Episode Summary
Garrett Sussman joins David Bain to discuss how AI search engines now personalise results for each individual user, requiring marketers to shift from broad keyword targeting to persona-specific content strategies.
Key Takeaways
- 1AI search engines now generate unique answers for each user based on implicit context like search history and Gmail, and explicit context provided in natural language queries, forcing marketers to create persona-specific content rather than generic keyword-targeted pages.
- 2Large language models already recommend different brands based on persona attributes: software engineers see SoFi and Ally; other professions see different banks, meaning brand positioning now determines who you are recommended to.
- 3Measurement is probabilistic, not deterministic: track trends over time using repeated prompt sets for different personas, and accept that individual results will vary even for the same person.
- 4Create search twins by setting up Gmail accounts populated with persona interests to test what AI search engines produce for your ideal customer profiles.
- 5The shift from broad keywords to hyper-personalised search is already happening: people no longer search for running shoes, they ask for grey running shoes in a specific price range that last four years and suit their lifestyle.
Contents
- Introduction
- What Is Hyper-Personalisation in Marketing?
- How Is AI Search Changing the Way People Search?
- What’s the Difference Between Implicit and Explicit Context?
- How Does Google Personal Intelligence Work?
- Should You Create Separate Content for Each Buyer Persona?
- How Do You Measure AI Search Visibility?
- What Are Search Twins and How Do You Use Them?
- What Tools and Tactics Work for Hyper-Personalisation?
- What This Means for Your Marketing
Introduction
Marketers now face the responsibility to market to an audience of one.
AI search engines like Google Personal Intelligence, ChatGPT, and Perplexity synthesise answers tailored to individual user context, making the traditional approach of targeting broad keyword phrases increasingly ineffective.
When two people type the same query into an AI search engine, they receive completely different results based on their search history, location, Gmail content, and even past purchases.
Garrett Sussman’s Research and Tactics
This episode of the Pump and Funnel Marketing Show explores how iPullRank‘s Director of Marketing, Garrett Sussman, approaches this shift.
Garrett has conducted real studies showing that large language models already recommend different brands depending on who is asking, and he shares practical tactics for testing and measuring AI visibility across different audience segments.
What Is Hyper-Personalisation in Marketing?
Hyper-personalisation means delivering content that requires the specific context of who someone is in the moment they are searching. Rather than creating one landing page that attempts to serve everyone interested in a topic, hyper-personalisation tailors the message and information to the individual.
This fits into every stage of the customer journey because AI search can be an entry point at awareness, consideration, or decision stages.
At any point when someone tries to solve a problem, discover a brand, or compare options, they will leverage AI search, and what works for one person will be different from what works for another.
Identifying Critical Customer Attributes
The challenge for marketers is understanding where the overlaps are for their business. For B2B companies, hyper-personalisation might focus on jobs to be done and firmographics. For e-commerce brands, it centres on demographics and use cases.
The goal is to identify the most important attributes that give information about customers and how they think about a product, then create targeted messaging across the entire customer journey.
How Is AI Search Changing the Way People Search?
The generalised keyword is becoming a relic of the past. When people interact with a large language model in natural language, they are not searching for running shoes.
They are asking for running shoes that are grey, in a specific price range, that last for four years, have great reviews, and are ideal for their specific lifestyle and location.
Content must match natural language queries, not generic keywords
“The way that people search isn’t going to be generalised…so we need to rise to that moment and make sure that we are creating content that imitates and resonates with the way that people will be and are now searching.”— GARRETT SUSSMAN, DIRECTOR OF MARKETING, IPULLRANK
This shift means marketers can no longer rely on capturing a broad audience with generic content targeting high-volume keywords. Instead, content strategy must focus on the specific questions and contexts different personas bring to their search behaviour.
The approach applies across industries. A lawyer serving a local market will need a very different content strategy from a national bank, and both differ from a SaaS platform serving multiple segments.
The common thread is the need to understand who the business is ideal for, who it can realistically address, and then build a content strategy from that foundation.
Garrett’s rapid-fire preferences reveal his marketing approach across ten key strategic decisions.
