Practical AI Marketing Guidance
AI is already revolutionizing marketing. But joining that revolution requires more than just throwing AI at problems. It requires the right approach: an Agile approach.
Benefits of Integrating AI into Your Marketing Strategies
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Today’s marketing AI enables you to personalize everything from product recommendations to ads almost instantly at large scale, improving conversion rates.
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By automating tasks like analyzing data, producing basic posts, and even inputting data, marketers can focus more on high-impact activities.
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AI can transform piles of data into actionable insights in seconds, saving marketers time and enabling them to act on those insights faster.
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AI-powered assistants and chatbots enable businesses to respond to customer questions faster and at scale, improving engagement and related metrics.
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From building content strategies to analyzing existing content for ways to improve, AI can have an enormous impact on content creation.
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By leveraging data more effectively and finding patterns, marketing AI tools can identify and score leads quickly and effectively.
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From answering questions to producing text, AI enables content production to move at a far greater pace without sacrificing quality.
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AI can automate much of the digital ad campaign optimization process by analyzing data and making fast decisions around your priorities.
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As a set of tools, AI enables marketers to simply do more. This offers an opportunity for them to get creative and find new ways to deliver value.
Challenges of Implementing AI in Marketing
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On the one hand, it’s important to test AI tools and use cases quickly so you can minimize the time it takes to begin seeing real value from them. But, on the other hand, sloppy and rushed testing can lead to inconclusive results that leave you stuck wondering whether AI is really adding any value at all.
Finding that balance requires beginning with your strategic goals in mind and choosing a few things to test during each sprint or set period.
For example, if you have three main quarterly goals for your marketing team, you might try testing an AI application aimed at tackling each one during a set two week period. At the end of that two weeks you might decide to continue testing one, mark another as a failure, and another as a success. Then you can choose another two AI use cases to test during the next period.
This approach lets you be fast, yet deliberate in your AI testing.
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Trying to select which AI tools or use cases to test can easily get overwhelming. The best way to overcome this challenge is through your mindset. Accept that there’s no “perfect” choice, and that the marketing AI landscape is changing so fast that even if you find something that seems “perfect” it won’t last.
So instead of worrying about which option is best, just choose one and test it.
Even if that tool doesn’t end up working for you, the learnings you’ll get will help you find a better option. As long as you keep moving forward and learning, you’ll likely do better than much of your competition.
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Modern marketing teams have an immense amount of data at their disposal. AI tools offer an incredibly fast and efficient way to analyze that data and turn it into actionable insights. But that can quickly turn into massive compliance issues.
To properly use AI and avoid these problems, marketers need clear guidelines.
That’s why the best way to avoid this particular AI marketing challenge is by working closely with compliance or legal teams within your organization. Bring them into the AI integration process early to lend their expertise and ensure marketing remains compliant. That said, this is often easier said than done, as we hear plenty of stories where marketers are bogged down waiting for their own AI use cases to be prioritized.
In these cases, it helps to get strong support from leaders. If senior management understands the potential value AI can bring to marketing and that unlocking that value requires prioritization from compliance, they can help make that happen. -
Even when you find an excellent AI marketing solution, integrating it into your existing systems and processes can present an entirely new set of challenges.
The first way to get around this issue is by looking at AI tools that are already integrated into your existing software. More and more products like CRMs, SEO, and project management tools now have AI capabilities built in. Some examples include Hubspot, Asana, Ahrefs, Semrush, and Salesforce.
Of course, integrated tools aren’t always an option. Here you want to get marketing operations involved early on.
If they’re involved in the AI integration process from the start, they can more easily lend their expertise to helping marketing deeply integrate the AI tools they uncover into their existing processes. Or, they may even help use those tools to completely rework those processes.
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Most marketing teams today are thinking a lot about building internal AI capabilities. The framing of this challenge is key, because implementing AI in your marketing is one thing, building the skills needed to manage and evolve that usage is another.
We can recommend building a culture of experimentation and continuous improvement around AI.
Marketers should be encouraged to regularly question assumptions, test new tools or use cases, and generally accept that the AI landscape is going to change constantly. AI implementation training and even basic Agile marketing training can both help create those mindsets.
Of course the elephant in the room is whether building internal AI capabilities like this entails reducing headcount.
AI in Marketing Use Cases
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So much of developing a marketing strategy comes from synthesizing learnings and information. Happily, this time-consuming process can be automated with AI. For example, you might feed an AI tool the transcripts of all your retrospectives and ask for an overall summary of learnings from the past quarter. Creating summaries like this can help your planning team get up to speed on what’s happening far more quickly.
In addition, you can use Hubspot AI tools or similar ones from your CRM to look at your pipeline and find weak spots to address.
You can also use AI to examine your team’s skillsets to pinpoint gaps, and then plan the most efficient way to build out cross-functional teams. This might include upskilling plans to fill human skill gaps, along with potential investments in AI tools designed to supplement the team’s capabilities.
Lastly, we’ve found it useful to build custom AI bots trained on our own brand, customers, and objectives to generate and refine marketing strategies for our business. While these strategies are usually far from perfect, they serve as an excellent way to quickly generate ideas that humans can then vet, debate, and evolve.
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Here at AgileSherpas, we’ve found AI extremely useful in helping with sprint planning and prioritization. For one, Asana and similar tools can look at your Kanban board and tell you how much capacity you have. You can also instruct such tools to consider things like holidays can also be automatically factored into these calculations.
You can also use marketing AI tools to better understand your capacity and what work you have left. Prompts like “Tell me all the cards that haven’t been started and have a completion date before the end of this sprint” can quickly get everyone up to speed on what needs to happen. Alternatively, you can ask AI to find patterns in the kinds of cards that don’t get completed during sprints to help spot bottlenecks. You can see examples of what happened when we asked Asana’s AI tool to do both of those tasks below.
