Marketing AI CEO Chats Welcomes Christopher Penn of

Marketing AI CEO Chats Welcomes Christopher Penn of

Transcript of the podcast:

John  (00:02): Hello, my name is John Cass, and I’m here with AIContentGen, I’m here today with Chris Penn. And we’re going to be talking about AI and marketing. Chris, thank you so much for joining today on the podcast. I think first met you at one of your pod camps many years ago, and, you know, I think you’re just such an inspiration. You were definitely an inspiration to me. I think the way that you approached marketing, you helped to teach the entire community here in the Boston area and across the country, actually. You know, what social media is, the value of it is. And then also you also had that connection to the PR industry. So thank you for joining me today.

Chris (00:49): Thank you for having me.

John  (00:54): You were one of the sort of the early pioneers in the industry with PodCamp and those, and those sorts of things. You know, what, what’s your sort of philosophy you know, in looking at the latest, latest in technology? Cuz now you’re in ai?

Chris (01:14): You know, I don’t know that I necessarily have a philosophy so much as it’s just what I’m interested in. I like to, to see what machines and tools and stuff can do. I am notorious for finding uses unintended uses of any technology. Just yesterday, I was cleaning out the toaster in my kitchen with the leafblower because I didn’t feel like seeing, I was sitting there shaking this thing and opening those little trays. Just take the leaf floor stick, unplug it, take the leaf floor, stick it in there, just blow all the, the, the crap out. And the same thing applies to, you know, all these things that we’re doing with data science and ai as, as an industry, just taking a look at what’s possible, what the tools can do, and then say, well, what are the, what are the applications that we wish we had solutions for? What kinds of intelligent automation should we be doing? What kinds of answers can we get that previously might have been inaccessible or might have been, you know, overly laborious to get? So that’s, that’s sort of just how things have pivoted over the years, you know, podcasting. Yeah, we were all early on, you know, I started my first podcast in 2005 and, you know, marketing over coffee has been on the air continuously since 2007, but the industry’s evolved and, and ideally everybody evolves with it.

John  (02:33):No, that’s very true. That’s very true. I mean, I also think it’s interesting cuz I mean, do you, do you call yourself a, a digital guy or a, a marketer or, because I think what’s interesting is that you spent quite a bit of time in the PR industry working for a PR agency. But what was fascinating to me is that you worked in tech pr, right? And that was a, you, you worked with a group of people who were very savvy and understood that it wasn’t just about the traditional ways that PR has done, even though it was done in digital context. It is being done in a digital context. So you have to bring in analytics and those sorts of things. How does that sort of color your perception of coming, having worked in the PR industry in relation to you know, digital marketing and ai?

Chris (03:27): I mean, AI really is, it’s just a tool. It’s, it’s, a lot of it is just math. And so the, the question to ask is what problems does any industry, public relations, advertising, marketing, what problems do you have that are mathematical in nature that you need answers for? One of PRS biggest problems is how do we measure this stuff, right? How do we know that public relations is working? And the limitations in the, in that industry have been largely because statistical modeling techniques for what PR does have been around for about 40 to 50 years. These are, these are not new things. However, no one in the PR industry has had the, the, the mathematical background to bring those to life. And so having this arsenal, or this toolkit of other things could bring into the industry helped for a period of time in improving measurement.

Chris (04:28): There are, you know, certain metrics and measures that I think should be in every PR person’s toolkit. And very, very few folks do it. Partly because, again, it requires some expertise and it requires stepping outside your comfort zone and part of it, because sometimes one of the trends that I’ve seen, particularly in the last five years, maybe the last six years, is people, and this stems from general society and politics. People only like data when it says what they want, right? People do not like answers that are uncomfortable. People do not like being told they’re wrong. People don’t like hearing bad news and or hearing something that does not agree with their perspective. And so when you do something like attribution analysis on a PR campaign, and the news is it did nothing, right? You spent 20 grand for nothing, right? People don’t like hearing that <laugh>

John  (05:31): <Laugh>. Well, well, Chris, that, that, that’s one reason why I think as a marketer and in the profession I’ve worked, you know, I don’t know if you know, but I, I worked in the agile marketing community for, you know, quite a few number of years. And part of it is, I think it reframes, it gives another mindset not just for the marketers. And it’s this mindset of, oh, we’re, we’re testing things, right? And so it also, but it also, but the real be, I mean, it definitely helps with the teams and also the leaders in marketing to think like that, right? But then it also gives a methodology that if you explain to the other stakeholders, you know, the C-suite and the other departments, they can say, oh, that’s what this market is doing. You know, they’re, they’re coming to me with this different mindset and they’re, so I think, I think something like a framework or a methodology like agile then re recast the frame. I mean, do you think that is kind of helpful? I know we weren’t going to talk about agile, but you, you, you brought that issue up.

Chris (06:33): It can, if the person understands the point of agile or understands the point of any framework, and it’s aligned with their goals, and this is the problem that, you know my c e o, Katie, Roberta and I have been tr trying to tackle around analytics and data in general for the existence of our company which is data and analytics and stuff. These are like cooking methods, right? You know, broiling, frying, et cetera. If the c e o wants McDonald’s, it doesn’t matter how good, you know, a, a, a stake chef you are or how good a sushi chef you are, if the, your stakeholders have a predefined, preset, inflexible rigid point of view, right? Unless you are aligned with that point of view, you’re not gonna make any headway. So someone who is, we call it opinion driven, someone who is opinion driven, isn’t going to become data driven, right?

Chris (07:33): That’s, it’s like asking somebody who is you know, one religious faith to become another religious faith. It’s very difficult. Something, something has to happen to, to motivate that change. And motivating change in human beings is really hard. It’s much easier to change code, right? Than it is to, to change humans. And so that combined with the systems and structures in businesses is very challenging because a lot of systems and structures reward the wrong things. You know, real simple example, if you are a publicly traded company, your reward is your stock price, right? And so you measure everything to your quarterly earnings. You measure everything to, you know, how much can you goose the stock price this quarter and keep investors happy? Not can I build a, a sustainable business for the long term and make bets that maybe money losing bets and, and not have it be a punishment, you know, for all of the issues and objections and ethical problems that I have with, with Facebook now, now meta you know, mark Zuckerberg trying to do the whole, you know, metaverse virtual reality, that’s a big bet.

Chris (08:43): And so far it’s really not paying off. But I have to at least acknowledge that they’re taking a big bet, even though they’re a publicly traded company. Most other publicly traded companies do not want to rock the boat. They’re unwilling to make those big bets. And as a result, when you start bringing things like analytics and stuff in and saying like, yeah, here’s what’s going on. They’re like, Nope, this, this, this data doesn’t help us make our quarterly numbers. Like, well, <laugh> then, then, you know, if, if that’s your point of view, that’s gonna be a lot harder to make change. It’s one of the driving forces why you see a lot of companies, there’s, you know, reasonably successful companies going back private, right? Privately held investment because they and their investors think, we know we can make some big moves, but we can’t do it if our, if our horizon of vision is measured by the quarter instead of by the decade.

John  (09:36): Right? Right, right, right. So ai, you know, how did you get into this area of, of marketing, you know, and what, and what are you doing in the area at the moment at at the company?

Chris (09:50): So I got into AI through analytics, right? So I, I started really paying attention to analytics and, and embracing it really, like 2004, 2005, you know, as websites started producing a lot of data and we all needed ways to analyze it. Google analytics became available to the public in 2005 after the Google bought a company called Urchin. 2011 is when they first introduced multi-tech attribution and assisted conversions. And it was at that point when like, okay, I guess I need to, you know, shrug off the shame and embarrassment of failing statistics in college and, and relearn this stuff. So when I joined the old public relations parameters with part of that you from 2013 on was reteaching myself, statistics, data science and stuff, and, and, and analytics. And, and AI was a natural outgrowth of that because that’s where machine learning, specifically classical machine learning is where data science and statistics have been going.

Chris (10:45): Well, they, they’ve been there for 50 years, but the computational power has been available to us over the last 20 years. You know, your laptop can do things now that 50 years ago where theoretical only. And so my career progression really started taking that turn into the machine learning space, or starting in 2013, predictive analytics, you know, forecasting regression analysis, complicated regression analysis and things. And then in 20, late 2017 Katie my c e o and I, we, we decided the, the agency we were working with at the time was going one direction. The, we wanted to go in a very different direction. So we, we split off and started our own company and focused around the, the more intelligent use of, of data and analytics. And part and parcel of that is using artificial intelligence. I think it’s, you know, worth pointing out that AI is kind of a, a umbrella term.

