Although AI is a hot topic recently, it has been around for a long time,
In our agency, Mechanism, we’re looking beyond the hype and exploring how AI will impact all aspects of marketing, including research, strategy, creativity, and performance.
And one sector ready for disruption? advertising.
Here, let’s take a look at how personal AI tools can transform your advertising strategy.
An AI tool is only as powerful as the data you provide
There’s a common characteristic we’ve observed throughout the adoption of AI tools: There’s nothing inherently special about many of the AI tools out there. What matters is the inputs we put into them, and the data we provide to each device.
Like many people will tell you with AI: it’s garbage in, garbage out. You want to make sure that with any AI tool the dataset you’re using is the best you can provide.
This means collecting whatever first-party data you can to make your output from AI as productive and personalized as possible.
For example, to get a powerful AI-generated marketing plan, you can incorporate customer expectations, first-party data around the consumers you are trying to reach, past examples of campaigns and their performance, etc. Would like to do.
Our thesis to address this problem: The future of AI in advertising will be to install custom internal AI tools that will protect customers’ data, and provide personalized marketing.
Five elements you need to include in your AI tools for strong advertising
When it comes to advertising, corporate AI tools will need a few key elements to be successful. Let’s jump into them now.
1. A shared prompt library.
A shared prompt library A collective resource in your organization that allows anyone to share their best pointers for getting work done. By sharing this information, you help engage your team members to take better advantage of AI.
Consolidating and protecting your instant libraries also addresses privacy concerns. Prompt libraries help centralize knowledge around AI, and minimize any loss of productivity when people leave the organization.
2. A document library.
A document library There is personalized training inside the internal AI tools that you bring to any LLM (large language model). This library is the “brain” of your organization’s AI and should include any relevant documents that can train the AI to provide more personalized results.
The library can include a brand’s past campaigns, competitors’ campaigns, results of campaigns, data around your consumer, and results of past brainstorming.
3. Brand tone and voice guidelines.
As part of that library, there should be a Brand tone and voice guidelines Document that clearly states what will and will not be included in any communications from your brand. This document should be given more importance than others in training to help maintain your brand in any content generated.
4. Approval flow.
An internal AI tool should also include an approval flow that allows any content generated from AI to be audited and checked for things like hallucinations and bias before it is used outside the tool.
As part of this approval process, other things may be checked by the AI, such as any claims made with the quote or any regulatory issues that certain brands may face within the language used. This approval flow is key to keeping the work human. By applying the good taste that only a human can use, we can avoid work that seems robotic.
5. Security.
Finally, and most importantly, these tools should include a robust suite of Security Measures to ensure that all generations remain private before they are approved to go public. This protection should also keep the document library secure, and perhaps even offline, to better protect any first-party data provided to the LLM.
What personalized results look like with personal AI tools
By adding critical first-party data to personal AI tools, a company can expect results that are personalized as well as potentially predictive in performance. Working on generative AI is a big task, but with enough information about past performance, AI can generate responses that mimic the best practices of past top performers.
Asking a simple question like “Create 10 ads about going back to school” will not only yield results with more brand-appropriate responses using personal AI, but will also yield predictable results with each response.
These tools can plug directly into the APIs of e-commerce platforms as well as social platforms to track organic and paid content performance to optimize their leads in real-time.
Personal AI devices that continue to learn
If our personal AI tools are learning from qualitative data points like click-throughs, likes, and shares, why not qualitative ones? This is really the power of the LLM tool, the ability to manipulate and perform calculations on the written word like numbers. These tools will also be able to take into account consumer sentiments through comments and reviews to create better generative outputs for brands.
One area Mekanism is currently exploring is collecting and measuring rich interactions with TikTok comments to better understand what consumers are thinking. As the usefulness of social listening from platforms like Twitter diminishes, comments are becoming more important in videos.
A common workflow for our social strategy team, whenever they’re researching a brand or topic, is to pull the comments of the top videos from that niche and then use ChatGPT’s Those conversations have to be run through code interpreters like LLMs. After this data is entered into ChatGPT, our strategies can “conversate” with these consumers and ask them more questions based on this data to improve their understanding of the brand or topic.
what will happen next
A lot of organizations are currently looking around and asking how they will use AI, and many are running into similar issues of copyright and security. Our hope is that we have provided an outline for how the advertising and marketing industry can move forward by investing in personal AI and adopting these tools.
If we want AI tools to meet our expectations of the future, we need to provide more useful data. And, for everyone to feel safe doing so, developers of these tools will need to provide organizations with the option to run these tools on-site, out of the cloud, or with tighter security options.
These are very exciting times for humans and AI.
