Welcome Francesco Magnocavallo to Marketing AI CEO Chats

Welcome Francesco Magnocavallo to Marketing AI CEO Chats

AIContentGen Chats with Francesco Magnocavallo, Chief Product Officer of Contents.com

Transcript of the podcast:

Scott (00:04): Hello, and welcome to  Marketing  AI CEO chats. And I’m Scott Sweeney and I’m here with my co-founder from AI content. Jen, John CA.

Francesco (00:14): Thanks, Scott. Great to be here again.

Scott (00:17): And today we’re speaking with Francesco Magnocavallo. I hope I said that, right? Francesco.

Francesco (00:25): Yeah. Scott, don’t worry. It’s a good

Scott (00:27): Line. <Laugh> and he is the chief product officer@contents.com. And today we’re gonna be talking AI marketing and learning more about what contents.com can do for you. And so thanks for joining us.

Francesco (00:44): Yeah. Thank you for having me. It’s great to be speaking with you for the public.

Scott (00:49): And so we’re gonna get right into it. We want a little bit of background first Francesco. You have a very, very interesting background looking through your LinkedIn. I saw that not only did you do some content for hears communications and luxury brands, but the dichotomy of spending 10 years in hip hop in Milan is really interesting. And I’m just wondering how all that melds into you and your AI journey and where you are now.

Francesco (01:21): Yeah, so I figure basically it’s always been about creating content. So I had the opportunity and the lack of meeting pioneers in NEP pop, like phase two. He was he’s been in Milan, been friends for years and learning, you know, very different kind of writing that was wild writing, a personal alphabet. Nobody really can replicate. And you know, that’s coming from your soul. It’s not even it’s very different. So those were crazy times getting to know this culture, this great culture, and then doing some systems. So basically you know, this was really multimedia <laugh> at mm-hmm <affirmative> and often they’re doing freestyle, just getting your soul out. And later through doing websites, I got into content and I’m fond of remembering the very early days of blogging, like years 2000, and 2001, the atmosphere was very different.

Francesco (02:21): Everybody was a developer then in blogging, it was easy to learn. You, you could study, you know, IBM stuff, materials just early days of content designs. That was great in learning your way around content. Great times then when blogs exploded, we launched the company, it was a clone of Google media and incorporated that to be companies publishing a gadget Giese model and all that stuff. And we eventually sold the company. So, I remember I launched something like one blog every month for five or six years. Those were crazy times. We had a thousand orders writing for us in all <laugh>. And then we sold the company. I did some consulting and ended up at Hurst where I spent six years doing digital strategy and launching the digital operation for six or seven websites. And I had, I think I had the privilege of launching Esquire magazine in Italy and our per so our is 150 years old brand. That’s something it’s, it’s even, you know, difficult to think about this because this there’s so much history in there. And the company now is, is got incredible infrastructure and strategy in New York. So it was very interesting times as well,

John  (03:39): Francesco I’ve worked in the custom content industry as well, and that’s basically the custom content industry is the magazine industry either you know, public magazines or, or private corporate ones. And one of the things that I found, although I’m a digital guy, I was working with those magazine editors and writers. I found the discipline of that industry to be really interesting because, to me, it was as a digital guy, almost like writing an entire website in a month when you’re changing a magazine every month. How did that discipline affect you and how you think about content?

Francesco (04:18): Yeah, I think this is a great concept indeed because the freer the ecosystem and the more possibilities you have, the more discipline you need, the more strategy. So this is very true for digital as well. What I learned from the first magazine was really journalism in the sense of not really high brow, but high-quality journalism. There was some high-brow content. We published it in Esquire magazine, but, you know, doing really premium quality stuff. And that’s something I learned the hard way with print journalism, doing digital transformation, which is a very difficult activity. Sometimes was really rough to deal with people with very different languages, different, you know, a very different mindset, different professionals com everything was very the two sides, the digital and the print merging the two sides was an incredibly demanding task.

Scott (05:20): I can see that Francesco and I think in many ways AI content generation has a lot to do with digital transformation too. So it’s not just about the tools, but it’s about the process. So so let’s, let’s kind of move into that. I don’t know if you’d agree with that, but feel free to comment on that. Tell us a little bit if you want more about your AI journey, but then let’s go in and talk a little bit about contents.com.

Francesco (05:48): Yeah. Yes. So ma Milano, our founder, and executive officer, we, we were acquaintances from you know, 10 years ago. And when he see me as a feeling for openings on the market he ended up thinking I was the right person to bring some journalistic mindset to work and design the system with the engineers. So, he chose not to have a journalistic system designed by engineers, but by a journalist. So that was you know, my point of view. And I think it’s been really rewarding work working with my data science team, and my colleagues in data science. That’s very interesting. So then we added a computational linguist and I think, by the time we started merging and having a common framework in languages, things really improved a lot. So when I speak to clients in meetings with my sales colleagues, I find that I often speak the same language because I had the same problems and the same workflows that the journalists have. And so it’s very easy to bring something that creates value for them, with automation basically.

Speaker 3 (07:06): And Francesco, what perhaps we could get into the service of the software. What are the capabilities of the software? What does it do?

Francesco (07:14): Yeah. So Scott mentioned that you need workflows. You obviously need some technology and we work with open AI by the way, but you also need talent. So what you know, the defining characteristic of contents.com is that we blend human talent with technology. So we try to get the best of both words. That’s a very basic concept in AI-assisted or human in the loop. So basically the company had a copywriting marketplace from a Francesco few years ago and it kinda went out of fashion. You know, you cannot really speak about this sort of product with DC anymore because it’s, it’s very old, but it’s coming back as a strategic point because you can have a finishing layer and and and have the machine designed by a journalist and finished by a journalist.

Francesco (08:13): So I think this is gonna be a very interesting concept if you been reading Andre in order with the magazine, that’s this article that came out just couple days ago, and this is their point. So there’s gonna be a deluge in AI content, and we don’t really know what school is gonna do in a couple of years or next year. So having a sort of blended approach where it’s AI, but it’s human definitely makes some sense. So this is a key concept. And in terms of a service we provide to the client, the client can often choose to have just a human professional working on his content, just the machine, if he wants to make it cheap and, and quick or a blend. So this is the this is something that’s very different because it’s kind of easy nowadays to just build you know, a front end on open AI or any other language model of your choice and build a little pipeline to do some pre-processing, but really to have a complex system. That I think creates a different scenario

Scott (09:21): For that’s very interesting Francesco and I, I love the approach because as we know many times you can’t, well, we, you never can just tell a machine what to write and have it come out. Perfect. Right? So you need, you need some inputs and you need some massaging. Tell us, I think everyone can understand human writing and everyone can understand machine writing. How does the blend work?