Quick Response Round Results
| Question | Answer |
|---|---|
| Annual strategy or quarterly strategy? | Quarterly strategy |
| In-house or agency? | Agency |
| Single channel or multi-channel? | Multi-channel |
| Paid ads or SEO? | SEO |
| Email marketing or social engagement? | Email marketing |
| LinkedIn or LinkedIn? | |
| X or X’d off? | X |
| Human or automation? | Human |
| Raw content or polished content? | Raw content |
| Data-driven or creativity first? | Both |
What’s the Difference Between Implicit and Explicit Context?
AI search engines use two types of context to personalise results. Understanding the difference between them explains why two people searching for the same thing see completely different recommendations.
Implicit Context: Data AI Already Has
Implicit context is information the AI system knows about a user without being told. Location has always been an implicit signal: someone searching for a grocery store in the US receives different results from someone in the UK. That level of personalisation is not new.
What has changed is the depth and range of implicit signals. AI search now draws on search history, meaning five previous searches about family members will contextualise a query about streaming services, prompting family-related recommendations even when the word family is never mentioned.
Google Personal Intelligence extends this further by pulling from Gmail transactional emails, Google Photos, Calendar, and past searches to build a profile of interests and behaviours. This happens automatically, without the user needing to supply the information in each query.
Explicit Context: What Users Tell the AI
Explicit context is the detail a user provides directly in the query itself.
A search that includes “I’m going on a trip to this location for three days at this time of year and I only want to bring one bag” supplies explicit context that shapes the answer, separate from anything the AI already knows about the person asking.
Natural language queries allow users to provide far more explicit context than the old two-to-four-word keyword phrases ever could. This makes the search more specific and the answer more tailored, but it also raises the bar for content creators who need to address that level of detail.
The balance between implicit and explicit context determines how AI search delivers results. Marketers must create content that resonates whether someone has supplied rich explicit detail in their query or whether the AI is relying more heavily on the implicit profile it has already built.
How Does Google Personal Intelligence Work?
Google Personal Intelligence is an opt-in feature connected to AI Mode in Gemini. It allows the AI to leverage data points from across Google’s ecosystem to deliver more personalised search results.
The feature looks at Gmail, Calendar, Google Photos, and search history to provide outputs based on that context. Garrett ran an experiment comparing a blank slate account with his own personal Gmail account, and the recommendations were starkly different based on implicit context alone.
What happened when I tested hoodie recommendations?
“When I connected to Google Personal Intelligence and I said, recommend the best hoodie for me…it went into my Gmail. It pulled out my transactional emails for when I purchased hoodies and said, oh, because you like Aviator Nation and because you like Reigning Champ brand of hoodie here, I recommend those.”— GARRETT SUSSMAN, DIRECTOR OF MARKETING, IPULLRANK
This demonstrates how AI search is already using personal data to make brand recommendations. The system identified two specific brands, Aviator Nation and Reigning Champ, from past transactional emails, then suggested similar brands the user had never mentioned.
Garrett also conducted a study with Profound that looked at how large language models recommend brands based on explicit persona context. When he framed queries around specific professions and income levels, the AI recommended different financial services brands.
Software engineers were recommended SoFi and Ally, which are brands aligned with tech-thinking individuals, while other professions saw entirely different banks.
This means brand positioning directly influences who a business is recommended to. How a brand has positioned itself in the market determines whether it appears in AI-generated answers for specific personas, and this effect will intensify over time as AI systems become more sophisticated at matching brands to user profiles.
Should You Create Separate Content for Each Buyer Persona?
Many SaaS platforms serve multiple audience segments. A project management tool might appeal to freelancers, agencies, and enterprises. The traditional approach has been to create one landing page that attempts to speak to all three, but AI search changes the calculus.
If a freelancer visits a SaaS product and the messaging speaks to everyone, they are more likely to choose a competitor who speaks directly to them. The same applies to agencies and enterprises. Generic content that tries to serve every persona often resonates with none.
Garrett recommends experimenting with persona-specific landing pages and content. The concern that these pages will compete against each other in search results is less relevant in an AI search environment, where large language models surface the best content for the individual in their specific context.
This does not necessarily mean splitting into three separate brands. It means creating individual content that speaks to a specific persona at the right stage of their journey. Over time, AI systems will become better at surfacing the most contextually relevant option rather than a generic page.
