How to Use Social Listening for B2B Marketing

0:00Social listening is one of the most

0:02underused tools in B2B marketing right

0:04now. Most companies either aren't doing

0:06it at all or they're paying 50K a year

0:08for it when [music] they don't need to

0:10be. Now, I use social listening to build

0:12my content strategy, track competitor

0:14moves, and root signals directly to my

0:17team automatically. And in total, the

0:20workflows I've created using it have

0:22generated meions of dollars in pipeline.

0:26They've helped triggery scale to over a

0:28thousand paying users. So, in this

0:31video, I'm going to give you the

0:32complete walkthrough of how to set up

0:34social listening for B2B marketing and

0:36then show you the three most effective

0:38use cases that you can implement today.

0:40So, let's get to it. So, before we go

0:43into anything, let me quickly explain

0:45what social listening actually means in

0:48a B2B context. Because a lot of people

0:50hear this term and think just of

0:52tracking mentions for your brand. It's

0:54bigger than that. [music] Social

0:56listening is the ability to monitor

0:58conversations happening across social

1:00media and then turn what you find into

1:03something your team can actually

1:05leverage in your marketing strategy. So

1:08that means you're tracking what people

1:09are saying about your competitors, what

1:11topics are trending in your industry,

1:13and what your customers are posting

1:15about and who's engaging with them for

1:17that specific content. The reason this

1:19matters is because if someone starts

1:21posting about a problem your product

1:23solves [music] or starts engaging with

1:26your competitors, that's telling you

1:27that they're in the market and without

1:30tracking it, you would actually never

1:32know that they are. And when you set it

1:34up properly, these conversations don't

1:36just sit in the feed, they get rooted

1:38directly to your sales team, to your

1:40marketing team, or even to your product

1:42team, depending on what signal you're

1:44looking for. Now, a lot of people think

1:46that there's just one signal in social

1:48media, but actually there's thousands.

1:50And I'm going to dive into a couple of

1:52those signals today. Now, if you were to

1:54look at a BTOC company, most of them

1:57have five or more dedicated roles for

2:00social data. They have content creators,

2:02social media managers, social insight

2:05leads, platform specialists, [music] and

2:07even editors. And they've been doing

2:09this for over a decade now because they

2:11figured out early that social data

2:13drives buying decisions. But in B2B,

2:16most companies don't even have one

2:18person thinking about it. And the reason

2:19for that comes down to the tools just

2:21simply haven't been available. For a

2:23long time, if you wanted to do social

2:25listening in B2B, your only option was

2:28to spend $20,000

2:31or more a year with tools like

2:34Meltwater, Brandatch, Sprout Social, and

2:37so on. And what you got for that money

2:39was keyword mentions, sentiment graphs,

2:42and share of voice reports. And the

2:44problem is no one cares about these

2:46dashboards anymore. They don't create

2:49content. They don't identify

2:50opportunities, and they don't build that

2:52competitive edge you've been looking

2:55for. They're great for presenting to a

2:57board, but they don't actually give

3:00anything tangible to work on from a

3:02day-to-day basis. And this is why

3:04outbound tools ended up getting all the

3:06attention and all the funding because

3:09your outbound is simply you send emails,

3:12you book meetings, you make money.

3:14There's a clear line between action and

3:17result. Social listening on the other

3:18hand has just been vague mentions,

3:22sentiment graphs, and vanity metrics

3:24with no clear connection to revenue. I'd

3:27know as I used to work for one of these

3:30companies. I know firsthand that they

3:33look great, but the value that you can

3:35actually take to a sales team, to a

3:38marketing team, or even to a product

3:40team is insanely hard to get out of them

3:43the way that they have been built, and

3:45they're [clears throat] just not built

3:47for modern-day agentic social listening.

3:50B2C has had this figured out for years,

3:53but B2B is just now getting access to

3:56right tools to make it work for them,

3:58too. And this is exactly why this

4:00matters so much right now because we're

4:03not just trying to collect data here. We

4:05want to act on it by rooting signals to

4:08the right team, fueling our content

4:10strategy, and connecting social activity

4:13directly to the pipeline. So, let me

4:16show you how to actually set this up.

4:18When it comes to social listening, most

4:20people either over complicate it or they

4:23don't go specific enough. That said, let

4:26me walk you through exactly how to do

4:27it. So, I'm going to be using Triggery

4:30for this, a tool I built that does

4:31exactly what we're about to walk

4:33through, which monitors conversations

4:36across multiple social media platforms.