Francesco (09:56): Yeah, so it’s blended by workflows basically. So what we, we have and we gonna improve and build and invest in is newsroom workflows, basically. So a full system where there are roles, there are teams, and there’s the process of commissioning a piece commenting on a piece, reviewing the draft, approving it, and publishing. So the last mile in the system is connectors to the CMS. So you see, we currently have a WordPress and a Shopify connector. We are building an Adobe connector for an enterprise large project with a big consulting firm. And so it’s, it’s really a lot of different pieces that mix for, for to have something that’s not easy to replicate. You know, that’s, that’s kind of like our mode, our defensive mode, it’s so complex, it’s kind of a hassle. You cannot replicate it faster.

Scott (10:59): Excellent. And so as you look out at the AI landscape, where do you see the strength of contents lying as opposed to other systems?

Francesco (11:15): Yeah. I’m sure there’s some room in Europe because we got all these languages. I don’t want to speak about Africa because they got thousands of languages, but in Europe we got, we have a few billion, so that’s a lot anyway. So in this disrespect, we building native data sets and one of the characteristics is many competitors. They just translate everything from English because of large language models, they are proficient, and they are mostly proficient in English. There are a few great projects in Europe at the moment, but it’s still something that that needs to evolve a little bit. So we got French, Spanish, and Italian datasets, and the language model is working natively in those languages. So this is something where I think we can be strong because we work with datasets in four different languages at the moment. And that’s also something that takes a little time. It takes a little love to have journalists working on this. We don’t really just scrape data from the internet. Everything is, you know, manually edited and proofread and worked on.

John (12:28): Let’s go through some quick, rapid-fire questions Francesco perhaps we can ask a couple of questions about how you support the clients you know content strategy team approach around some of the following areas. How about ideas and research?

Francesco (12:50): Yes. So the idea of the founder was to have a suite of services covering the whole customer journey. So basically a content strategy journey going from media monitoring. And we got a pocket solution I think, is N cause it’s got a very strong signal-to-noise ratio. You can it doesn’t require you to spend the money of one of the enterprise or mid-market competitors, which usually costs from one or 200 to a few thousand a month. And of course, it’s working across languages with MLP. So across industries, keyword entities, fairly basic stuff. Then we got a brainstorming service which is basically a text box where you can work with a language model and do a semantics search test, which is very new. And I think this is gonna be the future of search. So not just getting 10 links, but getting, you know, a proper original answer to your search.

Francesco (14:00): So this is also where we can peek inside the mind of clients and get a little fresh data to see what’s going on. What are the needs and the pain points and the jobs to be done? Then of course the main, the main service is content creation where we can do copywriting in. As I mentioned before in assisted mixed hybrid, purely human or purely machine, we got a couple of different input styles. So one is the instruct, the open I G PT three instruct model. So it’s natural language and clients like this, a lot, the possibility of input having natural language input. I want an article like this and that they enjoy this, but the other one is purely a CEO routine. So basically you input a keyword and we do some sort of data science that zoom gap analysis with competitors and building an outline for the, for the article that’s you know, geared to, to generate organic traffic on Google.

Francesco (15:01): Then we got translations. Again, you can do this with machines or humans, and we are moving into multimodel. Of course, this is the big, the big research race at the moment between the large companies. So we’re gonna start at the beginning of September. So I don’t know when this interview is gonna be polished, but starting from September, we’re gonna sell text-to-image services. And we are in the process of refreshing the audio services. So basically we do a basic speech-to-text that’s a utility but there’s a lot going on in text-to-speech. So you can go to the very high-end Hollywood-level voiceover, or you can do many different types of services in with synthetic voices. And so I’m, I’m very interested in in this and some of the clients we began speaking about this topic in July, they very interested because that’s very glamorous that’s hype, but they can speak to clients about something that’s cool, and nobody has at the moment. So basically that’s the suite of services at

Scott (16:19): The moment. That’s very broad. I’m really impressed. And I noticed that you’re hiring for someone that’s gonna be using Dolly too very soon too.

Francesco (16:27): Yeah. We hiring for Dolly too. Yeah. <laugh> and, you know, that’s very interesting because the ideal candidate is, is somebody who knows about photography, illustration, graphic design. So it’s, it’s, it’s really, you know, it’s, it’s very, it’s, it’s very generalized across visual disciplines. And we got a, I think we got an incredible debt for Italy at the moment it’s going out next couple of weeks to our sales team. So this is very, very, very promising.

Scott (17:01): That’s very exciting. I’ve been playing with Dolly too. It’s a lot of fun. <Laugh> yeah.

Francesco (17:05): Yeah.

Scott (17:07): Excellent. John, do you have any more rapid-fire questions that you wanna follow up on, or is

John (17:12): Well, you, you covered a lot of it, Francesco. Yes. I did wonder about expansion and metrics expansion would be you know perhaps doing the general AI content generation, but then maybe for social media or something like that, you know, splitting content up. Yeah. Do you have any aspects of that?

Francesco (17:32): Yeah. You know, when you mention metrics, I think this is very interesting for the gigs of the NP gigs and the energy gigs because actually, this is evolving so fast. There’s no, there’s often no coded KPIs for energy. If you go into research, the pre-print papers, you know, there there’s, there’s no real standard at the moment. So I think this is very interesting and we working a lot on KPIs for the quality of machine translation of NLG. This goes from, you know, very basic like syntax grammar the, the flare journalistic flare, which is very difficult to, to, you know, to digitize, to compile, certainly because it’s you know, it’s, it is the most difficult to plagiarise to that there’s a lot of to similarity of translations, the quality of translation. So I think this is a very interesting area of benchmarking and having common KPIs for energy because often you see as I said before, it’s very easy to build a system, but the large language model will just give you average input.

Francesco (18:43): That’s not enough for clients if you if they’re demanding. So I remember a computational linguist. We were working this winter, relaunching the product, and this was scheduled for spring. So one day we were testing different pipelines, different data sets, and different models with opening, I had just an incredible acceleration around the end and the beginning of the year. And she, one day came into Francesco. We jumped from I don’t know about school grades in America, sorry, be patient with me. But we went from, you know, Italian of 14 years old to Italian of 20 years old. Huh. This is great Francesco. We did it. <Laugh>. Yeah. So this is really, this is happening. And I think it’s very interesting to go behind the scenes and know what the data scientists are doing, what the, what the competition linguists are working on, what these then pleasure and satisfaction.

Speaker 3 (19:40): Well, that’s great. That’s great. Thank you so much for, covering those different aspects. I really appreciate it.