Building a Persona-Driven Content Strategy
The decision depends on market research and positioning. Who is the business genuinely ideal for? Who can it realistically compete to serve? Once those questions are answered, a content strategy can be built around the personas that matter most, with targeted messaging for each.
This is partly a brand exercise, partly market research, and partly a positioning exercise.
The answers differ significantly depending on whether the business is a local lawyer, a national bank, or a SaaS platform, but the underlying principle holds: create content for the specific people you want to reach, not for everyone at once.
How Do You Measure AI Search Visibility?
Tracking AI search visibility is not like tracking traditional search engine rankings. The same person will receive different results each time they run the same query, making measurement probabilistic rather than deterministic.
Marketers face a similar challenge to attribution. There has always been a desire to know exactly how someone arrived at a website, which touchpoints influenced their decision, and what marketing activities drove the conversion. That has always been messy, and AI search makes it messier.
Why tracking AI recommendations requires a probabilistic approach at scale
“It’s probabilistic. Even the same person’s gonna get different results each time. But at scale, if we have a good sense of the types of ways that they’re searching, can you start to get a sense of are you more likely to be recommended and is the sentiment more likely to be positive?”— GARRETT SUSSMAN, DIRECTOR OF MARKETING, IPULLRANK
The approach Garrett recommends is to create a framework that is repeated over time, then look at trends to see how visibility changes. This requires precision in the method rather than absolute accuracy in each individual result.
Marketers should develop a set of prompts representing the types of searches their personas are likely to make, then run those prompts repeatedly across multiple AI platforms. Track whether the brand is mentioned, whether the sentiment is positive, and whether recommendations are increasing or decreasing over time.
At scale, patterns emerge. A single result means little, but a hundred results across different prompts and personas reveal whether the marketing activities being undertaken are positively influencing the metrics that matter.
This requires locking into audience research and understanding what is important to the types of people searching for the business. What are they interested in? How do they phrase their questions? What context do they bring to their searches? Track these over time and measure trends, not individual data points.
What Are Search Twins and How Do You Use Them?
Garrett’s number one tactical recommendation for hyper-personalisation is to create search twins. These are Gmail accounts set up to mimic the interests, behaviours, and attributes of ideal customer personas.
The process involves creating an account and populating it with the interests of a specific persona. If the target customer is a software engineer interested in finance, the account should reflect those interests through subscriptions, searches, and other signals that AI systems use to build implicit context.
Once the account is set up, use it to run searches in AI platforms and observe what recommendations appear. This provides a manual, in-the-weeds way to see what personas are seeing in AI search, without needing expensive tracking tools or complex measurement frameworks.
Testing Brand Visibility Across Personas
Search twins allow marketers to test whether their content is surfacing for the right personas and whether competitors are being recommended instead. They also reveal what other brands are positioned effectively for specific audience segments, providing competitive intelligence that would be difficult to gather otherwise.
This tactic is practical, inexpensive, and immediately actionable. It requires time rather than budget, and it provides direct insight into how AI search engines perceive a brand’s relevance to different personas.
What Tools and Tactics Work for Hyper-Personalisation?
Garrett uses AI to make his own job easier, particularly for generating outputs, gaining insights, and receiving feedback that allows him to do more and do it more effectively. He specifically mentioned Claude as a tool he leverages regularly.
For dictation, Garrett is obsessed with Whisper Flow, which allows dictation with a layer of AI to improve quality. This lets him provide far more context than typing allows, and because he is a natural talker, it suits his working style.
He believes dictation will allow people to communicate more effectively than the shorthand of text, acronyms, and emojis that has dominated digital communication.
Exploring Connected TV Advertising
Looking forward, Garrett wants to test connected TV advertising. He has noticed that after searching for something, he and his wife see related ads appear on streaming networks at home.
CTV ads are the programmatic equivalent of display advertising but delivered through smart TVs during streaming content. The format is becoming more popular and allows targeting specific audiences on different streaming networks, similar to YouTube ads but as part of the home TV entertainment experience.
Marketers Worth Following
Amanda Natividad, who works with Rand Fishkin at SparkToro, is publishing her first book and coined the term zero-click marketing. She has a way with words and provides a real, raw perspective on navigating the intersection of AI and humans.