4:39We're talking LinkedIn, Reddit, YouTube,

4:42Substack, Podcasts, Instagram. We're

4:44adding Tik Tok, uh, Stack Overflow,

4:47Hacker News, so on. It's multi-ocial for

4:51us to start to connect different data

4:53points together. The way you set it up

4:56is through boolean queries or by

4:58monitoring a specific channel. Boolean

5:00queries is just a way of combining

5:02keywords to narrow down exactly what you

5:05want to track. And how you build these

5:07search queries determines the quality of

5:09everything that comes after. So let me

5:11show you how I structure mine. So here

5:15we're monitoring a industry term or an

5:17industry search inside of the triggery

5:21platform across X. What I'm doing is I'm

5:23basically monitoring anytime someone

5:25talks about pl code linked to marketing.

5:28There's been a massive push for people

5:30using this recently and I want to make

5:32sure that I'm tapping into everything

5:34that's going on with regards to this

5:37because I feel like there is a direction

5:39for triggery to go down when it comes to

5:42our MCP and everything that we do here

5:45inside of [music] claw code. Now, if I

5:47was to remove marketing, the challenge

5:49with this is this just becomes quite

5:51vague right now. So, by adding marketing

5:53into the mix, I'm making it a lot more

5:56specific. I can take this even further

5:58by saying, okay, I want to find anytime

6:02someone is talking about claw code

6:04marketing and skills related. So maybe

6:07they're building claw code marketing

6:10related skills that either I could use

6:12or again gives me that product insight

6:14or intelligence that I need to have to

6:17create a potential product strategy out

6:20of the back end of that. What I can also

6:22do is I can also go ahead and actually

6:23monitor a specific profile URL or

6:26particular channel or a podcast or

6:28whatever it is as well and again just

6:30get notified instantly as these mentions

6:33or as these posts basically come out.

6:35Without Boolean logic, you'd get random

6:38mentions of every clawed code coming in

6:41under the sun, which is thousands,

6:43hundreds of thousands on Twitter. And we

6:45want to make sure that it's insanely

6:48specific for our use cases. The

6:51beautiful thing about boolean is it also

6:53allows us to get a little bit creative.

6:56So, let's just clear this and let's say

6:58I wanted to find anyone talking about

7:00something that would have relevance to

7:02me in my business, which is a social

7:04listening tool, right? I could now enter

7:06in something such as social, let's add

7:09it into here, social listening or social

7:12signals, and then link these keywords to

7:15marketing, for example. So, now what I'm

7:18doing is I'm finding anyone talking

7:19about these keywords that has a level of

7:23high intent. Without this again, it's

7:25just going to pull back very vague and

7:27very generic content. Now, that's not

7:30always a bad thing. Sometimes you do

7:32want to cast the net pretty wide. And in

7:35that case, we then can go ahead and use

7:38agent to actually qualify the data and

7:41use obviously their intelligence to tell

7:44us whether this post is relevant or not.

7:47So sometimes going wider can be a play,

7:50but typically boolean allows us to get

7:5390% of the way and then we can use AI to

7:56overlay over the top of that data to

7:58save costs. Once you have your searches

8:00built out, the next step is organizing

8:02them into certain categories because

8:04this keeps everything clean as you start

8:06to scale it. You might want to think

8:07about the different categories and the

8:09different searches that you can do. For

8:11example, you'll have multiple searches

8:13around brand monitoring. This tracks

8:15anytime someone mentions Triggerfy or

8:17our product directly as a platform. The

8:19second could be around competitor

8:21tracking. So these are searches where

8:23I'm monitoring anyone either talking

8:25about our competitors or anytime our

8:28competitors themselves are actually

8:30talking as well. And the third bucket is

8:33industry keywords. These are the broader

8:36topics that matter to our space. Things

8:38like intent data, social signals, and

8:40B2B marketing automation. And the fourth

8:42is thought leaders. Think of these as

8:44influencer. This means I'm monitoring

8:46specific people in our space to see what

8:49they're posting about and who's then

8:51engaging with their content. For

8:53example, I I follow a lot of marketing

8:55influencers that post a lot around

8:57automation, social data, and signals.

9:01And there is a crossover between people

9:04engaging with that type of content who

9:05would be interested in mine as well. One

9:08thing I want to call out here is how

9:10important it is to monitor beyond just

9:13LinkedIn. I actually tend to find that I

9:16get most of my traction on Twitter and

9:19Reddit. I've got different flows built

9:22up. For example, anytime people are

9:24talking about high intent topics, my

9:26sales team get a Slack notification to

9:28go get involved in that Reddit thread.

