Full transcript
0:00Strap yourselves in, guys. This is
0:02probably one of the most insanely crazy
0:05agents that I have built. And we're
0:07using three tools for this. Triggery,
0:10Clay, and NA10. Now, what if you could
0:14create personalized lead magnets at
0:18scale? And so, what I mean by this is
0:20let's say you reached out to 100 people.
0:22Imagine if you could create a 100
0:24different lead magnets. that changes
0:27based on the job title, the company, and
0:30the type of content that that individual
0:33is engaging with.
0:36So stay with me and we're going to dive
0:38into this one. So first things first,
0:41what we're doing is we're using triggery
0:43to monitor anyone engaging with a
0:45particular piece of content. Okay. So
0:48here I am sending through leads of
0:50people engaging with particular thought
0:52leaders where they're engaging with
0:55particular content within that thought
0:56leader and it goes through to Clay via
0:59an evergreen campaign which in layman's
1:01terms means it never stops running.
1:04Anytime this particular thought leader
1:06that I'm monitoring posts, I then track
1:08that data and send it through to Clay.
1:12Well, you can start to see it coming
1:13through here. We get so much bits of
1:16data with uh this particular table and
1:19we don't use any credits which is cool.
1:22So first things that we're doing is
1:23we're just kind of fleshing all of this
1:24out and then we start to do uh an ICP
1:28check. Are they based in the location
1:29that I want them to be? Yes or no?
1:34Then is the post that they're engaging
1:36with containing data or does the post
1:40contain information around social
1:43signals, ABM strategies, sales
1:45automation, personal branding? So things
1:47that are relevant to me from triggery.
1:50If they are, then I want you to go and
1:52run some research on why this individual
1:56would would be engaging with that type
1:58of content based on what that post is
2:00about. and then understand if we could
2:03build on that post that they've already
2:05liked, how could we write a kind of
2:08research paper or a lead magnet based on
2:11that? So, for example, this one was
2:13like, how can B2B sales consulting firms
2:15help organizations overcome ABM platform
2:18adoption challenges to ensure successful
2:20implementation and value realization?
2:23And this kind of changes, right? Like
2:25depending on the type of post they're
2:27doing. Here's another one. How can AI
2:29powered personalization in B2B outreach
2:32be leveraged to optimize website visitor
2:34conversion rates and sales efficiency?
2:37So what happens is this basically
2:40triggers um what we call the HTTP API
2:44here which basically just sends this
2:47data straight through to this particular
2:50NA10 uh workflow via a web hook. Now
2:54what then happens is a sequence of
2:57events and we're using 1 2 3 4 five
3:00agents inside of this particular flow.
3:05So first of all that particular subject
3:08so if we go back to this one how can AI
3:10powered uh personalization power B2B
3:12blah blah blah gets sent through to this
3:14which is the query builder and I've
3:16already ran one and this one was on how
3:18you could use trigger fire to book 30
3:20meetings. So we'll go with that for the
3:21example. But the idea is that that
3:24information that Clay has just found
3:25gets sent through to this which is the
3:27topic. From there, this agent comes up
3:31with four different um conversations or
3:34kind of research questions that it needs
3:36to do. And that's all that this agent
3:39does. It just researches this particular
3:41topic and breaks it down into four
3:43subtopics.
3:45From there, it then sends it through to
3:47what we call the research leader. the
3:50research leader will create the initial
3:52research um for that particular
3:56individual or those particular subjects.
3:59And so how this then basically looks is
4:01it takes those four topic areas which is
4:03the ones that we looked at previously
4:05and then starts to run some deep
4:06research on those topic areas. From
4:10there, it goes through to what we call a
4:12project planner, which then starts to
4:14create the title, the subtitle, the
4:16introduction, the different chapters
4:19that are going to be involved in this
4:21research paper slash um lead magnet. And
4:25so, think of it like this. The project
4:26planner takes all that research that was
4:28just previously done and then works out
4:30what are the chapters that are needed
4:32and then it works out the prompts that
4:34are needed for that chapter. Because
4:38then it sends let's say those five
4:39chapters through to this team of
4:41research assistants which then takes the
4:45prompt and the chapter title and has all
4:48of the context about the article itself
4:50and then starts to run deep research on
4:54that chapter. And one of the key things
4:56that we're doing here is we are
5:00basically giving it the context which is
5:03hey we are writing this chapter the
5:05previous chapter was on XY Z the next
5:08chapter is on XY Z here is the prompt
5:11for you for this particular chapter and
5:14then it runs that research and then
5:15compiles it for you where it then sends
5:19it through to this next uh editor here
5:21which is the agent which just kind of
5:23combines it all together. It kind of
5:25checks grammar. It checks the style of
5:27writing. It makes sure that it copies
5:30that particular flow and so much more.
5:33And it also writes it in the language of
5:36the prospect themsself. So we know based
5:39on the location and the content and the
5:43post language that the individual is
5:45engaging with what language they speak
5:47and then the magnet is then written for
5:49that individual in that language should
5:52you wish to have that kind of option
5:53turned on.
5:55Some key things that we're doing here is
5:57we're using Triggery's knowledge base.
5:59So I have a trained model that is
6:02basically trained on everything that I
6:03do. So it uses that to help answer any
6:05triggery based questions if I'm wanting
6:07to use Triggery in the lead magnet. And
6:10we're also using Perplexity's deep
6:12research here.
6:14Once the editor has done its work and
6:17its magic, it creates a Google document.
6:20It adds that text to the document. It
6:23then makes this document sharable and
6:25then gets me the link where it then
6:27sends this data back to Clay.
6:31Um, let me pull up the right one
6:34with the documentation. So, here's the
6:36share link, which is really cool. And
6:39here are the individuals coming through
6:40that it needs to then send back to. And
6:42then you basically can just reach out to
6:44those individuals with that share link.
6:47And so you can say like, "Hey Christian,
6:49here is the um here's like the lead
6:52magnet that I've compiled or whatever
6:53kind of copy that you'd want to do." But
6:56this then works on clockwork for you.
6:59Anytime someone says yes, they would
7:01like to see more information. Or you can
7:02just chuck this in the very first
7:03message if you're reaching out via
7:05LinkedIn, whatever. And it is 100%
7:08personalized to that individual. Now, if
7:11you want this framework, let me know
7:12below. I will share it with you. the
7:14clay table, a full video breakdown on
7:18YouTube as well on how to run this thing
7:20and the NA10 template and how to set up
7:23trigger fire in order to do this. Just
7:26let me know.