Angela Skane recently moved in-house to Network Solutions after working across agencies. She is a TikTok marketing expert and spoke at SEO Week about using the psychology of TikTok ads for SEO and AI search. She has emerged quickly and is producing innovative ideas.
What This Means for Your Marketing
The shift to hyper-personalisation and AI search is not a future trend to prepare for. It is already happening, and marketers who continue to rely on generic keyword targeting are becoming less visible with every passing month.
Start by locking into audience research. Understand who the types of people searching for your business are, what is important to them, what interests them, and how they phrase their questions. Then create content that speaks directly to those people at each stage of their journey.
Accept that measurement will be messier than traditional SEO, but that does not mean it is impossible. Track trends over time using repeated prompt sets, and focus on whether your brand is being recommended more often and with more positive sentiment.
Create search twins, test what AI search engines show your ideal personas, and adjust your content strategy based on what you find.
Read the Full Episode Transcript
[00:00] Garrett Sussman: All of a sudden you have the responsibility to market to an audience of one, which is somewhat impossible. You really do need to lock into your audience and your market research and understand who are these types of people who are searching for me? What is important to them? What’s interesting to them? And track it over time.
[00:25] Garrett Sussman: What is hyper-personalisation? How is hyper-personalisation changing search? And why is this so important in the age of AI? That’s what we’re discussing today with Garrett Sussman from iPullRank.
The Pump and Funnel Marketing Show with David Bain.
[00:43] David Bain: Hey, it’s David. Now way back in 2007, I delivered my first ever digital marketing training session. Ever since then, I’ve been on a journey to help professional marketers pull together every disparate marketing activity into one unified model, the Pump and Funnel Marketing Model. Garrett, where does hyper-personalisation fit into the pump and funnel marketing model?
[01:05] Garrett Sussman: David, great to talk to you. So, we know the pump and funnel is really holistic marketing that you’ve been talking about. At any different entry point, you can start to search and become aware of a brand. And so I always think about hyper-personalisation in the context of AI search and the direction that we’re going. And so it fits into every part of the entire pump and funnel marketing framework in that at any given point when someone’s trying to solve a problem, when they’re trying to discover a brand, when they’re comparing brands, they will leverage AI search. But what works for you is gonna be different than what works for me. And so we need to make sure wherever they enter that we’re hitting them with the right content that resonates for a specific individual in the context of AI search. And it’s never been more tricky to do that.
[01:58] David Bain: So if you were asked to define hyper-personalisation, what would you say?
Garrett Sussman: Well, hyper-personalisation is this idea of whatever type of content you’re looking for in the moment is going to require the specific context of who you are. And so therefore we need to be able to target the messaging and the information for the individual to make it the most valuable in that moment.
[02:21] David Bain: So how do we get so personalised with our messaging if we’ve got perhaps multiple personas and a broad audience that we’re aiming our content at?
[02:35] Garrett Sussman: Well, I think that’s the tricky thing is you really need to do that much more research and dial in who are those personas. There are going to be generalisations where you can start to segment how you’re talking to people at different parts of the journey. But once you start to create this matrix of the different types of messages at the different stages of the journey for the different types of personas, then you can start to make sure that AI search, for instance, is pulling in that right message in that right moment. Stay on top of it and deliver it.
[03:10] David Bain: So essentially you’re talking about having a hyper-personalised persona for each stage of the buyer journey.
[03:18] Garrett Sussman: Yeah, I think it’s really about understanding where the overlaps are for your business. The hyper-personalisation can focus on, for instance, if you are a B2B, you might be focusing more on jobs to be done and firmographics. Whereas if you are offering an e-commerce product, B2C, then you might be focused more on specific demographics and use cases. And so identifying across those individual personas, what are the most important attributes that can give you information about your customer and how they think about your product, and then create campaigns and targeted messaging across that entire customer journey so that when they do search in an LLM, is that specific type of content going to be framed for your audience in a sentiment that positions you as the best solution or the most, the best to educate someone?