9:31Anytime people are engaging or

9:32commenting on things inside of Twitter,

9:34the same principle happens. But we also

9:37have an enrichment step that basically

9:39takes that Twitter handle, converts it

9:41to a LinkedIn, allows us to get their

9:43data, and then obviously reaches out

9:45from there. The other really thing, and

9:46I'm going to talk about this in a

9:48second, is podcasts and longer form

9:51content, a hugely rich data sources to

9:54act as context for agents. Do you want

9:56to train your agent on everything about

9:59social listening? or feed it a podcast

10:01which has so much written context which

10:04is gold for an agent. Maybe you want to

10:07feed it constant Reddit threads about

10:09people talking about XY Z. You can

10:12really use this to spot gaps for organic

10:15content strategies, training agents. The

10:17use cases are endless. So when you're

10:21setting up your listening, make sure

10:22you're not limiting yourself to just one

10:25platform. And if you want to follow

10:26along and start building these searches

10:28yourself, you can try out Triggery

10:30totally for free. You get 250 free

10:33credits on our pay as you go plan. I'll

10:36leave a link at the top of the

10:37description. Now, let's get on and show

10:40you the three most common use cases I'm

10:42seeing where this data actually drives

10:44results. First up is the competitor

10:47intelligence layer. The first use case

10:49is monitoring your competitors. And this

10:51is the one I tell every B2B marketer to

10:54start with because it gives you

10:56information you generally can't get

10:59anywhere else. What I'm doing here is

11:01I'm tracking every time someone mentions

11:03a competitor. You can see I'm monitoring

11:05for tools like Brandwatch, Mench, Brand

11:0724, and just general social listening

11:09tools. I'm monitoring for anytime

11:11they're talking about a feature, how

11:14they're complaining about a feature, how

11:16they're comparing products in our

11:18category. And because we're monitoring

11:19across multiple platforms, I'm not just

11:22seeing polished LinkedIn posts. I'm

11:24seeing the unfiltered conversations

11:26where people are being honest about

11:28what's working and what isn't. That's

11:30what makes this different from how most

11:32teams do competitive research. Effective

11:35social listening shows you what

11:38customers are actually saying, their

11:40frustrations, their workarounds, and the

11:42features people wish existed. [music]

11:44So, let's have a look. Here we have a

11:46competitor monitoring thread on Reddit.

11:49And ironically, this actually is a post

11:52about monitoring Reddit at scale. So you

11:56have people again talking about

11:58different things that they can do inside

12:01of here. So here they're talking about

12:03social listening tool in general, which

12:06is obviously one of the keywords that

12:07I'm monitoring linked to certain

12:09competitors. [music] Here we have a post

12:11talking about brand 24. Again, all of

12:13these posts are bringing in relevant

12:16content for me that's being spoken about

12:19across Reddit, but I also have this set

12:21up across X, across LinkedIn, obviously,

12:24YouTube, and podcasts. On top of direct

12:26feedback, I also use this to track how

12:29competitors are positioning themselves.

12:32So, if a competitor suddenly starts

12:33posting about a new feature or a new

12:36angle, I see it immediately instead of

12:39finding out weeks later. that gives me

12:41time to respond. Whether that means

12:43adjusting our messaging, creating

12:45content around the same topic, or

12:47doubling down on a differentiator

12:49content strategy. So, how I'm doing that

12:52is I'm actually monitoring the founders

12:54profiles as well. So, here we can

12:57actually track anytime they post on X,

13:00anytime they post on LinkedIn, we can go

13:02and then track all of that data coming

13:05back. So, let's just pick out a random

13:08individual here from a CEO. So, again,

13:11here I have all of the content coming

13:14out around what they're doing. January

13:16was focused on planning blah blah blah.

