filters in the last layer display high-level patterns that look much like fingerprints—this increasing complexity is expected of deep neural networks that process images. Furthermore, the ridge patterns in the filter visualizations are all generally the same shade of gray, meaning that we can rule out image brightness as a source of similarity. Overall, each of these visualizations resembles recognizable parts of fingerprint patterns (rather than random noise or background patterns), bolstering our confidence that the similarity learned by our deep models is due to genuine fingerprint patterns, and not spurious similarities.
And what are Guo et al’s derived ramifications? I’ll skip to the most eye-opening one, related to digital authentication.
In addition, our work can be useful in digital authentication scenarios. Using our fingerprint processing pipeline, a person can enroll into their device’s fingerprint scanner with one finger (e.g., left index) and unlock it with any other finger (e.g., right pinky). This increases convenience, and it is also useful in scenarios where the original finger a person enrolled with becomes temporarily or permanently unreadable (e.g., occluded by bandages or dirt, ridge patterns have been rubbed off due to traumatic event), as they can still access their device with their other fingers.
However, the researchers caution that (as any good researcher would say when angling for funds) more research is needed. Their biggest concern was the small sample size they used in their experiments (60,000 prints), coupled with the fact that the prints were full and not partial fingerprints.
What is unanswered?
So let’s assume that the study shows a strong similarity between the ridges of fingerprints from the same person. Is this enough to show:
that the prints from two fingers on the same person ARE THE SAME, and
that the prints from two fingers on the same person are more alike than a print from ANY OTHER PERSON?
Or to use a specific example, if we have Mike French’s fingers 2 (right index) and 7 (left index), are those demonstrably from the same person, while my own finger 2 is demonstrably NOT from Mike French?
And what happens if my finger 2 has the same ridge pattern as French’s finger 2, yet is different from French’s finger 7? Does that mean that my finger 2 and French’s finger 2 are from the same person?
If this happens, then the digital authentication example above wouldn’t work, because I could use my finger 2 to get access to French’s data.
This could get messy.
More research IS needed, and here’s what it should be
If you have an innovative idea for a way to build an automobile, is it best to never talk to an existing automobile expert at all?
Same with fingerprints. Don’t just leave the study with the AI folks. Bring the forensic people on board.
And the doctors also.
Initiate a conversation between the people who found this new AI technique, the forensic people who have used similar techniques to classify prints as arches, loops, whorls, etc., and the medical people who understand how the ridges are formed in the womb in the first place.
If you get all the involved parties in one room, then perhaps they can work together to decide whether the technique can truly be used to identify people.
I don’t expect that this discussion will settle once and for all whether every fingerprint is unique. At least not to the satisfaction of scientists.
But bringing the parties together is better than not listening to critical stakeholders at all.
I’m sure that you’ve heard the saying that “you can’t make a silk purse out of a sow’s ear.” Alternative phrases are “putting lipstick on a pig” or “polishing a turd.”
In other words, if something is crappy, you can’t completely transform it into something worthwhile.
Yet we persist on starting with crappy stuff anyway…such as surrendering our writing to generative AI and then trying to fix the resulting crap later.
Which is why I’ve said that a human should ALWAYS write the first draft.
The questionable job description
Mike Harris found a job post asking for a human copyeditor to rework AI-generated content. See the details here.
I’m sure that the unnamed company thought it was a great idea to have AI generate the content…until they saw what AI generated.
Rather than fix the source of the problem, the company has apparently elected to hire someone to rework the stuff.
A human should always write the first draft
Why not have a human write the stuff in the first place..as I recommended last June? Let me borrow what I said before…
I’m going to stick with the old fashioned method of writing the first draft myself. And I suggest that you do the same. Doing this lets me:
Satisfy my inflated ego. I’ve been writing for years and take pride in my ability to outline and compose a piece of text. I’ve created thousands upon thousands of pieces of content over my lifetime, so I feel I know what I’m doing.