[04:12] David Bain: So it’s certainly appealing to produce a piece of content that’s hyper-personalised towards a particular persona and really resonates with someone, but I guess many marketers will be scared to cut themselves off from many other potential viewers, readers, consumers of their content. So what would you say to people that say to you, marketers who say to you, well, it’s all very well doing that, but when we had a generic keyword phrase that we’re targeting to rank on Google, then we were potentially building this big audience and getting many people to our website. Can we not still do that? Will that not give us a better opportunity still?
[04:54] Garrett Sussman: Well, there are two parts of it, I think, right? Like, we all, when we think about who our total addressable market is as marketers for our brands, first off, there’s this responsibility to understand our business model, right? Like, you want to be everything to everyone, but that’s not great. Not many businesses can do that. So you’re niching down to begin with. Like, who are people who are actually going to ultimately be buying your product? And then it’s out of your hands. Like, if you have people who are changing the way that they start to search for information about your topic or information about your individual niche. Or, you talk about generalised keywords, before you know it, people aren’t going to be using generalised keywords. Like, that is a relic of the past when people interact with a large language model in natural language. I’m not going to be just looking for running shoes. I’m going to start looking for, well, I need running shoes that are grey, that are in this price point, that last for 4 years, that have great reviews, that are ideal for dads who live in the Northwest of the US.
[06:10] So the way that people search isn’t going to be generalised anyway. And so we need to rise to that moment and make sure that we are creating content that imitates and resonates with the way that people will be and are now searching.
[06:18] David Bain: So I can understand from your example there, from someone selling running shoes on an e-commerce website that a certain size of running shoe in grey might be quite likely to be bought by a male, and maybe a smaller running shoe in other colours might be more likely to be bought by a female. And you can be pretty sure who your product is going to appeal to. But how does that work from a B2B perspective? If you’ve got maybe a SaaS platform and you do have multiple types of people purchasing your product, do you have to create multiple pages of different content aimed at different personas in order to give yourself the best opportunity? Or is that going to be competing against each other? Those different pages are going to be competing against each other in search.
[07:08] Garrett Sussman: Well, you need to take a step back and look at your brand. I did a study with Profound recently, and I looked at personas over the course of 12 months with different B2B services, like a bank for instance. And when I put in explicit content around, hey, I make this much money and I’m a business owner versus I make this much money and I’m an electrician. What is the best bank for me? It started making recommendations based on that explicit context. Like, when I said it was a software engineer, it would start recommending like SoFi and Ally, which are brands aligned with tech-thinking individuals. And so we need to start to understand that how we have positioned our brand is going to influence how we’re recommended to specific personas, and that’s going to get more hyper-personalised over time, like it already exists.
[08:10] So I don’t think it’s as easy as, hey, create this landing page. I think it’s partially a brand exercise, a market research exercise, a positioning exercise. It depends on the market, like if you’re talking about a lawyer in a certain location, that’s going to be a very different kind of research journey and content creation journey than a national bank. And can you compete? Who can you compete with? How can you compete with them? So I think you need to do the research of who is your business, who are you ideal for, who can you address? And then start to build out a content strategy based on that.
[08:46] David Bain: So going back to this SaaS example, so say you offer a SaaS and it appeals to at the moment freelancers, agencies, and enterprises. Are you saying that moving forward it may be a good idea to actually split that up into 3 different brands so you can talk specifically for the audiences as opposed to offer your service to multiple different personas?
[09:17] Garrett Sussman: Yeah, I mean, think about it. If you are a freelancer and you go to the SaaS product and the business is talking to everyone, are you more or less likely to go to a different SaaS product who’s speaking directly to you? And so I think it’s clear. I think the way that we do it, we’re gonna have to figure out a way that we don’t cannibalise each other. But I would anticipate in the future that LLMs, Google, ChatGPT, Perplexity are going to get better at surfacing the best content for you that solves their specific problem in their context. So I think it’s something you should experiment with in your niche. But yeah, absolutely. You should be creating individual content that speaks to a specific persona.
[10:01] David Bain: And how’s this changed specifically because of AI? And what is AI doing to change the way that you would recommend considering this as a marketer?