13:18We ship seven product features, new

13:21Chrome extension that allows

13:23rearrangements, purchasing credits, blah

13:24blah blah. So again, what I do is I have

13:27an agent that's summarizing any feature

13:30releases, any major developments that my

13:32competitors are talking about and then

13:34feeds that back to me into a Slack. The

13:37moment they post, I get that

13:38information. I know what they're talking

13:39about. The second use case is the

13:42content strategy. And this one

13:44completely changed how I think about

13:46what to post and the biggest problem B2B

13:49marketers have with content, which is

13:51figuring out what to talk about and how

13:53can you get your team to start posting

13:56more. Right? It's ghostriting. It's

13:58employee advocacy. You end up sometimes

14:01staring at like a blank page or posting

14:03about corporate nonsense that's

14:05literally no one cares about. I think

14:07the world is starting to realize that

14:09personal brand is way more powerful than

14:12a corporate brand and social listening

14:15eliminates the guesswork because you can

14:17actually see what topics are trending

14:18within your space. You can train agents

14:21to understand those topics. You can

14:23train agents to understand your

14:25conversation style or your posting style

14:29and then you can get them to start

14:31posting about stuff for you. So, how

14:34does this work in principle? Well, so

14:36what we're doing is we have to provide

14:38agent with context to help us create

14:41content. So the first thing that I'm

14:42doing is I'm monitoring my own posts.

14:46Now why am I doing that? Because the

14:48first and most key part of giving an

14:50agent context is the style of writing

14:53that I have done the last several

14:55months. And this is going to go into an

14:58agent, and I'm going to show you in a

14:59second how it works, and train that

15:01agent on the style of writing. The next

15:04step is to set up the thought leader or

15:07the industrybased searches. I do both.

15:10So the first thing that I'm going to do

15:11is I'm going to follow people that talk

15:13a lot around interesting topics that are

15:15very relevant to what my business does.

15:17So I'm going to monitor prolific

15:19marketing people like Kai, Kyle Coleman,

15:21Sam Jacobs, and so on. So many more

15:23people that are again great content

15:26style. They're talking about really

15:28interesting things. They have great

15:30hooks that the agent can also learn

15:31from. and so it can start to understand

15:33what type of content it should post

15:35about the styles and the hooks that we

15:37should use. The next is feeding it

15:39topics that are relevant to my business.

15:42So I've got if we have a look here if I

15:44type in intent topics I've got two

15:46different searches that are running

15:47running around cold email intent data

15:49employee advocacy found brands

15:52influencer creating strategies creator

15:54programs so on and so on. This data is

15:56then going to feed through to an agent

15:59if it gains over a certain amount of

16:01traction. That way, my agent is being

16:04trained on the most viral topical

16:07content that can it can physically get

16:09its hands on. And this isn't just solely

16:12on LinkedIn. This is also across

16:14Twitter. This is also across podcast.

16:17And it's hoovering up all of that

16:19information. So, let's show you how it

16:20does that. We covered step one, which is

16:22setting up the social listening search.

16:24Step two is then building out a

16:26workflow. And we actually have some

16:27simple templates actually just to let

16:28you do this. Content agent one, content

16:31agent two, and then we have an agent

16:33idea generator. So let's have a look at

16:35how this works. First of all, we

16:38configure the posts, right? And so

16:40simply what we're going to do go here is

16:41we're going to go ahead and set up

16:43multiple different posts feeding through

16:46into an agent node. This agent node has

16:49quite a complex query or a boolean built

16:52out. In short, what we're wanting it to

16:54do is we're wanting to analyze the

16:57storytelling, the topics, the style,

17:00everything that's involved in how this

17:02post was written. And we're going to

17:04feed the industry post into here and the

17:06thought leader posts into here as well.

17:09And what it's going to do is it's then

17:10going to save this information into its

17:12own database. So you can see here I have

17:16writing styles, I have the content

17:18ideas, and I have like the content

17:20agent. So in here, I'm going to feed

17:22through the content ideas because this

17:25is the interesting ideas that I want to

17:27basically send through. So what happens

17:29is anytime any industry search comes in

17:32across those different socials, the

17:34agent qualifies and works out why is it

17:36interesting, what topics, how, what are

17:37they writing about, and then it saves

17:39that information into this database

17:41here, which moves us then on to step

17:44two, which is the next workflow. In the

17:46next workflow, we're then going to use a

17:49topic agent. The topic agent is

17:52basically going to use that database.

17:54So, it's going to use the content ideas

17:57and it's going to pull them out. All

17:58right? So, it's going to basically be,

18:01okay, I've already analyzeed these

18:03topics. I'm going to go ahead and find

18:05the most relevant topic that I've not

18:08written about before and pull that

18:10through as a use case for uh something

18:13to write about. It then sends that topic

18:16through to this agent here. And you can

18:19see that we're then giving it the post

18:21topics that was generated by that

18:23previous step. Now, one thing that I

18:25haven't covered yet is I have another

18:27workflow like the first one that's just

18:29monitoring my posts and those

18:32individuals that I think write really

18:33well. And that information is being

18:36saved to this writing style. And so what

18:38it's going to use, it's then going to

18:40use the topic and use the writing style

18:42database to then create me a post. And

18:45all of this then happens like clockwork.