Iterate on my work to make it better. Yes, your favorite generative AI tool can crank out a block of text in a minute. But when I’m using my own hands on a keyboard to write something, I can zoom up and down throughout the text, tweaking things, adding stuff, removing stuff, and sometimes copying everything to a brand new draft and hacking half of it away. It takes a lot longer, but in my view all of this iterative activity makes the first draft much better, which makes the final version even better still.
Control the tone of my writing. One current drawback of generative AI is that, unless properly prompted, it often delivers bland, boring text. Creating and iterating the text myself lets me dictate the tone of voice. Do I want to present the content as coming from a knowledgeable Sage? Does the text need the tone of a Revolutionary? I want to get that into the first draft, rather than having to rewrite the whole thing later to change it.
I made a couple of other points in that original LinkedIn article, but I’m…um…iterating. I predict that there’s a time when I WON’T be able to sleep on my text any more, and these days the “generated text” flag has been replaced by HUMAN detection of stuff that was obviously written by a bot.
And that’s more dangerous than any flag.
But if you insist on going the cheap route and outsourcing your writing to a bot…you get what you pay for.
Forensic examiners, YOU’RE DOING IT WRONG based on this bold claim:
“Columbia engineers have built a new AI that shatters a long-held belief in forensics–that fingerprints from different fingers of the same person are unique. It turns out they are similar, only we’ve been comparing fingerprints the wrong way!” (From Newswise)
Couple that claim with the initial rejection of the paper by multiple forensic journals because “it is well known that every fingerprint is unique” (apparently the reviewer never read the NAS report), and you have the makings of a sexy story.
Or do you?
And what is the paper’s basis for the claim that fingerprints from the same person are NOT unique?
““The AI was not using ‘minutiae,’ which are the branchings and endpoints in fingerprint ridges – the patterns used in traditional fingerprint comparison,” said Guo, who began the study as a first-year student at Columbia Engineering in 2021. “Instead, it was using something else, related to the angles and curvatures of the swirls and loops in the center of the fingerprint.”” (From Newswise)
Perhaps there are similarities in the patterns of the fingers at the center of a print, but that doesn’t negate the uniqueness of the bifurcations and ridge ending locations throughout the print. Guo’s method uses less of the distal fingerprint than traditional minutiae analysis.
But maybe there are forensic applications for this alternate print comparison technique, at least as an investigative lead. (Let me repeat that again: “investigative lead.”) Courtroom use will be limited because there is no AI equivalent to explain to the court how the comparison was made, and if any other expert AI algorithm would yield the same results.
As I said, I shared the piece above to several places, including one frequented by forensic experts. One commenter in a private area offered the following observation, in part:
What was the validation process? Did they have a qualified latent print examiner confirm their data?
From a private source.
Before you dismiss the comment as reflecting a stick-in-the-mud forensic old fogey who does not recognize the great wisdom of our AI overlords, remember (as I noted above) that forensic experts are required to testify in court about things like this. If artificial intelligence is claimed to identify relationships between fingers from the same person, you’d better make really sure that this is true before someone is put to death.
I hate to repeat the phrase used by scientific study authors in search of more funding, but…
Leftover pizza is the best pizza. Preparation credit: Pizza N Such, Claremont, California. Can I earn free pizza as a powerful influencer? Probably not, but I’ll disclose on the 0.00001% chance that I do.
Now that it’s time to write the “separate post,” I really don’t want to get into the mechanics of how posts that attract prospects (hungry people, target audience) increase awareness and help you convert prospects for your products and services.
So forget that. I’m going to tell a story instead about two executives at a fictional company that has a real problem. The executives’ names are Jones and Smith.
The story
Jones was troubled. Sales weren’t increasing, prospects weren’t appearing, and if this malaise continued the company would have to conduct a second round of layoffs. Jones knew that “rightsizing” would be disastrous, so the company needed another solution.
So Jones videoconferenced Smith and asked, “How can we make 2024 better than 2023?”
