Full transcript
0:00What is going on everyone? I wanted to
0:02go over one of the agents that I have
0:03just finished building and is currently
0:06whirling away for me right now running
0:09every single day, nearly every minute of
0:11the day at the moment, which is quite
0:12cool. And this is what I call my low
0:16usage agent for customer success. So
0:20before I dive into the complexities of
0:22this uh kind of monster looking agent, I
0:25want to go over high level what this
0:27thing is doing. So first of all we have
0:31segment and postto. So for those of you
0:34that don't know segment is like a way of
0:36um transferring data between systems and
0:39we have all of our data coming in from
0:42segment through to atio and our data
0:45being anyone who signs up on the
0:47platform. So it's any users that we have
0:50inside of the kind of triggery e
0:52ecoscope is coming in through segment
0:55from our database and from postgress
0:57through into
0:59also then using postog which is our
1:01product analytics um
1:04uh kind of uh system to then push
1:07product analytics into
1:09as well. Super super seamless super
1:12super easy to do. From there, we then
1:15are using Atio basically just to store
1:17all of the data telling us the features
1:19that they've used, when they last logged
1:21in, all of that fun stuff which we're
1:23getting through from segment and post
1:25hog.
1:27So once we have that, we're then using
1:29NA10 to start to query ATIO essentially.
1:34And what NA10 is going to do is it's
1:36going to pull through individuals from
1:39Atio that haven't logged in in the last
1:4210 days. Then what it's going to do is
1:46it's going to go and look up these users
1:48inside of Postto to get more of a
1:50comprehensive overview than what we
1:53currently have inside of Atio. From
1:55there, we're going to use Claude to
1:57basically start to compile information
1:59based on how they've used the platform
2:01and use cases that they could use on the
2:03platform before we send them through to
2:05Loops, which is our marketing uh
2:07automation tool that we use and then
2:09we'll update Atio. So, let's dive into
2:12this uh system going through it. So, the
2:16first thing that we have is we basically
2:19query ATIO. So this is using Atio's API
2:22and what we're doing is every day we
2:24basically go and look and we say hey go
2:27find me any users that haven't logged in
2:30in the last 10 days. And so in this
2:32instance there's 77 users uh who are
2:35using triggery that haven't logged into
2:38the triggery platform in the last 10
2:41days. So what that basically means is
2:42like they haven't uh triggered what we
2:44call like a page view event um and uh
2:48therefore they've come up here. So then
2:50what we're doing is we're going into
2:52these users and getting more details. So
2:55we go get the their initial information
2:56via post hog and then what we start to
2:59look at is we look at the different
3:01events inside of post hog that mean
3:04something to us. So these are
3:05effectively for for for those of you who
3:07don't know what events are, these are
3:09effectively the different features
3:10inside of triggery that someone could or
3:12couldn't use as well as looking at more
3:15of like holistic overview with these
3:16page views. So we can see everything
3:18that they have or haven't done.
3:20What then happens is if we dive into
3:23this one a little bit deeper, you can
3:24see, okay, well this person so far has
3:26used this event 162 times. They've used
3:30the uh sync event six times. have not
3:32used social signals and they've done 15
3:35active days since they've logged in or
3:38since they created their triggery
3:39account. And then we can see the most
3:41visited pages, all of that fun stuff. So
3:44what then happens is we go through to a
3:46series of events uh or agents I should
3:50say. And the agents are doing a few
3:53different things. So the first agent is
3:55effectively our user and company
3:57research agent. So this is going to use
4:00uh funny enough the 4.5 preview search
4:03or sorry the four preview search from
4:05GPT to search the web and understand all
4:08the information that it can find on that
4:10person and on that company. It's then
4:13going to feed that through to the usage
4:16agent which you can see is now running.
4:18And this usage agent is basically going
4:20to take all of this data from Postto and
4:23start to understand what they have or
4:25haven't done on the platform. And it's
4:27going to chat to this triggery knowledge
4:30hub. This is another agent that I've
4:32created which is a trained agent on all
4:34of the features and their use cases and
4:37how they use them inside of Triggery.
4:40And what it's going to do is it's going
4:43to create a use case for that individual
4:47that they haven't already done yet based
4:49on reading everything that they've done
4:50inside of the platform. So an example
4:53would be like it's like hey you've not
4:54used this feature here's how I would
4:56think about using it and here's how I
4:58would use it. Because what then it's
5:01going to do is it's going to feed it
5:02through to this email agent which is
5:04then going to start to construct the
5:06actual uh uh kind of uh email itself.
5:10And so for example, you can see here
5:11it's like one monitor competitor
5:13engagement to find high intent
5:14prospects. Go to the engagement tab. Add
5:17a competitor URLs like Jasper AI and
5:19copy AI. Export engaged prospects to
5:22your CRM. Track buying intent through
5:24social signals. Click on the social
5:26signal setup in the left navigation.
5:28Upload your target prospect list.
5:30Monitor marketing workflow AI automation
5:32signals. And then lastly, identify pain
5:35points via our topic search. Um add
5:37keywords for marketing automation
5:39challenges. And then track discussions
5:41around content production bottlenecks.
5:43So it's super super specific to that
5:46user in terms of an actual use case on
5:49how they should be getting started or
5:51re-engaging with their Triggery
5:52platform. And then what it's doing is
5:54it's firing this over to the email
5:56marketing system that we use which is
5:58loops. And then it's also updating
6:01uh atio to say hey we just sent them an
6:04email and it adds a time stamp because
6:07one of the things that we're doing back
6:08here is when we query ato we are also
6:13making sure um that we're not emailing
6:16anyone that's been emailed in the last
6:1810 days. I.e. if this usage email goes
6:20out to them they still don't log in. we
6:21don't want to hit them the next day and
6:23the next day and the next day. So, there
6:24is at least a buffer between that. And
6:27this is uh what I think an awesome
6:31awesome agent to help me when I've got a
6:33a small team to stay on top of our
6:36customer base and continue to re-engage
6:38and engage with our customer user base
6:41with a highly personalized element. Um
6:44looking at their current like usage and
6:47data uh perspective.