You can also use AI to conduct customer sentiment analysis across platforms to help marketing prioritize specific campaigns or messages based on what’s being talked about. This can be done by gathering text or other data about customers and compiling it into a single document before asking an AI tool to analyze it. Ideally, you’ll combine several of these techniques to help set priorities based on internal capabilities and customer needs.
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There are use cases for AI marketing automation throughout the examples on this list, but a good way to find your own is by analyzing your time. Try creating a detailed list of what you spend your time doing during a particular sprint. Then, look through that list and consider whether any of those activities could potentially be automated.
Once you have a list of ideas (data analysis, customer segmentation, sentiment analysis, personalization, chatbot support, etc.) it’s time to test them. You need to determine whether the time savings are ultimately worth it, as it’s possible the quality of the work might decrease (though it may increase as well). Either way, validation is essential.
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It’s no secret that a widely popular use of AI in marketing is generating content. But the quality of that kind of generated content isn’t likely to bring you content marketing success.
A better use case comes from using generative AI to explore perspectives and approaches. For example, inputting an existing article and asking it to be rewritten for a different persona.
AI can also be used to predict what type of content will be engaging for specific personas at specific stages of their customer journey. This can both help you plan what type of content to create, as well as hone your messaging and improve conversion rates.
Doing so begins with using Natural Language Processing models to analyze content from your customers like reviews, comments, etc. Then, you can ask them to predict what messages will resonate at what stages. For example, you might input reviews and ask “what factors are preventing people from becoming repeat customers?” From here, you can validate and test your results during sprints.
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While AI is unlikely to produce a world-class marketing email in one go, you can try using it at scale to get better results. For example, by asking for four versions of an email each targeted to a slightly different persona, or with different styles, or just of different lengths. Those emails can then serve as a solid foundation for combining elements for a more effective final product.
Another use case would be to ask AI to analyze a large batch of previous emails along with their performance data to identify patterns. This can help you pinpoint what’s working and why. Just be sure to test those assumptions to validate them afterwards.
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AI has long been used to optimize paid digital marketing campaigns, and that continues to be an excellent use case for the technology.
But getting beyond that application, AI can analyze content from customers like messages, emails, comments, etc. to uncover pain points you can address with ad copy. Generative AI can be used to quickly create simple images for use in ad campaigns. Just be sure to check each one and ensure there are no mistakes or other issues before using it.
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Like with digital marketing, a fantastic use case for AI in social media marketing is analyzing your prospective customers to create personas, uncover pain points, and generate content ideas. You might ask an AI tool to generate sample social media posts targeted to different personas you’ve built by feeding it information about your customers, for example.
You can also ask many marketing AI tools to create social media marketing strategies for specific personas, or built around specific goals. You probably won’t want to simply use these strategies as is, but they can serve as a useful starting point or simply as a source of ideas and inspiration.
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Here, you can use tools like ChatGPT to frame questions from the perspective of specific users. For example, “use the ‘4 Ws framing’ to tell me how a senior procurement manager might approach finding a new procurement management tool.” Like with other use cases on this list, you can also feed a tool text from your target audience to help uncover pain points, and analyze sentiment.
AI can also help you develop and segment customer personas.
By using tools like delve.ai or UXPressia to analyze customer interactions, you can group similar behaviors to generate personas. Then, you can validate and refine them through testing.
You can also try using AI tools to put yourself in the shoes of your prospects. Try prompts that ask how a person with specific traits might react to a particular message or situation. This can help get the creative juices flowing and help teams understand their prospects better.
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Building effective marketing campaigns with the help of AI begins at the strategic level. Begin by using the other AI use cases on this list to help better understand your customers, market opportunities, etc. This will help you begin with a stronger sense of what strategic focus your individual campaigns should have.
Armed with that information, you can use AI to generate content for campaigns. You can feed a tool large amounts of text and information about your customers and then ask it about specific messaging to gauge how customers might respond.
Once campaigns are live, try using sentiment analysis to understand how customers respond to your campaigns and quickly refine your messaging further.
The Best Examples of AI in Marketing
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While the internet is full of examples when AI chatbots fail, there are also great examples to follow. Sephora’s bot enables people to make reservations, color match makeup, and ask general questions. Customers have generally been quite happy with the convenience this offers while Sephora is able to help customers affordably at scale.
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Netflix has long been known for its algorithmically-driven content suggestions. Today their use of marketing AI goes further, analyzing customer behavior to both make suggestions and even personalize their experience through things like unique images for shows and movies. This large-scale personalization is only possible through AI and enables each user to get a more personalized and positive experience.
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The Economist has used AI to analyze the vast amounts of data it has about its readership to identify people who read the magazine on occasion but may be persuaded to read it more often or subscribe. By generating lookalike audiences, and honing in on how to target them, the publication has seen significant increases in readership. It’s a great example of data-driven AI marketing that really moves the needle even for organizations with over a century of history.
The Worst Examples of AI in Marketing
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Using AI to generate content is totally fine, but assuming the results will be 100% fine without checking is a huge mistake. This can lead to embarrassing mistakes that make your company look lazy and sloppy.
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The internet is full of people laughing at chatbots that pretend to be humans only to respond to AI-centered prompts asking them for things like cake recipes. Using AI for things like chatbots is totally fine, but you should be upfront about it with your customers.
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Whether this is used to deceive your customers about what they can expect or simply put out a subpar ad on the cheap, using AI to simply cut corners is always a bad idea. This approach can quickly cheapen your brand, leading customers to assume you do the bare minimum and can’t be trusted. That customer trust is far too valuable to exchange for a quicker turnaround of poor quality content.