Smith replied, “Increasing sales calls could help, and ads could help, but there’s another way to increase our awareness with our prospects. We could create content on our website and on our social channels that spreads knowledge of our products and services.”
Jones exclaimed, “That’s great! We could get generative AI to create content for us!”
“No, not that!” Smith replied. “Generative AI text sounds like a bot wrote it, and makes us sound boring, just like everyone else using generative AI text. Do we want to sound like that and put our prospects to sleep?”
“So we need a human writer,” Jones realized, “one who can describe all of the features of our products.”
“Absolutely not,” Smith emphasized. “Customers don’t care about our features. They care about the benefits we can provide to them. If we just list a bunch of features, they’ll say, ‘So what?'”
“OK, we’ll go with benefits,” said Jones. “But why is content so important?”
“Take blogging,” replied Smith. “The average company that blogs generates 55% more website visitors. B2B marketers that use blogs get 67% more leads than those who do not. Marketers who have prioritized blogging are 13x more likely to enjoy positive ROI. And 92% of companies who blog multiple times per day have acquired a customer from their blog.”
“Wow.” Jones was silent for a moment. “How do you know all of this stuff, Smith?”
“Because of the content that I’ve read online from a marketing and writing services company called Bredemarket. The company creates content to urge others to create content. Bredemarket eats its own wildebeest food.”
As we close out 2023, I’ve been thinking about a lot of things.
Will 2024 be like 2021, in which the new year (2024) offers mimimal improvement over the preceding year (2023)? Reply hazy, try again later. (“And while tech layoffs slowed down in the summer and fall, it appears that cuts are ramping up yet again.”)
What new ways will we develop to better ghost people? Or will the old ways of ghosting continue to dominate the (lack of) conversation? (And if the person who ghosts is the ghoster, and the person who is ghosted is the ghostee…who is the ghost?)
Most importantly for this post, I’ve been thinking about the following: why are target audiences TARGETS, and why are they AUDIENCES? And is the term “target audience” the best description? (TL;DR: No.) If not, what’s a better description? (TL;DR: I don’t know.)
So let’s look at target audiences. And if you don’t mind, I’ve asked William Tell, Cheap Trick, Steve Dahl, Google Bard, Andrew Loog Oldham, and others to help me examine the topic, which will probably be Bredemarket’s last blog post of 2023.
But first I’ll explain where I’m coming from. Rather than diving into great detail, I’ll take a more, um, targeted approach and just quote a bit from the relevant information page.
There are roughly 8 billion people in the world. Most businesses don’t care about 7.99999 billion of these people; the businesses only care about 0.00001 billion (or fewer) people who will buy or recommend the business’ product or service.
Your content (or proposal) needs to resonate with these people. The others don’t matter.
Daily Copywriting’s use of the word “target” as a pejorative got me thinking about my fave phrase “target audience.”
Let’s start with the first word. At its worst, “target” implies something that you shoot, in a William Tell sort of way. If your weapon hits the target, you and your son don’t die. If your weapon misses low, you kill your son. Is that what we marketers do? Hopefully not.
Even at its best, “target” is just something that you get right. You’ve narrowed the 8 billion people to the few that really matter. So what?
Then we move to the second word of the fave phrase “target audience.” An audience is a group of people that sit in chairs while you perform your song and dance in front of them. (“These are the seven questions your content creator should ask you. Thank you for coming to my BRED talk.”)
Sometimes the audience just sits. Not good.
Sometimes the audience claps. A little better.
If you’re the Beatles or Cheap Trick, the audience screams. But still not enough.
Clapping or screaming is nice, but this doesn’t count as true engagement. I mean, in a way it would be nice if you scream with joy at this post, but it counts for nothing if you don’t actually buy Bredemarket’s marketing and writing services.
I’m forced to admit that there was one time where someone truly engaged his target audience—and that was during Steve Dahl’s “Disco Demolition Night.”