18:48Every single day this thing runs and I

18:51get a notification into my Slack channel

18:54with a post to post that day. I'll then

18:56pair it attach potentially with a video.

18:58I'll then pair it potentially with an

19:00image. And this has helped me get over 1

19:04million impressions in the last 3 months

19:07alone purely [music] from doing this on

19:09LinkedIn. Everyone at Triggerfly is then

19:11tasked with posting two to three times

19:13per week. And this is how we keep the

19:15content pipeline full without becoming a

19:18bottleneck for the business. The third

19:20use case is brand and sentiment

19:22monitoring. And this is about protecting

19:24what you've built and staying on top of

19:27how people perceive you. So it's very

19:29very simple here. What I'm doing is I'm

19:32monitoring anytime someone is talking

19:35about my brand. If we have a look at

19:37this, I have so many different searches

19:39set up across podcasts, LinkedIn X,

19:43YouTube, Reddit, you name it. I'm

19:45monitoring my brand. So, let's go have a

19:48look here. For example, we can see that

19:49there's been um some recent mentions

19:51inside of YouTube, which is actually

19:53just our own company, which is cool.

19:55That's all good. Let's go have a look at

19:57another one. Again, 10 mentions recently

20:00on X. So, we can pull this up and have a

20:03look at the mentions inside of here. We

20:05can see people talking about things that

20:06we had. Amazing. This is so cool. So

20:09then what I need to do is I'm not going

20:11to have time to sit and monitor this

20:13every single day. And this is where

20:15legacy tools fail. So what I'm going to

20:17go do is I'm going to go build a flow to

20:20action this data. And it's going to be a

20:22very simple one. It's going to be a

20:24brand mention or sentiment tracking flow

20:26that I'm going to create. So what this

20:28is doing in short is anytime any mention

20:32that I get across any social media that

20:35data or that text is going to be fed

20:38through to a sentiment agent. This

20:40sentiment agent is then I'm going to

20:41monitor is triggery mentioned positively

20:44in this post or not. The issue with how

20:46most sentiment analysis works is it's

20:49done at a post level. So it takes

20:52positive and negative keywords. The

20:54challenge is is sometimes you might be

20:55mentioned positively but the overall

20:57post is negative. And so this is where

20:59we take it a step further and we can use

21:01agents to qualify that. If it is

21:03positive, we create here a signal called

21:06positive mention. And what's cool is we

21:08can go ahead and build subworkflows off

21:10the back of any time a signal has been

21:12created. If it's negative, we're then

21:14going to have this post analyzed by an

21:17agent. I'll typically use probably

21:19either the 4.6 six sonet model for this

21:22or maybe even the opus as well. And what

21:24we're doing is we're looking to see why

21:27this company was mentioned negatively

21:30and if they can summarize this for us in

21:32under 50 words because I'm going to be

21:34forwarding this to the executive team.

21:36It then summarizes it. It then sends it

21:38to the executive Slack channel that we

21:41have and then we generate a negative

21:44signal which triggers another flow which

21:46goes into more of a triage workflow that

21:49the marketing team would then work with.

21:51And this is how we're keeping our finger

21:52on the poles on how we're being spoken

21:55about across the board. But this is also

21:58how fashion brands are using us to root

22:01certain bits of information, questions,

22:04queries brands are getting negative,

22:05positive all across the organization.

22:08Certain B2B brands are using us that get

22:10tons of traction on X and different

22:12socials like that. You know, when your

22:13platform goes down, you need to respond

22:15to things in [music]

22:16real time. And this is the workflow

22:19that's going to enable that to happen. I

22:21think the key thing is what makes this

22:22different from just setting up like a

22:24Google alert is that sentiment layer.

22:26Using agents to go deeper is huge for

22:30us. And so that means whether the

22:31customer is happy or sharing a win, if

22:34they're frustrated with a feature or

22:35maybe they're comparing us to a

22:37competitor, that context matters. And

22:39that's why the agent matters because it

22:41determines what happens next. I

22:43mentioned I've got subflows built off

22:45the back of this. If it's a positive

22:47mention that goes into a new flow where

22:50we reach out, thank the individual for

22:53talking about us in a positive way. We

22:55can automatically give them more credits

22:57if we want to through an automation. We

23:00also can go ahead and collect the

23:01engagement that the individual collected

23:03of that post to try and push them into

23:06that. And then the same thing is

23:07happening over on the negative side

23:10where we're actually tracking how people

23:12are saying, what they're talking about

23:14and then triaging it accordingly. This

23:16makes it so that nobody on my team has

23:18to sit in a dashboard scrolling through

23:20posts. The relevant signals find their

23:22way to the right people automatically.

