How to Start Agentifying Your Franchise Marketing

In this podcast episode, we're examining the concept of agentic AI and how it can be pragmatically applied to marketing within the franchise space. 

How can you create AI-powered marketing teams that operate autonomously, yet collaboratively, at national, regional, and local levels?

The aim is to augment human marketing efforts rather than replace them. Key components include AI agents specialized in research, strategy, content, creative, and analysis, all working in synergy to optimize marketing execution. 

Vera provides a roadmap for implementing these AI agents, including steps to get your data organized, setting detailed goals, experimenting with micro agents, and documenting workflows. 

00:00 Introduction to AI in Franchise Marketing

02:03 Understanding Agentic AI

04:41 The Structure of AI Marketing Teams

08:52 Practical Example: Local Franchise Marketing

13:52 Steps to Implement Agentic AI

16:49 Final Thoughts and Call to Action

Transcript

0:12

Hey everyone and welcome to the podcast your go-to for real and relevant discussions on all things business, marketing and technology in the franchise space. Today I'm covering another AI topic, and as usual, I wanna talk about how you can use today's discussion to actually go away and make it actionable. What if you had a full team of marketing experts, researchers, strategists, copywriters, media analysts, all working 24 7 at every level of your franchise system, but none of them were human. Yeah. Okay. I know you've heard this many times, it's the good old discussion on AI agents. But today we're diving into a concept that's not just gonna be futuristic theory, but something that you can start implementing today. Okay, so let's talk about agentic AI marketing teams today. I wanna bring you into the future, but not in a vague, theoretical way. As I mentioned, what I wanna do is talk about something that you can start preparing for right now, and that's the rise of agentic AI marketing workforces. So this is really the idea of a fully AI powered set of digital marketing agents. That mirrors the way that you do marketing in your franchise. And again, what I wanna reiterate and what I will continue to reiterate during this conversation is that it is not a replacement for your marketing team. It's not a replacement for them, and it's not sci-fi and a long-term vision. What I want you to look at this as is a roadmap. And by the end of today's episode, I want you to have a clear picture of what that roadmap could look like and what steps you can take today to start building towards it. So what is agentic ai and why does it matter for franchise marketing? So let's start with the basics. Agentic AI refers to a system of AI agents. Each of them are trained to perform a specialized task, and they work autonomously, but also collaboratively with other agents. So think of this like a set of digital team members that have defined roles, workflows, and the ability to learn, reason and improve. And I guess to explain this, the real main difference between an agent and a chat bot or an automated workflow is that an agent is almost like a living, learning, breathing. Thing. And it doesn't just act upon conditional logic. So whereby in the old days when we programmed a chatbot, we would build it on certain scenarios. We would say, if this, then do this, if that, then do that. And it would work within those parameters. But if it came across a situation that wasn't outlined in our parameters, it would probably just break and you would need to keep. Giving it more information, right? Same thing with automation. Automation still requires a set of given parameters and guidelines and conditional logic for it to work properly. An agent, on the other hand, is something that has a body of knowledge and continues to grow and learn as that body of knowledge expands and it can use common sense and it can use expertise that it's learned over time to do tasks right. So an agent is where you are training essentially an LLM. You're training a large language model to do a specialized task, and this agent is gonna get better and better at doing the task over time as it learns and collaborates with other agents and with the data. If this agent is able to do actions on our behalf and interface with third party platforms like Google Meta your CRM or your analytics tools, it stands to reason that this can be really powerful for building a team, which is a virtual team, and that can free up time for your actual human marketing team to do other activities That AI isn't good at. So we're not replacing humans, but we are reshaping how marketing gets done. AI takes on the grunt work so your team can focus on the creative and strategic edge. So now here's where it gets really powerful for franchise marketing. My vision is to actually map these AI agents directly onto the unique structure of how marketing works best for franchise brands, and that is in the national, regional, and local levels. So here's the structure. You can build three groups of AI agents. The first group is a set of national marketing agents. The second group is a set of regional marketing agents. And