Chris (11:42): There’s, there’s three things in machine learning that you’re, you’re three fundamental tasks, right? There’s regression classification in generation regression is supervised learning. Can we figure out, you know, what happened? Classification is, Hey, I’ve got a big bucket of data. Can I make so sense of it? Can I sort it? And, and, and classify it and understand what’s in this giant bucket, which is a problem that many marketers have. And then generation is, Hey, I want machines to make something from the data that we have. This has been the, the talk of the town really for the last 18 months with services like open ai, Dolly and Dolly two stable diffusion mid journey and, and stuff like that. You know, making pictures of dogs and tutus on skateboards and such writing blog posts, you know, with, you know, one click blog post writing. But the underlying technologies are all pretty much the same thing, you know, regression classification and generation.

John  (12:38): While you were at that PR company, were you also looking at social listening? Because, and, and the reason I asked that question, and I had wondered was because I was director of blogging strategies at a company called Backbone Media back in, I think it it’s about 15 years ago. And, you know, we were heavily into that. We weren’t a PR agency, we’re a digital marketing agency, but I, I think PR was some of the first people to look at social listening. And that, to me seemed to be a really ready a application and an early application of machine learning.

Chris (13:13): Yeah. Listening and search listening were the, were the two things that were worth paying attention to. And search listening I think is actually more valuable which is seeing what people are typing into search engines. I mean, anyone can do that. You go to Google Trends, you know,, type in search queries. You can see search volume of, of a given set of terms over time. You know, you don’t need any technical skill for that. But there was a book, I want to say it’s like five or six years ago called Everybody Lies. And it was a, a book they’re talking about from the perspective of search engines, the billions and trillions of questions that people ask surgeons, they would never ask another person, another living human being because it’s too embarrassing, too private, or something that, you know, even in a professional context, if you are in a marketing role and you, and you know, your, your, your stakeholder says, Hey, let’s let, let’s get some attribution analysis going.

Chris (14:06): And you don’t wanna admit, I have no idea what that means, <laugh>, right? You’re probably not gonna say it in public social media unless you have like a throwaway account, but you will absolutely Google it and say, okay, what is attribution analysis? Social listening is really good for qualitative analysis to understand the, the language space of our problem. It’s really poor at quantitative analysis because there’s, generally speaking people only have conversations about things when something’s either very wrong or very right. Most people don’t have conversations about things that were okay, right? You know, if you, if you think about like a restaurant experience, you either leave a one star review or a five star review. You, Jim, people don’t leave three star reviews. It’s like, service was good, food was fine, it was good, right? <Laugh>, that’s, you’re not, not compelled to leave review. It’s like, you know, but the, the waiter threw my food on me and lift the table on fire and <laugh>, you know and you see this like on Amazon too. There are tons of either one or five star reviews on things. It’s, there really is not a, not a lot of middle ground. And so social listening provides you the qualitative context, and then you need to use other methods like surveying and things to quantify the questions that you’ve developed from social listening.

John  (15:22): Right? You know, it, it is interesting cuz I’ve actually used reviews to pull together insights for customer journey mapping where I wasn’t able to initially do surveying. So I’d like to see more tools in, in those areas. Now for trust insights, what, what are you doing in that? How are you, how are you working with clients? What’s, what’s the area of coverage?

Chris (15:49): So, with a lot of our clients, our, we’re a management consulting firm that focuses right now on marketing. Although, you know, the, the techniques can be used to anything. Many of our clients come to us to make better use of the data they have or fix the, you know, the infrastructure problems they have or get insights that they’re not able to extract otherwise. For example, we have one client where we, we process a lot of their NPSs scores. We look at the, their, their net promoter score data, and then do some, you know, fancy math to say, here are the things they’re probably driving, you know, this score that’s rising or this score that’s falling. We had another client in the food and beverage industry that said, here’s, here’s the inbox of our, of our customer service department, right? Here’s all the emails coming in, do some text analysis and tell us if we’ve got any blind spots.

Chris (16:43): And we did. We found, you know, this was 20 20 19, they had no formulation. They made thickeners, they had no formulation for oat milk, right? And, and it was, it was becoming a very hot topic. And so being able to dig into their, their existing data, people asking them, emailing them, Hey, do you have, what’s your solution for oat milk? Was a valuable insight for them. We did some work with a, a recruiting agency, and we, we sh they were like, we can’t get people to, to, you know, fill out the application forms as much as we want. So we, again, we did some analysis. Here’s all the things that you say in your 5,000 job listings, here are the transcripts of 17,000 calls of candidates with your recruiters. Notice that the conversations, the questions candidates are asking are not any answered anywhere in any of your job listings.

Chris (17:29): Like, you know, well, you know, what’s for the vacation time? What’s the, the starting pay and stuff like that. And we said, if you just put those common questions into your job descriptions, you’ll do better. They did. And they literally increased their conversion rates 40% within, like, overnight, just, just saying, okay, here’s the answers that people are, are really after a lot of our other clients. Right now Google Analytics four is, is the hot topic. You know, the universal analytics system is coming to an end on July one of the, of next year. And a lot of people are realizing that it’s not just a a, an upgrade. It’s not like going from Microsoft Word 2020 to, to Word 2022. It’s a totally new piece of software. And so there’s a lot of marketing operations changes we’re helping companies make to, to, to make that pivot to the new system and make it useful because there are a legion of gotchas that, that are not obvious. So that’s, that’s kind of who, who we try to help.

John  (18:25): Great. Great. Great. how do you see the future of AI marketing? Where do you think it’s gonna go? You know in relation to where we are today and in, in the next well, two years or five years.

Chris (18:40): <Laugh>, if I knew that we would not be having this conversation because I would already be retired, <laugh>, phenomenally wealthy. Here’s what we, here’s what we see and, and what you can see in the marketplace, machine learning of all kinds is just getting baked into products, right? Very few companies other than like really big companies with really big budgets are building their own. Most companies are waiting for vendors to build machine learning into their products. And it’s in, it is in everything now, right? We’re using Zoom. Zoom has, you know, live captions, which is using text speech to text transcription, which is AI based. That is, you know an example of AI just kind of snuck its way in there. We’re gonna see much more of that much more intelligent automation, just machinery being used to, to speed things up.

Chris (19:33): The thing that’s captured people’s imagination right now is generation. You know, all of the, the AI generated artwork pe we are seeing a mechanistic automation of certain parts of marketing, you know, content generation is, is, is a big part of it. And there’s a real serious dangers with that. Particularly on the intellectual property side. I just did an almost hour long interview with attorney Ruth Carter. They specialize in IP law, and they were saying like, yeah, there’s, there’s a whole bunch of, of legal issues with AI generated content that nobody seems to be aware of, but it’s gonna bite some people really, really, really hard. So those questions are gonna need yet resolved to the ex to some extent in the next couple years. There are some there, what’s happening in the industry overall right now is there is a trend towards privacy.

Chris (20:26): And this is not gonna let up anytime soon. We have legislation popping up all over the place next year in California. The California the consumer privacy act, C P R A, it takes effect January 1st, 2023. That’s gonna restrict companies from sharing personal, personal information of consumers, but it also allows consumers to know when there data’s being used by machines for decisioning, a k a machine learning, and to opt out of it. So there’s gonna be a lot of frantic scrambling to become compliant once the first lawsuits start rolling in a about the improper use of people’s data for ai. So a big portion of what is likely to happen in the next couple of years will be increased interest in things like synthetic data that’s modeled off of real data, consumer data, but is not using the, the consumer data to, to build, you know, functional models.

Chris (21:26): And there will be a lot of focus on behavioral data, because behavioral data doesn’t contain personally identifying information. I don’t care who you are, I just know that if you visit my website, you go to the services page, the about page and the contact page, you’re probably gonna convert. So anything I can do to nudge that along, again, with the no personal information at all, is gonna be a money maker. So those are kind of the, the, the areas where there’s definitely gonna be growth coming along. But there’s, there’s also so many unknowns. There’s a whole thing on from stability AI this morning from the founders saying we are holding off and releasing the next model be of our, our image generator, because we have a whole bunch of very serious unanswered questions about what people have done with the existing open source model. Some people are doing really bad things with it because it’s open source, right? It’s, it, it is like, you know, putting out a pile of kitchen appliances and somebody, yes, there is one person who’s running around with a cleaver hacking people to bits. You don’t have control over that once you open source something. So they’re, they’re struggling with that right now to figure out, okay, how do we live up to the ethos of open source, but also reduce harm?