[10:15] Garrett Sussman: Yeah, so I think there’s a lot of changes that are happening. You look at Google and they’re moving towards this idea of Google Personal Intelligence, which is an opt-in feature connected to AI Mode in Gemini where it can actually leverage different data points implicitly. So historically, we know Google’s leveraged your location. That’s not new for anyone. If I’m looking for a grocery store in the US and you’re looking for one in the UK, I would expect different results. That sort of personalisation already exists. It’s just going to get more hyper-personalised because, for instance, with Google Personal Intelligence, it’s going to look at your Gmail, it’s going to look at your calendar, it’s going to look at your photos, it’s going to look more intensely at your search history and give you a more specific output based on that context. So it’s already happening.
[11:07] I ran an experiment where I compared a blank slate account with my own personal Gmail account, and the recommendations were starkly different based on that implicit context. So you have to expect that this is going to become the norm going forward.
[11:25] David Bain: You mentioned the word implicit a couple of times there as well. What’s the difference between explicit context and implicit context?
[11:42] Garrett Sussman: It’s a great question. So implicit context is the data that whatever LLM already has about you, in the sense of your location. Wherever your device is, you don’t need to tell Google that, hey, I’m in Virginia. It knows that about you, right? So more implicit context is your search history. I already searched for these 5 different things, so Google already knows that my intent is contextualised by those prior searches. I don’t have to say, if I’m looking for a streaming service for my family, if I’ve looked, if I’ve made searches around the fact that I have a daughter, you’ll start to see things like Google making—if I ask for what is a streaming service recommendation, it’ll actually reference family-related streaming services because it knows that I have a daughter from those past searches. That’s gonna be your implicit context.
[12:39] Now, if I tell it, hey, I’m going for a trip to this location and I’m gonna be there for 3 days and this time of year, and I only wanna bring one bag with me, that’s explicit context that I need to convey to the search for a better result than implicit context that it might just take from me from the, from those data points that’s part of this profile that I have already provided. So we’re gonna see this balance between at some points LLMs like Google is going to take implicit context and then it’s also gonna leverage the way that we naturally ask questions for a more specific answer, this explicit context to ultimately provide a completely different search experience than we’ve been used to historically.
[13:29] David Bain: Got you. So essentially you’re saying that LLMs, AI systems, are building implicit personas based upon previous search behaviour to deliver personalised results.
[13:39] Garrett Sussman: Yeah. And it’s beyond search behaviour. This is the things like looking at your Gmails. For instance, in my experiment, when I connected to Google Personal Intelligence and I said, recommend the best hoodie for me, because I love my hoodies, it went into my Gmail. It pulled out my transactional emails for when I purchased hoodies and said, oh, because you like Aviator Nation and because you like Reigning Champ brand of hoodie here, I recommend those, but you should also check out these brands that are similar that I didn’t explicitly say.
[14:15] David Bain: So obviously this means that for the same query typed into an AI search engine, whatever you want to call it, Claude, Gemini, if a particular query is typed in by two different people in two different places, a completely different result would be received by each person. What does this mean for marketers? And how can marketers—what can marketers do, if anything, to actually try and ensure that their content is received more often than other content?
[14:50] Garrett Sussman: It’s overwhelming, right? Like, it’s really scary because all of a sudden you have the responsibility to market to an audience of one, which is somewhat impossible. We’re all such complex individual people. So that’s why you really do need to lock into your audience and your market research and understand who are these types of people who are searching for me? What is important to them? What’s interesting to them? And track it over time. It’s not going to be perfect when we are looking at tracking for AI outputs. It’s probabilistic. Even the same person’s gonna get different results each time. But at scale, if we have a good sense of the types of ways that they’re searching, when we look at a whole bunch of prompts in that topic for these different types of personas, can you start to get a sense of are you more likely to be recommended and is the sentiment more likely to be positive and what can you do to move the needle so that you’re recommended more and the sentiment is more positive? And this is going back to the pump and funnel, a more holistic approach to marketing.
[16:05] David Bain: And how do marketers measure this? How can marketers start measuring AI visibility across all these different audience segments?