Scott (19:48): So then Francesco, I have one kind of wrap-up question, and then if you want to make any final comments at the end feel free to do so, but I’d like to know you know, this is such a fast-evolving field right now, and everyone’s got all kinds of thoughts about the AI and content generation I’d like to know from you. One thing that most people believe is true about AI content generation, but you take a very contrarian view about it. And I think it’s not true at all.

Francesco (20:28): Yeah. So you see, I think a system that just helps you create an article is just some very basic task, and it doesn’t really give you a proper idea of what you can do with these models. They’re so powerful. You can do incredible things. And I think for large businesses, I mean, million URL websites a hundred million companies in, in the Italian market. So everything is different, in the United States on a global scale, but they got a different set of problems. They don’t want you to create one article for them. They want to manage a million hotel project listings. So I think that’s so much that we’ll come to the market in the next few years, because, you know, when I came to the company, I told the founder, I don’t really wanna do a high copywriting assistant, a Milano. This is so basic who cares about this? Let’s go after, you know, affiliation, let’s go after hotel businesses, let’s go after eCommerce. And so I think that lot is gonna come to the market in terms of specialized pipelines and companies.

John (21:36): Well, thank you so much for joining us, Francesco. We really appreciated the podcast. So it’s a great interview today. Yeah. Thank you very much.

Francesco (21:44): Thank you for us to me. It’s great to be here with you

John  (21:48): And thank you to our audience for joining us today. We’ll see you next time.

 

 

Marketing AI CEO Chats Welcomes Harish Kumar of CrawlQ

Marketing AI CEO Chats Welcomes Harish Kumar of CrawlQ

AIContentGen Chats with Harish Kumar, Founder of CrawlQ

Transcript of the podcast:

Scott (00:11): Hello, this is Scott Sweeney, and welcome to AI content CEO chats. Today we have Harish Kumar, founder, and CEO of CrawlQ and he’s joining us to talk about how their company is solving content teams content design issues. And this is my co-founder John Cass. 

John: Hi. Thanks Scott. Hi Harish. Thanks for joining us today.

Harish (00:39): Hi, thanks Scott and John having me here. It’s pleasure to speak to you and happy to answer your questions.

Scott (00:48): Fantastic. It’s great to have you. Thank you. So I, you know, I was, I actually curious, I know there’s an actor named Harish Kumar, are you any relation?

Harish (01:01): Well,  to be honest there are more Harish Kumar than you can imagine. <Laugh> so I’m one of them, but I’m not an actor and I’m not going to perform today. Like Harish <laugh>. I will be staying true to myself.

Scott (01:17): <Laugh> perfect. Great. Excellent. Well, I wanted to clear that up before anyone asks that question. So let’s jump right into our, some of our questions. Tell us about yourself Harish and your AI journey.

Harish (01:36): Thank you. So myself Harish I have more than 18 years of experience in the industry with data machine learning. And mostly my background is product design engineer. I think my AI journey started last in four or five years. And it was mostly because of my background with the product design engineering. I have been leading product teams in different complex environments from Ernst & Young, you know, EY, who’s a consulting company, and then big banks and be small or big. The most important roadblock that I faced in my profession was the silos between product silos, marketing and silos between sales teams. So when I started to explore this problem, the problem was deeper than you can think because none of these teams, even when I started within a marketing or within the product team they were not speaking in the same language and the root cause analysis that I did myself was that they were not clear enough on who is their target audience.

Harish (03:00): Mm-Hmm <affirmative>, they are up there going on, who, who is their audience, and that that’s the fundamental cushion that was kind of lacking. And I started thinking, okay, if we can create a common language across sales, marketing, and product, and then I realized this need to happen within marketing, within sales, within product development, because silos are everywhere. Silos are not only between these three arms but also within. And the problem was not only with the big organization, but the problem was also with the smaller companies, even a startup, even if I one person CEO, he’s thinking in a different sales marketing and product development, the three minds are working in silos and that’s a fundamental problem for many of startup failures, business failures in big organizations, projects are not delivered on time in, in budget many repercussions are happening. So I think that’s an interesting investigation that I started and underlying mechanism that I started because my background was not marketing or sales because I come from product design.

Harish (04:13): So I started thinking in terms of customers. So CustomerCentric centric approach, like what pain points my customer have. And I started applying this idea of jobs to be done by customers, understanding their pain points. Then obviously once you start talking in broader terms, then you see there are different concepts, like people are saying, I have, I know what is my ICP, ideal customer profile. Someone tells me, I know who is my target audience. Someone, someone is saying to me, I know what is my audience persona? And then someone says, okay, buyer persona. So everyone within marketing sales or product are talking in different terminology, even who is their target audience and the definitions around it. So the framework that I put around crawl queue is that you start with a broader niche within a niche. You go to sub niche and you go to micro niche and you apply jobs to be done framework to narrow down your micro niche.

Harish (05:20): And then within the context of MicroAge, I identify an ideal customer profile and I call it based on demographics. So roles, age, income, location, those kind of demographic factors, D remind within the micro niche, your ICP, then this is one set, right? The demographics mine, one set, and it is ICP. And that determines to whom you are going after. So for the sales team, for the product team, it’s very clear that we are going to this geography. These are the people with the experience. This is a group that we can study more or understand more. So you have a clear approach to whom we are going after, but that doesn’t solve the problem still. So you still need a lot of people, which are not only your ideal customer profile but also the people who are going to amplify the impact. Right?

Harish (06:21): So then I call another person and call audience persona. So audience person is more based on semantics, like the topics of interest it’s is, is more related what kind of authority or topics. And that includes everyone being an influencer in that field. So for example let’s take an example, like there, there are many people who are influencing CMO as a role, right? CMO is the role. And there are many people who are influencing and they, there are thought leader on the topics pinpoint of CS, right? So then this is audience persona. So now there is an intersection between audience persona and the ICP, your ideal customer profile. So based on demographics and based on semantics and the intersection, I call it buyer person. And that buyer person is dependent on the psychographic factors. So going into deeper into problems, desires, and outcome.

Harish (07:22): So to start with, I want to correct here that crawl queues, not an AI writing tool is a research tool is a personal building tool that starts with ICP. It helps you to do we get clear your audience persona then intersection it defines the buyer persona. And this is the buyer that you develop your customer journey from the top of the funnel, middle of the funnel, and bottom of the funnel. And once you are up with the research, the tool automatically also helps you to create content, which is highly to a stage of the, of the bio personnel are in the top of the funnel, middle of the funnel, or bottom of the funnel. I started this company in 2019. When I started, it was a very high level template with an Excel template, Google sheet with trying to ask a lot of ion with the people.

