218 | Zero to Leads: How to Build a Personalized LinkedIn Outreach Machine for Your ICP with Anna Bui

0:00Hello, and welcome to another live episode of the Leveraging AI podcast. the podcast that shares practical ethical ways to leverage AI to improve efficiency, grow your business, and advance your career. This is Isar Maitis, your host, and I'm really excited today. I'm really excited for two different reasons. Reason number one, we haven't done one of these lives in a very long time. Well, not very long. I guess for most people, maybe not that much, but about a month and a half since we've done the last live. And we used to do this every single week, but I was on vacation in Israel, and that's a very different time zone, and it would have made the whole thing a lot more complicated.

0:33But we're back. We're going to be back doing this every single week unless I'm traveling. So if you are with us live, first of all, thank you for being here. If you're not with us live and you're listening to this as the podcast or watching this on YouTube afterwards, we're going to be back to doing this every single Thursday at noon Easter time with an amazing expert that's going to teach you how to do something very practical, very effective with AI and other tools. And so if that's what you want to learn, come join us because A, you get to hang out with the cool people and B, you'll be able to ask questions, which if you're just listening to this afterwards, you cannot.

1:03Which leads me to the second reason I'm excited is our topic today. You heard me say on this podcast many times before that you can have a business without marketing. You can have a business without HR. You can have a business without finance. You can have a business without operations. You cannot have a business without clients because without clients, you don't have a business. That's the whole point. And so to get clients, you need leads. And to get leads is not necessarily an easy thing. And today, in today's episode, we're going to dive deep into how to use AI tools together with NA10, which is my favorite automation tool, to get leads, meaning look through relevant people on LinkedIn and reach the information about them so you know who they are and create relevant personalized outreach messages that drive engagement that lead to a much higher conversion rate, which leads to clients, which is what you need in order to run your business.

1:55And so if you are in business, that should be of a high interest to you because, as I mentioned, this is the bloodline of every single business. And our guest today, Anna Bui, has built a really cool, amazing automation that does each and every one of the steps that I just described previously, basically getting you from I don't have anybody to I have a conversation with a long list of people. Now, in addition to that, Anna is a clay.com expert, and she has a course where she teaches people how to use clay. Those of you who don't know Clay, it's an incredible tool that does a similar process. It does, shows you how to, or it allows you to grab people from different sources and then you reach the information and then reach out to them.

2:37So if you're asking yourself, why does Anna need a separate process other than Clay? Well, first of all, I will let her explain probably more in detail. But the main reason is Clay is expensive. And unless you get a very good return on ROI on your clients, then there might be a different way. And it also is a good entry point. So the process she developed allows you to do this without risking more or less anything. It's practically free to use NA10. And with a few tokens running through the APIs, you can do the process, figure out how effective it's working for you, finesse it, and then go to Ana and take the course on how to implement it on Clay.

3:12So with all of that in mind, I am really, really excited to welcome Ana to Leveraging AI. Ana, welcome to the show. in the next few years AI technology will change our world dramatically whether you are a business executive trying to catapult your business forward or just somebody who refuses to be left behind and want to advance your career this is the show for you I'm your host Isar Metis a serial entrepreneur and an AI enthusiast you'll hear invaluable practical tips from innovative business leaders AI practitioners, and some of the brightest AI minds in our world today on how you can leverage AI in ethical ways to advance your career and grow your business.

4:03Thank you so much, Isara, and so glad that everybody joining our LinkedIn line right now when I'm going to show you the workflow and going to dive deep into not just the technical, but also all the problem and all the headache I got from building this flow as well. and just a little bit about myself I was working as a project manager where I tried to dab into automation just to make the team life easier at my previous company and then I got that I got into like you know Zapier and make very popular and then I make a shift to NNN and I become literally an NNN enthusiastic I finally become recently an NNN creator officially so you probably will see my my verified profile later on but basically I got I become a coach at clay boot camp where I teach people and show people skill and how to use to like clay as a tool and now we're doing automation especially with anything because it's also my expertise as well so I'm really looking forward to continue our session now and then later on when people if anyone having any questions regarding clay or automation I love to answer them as well awesome and relating to the people who are joining us live on LinkedIn and or on Zoom.