finally, the last group is a set of local marketing agents. So the national AI agents would be focused on enterprise brand strategy, category trends, system-wide campaign themes, something. That's gonna be more on the higher level at the national or brand level, right? Then you have the regional AI agents and their job is going to be to tailor the national campaigns for more of a cultural, seasonal, or market specific variation. And then finally you've got your local AI agents, and those are gonna be focused on Hyperlocalized messaging. Promotional offers at the local level, creative asset swaps, and based on community data and performance. So think of it like a franchise org chart, but for ai, everyone has a lane, but they all sync and cross train and elevate each other. So within each of those, so at the national, regional, and local level, within each one, you're gonna have five different agents. So total of 15 agents. If you're going for the national, regional, local structure and these five agents are each gonna have their own swim lane, their own area of expertise., You're gonna have a research agent. That agent is gonna be in charge of competitive intelligence, industry shifts, emerging platforms and trends. The strategy agent is going to be. Really in charge of budget allocation, media mix, geotargeting, audience insights. Then you're gonna have a content agent who is going to be over overseeing the ad copy, posting, SEO, copy, landing pages, all of that good stuff. Then you have a creative agent. That agent is going to be in charge of image generation. Video generation branded assets. And then finally, you're gonna have an analysis agent. And this agent is gonna be responsible for performance reviews, ROI, insights, optimization recommendations, and everything on the analysis side. So each AI agent has a job, but when they all work together. They feed each other and they work together collaboratively. So analysis feeds strategy, strategy guides, content, creative adapts in real time, and you can create intelligent marketing momentum. So each of these agents will communicate horizontally with their peers. So for example, the content agent will talk to the creative agent, and the creative agent will talk to the strategy agent, but they also collaborate vertically. Within the same agent in their different tiers at the regional, local, and national levels. So it's kind of a, a structured, scalable, and synergistic model. And you're probably thinking, yeah, Vera, this sounds great. It sounds very theoretical, sounds like something which is a figment of your imagination, but really, how is this gonna work? So I wanna kind of dive into. Uh, A practical example of what this looks like in action. And then once we've looked at that example, I wanna talk about how we can get started to put things in motion, right? This isn't gonna happen overnight. It's, it is a long-term vision, but there are things that we can do today to get the ball rolling, right? So let's talk about a real life scenario. So say we have a local franchisee in the Tampa market and they need a geo-targeted. Marketing campaign for mosquito control. Say it's a mosquito prevention franchise, right? Home services. The local agents that we create will confer with the national and regional agents and. They've been working together over time. So if you think about this, you've put these agents together and they've been working together, so have been absorbing information not only about the Tampa market, but about all of the markets in your franchise system. So what we're gonna do is we are going to tap into the local marketing agent, and this agent is going to know exactly what marketing activation and messaging and tactics need to happen. At the local level for this to be successful because it already knows what's going on at the national and regional level. And it has been trained to put strategy and marketing in place that is not duplicative of what's going on at those higher levels, but rather synergistic to those, right? So the local agents put together a local marketing plan that is perfectly crafted to meet the needs of that local Tampa franchisee. So what the local research agent will do is pull real time weather triggers. So it's gonna understand what the weather's like in Tampa, whether it's conducive to, a mosquito repellent service. It's going to put together a competitive offer knowing what the local market is currently offering, and knowing what the local mom and pop competitors are currently pricing their plans at. Then the local strategy agent is going to adjust the media mix based on the budget and the locations that we need to target, like the zip codes, for example. Then the content agent is going to generate localized ad copy using the correct tone of voice'cause it's been trained on the brand content. The creative agent is going to build a localized image or video set of assets. And then finally the analysis agent is going to look at previous marketing activity and run ab comparisons in real time, along with the campaign that we're gonna run here for this local Tampa franchisee. It's gonna do AB testing and it's going to feed results back into the loop. With