Building AI marketing campaigns begins with understanding the relevant use cases like idea generation, data analysis, content generation, and campaign management. Then you should be aware of potential challenges like building stakeholder trust and combating bias. It’s also important to appreciate how valuable Agile frameworks are for AI implementation. Lastly, you can follow three basic steps to create a campaign: identifying strategic goals, experimenting, and iterating.
The pressure for marketers to start building AI marketing campaigns and implement AI into their processes is immense. Our latest State of Agile Marketing survey found that just 7% of marketers weren’t considering using AI at all.
So marketers want to start creating AI marketing campaigns, but the vast majority just aren’t there yet. This is particularly true at many large organizations, where data silos, compliance concerns, and cultural barriers are holding teams back. However, in B2B and B2C environments alike, there’s a strong need to unlock the personalization, optimization, and yes even creativity AI offers.
Here at AgileSherpas, we’ve been investing heavily in fully implementing AI across our organization. That’s enabling us to really understand how to practically use AI to do things like build effective AI marketing campaigns. Now, we’re sharing that knowledge with you, so you can harness the power of Agile marketing to implement AI in your campaigns, processes, and more.
Demystifying AI in Marketing
Let’s start by clarifying just what building an AI marketing campaign looks like. Because ideally, it’s about much more than simply using AI to generate a video for your campaign. So here are the main ways you can harness the power of AI marketing for more impactful campaigns.
Idea Generation
While content generation gets far more attention, this is actually where AI’s ability to influence campaigns really shines. Large Language Models (LLMs) like ChatGPT are excellent at generating, getting feedback on, and iterating on ideas.
For example, you might try a prompt like "imagine you’re a marketing expert, how would you approach building a B2B campaign targeting mid-level managers at SaaS companies with over 100 employees and $5 million in annual revenue?” We wouldn’t suggest using the first result you get, but you can try adjusting that prompt to get slightly different results.
Each time you do that, you can get ideas about how to frame your challenge. For example, you could try asking the model to imagine it’s an AI marketing specialist, or a more creative marketer. You can use that inspiration to write your own proposal and then ask the model to give feedback on it (again from the perspective of different types of people). You can use this approach to build audience segments.
This approach to idea generation is a great way to quickly and efficiently generate quality campaign ideas you can then put to the test. Just be aware that your competitors have access to the same tools you do, so being creative about how you use them is the best way to gain a real advantage in this area.
Choosing the Best Mix of Channels
Once you know what goals you want your campaign to achieve and have brainstormed some approaches, you can choose your channels. Here AI can help as well.
First, many CRMs like Salesforce and Segment have built-in AI tools to help you examine potential personas and understand consumer preferences. Here, you can get an early idea of the channels your audience use and the types of content they typically interact with. Next, you can use tools like LLMs to leverage historical data and market trends to make predictions about channel performance.
Armed with this information, you can make solid predictions about the best channels to use and how to divide your budget amongst them.
Data Analysis
It’s no secret that AI can be great at drawing conclusions from large datasets significantly faster than humans. Besides analyzing numbers, you can also use AI to conduct sentiment analysis and understand why people may be reacting a particular way to your campaigns.
So, you might feed a model information about a campaign’s performance as well as text you used and ask it questions like “Imagine you are a senior B2C marketing expert, how would you suggest this campaign be improved?” or “Imagine you are a customer that did not respond to this campaign, explain why its messaging didn’t resonate with you.”
The goal is to view data about your campaign in a different light as much as it is about saving time avoiding manual data analysis.
Content Generation
By now, marketers everywhere know that you can quickly and easily generate content for an AI marketing campaign using an LLM. But like with the previous examples, AI tools are excellent at enabling marketers and copywriters to hone their content until it’s just right.
For example, you might feed it a piece of copy and ask “imagine you’re a senior marketing copywriter specializing in B2C, rewrite this copy in a more playful tone.” Likewise, you could ask it to shorten or lengthen some copy, change the type of marketer the model imagines itself to be, etc.
You can use these techniques to generate content, but again their real value comes from their ability to hone that content. In today’s oversaturated marketing landscape, generic content just isn’t going to bring results, so you need to be creative about how you use AI.
Campaign Management
Lastly, most campaign management tools now offer the ability to use AI algorithms to help manage your campaigns in real time. Often this means things like adjusting ad spend in response to performance. In other cases, AI tools can optimize when emails are sent or automatically respond to the results of A/B tests. However, there are other ways to use AI to manage active campaigns.
LLMs can be used to analyze campaign performance and make suggestions, particularly when multi-touch journeys make attribution difficult. Likewise, these models can be used to predict revenue, church, and other key metrics based on past data. You can use insights like this to adjust your campaigns as they go.
Post-Campaign Analysis
This is where many marketers end up deep in the weeds, spending hours building spreadsheets to analyze performance. Fortunately, AI can automate a lot of that work. Using either an AI plugin or dedicated tool like Google Looker Studio can help you combine data from various channels into automatically generated dashboards that calculate your KPIs. But besides these basic metrics, you can feed the raw data into LLMs or dedicated tools like Mixpanel to help uncover patterns and insights.
If your campaigns generate things like comments, those same LLMs can help create summaries and analysis of all that text to streamline analysis. You can even ask the models “what if” questions based on your results to get some ideas about how to improve your future campaigns.
Navigating Compliance and Ethical Considerations
A major concern preventing many marketers from creating AI marketing campaigns is questions around the legal and ethical use of AI. Everything from data security to GDPR compliance can present major roadblocks. These are some issues you should be aware of and ideally get ahead of as you embark on your AI marketing journey.