5:13Feel free to ask questions. So I'm monitoring the chat on both conversations. If you have any questions, please just pop them in the chat. I will get them to Anna's attention and we're going to answer all your questions. Thank you obviously so much for being here. I'm sure you have other stuff to do on Thursday morning, afternoon night, depending where you are in the world. And with that in mind, it would be great if you introduce yourself in the chat, just say where you're from, what are you looking to get out of the session, just so that people can you can meet each other and network as well and share links to your LinkedIn if you're on Zoom and so on.

5:42But Anna, let's get started. Let's dive right in. Yeah, I'm just gonna share my screen quickly. Please do. Okay, perfect. So this is my workflow. First of all, you can grab this workflow on this template right here. Right now it's a three, but I'm trying to build more and more. But here is the workflow. This is a template where you can just go in and grab this for free this is this exactly is it is this workflow but i just add data to the dummy data to that one but again the what this workflow does okay i had to scale it back a bit when um i joined clay bootcamp natan lippy which is our ceo he encouraged me to post more on linkedin i never touched linkedin before i never like when i have a profile i never touch it and i post anything and since i like okay i'll start posting just the things that i built like the n8n workflow or my opinions when it's regarding to you know the platform itself or like different automation i just you know using the platform to showcasing my knowledge and what i've built because i think is i don't have any thought i was just like here's what i did and the impact that's from all the posts and then the encouragement from all from everybody but in my linkin post is definitely overwhelming hence i was building this work for just because okay i have not like a viral post i have a quite popular post and i now consider linkin is a warm lead right i believe it's you in sale you probably agreed with me that that is that is your nurture sequence that is where you nurture people that's where you interact with people that's where prospect find you that is basically where people heard about you and they come to you a lot of inbounds coming in so i got this somewhat popular post i'm not gonna say it's popular but basically it's have this is this one so i have uh 79 people interacting like interact with me this is all the reaction and I was like okay I have all these people in interested in the um in what I in my post so I was just wondering let's just consider them as leads let's consider them at least and then from because they are indeed people that you know interact with my post so maybe some of them would probably enjoy my content or probably become my ICP again I'm not I'm not a business owner so So this is all like, you know, I'm just taking myself into a position of an agency owner or a small business owner.

7:58And my ICP are people that are like small business or small agency or solo entrepreneurs that need help with automation. So that would be my ICP. Hence, from that, I just want to say, OK, all the people that interact with my post, they maybe like 50 something percent of them are probably my ICP. So how can I actually capture them and then nurture them from so far and so forth and probably become a prospect and then closing the deal with all those sort of things? That was my initial thought with this workflow. Any questions? Anything, Isar? No, so two cents. First of all, on a very high level, what is NA10 or make the tools that you mentioned?

8:33So they are tools, automation tools have been around for a very long time, right? So I've used Zapier the first time around 2015. So it's about a decade. And what automation tools know how to do is they know how to move data from one software to the other. So you have a contact on LinkedIn. You can grab the information, put it in your CRM. You can take the data from your CRM, put it in an Excel file, then in your outbox, right? It's like these kind of things. What these tools did not have any ability to do is to reason, think, research all the things that AI is good at. And now the combination of these traditional automation tools together with AI capabilities make them superpowers if you know how to use them.

9:08Two cents about Make versus Zapier versus N8N. Make is probably the easiest to use. Zapier is a step above that with more capabilities, but mostly a lot more connectors. But to be fair, at this point, it doesn't really matter because you can almost connect anything you want one way or another. And N8N is more of the geeky brother of these two tools. On one hand, the learning curve is steeper, like it's more complicated to use. And that's why it shouldn't probably be your first entry point. but because the way it's built, it's significantly more flexible. You can do a lot more things with it. You're practically almost unlimited with what you can do with it because it can run code and it can get any kind of web hook.

9:49And the other reason why it's becoming very, very popular is that it's open source and there's a huge community who's developing additional capabilities for it and you can self-host it, meaning you can actually grab the code, host it on your own hosting server, and then your data doesn't go to make or to Zapier, it just stays in the box that you're hosting. And it makes it a lot cheaper because now you're paying$6 a month for hosting. It doesn't matter how many automations you're actually running. So there are many different reasons why to use NA10. That being said, I wouldn't start with NA10 just because it's more complicated unless, Anna, you disagree.

10:23I agree with you. I started with Zapier. I started with Meg. And thank you so much for scaling things out. I was just like jump right straight in. No, no, no. So now we can jump straight in. And you can actually go through the steps on how this thing works. But I completely agree with you. The reason why I avoid Zapier, because it tends to get more expensive. Yes, it's had a lot more connector. It's going to make your first step into automation a lot easier because it's very straightforward. It's just step-by-step like a waterfall. It can do a lot, but it's going to get very expensive the more workflow you're trying to run.