23:25So, let's talk about social listening at

23:27scale. Now, let me show you what this

23:29looks like when you take this guide and

23:32run it at scale. So, a few months ago, I

23:35set out to understand what people

23:37actually think about Triggery versus our

23:39competitors. and not just the surface

23:41level stuff you find on review sites.

23:43The unfiltered conversations, the gaps,

23:45the frustrations that people only share

23:48in casual social posts. The volume was a

23:50challenge here. We were looking at over

23:5217,000

23:54posts [music] across competitors in 30

23:57days. There's no world where my team can

24:00manually read through all of those posts

24:03to find patterns. So, I built a

24:05three-step system to handle it. Step

24:07one, I set up Triggery to track our

24:10brand and every major competitor. Now,

24:13in this use case, they're not

24:14necessarily a competitor, but they had a

24:16competitive feature, and that is NA10

24:19and Zapia. So when we went to launch our

24:23workflow feature, we wanted to

24:25understand how does everyone talk about

24:28workflows. What are the challenges? What

24:31are the pains? What are the advantages

24:33points? You know, literally every

24:36nittygritty when it came to talking

24:38about those particular use cases. Step

24:41two, I ran the analysis on the incoming

24:43post and embedded the insights into a

24:45vector database so that the data isn't

24:47just collected, it's organized in a way

24:49I can search and retrieve later. Step

24:51three, I use retrieval on top of that

24:53database. So I pull out the specific

24:55insights through simple queries. So

24:58instead of reading 17,500

25:01posts or whatever it was, I can actually

25:03question about what top complaints or

25:06certain features or what are people

25:09generally wanting and requesting from

25:12these tools. I could go ask it what

25:14features existed within a certain

25:16category and I could get the answers

25:18back backed by substantial social data.

25:21And you can see here the vector database

25:23that I'm referring to is the database

25:26memory banks that we have pre-built

25:28inside of triggery. So a lot of

25:30challenges with this is you think okay

25:32vector databases are really hard to set

25:34up but in triggery it's as simple as

25:36creating a node and adding it and that

25:38creates a vector DB for your agent to

25:40use going forward. For those of you that

25:42don't know, a vector DB is just a great

25:45way of using what we call like a rag

25:47retrieval to pull information out of

25:49large embedded data. So in short, the

25:53agents monitored over 17,000 tweets

25:56about people talking about Zapier and

25:58NA10. They analyzed the product requests

26:00that they were asking for, the

26:02disappointments, [music]

26:03anything that was related to the feature

26:06set that those tools have. From there,

26:08they embedded that data, as we can see,

26:10inside of the database. And then I have

26:12an agent every week giving me the

26:14insight and pulling that information out

26:17of there whenever I wanted to. We

26:19discovered three major feature gaps that

26:21our competitors were ignoring. These

26:23gaps showed up in over 400

26:25conversations, but they never appeared

26:27in user research, product reviews, or

26:30even NPS surveys. They were buried in

26:32casual social conversations where people

26:34were being honest about their

26:36frustrations without even realizing that

26:39they were giving us an unbelievable road

26:42map. And the system runs daily. Every

26:44day analyzes new data and has the option

26:47to automatically create tickets for our

26:49product team. So this isn't something I

26:52ran once and forget. It's actually

26:54continuously feeding us intelligence.

26:56Every time we have up and cominging

26:58features that we're thinking about, we

27:00set this up. We just duplicate the work

27:02and it starts giving us the gold [music]

27:04dust and it tells us what to build next.

27:07A lot of the time as well, what content

27:08to create and where the opportunities

27:11are. The data is sitting there literally

27:13in front of you in public conversations,

27:15but most companies just aren't listening

27:18in the right [music] places. And even

27:19the ones that are listening aren't doing

27:22anything with what they hear. So, if you

27:24want to start using social listening in

27:26the most effective way possible to drive

27:28your marketing, you can try Triggery by

27:31clicking the link in the description.

27:33It's the first one there. You get 250

27:35free credits to start with. It's totally

27:38free to go and use and you can have your

27:40first set shut up within minutes. Again,

27:42links in the top of the description. And

27:44if you want a deeper look at how I use

27:46AI to book over 160 sales calls a month,

27:49then go watch this