Harish (08:17): And I started working with five clients. So everything that you’ve to see in CrawlQ is created from scratch as in framework, as in a combination of different ideas from job to be done, then a background from product design engineer and try to address the needs of marketing and sales as well into it. So it’s, it, it, it was a framework. It evolved into a research tool. And then I applied artificial intelligence, NLP GPD three, and all the data that I could pull from Google, Reddi, and other sources to get the right information and to create a very structured research output that also feed dynamically to the content creation. So that’s, that’s how the whole part, and of course, it was a bit longer, but you can ask me for more specifications.

Scott (09:14):  Well, what I find interesting Harry is that point that you started at the beginning where you’re talking about those different silos and there’s no conversation amongst each of the different departments. And so that focus on the research is, is both hopefully to solve that problem across the different departments, and then also have a single mechanism for communicating within those departments as well. So that’s really the focus isn’t it, which is, the research? And then you do the the actual content creation. So I, I would suspect that most of the clients that are coming to you, you know, when they look at the software, they’re looking at the AI content. So how does that, how do you meld that in what the software does between the research and the content creation, you know, what are the outputs like how does that actually produce for the clients?

Harish (10:10): Sure. So of course I applied this software for myself also, and I tried to find my buyer persona. And it was not an easy one because you see the AI content writing as a niche has evolved, and there are many players in this niche, and it was very difficult initially to differentiate, but when they start and they come and they see a completely different way to create or understand how the content should be created. And obviously it created a filter mechanism. And also I did a lot of rework on how I wanted to target my audience. So my current focus is on brand strategist and content strategist, because those are the people who are linking pin between product team sales team and marketing team. They’re creating kind of overlays on these silos, right? These, are the people who are responsible for brand strategy, and that’s where my current focus and target is where to solve the problems of the brand strategies.

Harish (11:16): And slowly, of course, SA founders, accelerators people who are validating their business ideas or coming up with the new business ideas tool can be highly useful, but I’m very conscious of how I open up my niche and also how I market it. So my current focus right now is on the brand strategies, and I cannot, of course I created segments, but most of my customers, they are staying or working longer with me are either content strategies or business consultant or agencies who are serving multiple crimes, both for research purpose, and also for content creation.

Scott (12:04): And what, what do you think is the strength of the product with the clients that you’re, you’re, you’re, you know, you’re working with those brand strategists, what do you think is the strength for them?

Harish (12:14): The core strength of the crawl queue is the ability to pin down a specific micro niche. And the moment they realize this they open up the main differentiating value of CrawlQ, because most of the other options that are available in the market produce very generic content. Why, because most of them start with either a topic or keyboard and artificial intelligence right now is a limitation because it’s probability best model. So if you, if you give any kind of input, which is generic, then there is a more room for this massive neural network to go in tangential direction. So people are okay, they want to get their time cut or short shortened, to write something. And many people are good enough when something is generic. They’re good is when good enough is good enough for them.

Harish (13:15): But when they come to crawl queue it’s eye opener for them that how crawl queue can zoom in to a specific micro niche or a specific marketing or sales angle, and then take them through a very unique content. So it’s, it’s like it’s some upfront work that needs to be initiated because not only the keywords that you input, but more inputs about your business problem desire. And also there’s a lot of automation from the, where we were one and a half year back. And now, so you only need to supply maybe two or three inputs, and then it creates a person or ideal person for you. And obviously, you are going to humanize those inputs and rework on those inputs to make it your business specific. And once you do that, the, the results are outstanding. They’re clearly diff different from what you can get from any other alternatives, for example,

Scott (14:13): That’s great. Harry said your approach is very different than almost any of the companies that we’ve seen. I don’t see someone else starting with persona and go to a micro niche, tell us how that helps a content designer or a brand manager in terms of working with research or briefing or the actual writing optimization of content expansion, etcetera.

Harish (14:47): Right? So when, when you zoom into a kind of a team structure where brand manager is responsible for producing content streamlining the different silos, marketing sales, and product, and also I think most of the brand managers in CMO CMOs are also responsible for creating a ecosystem where they can pull all the available data and technology resources together. What I observed most of the time is there is a mad address to get more and more tools, but nobody’s brainstorming like how to get best out of that, right? The available technology. So right now we are in the face where there is hype, but as, as people settle down, we are still going back to the fundamentals. And the fundamental here is to understand the pinpoint of your audience, the old method to do this was surveys, questionnaires, focus, and group studies, but these methods are hard to scale.

Harish (15:50): And also when people try to implement those methods they’re prone to biases of how you design those discussions. So most of the teams currently, they’re not aware that we can shortcut this process by using some, smart AI tools like roll queue, right? But the moment they start working on it, they can realize that how it can collect short their time. Now, in terms of organizing the team, I would say that you would be hiring agencies or expensive market research, or you will be reverse engineering this customer problem from the mass amount of data from Google analytics, from everywhere. But in my approach, you are still back to the basics. You’re trying to understand this one particular customer you’re making a hypothesis about this. And you are validating that hypothesis by creating content. So you have a research team, you, one person who’s going to do the research.

Harish (16:44): You have three or four people who are going to create content. They are rapidly going to they’re rapidly going to create content and validate the actual signals that come from customers from reviews, from their interaction at the customer support desk, and from the emails from internal systems that they have created. And then, the more they can give feedback, look back to crawl queue in the research, the person who’s doing research or responsible for research, they can speed this process faster and create more ROI because they a don’t, they don’t have to hire expensive research marketing agencies or to market research agencies, which is by the very, very expensive you see to collect that kind of consumer insights and data on that level is, is a very difficult job and very expensive job. So they don’t need to hire those people.

Harish (17:36): They can work with CrawlQ, but someone who is a domain expert in that area is needed to validate those inputs and also to validate the market feedback. Once you have that function sorted out, right then rest is very automated because you can hire as many as virtual assistant. They don’t need to think about again and again, who is their neat micro niche or ideal persona. Everything is set to preset as a, as, as research. So you can create multiple preset or research. You can clone them, you can AB test them. You can apply different marketing angles to the different stages of the customer journey. So I, I think it’ll be great cost reduction because they can consolidate this research function into one person, and then they can scale the process of content creation by hiring virtual assistant, which need not to reinvent the wheel, but they just follow the, the, the research that is already there. And there is an internal AI within crawl queue, which take care of your research and the content creation. So there’s a linking pain, I call it Athena. So every time you can train Athena is almost like your virtual AI assistant, which makes sure that all the research that is done by Athena herself based on your initial input are also connected to the output. And she’s writing intelligently on all the information that is there and validated by your team.

Scott (19:03): So is there some metrics and dashboard that your customers receive that they’re able to review and maybe provide human input in terms of the waiting of the importance of the data that’s coming back in?