John  (22:41): No, that makes sense. And I think you’ve seen that happen with some other players where they’ve taken a much more careful approach. And I also thought in this discussion, it was interesting you were talking about how companies are thinking about how AI applies to their setting, right? I I very much, I’m starting to see that as well. You know, you know, you, you might be I, I think what’s interesting, you, you bring up AI content generation, but I think that you know, you might be in one particular industry where you’ve already got industry players who are really successful, and then all of a sudden they’re saying, oh, we might have a competitive advantage if we start developing it. But what do you do? Do you build, do you start building it yourself? Or is it better to go and partner with someone or buy someone? You know, what, what, what are your thoughts about that in terms of AI content generation and the opportunities that apply to existing leaders in their particular industry?

Chris (23:41): You know, one of the things we have, we have a five part framework we call the five Ps purpose people, process, platform performance. And the big question for any company that’s looking at AI is, you know, number one, are you using it as, as, you know, incremental improvement, efficiency building and things like that? If that’s, if that’s your purpose, it’s probably safe to just buy it from a vendor. If it is gonna be part and parcel of your secret sauce as a company, you probably should own it because you don’t want your, the, the fate of your company in another vendor’s hands, right? Unless you’re just outright by that vendor. The second question you have to ask is, do you have the people with the right skills internally to support a build decision? If, if you go the buy route, then you don’t have to worry about that.

Chris (24:26): That’s the vendor’s problem to, to acquire the necessary talent. Do you have the right processes in place, you know, data, governance, operations, et cetera, to not only construct AI models successfully, but also adhere to the labyrinth of regulations that are cropping up around it, right? They’re, there are some regulations about the use of consumer data around the world that have intimate personal consequences, right? China has, its P I P L law, people’s internet privacy law. If you violate that law and you’re, you’re, you’re, you’re found guilty of it. Not only do you get whopping fines from the government of China, but if your executive set foot on Chinese soil, they go to jail, right? So, you know, the, the process and the governance is, is really important. Do you have the right platforms and technology stack to, to support ai? Some companies do, some companies don’t.

Chris (25:18): And then, you know, ultimately, can you, can you successfully use AI as to generate the outcomes you want? There are whole swaths of problems that AI is really bad at solving, right? Because they’re, they’re problems that either don’t have a lot of data, they have very sparse data, very poor quality data or they’re just a problem that you can’t solve with AI because it’s not a, it’s not a, a mathematical problem, right? Think about how, you know, the, the most common examples, think about something like getting people to wear masks, right? That is not a, a, a mathematical problem. That is a cultural problem, right? There’s a an education problem. You’re not gonna solve that with ai. There’s no way to, to automate or to, to even, to, to effectively measure why something as simple as, you know, stick this cool looking thing on your face is, is happening or is not happening. So that, you know, to, to look at the adoption of AI and, and making decision on it. You gotta look at all five of those factors.

John  (26:26): Great. Great. So what about your thoughts on AI content generation? And you also mentioned images, cuz I, I think to a certain extent, what’s interesting to me over the last couple of months is that that’s the aspect of AI generation, content generation. That’s really blown up partly because of that, that incidence in Colorado where you know someone was using one of the AI tools to develop an image that they put into an art competition and, and they won the art com competition mm-hmm. <Affirmative>. And there’s been a, a lot of blowback on that. But there’s also text generation as well. And I think what’s interesting to me is that there are different you know, there are different approaches. A lot of these companies, they might succeed on blog writing or general article writing, right? But then you’ve got companies like the PR industry where I don’t see companies doing as well as that type of content. And I, I wonder if, if that’s partly because there isn’t the demand for PR agencies in, in, in terms of what they’re asking. Although I’ve, I’ve, I’ve spoken to some that, that do. So what are, what are your thoughts about AI content generation, where that’s going and, and then maybe specifically that example with pr. Cause it goes back to your, you know, your work over time.

Chris (27:55): As the tools become easier and more accessible, PR will be probably one of the first consumers to use extensive text content generation, because a lot of the tech, they generators, boiler plate, right? Machines can already very capably write press releases that are better than what your average junior account coordinator is gonna crank out. The, the challenge with the content generation in general is that these models are trained on very large data sets, right? Eluthra, AI’s the piles 800 million documents, basic mathematics, most, most content is mediocre, right? The most content’s, okay, it’s not great, it’s not bad. Like there, there are are a few, you know, winners there a few like total losers, and it’s a whole bunch of meh in the middle. And all of these models are trained on as much data as can be acquired.

Chris (28:52): Most of that data is gonna be in the middle. So the, what the models produce right now is perfectly adequate mediocre content, right? The you, you use any of the major tools right now that they’re on the market and they create readable, coherent, okay, content it fits what Google calls. Nothing wrong, but nothing special, right? And that’s problematic because if everyone and their cousin is creating nothing wrong and not, but nothing special, nothing makes you stand out, right? You don’t have that unique creative edge that humans tend to bring. Now here’s the bigger issue for all of these companies and all these, these companies using AI generated content. Again, I talked to, to attorney Ruth Carter about this AI generated content cannot be copyrighted, period, because copyright can only be held by a human. Even if you are using the tool, the tool did the work.

Chris (29:54): And there are a number of court cases that we went through that illustrated very, a very clear chain of evidence by a court saying, if you use AI degenerate content, you cannot copyright it. And if it if someone rips it off, you can’t do anything about it because it is inherently in the public domain. There was a case, Naruto versus peta where a chimpanzee took a camera, took a selfie, and the photographer tried to copyright it, and the court ruled human didn’t create the content. No copyright. That image is, is in the public domain. So the, the person who created the mid journey image that won that art contest, that’s public domain. You can use that image as much as you want, and the artist can do nothing about it because they cannot hold a copyright on it. And this is one of the challenges that people don’t realize when it comes to AI generated content is legally it is a very different than human led content.

Chris (30:47):Now you can do things, for example, like have a machine provide you an outline, and then you write the content from that. You can take a first draft from a piece of con of AI generated content and substantially rewrite it and improve it. And generally speaking, if you think of AI generated content as being in the middle, literally it’s the, the, it’s the definition of, of average. If all of your staff are below average writers, then guess what? AI is actually going to improve your business to, to get it to mediocre. But if you, if you aspire to anything above mediocre, you’re going to need to, to continue investing in humans to not only deal with the copyright issues, but also to stand out. Because right now, again, machines create stuff that is okay, even in the image generation side. You know, there are still enough artifacts and enough oddities that you can tell the difference between machine generated and human generated.

Chris (31:46): Now, the machine generated stuff some way it’s pretty darn and cool. It is, it’s fun to look at and think, we’re starting to see models generating images and animations and stuff. But we are a long way off still from providing a prompt, having a machine write a novel that is, you know, coherent or producing a motion picture that will, it’ll be a little while for what companies should be thinking about though. A, you should be talking to your lawyers A S A P to figure out how to integrate AI processes in your company without endangering your ability to protect your intellectual property. That’s a big deal. And, and expect to spend a lot of money with your legal team to do that. And then b, figuring out from a purpose perspective, what problem does this does, does this technology solve for you?

Chris (32:35): And does it actually solve that problem? Like if, you know, if you’re in the example of a PR firm, if you wanna relieve an account coordinator of doing the first draft on press releases, that’s a good application, right? You can take that person and then have them become an editor and, and level up those, those releases. Or maybe not. I mean, if no one reads press releases anyway but if you are, you know, creating a content shop you’ve gotta create content that is helpful and useful. One of the things that has happened recently is Google pretty much straight up said, Hey, we’re not gonna allow content that is low quality to rank anymore machine generate or human generated, which we’re, we’re, we’re trying to curtail any benefit that that content mills and spam farms generate. And that bar isn’t just a one-time announcement that’s, they’re gonna keep ratcheting that up to say, okay, we expect your content to be higher and higher quality and AI won’t solve that for companies.

John (33:28): Right? And, and Chris, you know, a point there, it doesn’t mean that Google is saying you know, you can’t use AI generated content. But I think the point that you are making and is in the industry, and as I spoke, speak to people across the industry, you have to have that editor. You have to have that marketer, that communicator there who is coming up with additional ideas. I mean, that’s really the value, right? With the with these tools. It’s, to me it’s that initial process of what do I need to write? You know, you, you made that earlier point about SEO and search analytics. So I, I, I think you’re making a mistake if you’re, you’re only, you know, you’re only using the tool cuz the production and the content that you get back is, is just not gonna work. You have to have a human involved. But the thing is, it’s a cultural change, right? For those writers, you know, in AI content donation. Have you have you seen reluctance on the part of companies to do this? Not for some of those issues, but for the problems with, you know, that digital transformation on the part of employees,

Chris (34:38): People, you know if this was before March of 2020, I would say that companies are, you know, were obviously being held back, but beginning into March of 2020, digital transformation was kind of forced on the entire planet, right? We spent three months all trying to figure out how to take every possible business and make it work from our living rooms because we had to, and if you look at the history of digital transformation stuff, what happened during the early years of the pandemic was we basically accelerated some companies in some industries, 10 or 25 years in three months, right? You take old school, like manufacturing companies, like, okay, now you’re gonna learn how to be a hybrid company because your accounting team can’t work in the office anymore. And, you know, seeing something, companies that were a little more forward thinking say, yeah, you know what, our company doesn’t need an office.