10:54So that's why I, same as you, with everything that you just list, that's why I choose NNN as my favorite automation platform. You can self-host it or you can have these cloudings as well. and one of the things I probably in the future you'll probably see more and more is the MCP server you can host the MCP within any within the cloud or like or like having on your local on your local hosting itself so it's definitely a very powerful platform but I one of the things that people keep reaching out sorry before we jump into the flow people reach out to me and ask how can they start with NNN I think the best way to start is just grab one of the templates that you see on the community, they have more than 4 ,800 templates.

11:38So just you can search it out. And then even with your tech stack as well, just grab a template and play with it. And the more you play with it, the more you feel that it's very straightforward. The community of NNN is very powerful as well. Everybody contributing. You will see a lot of people posting about their workflow, about their problem. And everyone that I ever encountered that enjoyed building things on NNN, they're very kind. So please feel free to just reach out to them asking a bunch of questions. But I highly recommend you just jump straight in and just start playing with the template.

12:08It doesn't have to be perfect because they are templates. You're supposed to modify them to suit your need. So the more you build the workflow that solves your own problem, the more you learn it very quickly. A hundred percent. I'll add one more thing and then we'll dive into the actual flow. When you do grab these templates, you don't have to use the entire template. So you can consider the building blocks of the template. Say, oh, this segment, these four steps, they do this thing as part of the 20 steps that the automation in the template do. And I can use these four steps somewhere else because I need to do this one step for a different thing.

12:41So this way you can mix and match between different components and different templates, knowing very, very little and still building something that works because you're taking these Legos that somebody has already built into a part of a solution and combining them together into a complete solution. But now let's dive into our actual workflow. We definitely given enough intro to this. Yeah. So the workflow is going to split into three sessions. We have the trigger right here, and then we have the whole process, as you can see, starting from here. And then this is the end. Like the end result is going to have the record created on your Airtable or whatever database storing you use.

13:16For me, I use Airtable because it's very straightforward and easy for beginner. So basically, these are the two Appify actors. So this Appify actor is served to scrape the link in post for you like the strip the link in reaction for you is basically a like a little special e that going like unlock the door of link in and like okay this is post reaction let's just unlock it get all get everything else like a little teeth because well we all know how link in be very particular with people with automation and scraping so they are like a little teeth unfortunately and there's two actors i'm using so the first one is okay i have my post with 79 reactions, how would I be able to get the profile of those 79 people?

13:59And here is the one that I use. Oh, sorry. This is the first one. So this is, yeah, reaction. Oh, sorry. It should be this one. My apology. So this one is the Lincoln Port Reaction Scraper. So this is your Appify console. And I'm just going to show you two things that you just need to look into. Just one second. To those of you who don't know, Appify is a platform and you can get multiple different tools that are all API-based that you can use in automations and applications that you're building and so on. And they are geared to do things that are very, very specific. So you go into Appify and then you can find, again, many of these different things.

14:37This particular one knows how to scrape LinkedIn for post reactions. Yeah, thank you so much. Yeah, Appify has a lot of different API, like endpoint like this. So you can even find like, okay, Google Map and vendors or like Instagram followers, like all those sort of things. And they have a lot of, you know, actors, like serve specific need. But for this one, we're focusing on the LinkedIn. And we have the, first of all, the LinkedIn post reaction scraper. So we scrape the reaction up that particular LinkedIn post. The information that you probably need to see is that the, first of all, the input parameter.

15:13So basically we need to send this parameter to the endpoint. So basically you go to input and you will see here, it had like this. And this information for post URL, you can find it in here. So when you go to this link, this particular number is here, is the unique number for this link in post. So you add it there. So this Appify will start extracting the reaction from this one. So again, for those of you who don't see, it's the end of the URL of your post, right? So those of you who are not watching the screen because you're listening to the podcast as you're driving, it's the end of the URL.

15:55There's like a long number character kind of like thing. You grab that. That's what Appify needs to know what post to scrape. Yeah, perfect. Right. And so basically this is, I'm just called, so this is, so, okay. In NNN, you have a lot of native node and all the native node I'm using in here is, you know, you request for an HTTP request, you have a wait node if, so this is our native node within NNN. And in the first one we're going to use is the HTTP request. So this is the endpoint or the URL to call this, to call this Appify actor. So you can see right here, it's actually the link to this endpoint.