Harish (19:22): Sure. So right now we, we are still developing other metrics, but mm-hmm, <affirmative> the most important KPI right now is every content that you produce from crawl queue, you get in a score, how much it is semantically related and co with your initial audience research. And there are some parameters, of course you can play around, but the, the, the better the score, I mean, if score is a hundred percent, then you are almost repeating your research in your content, right? And the score is zero. That means your content doesn’t make sense with your with your initial research, but the content is good enough. Let’s say 75. And about then I call it. You are getting just from your Fred I, well, Fred, your audience is a thousand audience. So, Fred, Fred is a person who is, who has peers. He wants results. He has desires. And if your score is more than 75, then you are going to get definite definite just from, from your Fred, this how we play around with single metric right now. And that’s my goal, also not to create multiple metric and confuse user, but play around with one single metric and if necessary, try to create additional signals, which form the computation or explanation of this single metric. So I still want to drive a single metric in terms of ensuring that what you create as an output is very consistent and streamlined with your audience persona.

Scott (21:02): Excellent. Good. Thank you for that explanation. It’s very unique and I think it has a lot of value for the target audiences that you’re talking about. I have one question that I like to ask because it usually spurs some really great thought and ideas. And it’s this, what’s one thing that most PE people believe is true about AI content generation, which you don’t agree with whatsoever. You think it’s false,

Harish (21:37): Right? Right. most people who use AI, content writers, believe that AI can write unique content or create unique information, which I don’t think is true because all AI models are trained on an existing set of information. So unless you heavily I intervene with this process or humanize your input. You cannot create unique content because if you start with one topic, whatever you feed into it, it cannot generate unique content because it is already trained on a vast amount of internet data. So regardless of this perception, people are happy what content they get, but they are just diluting the value on the internet because there’s already such a content existing there. And that’s where this neural network has learned this. So my approach to this is to really break at every point of entry your data into this neural network, where you’re calling this machine big machine to inject your interventions, and more, you do more possible that you get an outcome, which is unique.

Scott (22:56): That makes absolute sense. You, you’re still including the, the human in that element. So, well, thank you. Thank you so much for joining us, Harry. You know, we really appreciate you joining us on the AI marketing CEO chats podcast today.

Harish (23:15): It’s my pleasure to talk to you, both of you

Scott (23:19): And thank you to the audience for supporting us, and we’ll see you the next time.

Scott (23:24): Take care,

 

 

 

 

 

Marketing AI CEO Chats with GoCharlie AI Co-Founders Kostas Hatalis & Brennan Woodruff

Marketing AI CEO Chats with GoCharlie AI Co-Founders Kostas Hatalis & Brennan Woodruff

Let’s chat AI Marketing! Welcome, Kostas and Brennan of GoCharlie to our inaugural podcast!

Transcript of the CEO Chat

Scott (00:04): Hi, and welcome to the AIContentGen video podcast. And today we’re delighted to host Kostas,  the CEO of  GoCharlie, and also Brennan, the COO of GoCharlie. And we’re gonna be talking about how GoCharlie helps marketers get their work done. And so thanks for joining us. My name is Scott Sweeney. I’m one of the founders of AIContentGen.

John (00:33): And I’m John Cass, one of the co-founders of AIContentGen. Thanks so much, Scott. Well, it’s nice to see you, Kostas and Brennan. Perhaps we can start off by asking you to tell us about yourself and also your AI journey.

Kostas (00:50): Yeah. I’ll introduce myself and then Brennan. I think it’d be for him to introduce himself. I founded GoCharlie about a year ago, it’s our first birthday coming up soon. We founded coming outta my PhD, where I saw a massive opportunity that only a few short years ago, content generation of AI, whether it’s text now, images and audio and video was really considered science fix. And it’s still in the realm of academia and universities. And now we’re seeing, we’re starting to see the early days of this explosion and it’s only gonna pick up from here. So that was my main inspiration for starting the company.

Brennan (01:39): Yeah. So Brennan Woodruff here, I’m COO co-founder of GoCharlie.ai. My AI journey was, a little bit different than Kostas who’s obviously had a decade worth of experience in the field, I personally joined SoftBank back in 2019 working on the vision funds because I wanted to learn more about artificial intelligence.  I saw that that’s where the world was going whether or not it be AI replacing humans or where I think we like to place is the enablement of human capabilities through artificial intelligence type technologies. What I found when I was at the vision funds is that there were a lot of different flavors of artificial intelligence but generative AI, I think has the potential to be the most transformative of any of the AI technologies I saw during my time there. And so when Kostas and team offered me the opportunity to jump aboard, it was a bit risky, but I wanted to take the plunge and learn as much as I could from some forefront thinkers in the space

Kostas (02:42): To mention our third co-founder could make the call. She’s she also has a PhD in AI and together we’re, we’re building our own technology from the ground up. So that’s one unique aspect about GoCharlie, is that unlike many of the other players in the space, especially for writing and for marketing it’s rare to find a company that’s developing their own technology, whereas instead of just plug and play from open AI or Google or IBM or existing technology is.

Scott (03:15): That’s really exciting. So does that mean that you don’t actually use any of the other technologies just your own AI?

Kostas (03:23): We do use some of the other technologies for three purposes. One is experimental to see if our AI meets or surpasses two for data augmentation, we do use, for instance, GPT-3 to help create data to train our own models. And we do also use a few other tools to augment our capabilities. Our plan by end of the year is to become a hundred percent self-sufficient. But that this, does take time. And as you guys may know that GPT-3, which we see as one of our competitors took a team of 30 PhDs years and I think 5 million to train. So we have a pretty big, you know mountain to climb.

Scott (04:14): Absolutely great. Well, that’s exciting to have a goal of being 100% your own AI by the end of the year. 

John (04:24): So next, what’s the what does the software do, perhaps you can explain that to the folks.

Kostas (04:32): This is where it gets really unique. We’re proud to say that we’re one of the first to be able to analyze images, to create content. So first off we’re a platform to help create digital marketing content, primarily co caps text. So sort of a co-writing tool for the time being we focused initially on social media posts and ads, and now we’re slowly expanding into almost every use case. And as I mentioned in the beginning, we pride ourselves in that we are trying to be the first to be multimodal, to incorporate other types of media into the content process. For instance, we can take an image and create an entire or, or post out of it. And one of the first ones to do that, we’re working on prototypes with video and with audio, and soon towards the end of the year, next year, we’re also doing the opposite, which is to go from text to an image. You guys have definitely heard of Dolly too, so there’s another mountain to climb there. And that’s definitely a unique obstacle in the sense that those are great artistic pictures, like paintings, but none of them are quite there yet for professional, like marketing purposes. Brennan, I think you definitely have a lot of ideas there. A lot of opinions there too.