Chris (35:30): We can, we can still get work done from wherever. And that bleeds over into the use of other things like AI and things. Because the more accepting you are of change and the more tolerant you are of, of change in new technologies, the faster you’ll get benefit out of any kind of machine learning tools, right? If you are willing to adopt, you know data-driven attribution models for your a your analytics, you will pivot faster and deal with unexpected changes in the market faster than the competitor that is purely opinion led that doesn’t see the, the, you know, the ground changing underneath them. The, the last three years have been massively disruptive to every single company, you know, regardless of, of industry. But those companies that had the resilience and the agility to deal with it are also inherently the kinds of companies that will benefit more from ai.

John  (36:30): Great. Well, Chris, I, I really appreciate you spending some time with me today in the marketing ai chat podcast. Thanks so much.

Chris (36:42): Thanks for having me.






Marketing AI CEO Chats Welcomes Ankur Pandey of Long Shot AI

Marketing AI CEO Chats Welcomes Ankur Pandey of Long Shot AI

Transcript of the podcast:

John (00:03): Welcome to the AI marketing CEO chats podcast with AIContentGen I’m John Cass, one of the co-founders of AIContentGen, and today we’re interviewing Ankur Pandey who is the co-founder of Long And he’ll be talking about how his company is providing content solutions for content marketing teams and strategy welcome an Ankur. How are you doing?

Ankur (00:38): I’m great. Thanks, John. Thanks for having me.

John (00:41): So I know that we share a distant passion on mountains. I know that you sort of worked in you’ve done some things in the past in mountain airing and skiing and so forth. Do you still have that passion?

Ankur (01:00): Yeah, I mean, not skiing. Yes. Skiing, rock climbing and you know, things like those. Yeah. So I definitely used to have a bit more than in, my you know, prior to entrepreneurship days, somewhat lesser now. But I think that’s part of the life maybe who knows, like, you know, I can definitely go back to it someday.

John (01:21): Yes. Say same here. When I lived in California and, and, and Seattle Washington state, I was always up in the mountains <laugh> but, but now I’ve got a family and everything it’s a little bit harder, so I’m probably on the same place with you. <Laugh> well let’s get into some of these questions then. So, you know, tell me a little bit about your AI marketing journey.

Ankur (01:45): Great. So you know, my back background in AI has been pretty technical starting from my education days to you know, the kind of jobs I had had where mostly, you know, in the realms of data science, machine learning AI since about a decade. So I have had an opportunity to venture into various aspects of, you know, like the, the fashion keeps changing, but then more or less, I, I would, I tend to see data science, machine learning, AI, the part of the big spectrum. So I had various roles of, I had a lot of technical background and experience in those, but I also had in this article and would also, you know, wonder about how can we leverage AI to deal with that now, specifically when it comes to content AI you know, which largely which involves, you know, a lot to do with natural language crossing, I’ve been fortunate enough to have some hands on training in those things.

Ankur (02:40): So it was natural for me and my team to explore that, oh, you know, what, what kind of cool things can be done? Why? So it is, my background has been AI first. I was not a marketer as a you know, by training, but this is something I picked up. We, by working with clients, we realized that the, they, you know, the existing, there could be possibilities of solution, which are much better than the kind of things they were doing. So, you know even before Long Shot, the kind of products we had developed or had the clients with made us again really in depth, understanding of the marketing word in general. And then, you know, as, as the startups tend to do we learn on the go everyday learning. And I believe that I learn something today,

John (03:27):Well, as a marketer Ankur you know, it’s always good to listen to those clients and, and hear what the triggers are and the problems are, and then, and then address them. So that’s always a great path. Tell, you know, tell us a little bit about what your software does in this space.

Ankur (03:47): Sure. So Long is basically a platform to research generating optimized content, and we focus a lot on long form content. So what I really mean is that you know, the problems with the writing content is generally people. And specifically, when you are writing content as a marketer, you want to do something with this. You’re not writing as a hobby or a, or a fiction writer, right? You there, you have an audience in mind. So, and, and there’s a lot of content out there. So, I mean, think of a typical content marketer, content, strategist even like, you know, somebody who’s writing themselves, they would do tend to do some research, then they’ll take their notes and then they’ll write something they would know inherently intuitively what kind of things should click and then they’ll do as the optimization and what will click with readers.

Ankur (04:41): So we decided, we thought that why can’t it be done in you know, like in one platform as like, you know, in, in a simpler fashion what ha used to, we used to realize that existing solutions that kind of scattered. So we wanted to weed them together and always emphasize more on the simplicity aspects. So while we, there are, there could be some extremely technical type of, or you know long winded approaches to optimizing issue. Our focus of the focus of long short as a platform today is to have like a four step process how to do, how to research your content, then generate something and then see if it is fit for you. So this is, this is the current sort of avatar of what we are doing long short.

John (05:31): Well, that makes a lot of sense to me a, as a content designer and content strategist. The biggest question I always have with my clients is you know, what content should we produce <laugh>. And so that research is really important. Definitely. what, what would you say is the, the strength of the software? You know, you talked about those four aspects, you know?

Ankur (05:59): Right. So, I mean, I would say that the strength lies mostly on intersection between the second and the third part that it can generate. I mean, while research can have a lot of meaning for a lot of clients or a lot of different types of users the focus or rather current USB, I would say, is how it generates while at the same time? Not you know, you know, like not trying to kind of clutter in the keyword. So one of the things which we often notice in content marketing, making marketers, make the mistake is they do some kind of keyword. Interesting. So that is what we avoid, really. We try to make it organic and, and another us P five to put it that way is that we also are very vigilant that the content should be as fresh as fact checked as possible.

Ankur (06:52): So, I mean this is, you know, I’m sure that our audience knows this already, and I’m sure that we’ll touch upon it more in the, in few minutes. But one of the issues, while AI content is, has a lot of you-know qualities to it. One of the issue where it kind of F is sometime it produce nonsensical type of content. So we are very vigilant in how we can kind of it, how we can at least minimize it, or we can be very clear about the user that, you know, this is the part where you should kind of deep dive, check it a bit more. So, before even you me not even ask this, but I would just say that this is a software which has to be, this is not like a plugin place software. You cannot just like rely on it 10%. This is never our goal, and this is not going to be ever go. The idea is that this is a tool in a content marketers in a content writer, in a content strategist hand, which can guide them. It’s like, you have some ideas in mind, but it, it kind of, you know, whatever you would’ve done in, maybe, I don’t know, like two days doing a couple of arts, right. So that’s our hope.

John (07:55): I love that. Ankur, I think you did a wonderful job there explaining you know, what your tool does, but for the whole industry as well. I think you’re making a great point there, which is it has to have that, that expert, the marketer, the writer, to be able to run things. So makes a lot of sense to me. And, you made a great point, I think, on, on the quality, you know, how, how you helping, you know, just to improve the quality of content. So I’ve got a couple of quick fire questions for you to sort of go through the whole process of you know, content development and content strategy through AI content generation. So you know, what’s you know, how do you support your clients strategy in the following areas say ideas and research specifically?

Ankur (08:45): Hmm. Yeah. So often the clients we have they, they definitely have some of the niche they already write, or, you know, some of the area, some of the niche of the clients they’re serving, right. So what we do is that we kind of fire up the kind like within a given topic, within a given niche, we fire up the kind of things you should, they should focus more on. So let’s say you are going to write something on Tesla, electric vehicles. This is, you know, this, right. This has been a requirement given to you as a company, or, you know, as somebody who’s providing consulting to their own clients right now. But, but what exactly is popular here? What is, what are people searching for, what they’re asking and then what you should focus on so that you know, your content’s very full-fledged.

Ankur (09:35): So, you know, I mean, there has been lot of, kind of, you know, chatter about that like what is good content and what is a helpful content? And we’ll probably touch upon that. But our approach to it is that you should write content, which people are looking for, which people would want to read about your specific niche. That is so we give you all those indicators, right? We have a facility where you can, let’s say, you know, let’s say you are a big content team and maybe your work is scattered. So you can create an amazing content brief using Long Shot  and pass on to others who can collaboratively edit it. They can give their own inputs, edit it out and stuff like that, all with the power of AI. Right. So when I say I edit it like I’m, I have created a content draft, let’s say, right.