Dahl was not a disco fan, since he lost his WDAI radio job when his then-employer switched to a disco format. As a counter-reaction to disco, Dahl entertained baseball fans on Disco Demolition Night by destroying disco records between two games of a doubleheader. 7,000 fans stormed the field in delirious joy, wrecking the field, and the second game of the doubleheader was never played. This was NOT a good thing. But the White Sox survived, and even celebrated the 40th anniversary of the promotion. Dahl threw out the first pitch. Nothing exploded. But nothing was accomplished.
So if “target audience” isn’t the right term to use, what is?
I’ll grant that “needy people” has a negative connotation, like the person who is sad when people forget their birthday. (Not “ghosting” per se, but perhaps a little “boo.”) But “needy people” is certainly better than “target audience.”
“Needy” is stronger than “target.” Rather than just representing a demographic, it actually represents people who truly NEED things. Just because a company needs content for its website doesn’t necessarily mean that it needs Bredemarket. Many companies have their own people to create content.
And for the reasons stated by Daily Copyrighting, “people” is stronger than “audience.” You are not a faceless audience that claps (or screams) when Bredemarket does its song and dance. You are people who work in a certain way, which I why I talk to people before creating content for them.
This “needy people” phrase sounded good a few days ago, but now that I’ve thought about it some more I see some problems with this formulation also. In addition to the negative connotations of the phrase, the mere fact that someone is “needy” doesn’t necessary mean that they will buy Bredemarket’s services. Take my identification of “needy people” from a few days ago:
My mini-survey shows that of the 40+ identity firms with blogs, about one-third of them HAVEN’T SAID A SINGLE THING to their prospects and customers in the last two months.
Yes, these companies are damaging their future prospects and need Bredemarket. But none of these companies has approached Bredemarket, or any other marketing and writing consultant, or their in-house people.
Perhaps they don’t see the problem at all.
Perhaps they see the problem, but don’t want to spend money (even a few hundred dollars) to fix it.
Perhaps they see the problem but don’t consider it as critically important as the other problems they face. Rather than spending a few hundred dollars, some companies are saving millions of dollars by “rightsizing” by 10-20% and cutting marketing budgets. They’re just fine with spiderweb-covered blog pages.
Because “needy people” doesn’t capture my meaning, I’ll do the recommended thing and use this blog post to throw up another idea.
The problem with the term “hungry people”
My latest iteration of “target audience” is “hungry people.” I figure that unlike “needy people,” “hungry people” are more inspired to act on their needs. They don’t just clap or scream; they are motivated to search for something to eat.
Using my example of the non-blogging identity firms, perhaps some of those quiet firms are troubled by their lack of communication with their prospects and clients. Rather than doing nothing, these firms are ready to plug their communications gap. But will they plug it with healthy food, or with junk food?
Even without writers, companies can unleash a content creation boom with generative AI. By feeding the AI brand guidelines, target audience data, and product specifics, they can churn out blog posts, ad copy, product descriptions, and even social media snippets. This AI assistant generates captivating headlines, outlines engaging narratives, and drafts basic texts, all while maintaining brand voice and SEO optimization. AI handles the heavy lifting, freeing up resources for strategic planning and audience engagement, boosting content output from silence to symphony.
I know this may surprise you, but I didn’t write the paragraph above. Google Bard did. And perhaps some hungry companies will opt for the free route and let generative AI write their content rather than contracting or employing a content marketer. Silence to symphony for a $0 budget! I consider this bittersweet.
I let the melody shine, let it cleanse my mind, I feel free now. Even though the melody was from Andrew Loog Oldham, uncredited, resulting in a decades-long copyright dispute between the Verve and the Rolling Stones. From https://www.youtube.com/watch?v=1lyu1KKwC74.
So “hungry” doesn’t quite cut it, since I seek the people who are not only hungry, but who will pay for quality fare such as Bredemarket’s marketing and writing services.
To be continued
In this case, the blog post IS a temporary expression of thought. I’ve determined that “target audience” is an imperfect phrase and that I need to replace it with SOMETHING, but I haven’t figured out what that “something” is yet.