Brennan (06:01): Yeah. Yeah. I mean, the way that we’ve approached generative AI is transformative technology. And while we see a bunch of competitors having success in purely the writing space, we think that that’s almost a disservice to the technologies capabilities. You, as you’ve seen with DALL·E 2  and a couple of other WebOs dream apps. There’s definitely an appetite for apps where you can go from text to an image. We also have seen a significant appetite for having a video and being able to turn that into text content. You know, we really want to make the starting point wherever the user is. So maybe the user has a photo or they have a video, but they have no idea what to say to optimize their content and engage their target audience. We want to be that bridge between the modalities with our AI. So we’re looking a little bit beyond writing, but we’re finding that that’s a good space to start out in because the learning there applies to so many different other mediums of media.

Scott (07:06): Great. Our next question is kind actually, you may have felt like you already answered it, but I’m gonna ask you anyway, is what’s the strength of your software

Kostas (07:19): And in its current state, I would say three main strengths first is that we’re trying to be driven by, our customers, our users, like building this in public, getting instant feedback. Again, we’re very small. We have several hundred users but we listen to every single one of them. And to that end, because we’re building a lot of this ourselves, we are going this multimodal aspect. So, and we hear a lot of, you know, demand, Hey, I want to use this image or this video and create content out of it. Another thing is it, blogs, Brennan and I were discussing the other day are like, Hey, can you take this blog and gimme 10, you know, tweets or, or Facebook posts out of this single blog? So that’s, our core strength is being able to innovate very fast with our own ideas and implement them from the ground up.

And the third is not to be that strength, but something I would say is that we try to be fun. And this is actually Brendan’s idea which I absolutely love this is that we have, for instance, these tones. So when you’re creating content on our platform, you can choose a tone and we’ve added some fun tones like a pirate, Brooklyn, and Shakespeare. And, when you start playing with ’em, they’re so incredibly fun and you can’t just help yourself, but giggle a little bit or just smile. So that’s a philosophy that we’re adapting is that when you’re using our tool so not just help you be more productive, but it should be fun to play with and just make you happy to use it.

John (08:57): Yeah, almost like the old Groupon model where they had that different style. That was the whole approach. Wasn’t it using a content style?

Brennan (09:07): definitely. And I think Kostas hit the nail on the head, you know, in a post-pandemic world where everyone’s experiencing burnout. We’re trying to introduce tools that make work feel as fun as playing with a puppy. So that’s, that’s GoCharlie. But we think some of those new tones are definitely a massive step towards that in making work feel like play.

John (09:29): So how do you support the client’s content design approach, you know perhaps in the areas of ideas and research briefing, actually writing the content, optimizing the content, you know, you talked about doing some of those extra things, even expansion metrics, you know, do you have how do you, how do you follow that content design teams sort of framework for how they do things?

Kostas (09:56): So, Brennan, you wanna talk about the customer aspect and I’ll talk more about the technical aspect next.

Brennan (10:01): Yeah, yeah, definitely. So John, I, I think you’re hitting the nail on the head, as you know, like when we think about a land and expand strategy, we’re, we’re starting to think about all right. Yeah. We’re generating the content, but, the creative process for humans and marketers and anyone that needs to create content really is like, you have to start from an idea. And so for some people that idea, we see more with like an influencer type customer. That idea is more life experience. It’s something that’s like really created this learning. They want to share with people to engage their audience in a more organic storytelling-driven way. For marketers, it seems to be a little bit more driven about like, what’s trending. So while it’s not there yet we actually have a hashtag and recommendations and trending functionality that’s being developed to really start you at that ideation point based on what’s performing what’s working well in your industry what topics are trending, which hashtags are trending that gets us a little bit more into that SEO space.

Brennan (11:03): So it’s still to be determined if we’ll grow that through partnerships or not. But then as we kind of go from ideation into that content creation piece, that piece, I feel like we’re completely addressing right now, but expanding the number of use use cases we go across then the next piece which I think is a huge differentiator for us is that we have content scoring. So content scoring too, if you’re unfamiliar is the ability to assess the content that we’ve created against the industry’s most engaging pieces of content that we’ve analyzed indexed, and fed through our models and really give you actionable insights as to how you can improve it. So not only are we giving you insights on how to improve and edit, but then if you think about the application of this in an enterprise setting that can, our content scoring can become part of your review process.

Brennan (11:52): So rather than living independently through a bunch of different emails, you just check the box that, Hey, you’ve scored an on GoCharlie’s content scoring. Therefore it’s good by me. And then the last piece obviously is, you know, publishing that content. And we’re, we’re happy to say that in the next month and a half, we’ll have the ability to post directly from GoCharlie into 10 different platforms that are most commonly used by our marketers that we’ve talked to. So that’s Facebook, Instagram, Twitter, YouTube TikTok. So we’re really trying to land and expand from just the content generation piece to a full sort of content creation platform methodology.

Kostas (12:31): Excellent.

Scott (12:32): That’s great. I, I have a question about that. How are customers doing with the scoring you know, one of the areas I’m always interested in being several businesses over the years is metrics? So is there a way to are there customer success stories or are there proof points on how that works?

Kostas (12:56): So still in the early days, we’re actually in the process of creating use cases now and hopefully band of the summer, we’ll be able to publish a few white papers on those use cases. Content scoring is, is becoming quite popular with our users. There’s still a lot for us to figure out what’s the best way to deliver it. We have quite a big UI. So we’re trying to take out an approach of optimizing for mobile, which we’re realizing almost all of our customers are on their mobiles. And that’s something none of our competitors are doing either is, okay, how can you deliver an immense amount of value on a much smaller screen? And we’re also seeing quite a bit of demand in addition to the quality of the writing is to what audiences will, you know, a content resonate with specific demographics, gender location, so forth.

Kostas (13:48): And part of our multimodality aspect is to analyze we’re building right now, the ability to analyze the content and tell you, okay, this is great for gen Z, or this is great for millennials, but also given an in image or video, okay, will this also appeal? So it’s more of a complete all-in-one packet analyzing quality than analyzing the appeal. And we’re in the process of seeking out a few partnerships with something data marketing companies to then start getting more performance statistics and say, okay, we can give you a quality score and audience score, but now let’s start predicting how many clicks, how many likes we will. We also start getting with your content especially as it gets mixed up with videos and images in different modalities, that’s something we’re also cooking up in our, in our MADLAB, <laugh>,

Scott (14:38): It’s exciting. So I think that leads us to our, our final question for today. And it’s one that I always find very insightful from business leaders, in general, is if there’s one thing that you believe that most people believe about AI content generation that most people think is true, but you actually take a contrarian view. You don’t think it’s actually that true.