Ankur (10:23): And my teammate can, you know, chip in and then they can, let’s say, you know create a few paragraphs based on their understanding, right? So, so it’s like, they, this is something which in, in, in some form of fashion was already done, right. Was, was already something which people were doing in collaboratively, but we make it 10 X fast. We make it 10 X fast. And with the power of a, I also give you, keep on giving you ideas. So you are never blocked. Really. So, I mean, I, I, I remember a case when somebody told me that they wanted to write a piece and they were stuck on it for three months and they could finish in a day. Right. And this, these kind of things are, you know, big, big, I mean, you know, I, I don’t even want to translate into what is the revenue and those kind of metrics, but these are some personal win, really, I think like that, right. That some who was stuck at the piece and we are kind of, you know, somehow not following through could kind of, we could Delete them and they could eventually finish up.

John (11:23): So, yeah, Ankur you’re reminding me of a, a past client who is in the legal field that literally took three months to write a blog post <laugh>. Yeah. So it’s, it’s kind of, so that, that certainly does

Ankur (11:33): Speak of legal. Yeah. Just, just like, just a note here. So speaking of legal, like one of our one of our user is a law professor in Texas. And he has, he, he just told me that he has written a book using our software. Right. Wow. And the great, the great thing about it that this book is fetching him a lot of clients, right. A lot of consult for consulting services. So this is how we, these are the kind of examples how we offer our, you know, help.

John (12:01): Well, that’s great. That’s great. And I think you, you know, you did an excellent job of pointing out that, you know, it’s, it, it’s that unknown, you know, it’s the unknowns and, and the ideas and the research can, can help cover that so that the market, or the writer knows that they’re, they’re doing that. You touch the brief you touch the covered this, which was briefing. What about briefing? I think you said drafts. So you know, how do you structure those that briefing those briefing tools,

Ankur (12:30): Right. Sure. So, so the idea is that, you know, what we notice is that, you know, content industry tend to go in a, have a, have a structure, which we respect, and we think it’s great. So we have also tried to you know, capture those essence in our product. Now, what I mean is this, so typically when you set out to write a content piece, you would from your idea to you, you would first do some kind of content research. And then you first, before actually writing the whole piece, you will first have a content brief. At this point, you can finish it yourself. You can, you know, invite collaborators, reviewers, etcetera. So a lot of times our clients would use these briefs which they can share or add their team members, and then they can collaboratively develop it. So the brief is basically a way of saying that, oh, you know, I have this article outline ready.

Ankur (13:21): I know this is my article headline. These are my, you know, like sub-headlines, these are the kind of things I would like to say. But why I’m not really finished the 2000 word articles, but here are some couple of hundred, three, 400 words, which I have finished. And this will give you a brief idea to anybody in their team that what is to be written, right? So this is this sort of intermediate step in the content writing journey is traditionally in the content marketing pile, land term as a content brief. And you know, like so therefore we do not you know, just so that somebody’s may have misunderstood it. We do not tend to create a create like a content in one shot. Although, you know, there is a shot in the name, but the idea is that you have to go step by step. And the reason is that this has been a tried and tested process, and this is ensure not just high quality content, but also something which has been actually developed by over the years or the really by, you know, content writers. So we products great to also emulate that very thing in our product.

John (14:28): So you’ve touched on the next area, which is in the process, I think, which is AI content generation. And I, I think I, I, I pick out there sort of a tip there, which is that you’re not generating it all at one, you know, one at one time, but it’s, it’s done perhaps in sections, right? Yes. And that, I mean, to me, that’s like the biggest, that’s the biggest learning you get from using these tools, which is, don’t think it’s all going to happen at once. So but tell me more, you know, how do you, how do you address that point or any other S

Ankur (15:02): So this is a question you know, I’ve been asked a lot and I, we have also, as a team dealt really deeply on, on this one, the, the, I would say the crisp best answer I could offer is this. So think you are a content team already. Would you rather write an article on one go, you would not really like, you know, sit down and write everything in one shot, right? Could you, I mean, the idea is that you, you would go in an high fashion, you would’ve some idea, then you’ll create a structure. And then you’ll like, okay, I have all these sections, and this is the bigger, and the more complex, and the more demanding article is which most of our users tend to write. The more important for a team is to follow this very structure. So, so like, it’s not even, it’s not even a choice for us.

Ankur (15:46): It’s basically, this is how this is the order in which a content should be written, especially a long form content should be written. You have to create various sections. They need to see what makes sense in this. What does not make sense in this and other is, you know, speaking from a slightly technical angle, even if you let’s say, you know I mean, without naming, I mean, there might be, you might find some solutions which might offer you, you know, audacious solutions, like, you know, one short full block post, but we have seen those never working at all because you know, you’ll, it, it’s just rarity that you’ll, you know, see a meaningful point because it’s like, if you are generating the whole article from the cube, how does it even make sense? You are just, it’s like, you are not even leashing the content.

Ankur (16:33): You have to have some checks and downs, you have to guide the content. So, but the only thing which long shot is doing, it’s doing it like really, really fast, because you are never stuck. It is always presenting with the lots of information to fill everywhere at the same time. It is fact checking, checking for plagiarism, checking for D checking for your own tones also at times that, oh, is it something which you would’ve, it might be correct information, but would you write, would you as a write it? So we also we also, we have also noticed that lots of our users also IISE on that their tonality or their way of writing should be present. So I think a good long form article, and specifically when it is written for the web by the marketers should have all those qualities. So by the way, I mean, I’m not suggesting that we are like the perfect solution and, you know, we have kind of checked all the boxes, but this is the direction we are going in. And I think to it, to, to, to, to a decent extent, we have covered many of these points.

John (17:34): I, I, I love that. Ankur, I, I think you’ve made a wonderful point there about you know, it’s an incremental process and mm-hmm, <affirmative>, you know, when writers and marketers are, are putting that process together outside of AI content, that’s, that’s how they do it. They do it incrementally, you know, they’re doing research and they add more to the brief and then they may write some content and it isn’t complete. And they realize they have to put more in, but these tools, they help to speed that whole process up. So, you know, you can concentrate on really improving the content, even though it is in, in different sections. So I, I, I think you did a great job of explaining how that works. And, and, and I, I, I think that framework really helps right with the writers and the marketers, knowing how to use the tools, if, if they understand how it works.

Ankur (18:24): So speaking of frameworks you know, I would also offer that we have noticed that, oh, in the journey, this is relatively recent development. So in the journey of, you know, constantly creating this product, we realize that different types of long form content tend to have at least slightly different structure. So for example, somebody’s, who’s writing a product review would have slightly different structure to it. Let’s say somebody who is writing you know something else, let’s say somebody who’s writing a content for the video, let’s say, you know, a video script, right? So therefore those have to be dealt a bit differently. So we support it by offering what we refer to as recipes. We have a lots of recipes where we kind of, you know, suggest users that depending on what you intend to write you should follow these things.

Ankur (19:13): And this is the structure, which is fit for such a type of. So, I mean, like the thing is that when we develop something, then with the feedback, the users and all the, the content community, which we are always listening to a learning from, we try to answer. And you know, so it’s not like we have, we know everything and we are developing the product. We have developed the record for the content, what to use it. No, it’s like, in some sense, I see this as this whole sort of era is a great sort of, you know, coming together of AI tech and content wherein collaboratively, collaboratively, you know, people learn from each other and then they adopt, right? So this is precisely where our sort of expectation and our vision with the product is,

John (19:58): Well, that’s a great point about the recipes and, and how you have different structures within depending upon the type of content, you know, I can, I can think of product descriptions and yeah. And having to, you know, work with an eCommerce provided where they’ve got 10,000 products and you have to produce it. I think AI content generation is a, you know, a superb tool for that. So I think you’ve touched there on my, my term of expansion, which could either be a recipe or it might be taking an existing piece of long form content and doing something else with it. How do you, how do you address that area expansion?

Ankur (20:38): So you’re saying that you have an existing piece of content you would like to expand it, right? This is, yeah. Yeah. Okay. Yeah. So, I mean, this is, this is also, a common use case we observe. So, and this is like, this happens a lot when like few of our users who, I mean, who have already written tons of content. Right. but they have to freshen up their older content because they’re more data. And then they want to sort of, you know, think, see that are there opportunities to make it even better. Right. So, and they do not want to throw away the entire thing, because of course that has been a labor of love. And that, that has been really good, our, our content already. So, so this is very natural to our workflow when it comes to long short what you do is you just upload our copy paste, your existing content, and then what we do, and depending on the type of content, as I said, like, you know, the recipe still holds because your content would follow most more, more, most of the time in one of these buckets, we have a tons of recipes, really.