16:45And then after that, you add your token. So when you opened an Appify account, you will have your unique token and you just add it at the end. So that means it's going to use a credit. It's going to use a credit of your account. And then as you can see here, I put money here. So you want to use a credit of your account to start using this actor. Does that make sense? Yeah. And then from here, we're going to send, as you can see here, we said, we send the, we add the port URL here and then the page number is one. So going back to this actor, the information that you would like to add in, as you can see here is page number.

17:22So page one return one to 100. So a reaction. So that means if you have a post at under 100, you just need to post that one or by default, then this will be the page number one. But if you have a very viral post, you have like 500 people interact with it, then you have to do like page three and four and so on. But to keep it simple, it's going to be one and it's going to extract all the 79 reaction from this LinkedIn post through here. As you can see, there's an interesting question on LinkedIn. Do these tools comply with the LinkedIn user agreement? Basically what LinkedIn allows you to do from their perspective?

18:03I'm not sure about that 100%. I just know that from my knowledge is whenever you go to Appify and whatever with LinkedIn, specific scraping actor sort of thing, try to use no cookie. So if you use no cookie, that means like whatever action, the action I'm doing right now, which is scraping the reaction from my LinkedIn post, right? Is from the person who built the actor itself, not from me, because I didn't associate my LinkedIn account with this scraping process. It's just an API call from another thing that the other person built that my LinkedIn profile didn't attach to it. Yes. So there's no cookie associated, nothing to kind of like track my behavior, if that makes sense.

18:42So that is why - So there's, what I will say is when it comes to LinkedIn automation, there are two aspects. One is what you can get off LinkedIn and it is very hard for LinkedIn to know who is actually doing it. Like Anna just said, there's no way for them to know who is actually scraping LinkedIn. Like they can try to block it, but they can't know it's you because it's coming in from a specific application. The other aspect of LinkedIn automation is if you want it to post on your behalf on your regular basis, and this is where you might get in trouble, right? If you're building automations to engage on your behalf automatically, this is where you might get in trouble because then it's you.

19:17It's engaging on your behalf. And then usually the best practices on these tools is just to use them at whatever levels they tell you that they checked and that are okay. it's still against LinkedIn's rules and regulations. Each and every one of these tools will tell you, okay, if you do no more than 30 a day, you'll probably be fine. And then you can decide what your level of risk to decide how much you want to push that envelope. Yeah. And I recently came on a call with Krushaka. I probably put your name, but basically he's like an expert in the Clay Bootcamp when it's come to LinkedIn outreach.

19:50And he told me from the very beginning, there will be a risk. there's always going to be a risk when it comes to LinkedIn outreach. It's not really something that LinkedIn promotes. It's not something that LinkedIn wants people to do. Like they'll sort of automation or, you know, interact on your behalf, all those sort of things. So there will be a risk if you decided to do a sort of LinkedIn outreach campaign. But that will be on your own risk and you probably have to accept that. For this one, go back to what Isar just mentioned, is an application from another person, etc. So I guess I was just being like hidden at all.

20:24Yeah, okay. So the first step, we used Appify to scrape the people who actually engage with the prompt. What happens then? What would that engage with the post? Sorry. Yeah, engage with the post. And after that, we have like 77 items coming in. It's like, oh my God, it's too much, too overwhelmed. So that is why we have this one, is a loop over item, which is another native loop. Sorry, another native node in NNN. It's basically split the thing in batches. So it's break down the 77 item to just do this thing one item at a time. So that means one item is going to run through this whole process.

21:00And when it's done, then the second coming in and the third coming in and the fourth coming in. So it's basically try to avoid over dry, like overflow, over flooding, sorry, over flood the system. and also let each person have time to, sorry, each of the items have time to actually process and go through whatever you desire the outcome. And so here is the first item coming in. I always recommend people to try this set note. So this is another native note in NNN. It's just basically to clean your data. So whenever you, how to say this, the more I work with this data and the more I work with scraping, I realized that you have to start cleaning your data from the very beginning.

21:42So that from to the very end, it's not going to get you a lot of headache. The same thing when you start building your play table, always make sure your data is clean. So I guess like for everybody who enjoy data and then data analysis or data strategist, you probably agree with what I'm saying. Let's explain in simple English what that means, cleaning the data in this particular use case. So we got scraped information from LinkedIn. We're now sending the first item, right? Because we're going to do this one by one. So the information that comes is profiling, first name, last name, company, you know, everything it knows that it can pick from that.