Kostas (15:08): I’ll go last cause as a Ph.D. in AI, I have a lot of opinions about <laugh> the world is different than actually is.

Brennan (15:15): So just to make sure I, I understand the question correctly, Scott, you, you wanna know what the masses think about generating content with AI that we don’t necessarily think is true? Correct.

Brennan (15:27): Got it. I think that there is a large portion of folks that look at AI content generation as a spam tool. And they, you know, they’ve cherry-picked validation of that belief through some of the Google policies about AI content and, and rather than delving into, you know, the science behind that and what was actually said, they just believe the headlines for what they are and, you know, we don’t, we don’t really subscribe to that, that idea. We believe that there could be bad users in, in abusers of such technologies to create content that is spammy or that is trying to be manipulative. But, but we believe that the majority of users of an AI content generation tour are, are really trying to pursue just making their dreams come true, whether that’s, you know, creating content to help grow their business or creating content, to create awareness around a passion that they care about, or, or maybe even just helping their friend grow their business so that they can be sustainable in, in this economically wild world that we’re living in. So, so for us, we, I, I would say that we don’t really subscribe to the AI content generation being a spam tool. It’s more an enabler, of people’s personal pursuits. And, and that’s how we choose to view AI is just, we’re enabling, what’s already there. We’re just giving you a way to unlock it.

Kostas (17:01): And I also add, cause that comes with two fears is one, AI is gonna spam you, but also AI may replace you. And that’s another belief we don’t have is that it, our whole philosophy is that it’s gonna augment your life, make your life easier, the same way that Photoshop 20 years ago made designers’ life easier instead of just doing things by hand. And that’s how we see AI. Maybe in 10, or 20 years, it’ll start replacing jobs, but we’re nowhere near there. And to be honest, we don’t wanna be in the business of replacing people’s jobs either. We wanna make them as productive and as fun as possible, really that that’s our whole core mission at GoCharlie.

Scott (17:43): I love the comparison to Photoshop and yeah. And especially for your application, I, I think that’s a really good analogy and you know, who knows what kind of jobs are gonna be around 10 to 20 years from now. Right, exactly. So so that’s a kind of a long time frame.

John (18:05): And, and I agree Kostas, I mean, isn’t it true when I’ve spoken to so many folks in the industry where I think these tools are, are helping those marketers and writers to get more out of their profession by doing more so I think it’s, I think that’s very true. So I think it’s a good idea. I think that that saying that you have about making it fun again is, is, is pretty insightful. So I really really appreciate Kostas and Brennan for joining us on the content video podcast. I also wanna thank the audience for supporting us. Thank you, Kostas. Thank you, Brennan. 

Scott (18:47): Thank you, guys. Take care. We’ll look forward to talking to you again sometime soon. <Laugh> thank you.

 

 

 

 

Is AI Marketing Legit?

Is AI Marketing Legit?

Is AI Marketing Legit?

Just Ask IBM Watson! Are brands like Toyota, Best Western, and CVS legit? They have all used AI-Powered IBM Watson to attract and engage prospects at critical times in their customer journeys resulting in impressive results. Wayfair has developed its own Machine Learning models to predict their consumers’ preferences concerning email marketing.2

So the simple answer to “Is AI Marketing Legit?”  is a 100% Yes!  AI for marketing is a legitimate way to use artificial intelligence to help with marketing tasks. 

It can automate tasks, such as customer segmentation, campaign management, and product recommendations. It can also improve accuracy and targeting and free up time for marketers. And it can assist in content creation.

What is AI for marketing?

AI helps with marketing by automating tasks since AI can process large amounts of data quickly and effectively. This can free up time for marketers to focus on other tasks. 

AI can also lead to more accurate and targeted marketing campaigns. 

Can AI-generated content be as good as human-generated content?

AI-generated content can be just as good as human-generated content, and in some cases, it can even be better. This is because AI can analyze data more quickly and accurately than humans, and it can also create targeted content that is more likely to resonate with readers.

Is AI Marketing becoming the new norm? 

As artificial intelligence (AI) continues to grow and evolve, technology is beginning to create content that writers would otherwise write. While some AI-generated content may be of poor quality, there are cases where it can be legitimate and even beneficial for businesses. In general, AI-generated content can be a helpful tool if used correctly; however, it is essential to remember that artificial intelligence is still in its early stages of development and thus should not be relied on exclusively. The use of AI in marketing is still a relatively new concept and one that not everyone is familiar with. 

AI is currently being deployed in personalization, targeting, PPC advertising, chatbots, you name it!. And, AI powered Predictive Analytics is helping companies to deliver the right content to the right customers at optimal times. Anywhere that big data or repetitive tasks are involved are a great place to apply AI for Marketing.

What is AI-generated content?

In short, it refers to any type of content that has been created with the help of artificial intelligence. This can include articles and blog posts, social media posts, and even product descriptions. While some people may be hesitant to trust the content that a machine has created, there are actually several advantages to using AI-generated content for marketing purposes.

For starters, AI-generated content can be highly accurate and informative. Because it is generated based on data and patterns, when writing certain types of content, such as reporting on last night’s sporting events,  financial results, or healthcare information, there is less room for error than if a human were creating the same type of content. This makes AI-generated content especially well-suited for businesses in industries where accuracy is essential. Additionally, because AI can generate large amounts of content quickly and efficiently, it can be a valuable tool for businesses that need to produce a lot of content regularly but may not have the staffing to do so manually.

Of course, it’s important to remember that artificial intelligence is still in its early stages of development. There are certain limitations to what AI can do when it comes to creating marketing content. For example, AI typically struggles with understanding context and subtleties such as tone and style; as a result, the quality of AI-generated content can sometimes be lacking compared to handcrafted copy written by humans. However, these limitations are gradually being addressed as technology continues to evolve; as more advances are made in the field of artificial intelligence. 

What is the Future of AI For Marketing? Learn Here! 

Will AI Content Generation Replace Human Writers?

In some cases it already has. The Associated Press began using AI in 2014 to automate the writing of news stories about corporate earnings. This freed writers from the drudgery of translating numbers into words allowing them to focus on stories that required higher levels of journalistic expertise.

While some people may be hesitant to trust the content that has been created by a machine, there are actually several advantages to using AI-generated content for marketing.

As artificial intelligence continues to evolve, it is becoming better at understanding context and subtleties such as tone and style. This means that the quality of AI-generated content is improving all the time, and it is beginning to rival copy written by professional copywriters.

Additionally, AI might struggle with understanding complex concepts or humor. So it’s essential to have a trained writer pulling the strings, putting on the finishing touches and reviewing the copy for bias, plagiarism and adherence to corporate style guides.  In the future, we can expect AI to become even better at creating high-quality content that engages and informs readers.