[16:11] Garrett Sussman: I think it’s hard. I think we need to make sure that we—what we are doing is precise and hope that the accuracy is a leading metric, that you have a framework that you do repeatedly over time and you look at trends to see how it changes, and then you have hypotheses on what you can experiment and try to change. But it’s not going to be, we have the same issue with attribution, right? Like we desperately want to know exactly how someone got to our website and made the decision and all the different touchpoints in our marketing that influenced them to buy from us. And it’s messy. It’s gonna continue to get messy. It’s going to be messier, but that doesn’t mean that you shouldn’t try to track and measure at a high level. And see how it’s changing over time and what your marketing motions are doing that is positively influencing the KPIs that do matter.
[17:08] Find out more and download your free copy of the Pump and Funnel Quick Start Guide over at pumpandfunnel.com. We’ll see you at pumpandfunnel.com. Now it’s time for the second part of the show, delving into Garrett’s marketing journey to date and uncovering his top marketing tips. Starting off with work window. Garrett, can you give me an insight into your current working life? What differentiates the way you operate from others?
[17:33] Garrett Sussman: I love trying to leverage AI to make my job easier. I know it leaves a bad taste in marketers’ mouths, but I feel like you can responsibly and maturely use all these great tools like Claude to ultimately get outputs and insights and feedback that can make you, allow you to do more and do it more effectively.
David Bain: I’d like you to look back on the early stages of your marketing career. What didn’t you do so well? What do you wish that you would’ve done differently?
[18:03] Garrett Sussman: I wish I had more agency. I wish I did more stuff and failed and did more stuff and failed. I think that learning from experience is so critical. It took me too long to get into marketing in the first place, and now I wish I’d been doing this for the past 20 years.
[18:19] David Bain: Staying with your early working life, what’s one thing you did in the past that’s fast-tracked your future marketing success?
[18:25] Garrett Sussman: I did try some new tools. I’ve always been a tech enthusiast, and I think it’s really valuable to have a growth mindset where you can try new things and see how it can improve you and then mix and match and move forward.
David Bain: So this is the quick response round. 10 quick questions. Just 2 rules here. Try not to think about the answer too much, and you’re only allowed to say the word both on one occasion, so use it wisely. Are you ready?
Garrett Sussman: Do it.
David Bain: Annual strategy or quarterly strategy?
Garrett Sussman: Quarterly strategy.
David Bain: In-house or agency?
Garrett Sussman: Agency.
David Bain: Single channel or multi-channel?
Garrett Sussman: Multi-channel.
David Bain: Paid ads or SEO?
Garrett Sussman: SEO.
David Bain: Email marketing or social engagement?
Garrett Sussman: Email marketing.
David Bain: LinkedIn or LinkedIn?
Garrett Sussman: LinkedIn.
David Bain: X or X’d off?
Garrett Sussman: X.
David Bain: Human or automation?
Garrett Sussman: Human.
David Bain: Raw content or polished content?
Garrett Sussman: Raw content.
David Bain: And data-driven or creativity first?
Garrett Sussman: Both. Yay!
[19:30] David Bain: I didn’t think you were going to use your both, but you went for the very last one there. Raw content or—sorry, data-driven or creativity first. Both of them. Do you wish you’d used your both earlier?
[19:41] Garrett Sussman: Which is impossible to be both. No, I’m actually, I’m actually surprised I didn’t have to. I, that was like the only this or that I actually felt like, okay, I can stand behind these.
[19:53] David Bain: And I assume that’s in-house or agency was comical for you because you work in an agency.
[20:01] Garrett Sussman: I—well, what’s even more comical is I’m the in-house role at an agency because I don’t do as much client-facing. See, I’m like literally marketing the agency. So yeah, it was a conundrum.
David Bain: Share a lesser-known martech tool that’s bringing you a lot of value at the moment and why that tool is important to you.
[20:18] Garrett Sussman: We were just talking about this, but Whisper Flow, I am obsessed. I, so it allows you to dictate, but like a little bit of AI, so you get quality outputs. And I’ve become a dictation machine when it comes to working with all my tools. I can provide so much more context than text, quickly. And I’m a talker, so.
[20:38] David Bain: Yeah. You just wonder if typing is gonna become a thing of the past.
[20:43] Garrett Sussman: I don’t think that’s a bad thing. Had a quick conversation with, the idea of like, we’ve been so used to writing text, LOLs, acronyms, emojis, I think talking will allow us to communicate more effectively with each other, and that’s a good thing.