these AI agents that have been set up, all of this activity happens autonomously, right? We just need someone to say, Hey, we need this campaign at the local level. Go right? And it's gonna be governed by the rules that we've defined ahead of time. But it's gonna go ahead and start doing these things that can be automated and can be driven by an AI agent. But again, I wanna reiterate, it's not replacing marketers, it's amplifying them. So we still need a human in the loop. We still need actual human beings who have marketing expertise to be checking in on this. And to be refining this and continuing to help train the models, right? So what makes this work? There's a key aspect to all of this that makes this successful versus potentially creates problems for making this successful, right? And that is a shared set of intelligence or a data repository. So these agents that we create. We'll be connecting to a centralized structured data repository, and that could be your CDP, your digital asset management system, your CRM or a cloud-based AI hub. But at the end of the day, we're gonna need a central repository where all of our data sits and where these agents can dip in and tap into that data. So this will ensure that the agents aren't working in silos, but that they're actually working in collaboration and that the learnings that they are making or the learnings that they are getting are compounding across all the campaigns and all of the levels. So caveat here is that this idea of agen AI only works if you get your data house in order. So that would be the first. Step that you need to take is really a deep data analysis and then just a set of kind of steps to architect your data so that it is sitting either in a data lake or data warehouse that can be accessed by all of these agents. Alright. Let's get tactical and let's talk about what you can start doing right now. So step one, I already mentioned it. Get your data house in order, right? Talk to your IT team. Talk to your data analysis team, your data scientists, and come up with a plan to at least get the main aspects of your marketing data into one place. If you haven't done that already, step two would be literally take your national, regional and local marketing goals. And map out each one's objective across these agent areas. So research, strategy, content, creative, and analysis. You may not have the regional level in your plan yet, and that's totally fine. You don't need to have regional, you can stick with just national and local. To start off with, the next step would be start training. With micro agents, right? Start actually using what we have in terms of tools to create what I wanna call micro agents. So that could be chat, GPT custom GPTs, or it could be using Microsoft Copilot Studio or anything similar. Depending on what kind of infrastructure you have, whether that be Google based, Microsoft based, or if you have an enterprise chat GPT subscription, and start creating these mini agents. For example, you can create a content strategist, GPT. You could tell it how to handle content briefs and what kind of outputs you want it to provide you. For example, you want copy aligned with your brand tone, et cetera, et cetera. So start experimenting by creating these micro agents, next step, document the workflows between your agents. So define how you want data to move from one agent to the other. So how does data move from the research phase? To the strategy phase, to the creative phase, and then finally to the performance phase. If you're doing it manually today, this documentation will set the foundation for automation that you're gonna be doing tomorrow. And then finally. Pilot one agent use case per level. So start small. Don't try and boil the ocean. Create an agent team at one level to start with. So say national, right? We're gonna start at the national level and maybe start with just strategy and creative, right? So just pick those two agents, see what works, and then iterate and learn. So again, it is just a matter of experimenting. With a small bite at a time, and then eventually this is gonna snowball and it's gonna grow into something much bigger and much more effective and efficient. Final thoughts on this the Egen AI workforce, again, I will say it again, is not about replacing your team, it's about redefining your team. And this is your opportunity to lead the charge in how franchise brands can scale marketing execution while maintaining consistency, intelligence, and innovation across all levels. So if you are a CMO or marketing leader listening today, this is your blueprint and this is your time to take action. And if you start now, you will be months ahead of the brands that are still doing those random acts of ai, right? And not really structuring what they're doing into a bigger scalable use case. So if you wanna see what this looks like, diagrammed out, or if you're already building your AI agent workflows and you wanna swap ideas, please send me a message on LinkedIn or email me at hello@verashafi.com. And as always, subscribe and leave a review if this episode sparked new ideas for you. Until next time, keep innovating and let's build smart together.