Is it time to Develop a Strategy for AI?

You betcha! 

If you’re a Content Manager, PR or Marketing Agency, Writer for hire,  CMO, Product Marketer, Ecommerce Company, you know what to do. Jump on the AI Bandwagon. Yes, AI for marketing is definitely legit! It’s a growing field that is already helping marketers to automate tedious tasks and improve their campaigns.

Need Help with Developing a Strategy, Plan or Jump Start in AI? 

We’re here for you with reports, custom AI Marketing training sessions and advice. All we do is AI for Marketing. 

Contact AIContentGen to learn how to get started.

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See the AI Content Generation ScoreCard and Analysis

Cover Page AI Contnent Generation Scorecard

Take Me to the report!  (Click)

From Typewriters to AI Content Generators – The AIContentGen Market Survey

From Typewriters to AI Content Generators – The AIContentGen Market Survey

From Typewriters to AI Content Generators – The AIContentGen Market Survey

In 1979 I left for my Freshman year in college at the University of Massachusetts, Amherst.  I jumped into my Chevy Chevette packed to the hilt that included a high school graduation gift from my parents, a portable, manual Smith-Corona typewriter.  Little did I know that my popular typewriter came from a highly concentrated industry that a decade prior was dominated by only five companies accounting for 79.7% of the market, spurring action by the FTC.1

Now we’re looking to identify a similar but 2020’s market size and share, albeit at a different maturity curve.  AIContentGen is working on a market size survey for the AI content generation industry. We are using various approaches to gather information and estimate the market’s current size for AI content generation software. 

The survey will be sent to AI companies to ask a series of questions about their sales of AI content software and information about growth rates. As a result, AIContentGen will develop research on: 

  1. The size of the market
  2. Growth rates

The objective is to estimate the potential market for AI content creation and the percentage of customers willing to adopt AI content software. If you had a market of 1000 customers, 20% of customers are willing to buy and use AI software, the market size would be 200 customers. 

Another way to estimate the market potential is to conduct surveys with customers and determine the percentage of customers in a market using the product and the number of customers considering jumping in. 

In addition to estimating the potential size, we can also conduct a survey of AI companies and ask how many subscribers they have using the software. 

Some AI companies might be concerned about not participating in such a survey. Here are some reasons why it’s important to give some numbers on the market’s potential size, the current size of the market, and the growth rates. 

  • Start-up AI companies are looking for venture or angel investment; if the market can demonstrate a potential business in the industry, it will make it easier to raise additional investments. 
  • Customers waiting on the sidelines will be more willing to jump in and start using AI content generation tools if they get a sense of the current scope of the market. 
  • Sizing the market by industry or type of content will help you focus marketing efforts or avoid working in a market that you think has sufficient customers but doesn’t. 
  • By understanding the market scale, AI companies can estimate their profits and what resources will be available to generate revenue based on potential profits. Further, suppose there are more players in segments of the market. In that case, a market size report can be used to understand the potential profit that could be made based on the market size for an industry or type of content and the number of competitors in the market. 

Quotes from investors: Why a market size view is important

Ray Chang  – Founder, Advisor, and Investor

“Knowing and understanding the market size helps investors understand what the available profit could be.  Some investors may be less risk-averse and would rather play in a small market than a bigger market.  Or, from a strategic point of view, a smaller market would be better for one application of a product to test it out.  If it’s successful, perhaps they could expand to other markets with changes such as language or color, etc.”

 

Lauren Nham – Product + Ventures + Investor – NewChic Capital, MetaCap Ventures & GovingVC Partners

In short, attractive underserved markets. Market opportunity & market timing.”

“What’s the current state, and where will it go? Where does it cross over and connect across industries and functions and geopolitical regions? For example, consumer behavior and needs evolve, and how well the market serves (or underserves) target market segments constantly fluxes. Too large and the market is only suitable for late-stage, too early, and the mainstream PMF (Product Market Fit) may exist beyond target return windows.

A true understanding of the market would identify interlocks across different sectors and tranches of the value chain. The ability to see the market in a truly multi-dimensional view is where the opportunity lies.”

Examples of  Market Size Studies Impacting New Industries

Word of Mouth, MarTech & ABM

The Word of Mouth Marketing Association was founded in 2004. The association was a trade group set up to promote Word of Mouth Marketing, which was the term used in the industry before the adoption of social media. WOMMA had companies as members, and the trade group held industry meetings, conducted important research on the value of word of mouth marketing to companies, and generally promoted the industry. Without WOMMA and its work, social media would not have been adopted as quickly as a strategy by as many companies as it was in the United States; the trade group helped demonstrate the market potential and size. The Association of National Advertisers acquired WOMMA in 2018.

MarTech

Scott Brinker’s now famous Marketing Technology Landscape Map infographic showing the extent of the Marketing Technology industry helped popularize the growing importance of Marketing Technology amongst marketers. The series of yearly infographics illustrate the growth of the industry and its weight in the industry. Without the infographic, customers will not be as aware of the different marketing technology categories or that their peers and competitors are using so many kinds of marketing technology. The landscape Map helped set the industry’s understanding of the importance of the industry and fueled lots of interest from customers in what technology to pick. 

Martech Landscape 2020

 

Jon Miller’s ABM Market Map

Co-founder of Marketo, Jon Miller, helped explain the benefits of Account-Based marketing with his simple ABM Market Map; again, he helped scope out the different categories of marketing technology that make up the strategy of ABM. Marketers used the map to follow the strategy of ABM and select vendors for marketing technology. 

The AIContentGen Market Survey

Will our Market Survey show a highly concentrated market for AI Writers? Not likely. But this first market survey will put a stake in the ground and we hope that it will benefit all stakeholders in the market. 

Do you work at an AI Content Generation company and would like to get a hold of an AI Content Generation Market Size Report?  The first step is to contact AIContentGen to answer our Market Size Survey. 

Footnote:

1 “In 1968 the year prior to the acquisition in question the two top ranking firms IBM and Royal accounted for about 50.3 percent and the four leading firms IBM Royal SCM and Olivetti Underwood for about 79.7 percent.

FEDERAL TRADE COMMISSION DECISIONS V FINDINGS OPINIONS AND ORDERS JANUARY 1 1973 TO JUNE 30 1973 PUBLISHED BY THE COMMISSION VOLUME 82 UNITED ALTRADE 2903 S OFAMER Compiled by Rules and Publications Section of the Office of the Secretary US GOVERNMENT PRINTING OFFICE WASHINGTON 1973 sale by the Superintendent of Documents US Government Printing Washington DC 20402 Price 14.95 STOCK NUMBER 1800 00159 p. 1009.

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