Ankur (21:33): So the thing is that you can start anywhere really like, depends. Like for example, you have an article today all you want to see is that, are, is it like, you know, first typical workflows that you would like to see that is this plagiarism free, or, or if that’s the case, then are there some factual inequities to it? So we tend to first provide all those checks and balances. Then we’d see that depending on what today is being searched, what the audience of this, your current article might have been written, let’s say, you know, a year back, but today’s, people are searching a bit different thing right. In your niche. So we offer keywords and phrases and questions, suggestions, which your article should follow for, for it to be relevant today. So, and then you can tweak your content accordingly. So which reminds me that one of the thing, one of the approaches, which is slightly opinionated also we take is instead of relying heavily on, you know, key that you should use this keyword here, we tend to really focus on is your content answering what people are asking.

Ankur (22:37): So it kind of encompasses all the things, just like a, it’s like a meta view of looking at things rather than a very keyword centric. We are looking at things. So what we really do is, but if you have a piece of content, I mean, if your content is really answering what people are looking for, and we have a mean to sort of discover that using some scores you have a very direct view of it, right? You do not have to actually go in circles in the sense that, oh why should I use this keyword? Should I use this keyword in one or two? So what we have done is we have simplified all those process and said if your content is really asking those things, it is good. Really it’s, it’s good. And, and, and as SU community would be aware that such and Google’s Google and, you know, predominant, which is a predominant search engine is actually moving in that very direction. They have released helpful content update, which actually suggests that if you, if it is a people first content, if it is actually looked answering the intent, that is what is important really. Right. So that makes a lot of sense, actually, slightly more than you asked, but oh,

John (23:45): No worries. No worries. I think it’s; I think it’s all good. And Ankur I really, you know, get that point as an SEO. I mean, I think, you know, Google for so many years now has been thinking more about the topic than it has keywords and yeah. When you think about topic, it’s, it’s the comprehensiveness. So actually answering the questions you make that, you know, you make that excellent point and is it, is it really covering it and, and is it competitive and it does it have the quality there? So I think that makes perfect sense to me. So you, you covered some of those aspects of expansion and then also optimization, but what about metrics, you know actually measuring you know, how do you, how does the tool or help with that aspect of it?

Ankur (24:29):Right. So before answering this question, so like, first of all, in the SU or the content optimization word, there are lots of metrics like many are technical metrics, for example which, which has to do with the website speed and stuff like that. So as a product, we focus more on content, not things like, you know, because this is, oh, yeah. So, so we, we, of course like, you know, your issue eventually be dependent on all those things also. And we are not, I mean, by no mean kind of, you know, shying away from that, but when it comes to you know, expos and metrics in context of long short, we really mean content your score. Right. And the, so we have like, you know, way of measuring readability, I just mentioned semantic your content, your score, that, how much is your you know, how much out of the high intent queries, high intent, the keywords your content is actually answering.

Ankur (25:25): We also give you based on that, you know, what are the, like, based on your competition, based on the, you have written how, where do you, does your content stand and then you know, various other things like plagiarism, which I already touched upon and we also offer, so instead of just saying a very qualitative answer to it, we actually give you a score because score is something which you can actually understand, right? And then you can try to say, oh, I should kind of reach this score, not just that score. We also give you the competition score because your score might be good or bad, but it is good or bad only in comparison with your, whoever is doing good, right. In, in whatever you have written. So we have all those things. There are of course, you know, revisions every day.

Ankur (26:08): Like sometimes people say, oh, you know, maybe this score can be tweaked, but so, you know, tomorrow, if you let’s say, if somebody’s listening to it a few weeks from now it’s definitely possible that some of these metrics have been tweaked changed upgraded because you know, the product is not you know, kind of stuck somewhere, right. Evolving every day. Just to give you a point of there’s a lot of questions we are asked about the helpful content update. So we are just releasing a feature next week, which, which is like a checklist type of feature that, oh, if you have all these checklists, you are good to go, you know, in eyes of Google, especially with, with respect to this helpful content update you are sorted and not just that you don’t even have to use our whole product.

Ankur (26:52): Like if you might have generated a content elsewhere, you can just paste it here. And then you can just see that is your content in, you know, sort of in accordance with the latest U updates. Right. So I think, and we are actually releasing it next week for free. So this is a tool which anybody can use for free. So just to shout out there, because the idea here is that we are not like an AI generator tool, to be honest, just to kind of, just to be very clear because we are generating content to be meaningful, to be helpful. And for primarily for marketers, because it already encompasses that when marketers write something, they are writing for an audience, right. So this is our primary goal.

John (27:38): That makes a lot of sense. I really like that point that you made about quality of the content. I think that makes a lot of sense and how the tool is, is helping writers and marketers to produce you know, that sort of better quality content by, by using the metrics, trying to figure out what you need to put in there and what isn’t in there as it as it is. So one, one last question, you know, what’s the one thing that most people believe is true about AI content generation but that you think actually isn’t true. So

Ankur (28:25): <Laugh>, yeah, sure. So, I mean, I think, I mean, there, there are lots of misconceptions, but the most important these days is that AI content is bad in the eyes of Google. So, I mean, first of all, this is falls for us. We, our own content is produced by AI and all of our clients, and also like other people who use AI content, the idea is how you’re using it. And I would even, you know, say that Google itself produced tons of content, any, but like, for example, you might have noticed so the meta descriptions Google would rewrite them right. Often because, you know, in order to be slightly different, more meaningful, whatever their thoughts are, they would so there are, there is no it’s not to say that AI content is bad. It’s the idea is how you use, of course, if you are using something like what, what the blackhead, your folks used to do that infest keywords in older days, right?

Ankur (29:19): And sometimes it’ll pass through these, your filters and also rank kind, remember, you know, 2000 early, 2000 and, you know, late 2000, 2010, and something around that, you would see blog posts, which have completely non thing, and they would rank high. So I think we have come a long way since then. So if you are trying to kind of game Google, really, you know, then it is not going to cut out. So AI content is not bad. I mean, of course, you know, you it’s like, I mean, when I say this I don’t want to kind of push this too much because of course I have a vested interest and I acknowledge it. But the idea that you can see for yourself really, right. I mean we, our self-produced tons of content for our own product and like all of our client and so other product people. So make sure, so, you know, just the idea here is that I think I completely disagree with it, that AI content means that it is not going to be, it is going to be penalized by Google that is completely false, and that Isly false. And now that the Google helpful content update has actually gone live, I can say it with even more confidence. Right. So we are seeing completely optics all the, all the day. Right. So

John (30:33): Ankur, I think you make a great point there. And I’ve been doing SEO for 20 years and as long as the contents that’s produced, whether it’s, you know, human generated completely or AI generated, which means that there’s a human involved <laugh>. Yeah. And you know, they’re stitching the content together. They make sure it’s good, they’re making sure it isn’t, you know, duplicate content and that the style makes sense and the quality’s good and, and all those factors then how, how is Google going to be able to tell now if there are grammatical issues in the particular language that it’s in and it’s repeating stuff yes. Then I think Google’s going to find it, but it, it found that type of content before, you know, you mentioned back to 2005 to 2010. Exactly. You know, that the content was terrible, but Google wasn’t doing a good job. That’s why they were getting criticized so much. And, and, and Bing was actually starting to beat Google, but then, you know, the founders came back from working on that Android operating system. And, and we’ve had 12 years of better content quality. And so it’s, it’s been a cool process. So

Ankur (31:45): I also sometimes say this, you know think of a product, a big popular product. I’m, I’m sure everybody who has written content, as you would know, and maybe used it Grammarly. Right. now, so I would say like Grammarly also sometimes rephrases things. It is also AI power. Is it like that? Of course not. I mean, I would even say Grammarly is a tool which has empowered a lot of people who might not have a lot of, I mean, who might not be grammatically gifted to, you know, kind of overcome that issue and just have like, you know, whatever great ideas they have can kind of come alive. Right. And I think we are nothing but Grammarly 2.0, if you can call it that. Right. So I think we are in the, we are doing exactly the same thing. I mean, I’m, I’m not, I’m not pretending to speak to, to imply that of course there might be sometimes people who kind of overstep and try to use it, or even create products you know, to, to create blogs on the fly in one shot and things like that. Right. So I’m therefore not trying to speak on everybody’s behalf, at least from my perspective and at least the decent products, which I think even in the realm where I operate follow all those guidelines, because this is the long way to go.