22:15What does the data cleaner do? Yeah. So this basically is just to split out, how do you say this? Split out the, as you can see here, the data, there's a lot of data here. They have like the profile picture, like everything like that. We don't need that. You only need what's important. So this is just basically clean it out and able to just give you what you desire. so i just want so profile urn i just i just want to get the okay i have to explain it a little bit we have the linkin url which is your own linkin and your own profile link yeah profile link and then you have the urn which is a unique number associated with your account with your linking account so the url can be changed like you see like with my name and i buoy something something maybe i can change this to a different name so the url can change but the urn will never change so that is um this is like a primary data if you want to do some sort of lookup so it's because it's reliable it's reliable data the ur end okay going back i just want a job title i just want a link in url i just want this and the name that's all i need for the next iteration of scraping to use which is just a very clean straightforward data so it's not going to mess up the system or like mess up the sequence of scraping because now it's like okay i'm not going to give you all this crazy thing.

23:34I'm just going to give you this full straightforward information right here. Perfect. So it does two things. A, it filters the lots of data you got into just the little data you need. And the second thing, it picks what particular type of data you're going to get. So like you said, the URL may not be the right thing. The URL is the right thing. So that's what I want to transfer forward as far as the ID of the person. So this is basically as the first step of everything else because this will set the stage to these are the attributes that I'm actually going to use moving forward in my automation.

24:06Awesome. Perfect. Great explanation. Next step. Yeah. I just want to add one more thing. The reason why I had to clean it from the very beginning because all this other node can actually use this for reference. So it's going to be like a very reliable data set. And then after that, I'm checking if there's any duplications in my um, in my air table. So as you can see right now, it's blank. So I'm just going to check. Okay. If it is actually blank. So this is mean that if there's no data, I'll say this is data. So that's means the data length. So that's, there's like information here is true. Then it's mean it's going to start creating, it's going to start with all this increment and then it is already exist.

24:48Then it's nothing to do, but basically just to summarize, this is the check note, just, it's just basically checking. if this person already on my database. If this person haven't, then okay, let's start with the next one. Yeah, makes sense. So now it's checked that, okay, this person haven't exist. That means it's going to create a new record. On this new record, it's going to have, it's going to add into all this field on Airtable here. It's going to start mapping it out. Right now it's nothing, but when it's run, you will actually see the information coming in. And it's, you know, this is Airtable node that with NNN is interrate in NNN.

25:23So it's very straightforward. You just kind of just map things out. And as you can see here with the URL, I just put it like this and it's reference the node. That's why you clean the data. So again, for those of you who are not watching and don't know Airtable, Airtable is like Excel with a better user interface. If you want, it's just a database where you can define rows and columns and you can put data in them. And to connect the two together, so now we have four pieces of information on each individual that we scraped. You basically connect the Airtable node and that will bring into NA10 the four column headers that already exist in Airtable.

25:56You can do the same exact thing with Excel. And then all you have to do is drag the parameters that you brought from the scraper and drag them into each and every one of the columns. So you drag the name to the name, the ID to the ID, the link to the link, whatever it is, and that's it. And that's how you set up. And now what's going to happen is as this first data comes in, it is going to check, does that data already exist? It's probably going to check based on the URN that we now know that's the unique identifier of the person. if that does not exist, it will create a new line in the database, or in this case, on the Airtable table.

26:30By the way, I'll say one more thing. You say, okay, why do I even need to check? Why do you care if there's duplicates? There's two reasons. A, duplicates are never a good thing when you're creating a database. But the other thing is, some of the few steps will actually send you to different large language models, which means you're going to be paying for tokens. And if you can minimize the amount of tokens you're paying, then you're just going to save yourself money and time. Oh yeah, definitely. I've been there and done that. I was scraping one person multiple times because I was testing it out.

26:56I highly recommend you have something to check to look up your data so you don't waste your time. Okay, now for the fun part. The reason why I have people asking me, okay, now you have this person. So from the previous Appify actor is give us this person profile ID and then the website, the URL, right? Now we actually need to enrich that. Now we actually have to scrape this person's personal link-in profile. Hence, we will move to this actor right here. So there's two actors used in this workflow. The first one is to scrape the reaction of the post, and the second one to actually enrich the individual profile that interacts with your link-in.