Stakeholder Trust
With AI-specific regulation still in the early stages in most regions, the technology presents a lot of open-ended questions about compliance. That’s why there’s no single silver bullet for ensuring your AI marketing campaigns will always be compliant and your compliance-focused stakeholders happy. So what can you do?
The first step is to get your marketing team to view and treat compliance and legal teams as stakeholders. That means involving them in strategic planning, regularly getting their input, and ensuring communication with them is clear. That goes for your customers as well. Transparency about how AI is being used is the best way to build trust around it and avoid costly mistakes.
Bias
AI models are generally a reflection of the datasets they’re built on and the people who built them. As a result, they tend to perpetuate biases. This isn’t just an issue from an ethical perspective, but can skew the data marketers rely on to make decisions about their campaigns.
The first way to avoid bias is to select the AI tools you use carefully. This isn’t always easy but a quick search can often uncover known issues. For example, some scientific papers break down how bias can be avoided and list examples of observable bias in common tools.
Next, providing some training to make marketers aware of how algorithms and the way they’re used can perpetuate biases can help. Things like wording prompts differently can go a long way towards avoiding many examples of bias. Lastly, if you’re using your own datasets, consider whether biases might be present there and how you can improve their quality.
Integrating AI within Agile Marketing Frameworks
Before you actually start building your first AI marketing campaigns, you should know about the secret weapon enabling marketers to dramatically improve their chances of successfully implementing AI: Agile frameworks.
This year’s State of Agile Marketing report asked hundreds of marketers around the world about their level of agility and their AI usage. The results were clear. Fully Agile teams were more than 3x more likely to fully implement AI compared to somewhat Agile teams.
Okay, but what about non-Agile teams? We weren’t able to find a single non-Agile marketer who had fully implemented AI on their team. But why are Agile marketers so much more effective at integrating AI and building AI marketing campaigns?
There are a few things. First, our survey found that Agile marketers had far more autonomy, focused on a smaller number of high-value activities, iterated more quickly, made data-driven decisions, and were more experimental. All of these elements are crucial for testing AI tools and figuring out what is going to actually deliver value to your campaigns.
If you want to build a solid foundation for the long-term success of your AI marketing campaigns, agility-focused AI marketing training is the best place to start. That foundation in Agile principles and ways of working will help teams adopt, integrate, and improve their AI usage with time instead of getting bogged down or stagnating.
Actionable Steps for Implementing AI Marketing Campaigns with Agility
Begin with Strategic Vision
It doesn’t matter how fast or efficient your car is if you have no idea where you need it to go. The same applies for AI marketing campaigns. Without a clear sense of how you want these campaigns to contribute to broader strategic goals for your organization, their effect will be limited.
The first step to effective AI marketing campaigns is an effective AI marketing strategy. Create some KPIs that reflect strategic goals and pick one or two that you’d like your AI marketing campaigns to contribute to. Identify the best metrics to gauge success and begin testing use cases.
Implement Structured Experiments
Anyone can just start using various AI marketing tools in their campaigns. But the marketers that are going to see the best results are those that structure these uses as experiments to actually quantify their impact. This data-driven approach enables marketers to understand the value AI tools actually provide.
Based on that information, decisions around whether to invest more in specific tools, how to further incorporate tools into your processes, and what types of AI tools should be tested next can be made. For example, if you want to improve the time to market for your campaigns, you can run sprints using various tools and see how they affect that metric.
Iterate, Iterate, Iterate
The AI testing phase is never complete. New tools are being released every day and no matter your industry, the competition will be building AI marketing campaigns of their own. Let’s say you use an LLM to conduct sentiment and data analysis on your campaign results. That improves your time to market by an average of one day. That’s great, but now you can try using a tool to help speed up the content production process to test how that further improves your time to market.
The goal is continuous improvement. No marketing team can simply rest on its laurels and assume its processes and tools will give them a competitive edge forever.
AI Marketing Campaign FAQs
Can AI create a marketing campaign?
Yes, most Large Language Models (LLMs) are capable of producing an entire marketing campaign if you ask them to. That said, you will produce more effective campaigns if you iterate on those results and use human marketers to further improve them.
How can AI be used in marketing?
AI can be used in marketing in a huge variety of ways. It can help marketers develop strategies, analyze data, automate processes, produce content, hone ideas, manage campaigns, and more. New tools and capabilities are being released all the time, so this list only looks set to expand further in the future.
What brands have AI campaigns?
While many famous brands like Nike, Coca-Cola, and BMW are already well-known for their AI marketing campaigns, they’re not alone. However, thousands of other brands are also running AI marketing campaigns right now. Some are upfront about this fact, others prefer not to mention it. But AI is deeply integrated into many marketing campaigns and this trend will continue.
AI Marketing Campaigns Are the Future: Are You Ready?
A few things are crystal clear. AI marketing campaigns are fast becoming the norm and the teams that are able to create them more effectively will have a serious advantage. Also, Agile ways of working make it far easier to implement AI in this and other ways by empowering teams with autonomy, experimental cultures, and data-driven decision making.
If you’d like to chat about how Agile enables greater AI implementation, just get in touch! Between our annual report and internal experiences, we have plenty of insights into implementing AI-driven agile marketing campaigns in all kinds of organizations.
Oh, and before you leave, why don't you pause for a second an get your copy of the 8th Annual State of Agile Marketing Report?