John (33:02): Right. Cool. This is wonderful. Thank you. What, a great job you did today in the, in the podcast. I want to thank you for joining us on the AI marketing CEO chats podcast. Thank you.

Ankur (33:13): Thanks, John. And it was an engaging discussion and completely enjoyed it.

John (33:17): Great. great. And so thank you everybody to the audience, and we’ll see you next time.






Marketing AI CEO Chats Welcomes Pankil Shah of

Marketing AI CEO Chats Welcomes Pankil Shah of

Transcript of the podcast:

John  (00:02): Welcome to the AIContentGen CEO Chats podcast with AIContentGen, today Pankil Shah CEO, and co-founder at will be joining us to talk about how their company is solving content teams, content strategy issues welcome Pankil How are you doing?

Pankil (00:27): I’m doing good. Thank you so much, John, for having me

John  (00:31): I noticed in your background that you have an interesting space background aeronautical, does that mean that you’re you were looking to the stars?

Pankil (00:41): I was, yeah. At one point in time, right. <Laugh> when you’re growing up, you have different passion and when you are all grown and you know, where your true passion, like it’s like a totally different story, but yeah, I, my background is aerospace engineering mostly propulsion. So yeah, with looking to build aircraft engines at some point in time.

John  (01:02): Great, great, great. And also you are still in technology though. You’re in the AI space and AI content generation. Tell us about a little bit more about yourself and also that AI journey.

Pankil (01:16): Absolutely. show my like right out of college, right. Like I was we were pursuing this idea that failed terribly eventually, right? Like, and we all learned from the mistakes and that lead to another one and that lead to another one. And that lead me to enterprise company where we had a different sort of problem. So I was leading growth for a enterprise database company and we were having issues scaling content because of the complex nature of the content. Like you need subject matter expertise and experts don’t often have time cuz they’re coding or they’re doing other things. And if you get it written outside, it will cost you a bond <laugh> so it’s how do you create a process that can help this engineers understand the value of what you’re doing? What sort of exposures will get them creating this precise guidelines that encourages them when they see that results like, oh my God, my topic is ranking on the first page and now all of a sudden they want to write five more.

Pankil (02:19): So those, you know, nurturing that kind of program and we’re able to really do well. So keeping that process in mind, we got into this this product which we wanted to build essentially around analysis of ranking pages and coming up devising a strategy that can help you create content, which has pre predictable rank. I mean, a predictable rate of success, right. You know, that it’s going to perform well, at least majority of the ones are going to perform well. So that’s what we were after. And then we started then that’s when like the AI all new and well, and we said, okay, how can we do this? So we can solve problems that our engineers had is not having base information to work from so that they can start adding value to it and, you know, reduce that time.

Pankil (03:14): So we were still doing all of that manually. So when we started this and the first thing that came to our mind when we implemented AI with, we don’t wanna generate block post in a minute, that’s not the point of it. And it can work for simple SEOs, but simple SEO stuff, but it can’t really work for complex things that we were trying to solve. Right. writing about databases and technology and products that just came about yesterday, AI is not trained to do that, right. So you need some form of research that goes behind it. And that’s how we evolve into the product that we are like, we are researched to writing platform for SEO. And yeah, that’s, that’s a quick story to the AI journey to our ranking.

John  (04:00): Well, you’ve covered some of this next question, but could you tell the audience what your software does?

Pankil (04:07): Totally. So our, like I said our product is a research writing platform for SEO content. Right? So what did that really mean is when you’re writing content for SEO, there’s, you know, user intent that you need to satisfy. There are there’s types of pages and there are types of trends that make a particular page rank. So you wanna not only do analysis of that and give something digestible to the writers because they’re not SEO, right? Like subject matter experts might not be SEO. So you wanna come up with guidance that guides them enough. But with AI, what we’ve been able to do is look past that, perform some deep research and extract information, which is relevant to what you might be writing about, right? Like for each section or for each paragraph, or however you are guiding your content.

Pankil (04:55): And that adds extra layer of, you know information that might be 90% there. And 10% is up to you to add value and, you know, elevate that with your subject matter expertise, but getting to a point where you have amazing research extracted for you automatically about what you’re writing about. And I’m not talking about like bland paragraph, right? Like I’m talking about perceived research. And that’s what we do. So we build a workflow with this new AI in mind, which was not there. People still were doing things. They were going from writing brief generating, outline, generating content you know, getting different people involved to get approvals and then edit the content, published a content. The process is still the same, but what we got is an AI, which sits in the middle now a little bit, or at least people are trying to figure out how to use it.

Pankil (05:49): So what we’ve done is we build a workflow that still understood this process, but uses AI as a centerpiece in helping you facilitate all of these tasks super fast. So that the evaluation part, which is the list, that’s where you get the most amount of time. We’re not saying that you’ll the end result will be a thousand times, times saved, but we are saying it’ll be a thousand times better content, which will have predictable rate of success. And it’ll be a delight to read, right? Like it’s well researched. It’s well thought out and it has all the elements. So that’s what our software does is the research writing platform for SEO. And we help not only build SEO content, but strategize is at well. So like you can create a strategy around what topics you wanna execute and we, we help you prioritize this also based on AI. So using AI in a few creative ways and the same underlying technology GPD three, right. But instead of just writing, writing, writing, or template generations, we are using it to find trends in your ranking data and sort of come up with a plan that can help you rank even faster.

John  (06:56): Great, great. My background is content strategy and SEO. And when I work with clients, the biggest question I always ask myself is what do I need to write? So it sounds like the software is really helping your clients to, to answer that question and, and produce the best content as a consequence.

Pankil (07:15): Absolutely. Right. Like there’s so many nuances to doing keyword research. And when you have your own side, old side, new side and with the different levels of expertise in SEO, not all companies can afford that. Right. So if you have a profit that you can adopt and reach to a go, you know, reach to an end point where you have an editorial calendar set for six months, that drives consistent result. That’s amazing.

John  (07:41): Absolutely. Absolutely. What, what do you consider to be the strength of your software?

Pankil (07:49): It’s mostly research writing. We’ve developed a technology that can do some semantic searches identify information from ranking pages or even beyond and pull in and, and transform that research into fragments of information that is easy to write around. That is easy to include that is easy to elevate, right. Like, and add value upon. And that is how most people are creating content without ranking. Like, so you get to that first draft in 15 minutes, but then you can really start working that content and adding more value. So your time to writing content is significantly reduced. Not something very observed, but it’s reduced which gives you more time to add value to it. So people on our platform many niche sites owners, right? Like they’ve scaled at traffic, like these stories about them scaling traffic from 3000 to 45,000 in less than six months and things like that, but it, it takes consistent effort. So that’s what we’re doing.

John  (08:52): <Laugh>, that’s great. That’s great. Let’s go through some quick fire questions. How do you support the client’s content strategy approach in the following areas, ideas and research.

Pankil (09:05): So in, in terms of strategy, right? Like the first strategy is what content do I go after? What content do I optimize and what content do I write new? Right. Like there’s three buckets that fall in that you have to figure it out. So we help them across all of these journey from handholding part, right? Like, so we’ll guide you to the entire process of finding the right keyword that you can build perceived authority around. And if you have your own strategy, you can blend that right in it can even read your websites vital and understand what is a gap in the content that you should be actually ranking for topical authority versus not. So it can help you prioritize this content and come up for an editorial plan of six months in which it can also suggest you pages that are low hanging foods that can drive you significantly more traffic.

Pankil (09:57): If you made a few tweaks not content tweaks, but other on page tweaks. And the last part is where you create content. And we help them create, you know, we have this entire workflow, which takes them from the start to end of publishing content, starting with a brief all the way to the finished content and publishing it on their website. So we’ve all this entire process said. So it’s all about content, right? Like how do we optimize and find the right content to optimize actually, how do we find the right keyword and how do we create new content around it?

John  (10:30): Great. And then what about briefing those outlines?

Pankil (10:33): Absolutely. That’s the first step in creating content, right? Like, so a content creation process, we starts with a content brief. Then you have research or you have, you know, prepared guidelines for writers. If that encompasses in your brief, then that’s well, but then the editing happens or writing happens. And then the editing happens, right? Like proofreading happens and then publishing happens, right? So like three or four phases that you have, and we help you facilitate each of those phases. And if you, if you want, you use less of AI, you want to use more of AI, it’s all up to you. You can manage the process and build your own workflow that sets your team. So it starts with content brief and our content brief are extremely detailed, like to the detail as in we’ll create a plan for what needs to be written in each section using AI. So it’s partially human driven and partially AI, you know, generated. But it’s a perfect match because we can read through the re related keywords and the signals from ranking pages automatically and train the, you know, AI to perform some of this task in a very much efficient way. So it’s guiding you to the entire process of creating that brief, that most people who don’t have SEO knowledge can also perform just fine.