Until I get a better idea, I’ll use “hungry people” in my internal work, but I won’t change my customer-facing work (such as the e-book) until I come up with an effective alternative to “target audience.”
And one more thing
And unless I get a sudden brainstorm, this will probably be the last Bredemarket blog post for 2023, and I’m currently ruminating about the first question I asked in this post regarding prospects for 2024. While there were some positive things that happened this year, the negatives during the last seven months tended to outweigh the positives. A lot of other people are also experiencing this, so I’m sharing this song for those who are glad that 2023 is almost over, and who hope that 2024 isn’t more of the same.
A firm’s content should address the needs of its target audience. For Bredemarket, that is people in identity, biometrics, technology, and general business whose firms aren’t marketing effectively.
Narrow target audiences are the enemy of virality.
Unless you create attention-grabbing content that doesn’t contribute to your core business, but only grabs attention.
For example, you could proclaim that “Kokomo” is the absolute best Beach Boys song ever.
Fair
But I couldn’t bear to write such drivel myself, so I assigned the task to Google Bard, who wrote all the “hot take” text under this paragraph. (I hate myself now.)
Kokomo: The Underrated Gem that Shines Brighter than California Sun
Beach Boys fans, brace yourselves for a hot take. While classics like “Surfin’ U.S.A.” and “California Girls” hold their place in the pantheon of summer anthems, there’s one song, often relegated to the realm of guilty pleasures, that deserves a crown of seashells and a chorus of seagulls: “Kokomo.”
Yes, I said it. Kokomo is not just The Beach Boys’ best song, it’s a pop masterpiece, a sonic escape hatch to a tropical paradise, and a testament to the band’s enduring musical magic. Here’s why:
1. The Escape: From the first steel drum lick, “Kokomo” whisks you away from the everyday. Mike Love’s smooth vocals paint a picture of turquoise waters, swaying palm trees, and worries lost in the trade winds. It’s wanderlust in a song, a daydream of bare feet in the sand, perfectly capturing the yearning for escape that made The Beach Boys icons.
2. The Melody: It’s impossible not to hum along. The chorus, a catchy earworm built on layered harmonies and a driving beat, burrows deep into your brain and stays there like a souvenir seashell. It’s a pop hook that rivals anything in The Beach Boys’ discography, a testament to their mastery of crafting unforgettable melodies.
3. The Production: Lush, vibrant, and layered, the production on “Kokomo” is a sonic tapestry. Tropical instruments, steel drums, and gentle percussion create a soundscape that’s both playful and sophisticated. It’s a sonic cocktail that transports you to the heart of that island paradise, with every layer adding a new dimension to the musical journey.
4. The Unexpectedness: Released in 1988, “Kokomo” arrived at a time when The Beach Boys were navigating a new musical landscape. But instead of feeling dated or derivative, it embraced a contemporary sound while retaining the band’s signature harmonies and sun-drenched spirit. It’s a testament to their ability to adapt and evolve, proving their musical magic had never truly faded.
5. The Heart: At its core, “Kokomo” is a song about longing and connection. It’s about finding solace in the arms of a loved one, escaping the mundane with someone special. It’s a love letter to shared dreams and simple pleasures, reminding us that sometimes, all we need is a little sunshine, a sandy beach, and the one we love.
So, next time you reach for a Beach Boys record, don’t just skip to the hits. Give “Kokomo” a chance. Let its infectious melody wash over you, close your eyes, and imagine yourself beneath the swaying palm trees. You might just find yourself singing along, realizing you’ve stumbled upon a hidden gem, the one song that truly captures the essence of The Beach Boys and their timeless sound.
Now, go forth and spread the gospel of Kokomo! Let’s give this underrated masterpiece the recognition it deserves. After all, it’s not just a song, it’s a state of mind, a passport to paradise, and the ultimate proof that The Beach Boys’ magic truly is endless.
P.S. Don’t forget to pack your sunscreen and your dancing shoes!
Bredemarket 