[20:55] David Bain: Yes. Yeah. And I’m not the best typer. I, I don’t know why I always type out the word the, T-E-H, it just comes out wrong and just have to correct it. Do you want to correct it to the? Yes, of course I do. The other one doesn’t exist. But I, I’ve tried Whisper. I really did like it. I’m not using it for absolutely everything, but it’s a great recommendation. So moving on from something that you currently use to something that you’re going to use. What’s one marketing activity or tool that you haven’t tried yet, but you want to test soon?
[21:27] Garrett Sussman: I mean, my—so it’s funny, my wife and I will search something and then we’ll see it come up in a CTV ad, like you’re watching at home on a streaming network. And we’re like, how, what, how does it know us? I want to try CTV ads. I really kind of want to make a big commercial and have it beamed in directly to you. We still watch commercials, right?
[21:50] David Bain: So for those that don’t really know the acronym CTV, what is it?
Garrett Sussman: I don’t know, it’s, commercial TV. I don’t know, it’s, I’m an awful marketer, but it’s basically—it’s the programmatic commercial that shows up for a smart TV, specifically like networks. It’s basically the in-house TV version of programmatic display. And it’s becoming more and more popular where you can test on different streaming networks, the equivalent of like a YouTube ad that’s shown to a specific audience but as part of your home TV entertainment experience.
[22:29] David Bain: Great, great description there. That’s exactly what I was looking for. It’s actually connected TV. That’s what it stands for.
Garrett Sussman: It’s new, we’re trying it.
[22:37] David Bain: Exactly. Now let’s move on to credit a couple of people who’ve helped you along the way. Name 2 marketers who’ve inspired the way you do things? Firstly, an established marketer, and secondly, an up-and-coming marketer. What can we learn from them and where can we find them?
[22:50] Garrett Sussman: Amanda Natividad is publishing her first book. She is working with Rand Fishkin at SparkToro, and she has a way with words. She’s on LinkedIn and she gives a very real and raw perspective that I think’s fair in a world that is navigating AI and humans. And she coined the term zero-click marketing, which is the way that we’re all kind of interacting with our digital media, and what that ultimately means for marketers. And then the second is—
David Bain: No, go for it. Yes. Yeah. The second.
[23:26] Garrett Sussman: Yeah. Second is Angela Skane. Angela has been working across a bunch of different agencies. She went in-house recently to Network Solutions, and she’s also a TikTok marketer. I’m not a big TikTok marketer myself, but she joined us at SEO Week and gave a really great presentation around using the psychology of creating TikTok ads to, kind of use those TikTok ads. Yeah, no, TikTok ads for, for SEO and AI search. And I think she has a lot of really cool ideas and she’s kind of just come out of nowhere and is crushing it.
[24:11] David Bain: Garrett, what’s your number 1 tip that you shared in the Pump and Funnel marketing show?
Garrett Sussman: Number 1 tip is to, in terms of hyper-personalisation, to start to create search twins, to create an account in which you can leverage all of the interests of your ideal persona, bake it into a Gmail account, and start seeing what the AI search LLMs are producing, as a kind of manual in-the-weeds way to get a sense of what your personas are looking—are seeing in AI search.
[24:40] David Bain: I’ve been your host, David Bain. Garrett Sussman is Director of Marketing at iPullRank. Find out more over at ipullrank.com and grab your free copy of the Pump and Funnel Quick Start Guide over at pumpandfunnel.com. Until we meet again, stay hungry, stay foolish, and stay subscribed.
Further Reading
- AI SEO Trends 2026: Strategy, GEO and the Future of Search
Covers hyper-personalization as a key predictive SEO trend and explains how AI enables content tailored to individual preferences to increase engagement. - Your Inbox Might Be the Next AI Search Signal
iPullRank’s controlled experiment showing Gmail-based signals significantly increased brand visibility in Google Personal Intelligence, directly validating the podcast’s core claims about email and persona data shaping AI recommendations. - Winning genAI search requires structured, persona-driven content strategies
Argues brands must focus on niche content strategies that align with user intent and context to be surfaced by AI search, reinforcing the episode’s persona-specific content approach.