John  (11:47): Well, that’s great. And then what about the writing? I, you know, I’ve used a lot of these tools in the last couple of years, and I, I think the number one tip I always give is think about writing in sections. What, how do you, you know, you mentioned earlier you, your intention wasn’t to write a whole blog completely, but to make sure that you have the research, what how, how does the writing work and, and, and what’s the best way to get the, the most out of it

Pankil (12:17): Probably be a little bold here and say that we came up with this process of section writing and the, the first thing that we launched around it was concepts. And what concepts are, is let’s say if I read through a thousand line of text and from those thousand lines of text, I am only trying to find information about out ranking features, for example, right. I, a human would have to read that 10,000 words grab a little fragments of information, compile it into a research and then create proper, you know, proper paragraphs, the proper sections around it, right? That’s what you call section writing. And humans have been doing this long, long until AI came and said, I’ll do right for me. And then I’ll build out the remaining part of it without thinking, right? Like, so let’s forget that what we do is we read this information, we know what you want to write about, and we can extract this fragments automatically past this fragments automatically to open AI or any AI and help you write around it with E so we, you see it’s mimicking the human behavior, not saying it’s going to replace it because still a great copy still needs a lot of additional facts and things like that.

Pankil (13:28): But what it does is that even if you take the state of the research, only that content to read is still a delight, because it only talks about information that is really solid like that. If I was to read a scan a page, and I just saw that and remove all the fluff out of it, right? Like I’ll be 10 times quicker. Is that, that’s how well the research is brought out by, you know reading through these existing pages. So then we use that to write sections after sections, after sections. So they’re, they’re, they’re factual. Like if, you know, AI tend to lie, right. But since we are influencing it with the research, it doesn’t lie. It stays on track. It writes about what you wanted to write. And while adding a little bit more of what’s already in the brain that we missed out to form proper, you know, structure.

Pankil (14:19): So that’s, that’s the process behind writing sections writing it’s still not cohesive, right. But because one section might not lead into the other and then the other and things like that. So that’s where a human comes in and makes that, you know, content flow naturally at the tone and that elements of surprise and the content that, you know, the, the user cannot stay away from. But yeah, that’s, that’s I, I would say that’s, that’s how content will be written. The moment you start thinking that I can write the entire blog in a continuous format using AI you are telling it to do a lot of processing that needs to be done in batches, but you are asking it to do it together, not going to work. If, if it works, the content produce will be great, but lacks like substantial facts. So it’s really not useful or it is very useful. It’s very short and you have to add so much other things around it. So it’s, it’s one way or the other, but continuous writing for AI and blog post will not, it’s going to be used mostly for ideations extracting research and writing better than what we found on the web.

John  (15:33): Pankil, I think that’s a great explanation for folks you know, as they come to this type of industry and also specifically your product and, and try and understand how to use these tools, cuz I think it’s a big point that you make that. What about optimization? And what I mean by that is I’m, I’m probably really thinking like, although there’s, there’s a couple of instances there, but, but really I’m thinking of maybe a writer write something. They could have used AI content generation or not maybe they didn’t, but then they write it and they use the tool to, to, to make sure that it’s doing okay, you know, the content’s doing okay, what else they can do with that? How do you, how do you help folks and riders with optimization?

Pankil (16:16): So primarily for optimization there’s a few types of optimization that you might wanna do through your content, right? Yeah. one’s in terms of readability and integrate with Grammarly. So you can easily check all of those stuff right. In our platform, but in terms of SEO optimization as well, that’s important, right? Like if your content is not going to perform, then nobody wants your content anyways. Right? Like it’s gonna sit on the third pile. So what we do is we have the most, you know, comprehensive on page SEO scoring mechanism. And what that means is that it collects this NLP data entity data from the ranking pages, establishes a trend and then suggest a pathway and optimizing each of those elements as you go about writing your content. So whether that’s entity to include in your content related keywords to include in your content, how should you include it? Where should you include it? Right. And with suggesting all of this, not based on a wimp we’re suggesting it based on what we already see, what we’ve already established trends in. And we, you know, carve a better path for the writers to follow and optimize their content.

John  (17:28): Great. What about what about expansion? So you might take it taking an existing article and then making it into different types of content. That’s probably one way to think about it. Yeah.

Pankil (17:44): Different types of content. You can transform it into summaries. You can transform it into social media blog post. We’re not doing social media blog post. We’re just doing SEO. So in in terms of expansion, you can also convert let’s say for example, a YouTube video script into an entire blog post you know, well written like things like that, it’s, it’s already available in the platform. But expansions, you know, that tend to, you know, gimme a thought of how do you help them expand into different content areas that they’re not focusing on and things like that. Right. So because we’re tracking their website because they’ve created strategy projects in out ranking, we can track all of these things and we can suggest ongoing improvements for new content creation for optimization of existing one to scale the traffic. So for expansion, to me, like it’s, it’s a little different. So about two to 3% of keywords for any website ranks in the top three. But there is a significant 40% that ranks between the, you know, the top fifth position to 38 position. Imagine if you can improve 10% of those keywords and elevate the value, right? Like, so that’s where we truly help again as well is how you can create or the processes around optimization of your existing content to expand your traffic matrix quite fast.

John  (19:12):What about metrics? And, and here I’m sort of forward thinking and, and sort of mechanics for, or metrics for looking at content. And then, and you’ve said some of this already, I think, and what you just described, but sort of tracking how the content is doing and then coming back over time and, and, and seeing where it progresses. You may not have all of these elements, but it does sound as if you’ve got some of them. So,

Pankil (19:37):Yes. And one part about creating amazing con like you need to track it if you’re not tracking it, then what you did yesterday, if not producing results tomorrow, and you’re just sitting on it and you have no idea what to do with it. So what we do is we actively look into your Google search console data and we help you track this keywords that you’ve just published, right, for example. And as they move in search, you know we can suggest improvements to them. There is a possibility, not, not a possibility it’s always happened. You’re trying to rank for one keyword, but you tend to rank for three or four or a hundred or 2000 S more. And the term that you would essentially trying to rank might not be the most valuable you uncover other terms that you’re ranking for that are more valuable for the same keyboard, right.

Pankil (20:24):Even optimize better for them. So we even suggest those optimizations to help you improve your traffic. And the end goal is really revenue. So you need to figure out where I’m going to spend the most amount of ad spend and things like that. Even in the preliminary stage our tracking software can suggest that that this is how much you’re looking to save. And this is where your true focus needs to be in optimization, right. Things like that. But again, a human is driving it tools only help <laugh>. So there is still a significant part that is left for the brains to run and figured out.

John  (21:02): Absolutely. so let’s end with sort of thoughtful question here. What’s the one thing that most people believe is true about AI content generation, but that you think is not true.

Pankil (21:20): Oh, it’s, this is controversial VR and AI software. So it’s, there’s actually a couple of things maybe. And, and the fir the first thing is that AI will help you speed up your content writing process. That’s, that’s totally true to a certain extent but because it lacks the value, the end result is essentially around about the same time it took you to create content. So now you use it to write better content in the same amount of time. That’s absolutely possible, but yeah, you go to organizations, right? Like you have single line written nonsensical. It’s not gonna float by, right. So there’s sign significant amount of work needed after that. So I think that’s one statement that people need to really understand it. Sure. You can do faster things. But the end goal, although the end result might not be really fast.

Pankil (22:19): Like you’re doing the entire process, you’re dividing it into two, you’re doing one task really fast, but that means that it’s giving you more time to do the other task. If you take shortcuts than, you know, people are writing good content, they’ll overtake you over the period of time, your content will not perform well. Even if it did right now, there will be a suitable replacement later on. So you know, you need to focus your energy on creating better content at the end. So I think that is not true. <Laugh> and the second part is AI will do all my SEO. It will do simple SEO. It will still help you create content for simple SEO, but complex things like I just talked about, right? We are talking about post skill technology. We’re talking about a can and camera that just came out yesterday. Things like that. It still requires human intelligence or research intelligence. And that is why AI cannot completely replace that either so

John  (23:15): Well pen <laugh>. That’s great. That, that’s a great answer. I, I really want to thank you for joining us today on the AI content, Jen video podcast. We really appreciate it. I think you’ve done a great job.

Pankil (23:28): Thank you so much, John, for having me, it was a pleasure.

John  (23:32): And I want to thank the audience for joining us and we’ll see you next time.






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

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 And today we’re gonna be talking AI marketing and learning more about what 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

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 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,