The essential
- Understanding of technology. AI does not only rely on knowledge of technological features.
- Marketing potential. Prompts open the door for marketers to leverage a number of insights and skills.
- Diversity of skills. A variety of skills can help develop better answers from LLMs.
In the game of digital technology poker, many professionals consider syntax to be the trump card of the game. However, as with a seasoned card player, a combination of skills makes the winning hand. Let’s take a look at the skills needed to successfully use AI in marketing.
The ascendancy of AI has placed emphasis on combining skills to get the most out of the models available today.
So what skills are actually needed for marketers when it comes to working with AI in marketing?
In today’s dynamic and volatile market, the skills required in marketing AI have moved from focusing on the intricate details of programming to understanding the programmatic activity behind instructions. Prompts go beyond simple requests; they allow users to formulate their questions based on their knowledge. This expertise paves the way for various skills that marketers can leverage to improve their rapid responses and AI workflow productivity.
Here’s a look at how these skills can potentially express themselves in a workflow that includes ChatGPT, Gemini, Claudeor any other generative AI solution.
1. Domain skills
Large language models (LLMs) can generate large amounts of content, but it is crucial to evaluate its quality and accuracy. Marketers need to hone their critical thinking skills with respect to the specific area their LLM is being used to evaluate effectively.
For example, if I’m creating a ChatGPT prompt about car buyers, I need to have knowledge of the automotive industry. This involves applying content evaluation criteria to evaluate the output generated by LLMs and confirm that it meets brand standards.
2. Data retention
Data curation is often guided by domain knowledge relevant to its application. As models evolve to become more multimodal, data can manifest in a variety of forms, from metadata descriptions to various media types. Therefore, marketers need to understand the potential of AI-enabled information curation to identify the best workflow for using AI models.
LLMs are data-driven, so marketers must be proficient in organizing and preparing high-quality queries to optimize LLMs for specific marketing tasks.
Consider describing data from SQL databases. Many tools allow you to map a data schema with minimal syntax, allowing you to grasp potential relationships between tables. The graphic framework Mermaid can be used to map interrelated tables. Likewise, the solution DrawSQL can map interrelated tables and write SQL schema.
With AI in marketing, marketers have the opportunity to create a prompt which can generate a preview of a diagram, using preview tools such as DrawSQL for additional guidance. The resources mean that marketers need to have a very good idea of what data will be frequently accessed and what queries are typically possible, and then describe that data and its structure in a way that the AI tool can understand.
Related article: Top 5 ChatGPT Prompts for Customer Experience Professionals
3. Understand the fundamentals of LLM
Marketers need to understand how LLMs work at a slightly better level than the casual user of the technology. This means understanding what information was used in the datasets for a model, as well as what information would be contained in a model. Recovery Augmented Generation (RAG), the augmentation vectors used in a query. This can help create useful prompts much faster in the first few iterations.
Marketers must also appreciate the limitations of a model, such as the characteristics of different types of LLMs, how they are trained, and the potential biases they may contain.
Related article: Rapid Engineering Basics for Marketers, Advertisers, and Content Producers
4. Rapid engineering
Developing effective prompts is essential to achieving the desired outcome of LLMs. Additionally, the skills needed to create a prompt have evolved rapidly. Researchers are discovering new insights into performance, such as variations in a chain of thoughts and automation techniques. Marketers need to hone their engineering skills to clearly communicate their goals and desired content.
Mastering rapid engineering techniques allows marketers to clearly communicate expected results and guide the LLM toward the desired response. My post about rapid engineering explains some of the quick basics marketers should put into practice.
5. Visualization skills
One of the benefits of AI in marketing is that it allows users to create visualizations without relying too much on syntax or technical language. This allows users to edit content much faster.
For example, in my article on ChatGPT ADA, I showed how to correct a bar chart when an error in the data was discovered. Instead of revisiting Excel and reloading the data, I was able to tell the AI to ignore the error, and it recreated the bar chart perfectly.
Visualization skills go beyond data; they can also involve shaping an image, as many models now have image-making capabilities. For example, Mid Road Prompts can include photogenic details like lens type and even camera model to perfect an artistic image.
Ultimately, choosing the right visualization for your data or creating an image accurately can result in a stunning visual result.
6. Data analysis
In data management, curation and analysis are separate workflow steps. Curation is essentially cleaning the data, involving changes to formats, while analytics focuses on discovering the meaning of the data after it has been processed.
Analytics offers several benefits in understanding AI models, beyond simply interpreting model results. It also highlights the performance of an LLM and helps identify areas for improvement, from refining prompts to improving the RAG that supports the model.
Analysis is about breaking down complex information. Marketers must dissect the results of an LLM to examine the data generated and evaluate the performance of the model, ensuring that the answers are well aligned with the queries asked.
7. Human insights into an AI workflow
LLMs are powerful tools, but they cannot replace human creativity in adjusting the deployment of AI within a workflow. Marketers often establish this workflow, so skills that effectively blend LLM capabilities with human expertise are crucial for optimal results. In areas like home loans, college grants, and hiring, where approval decisions affect people’s access, it’s essential to include a human in the loop.
8. Ethical considerations impacting data privacy and security
Ethical considerations, such as bias and abuse, have become increasingly important with the rise of AI and LLMs. These concerns are reflected in the decisions models must make regarding data privacy and security. I’ve covered five key questions marketers should consider in a previous post.
Marketers should be aware of these ethical considerations and use LLMs responsibly. In doing so, they can develop better strategies to mitigate potential issues with AI in marketing.
9. Tell stories with information
Despite the help of LLMs, crafting compelling marketing stories remains crucial. Marketers need to hone their storytelling skills to clearly communicate how the model results support their campaign plans. Collaborating with stakeholders to generate the right ideas and media is often essential in using LLMs for content creation. A thorough understanding of LLM capabilities, limitations, and insights is essential for effective collaboration between marketers and content creators to shape a story around a product, service, or event.
10. Stay up to date
This last skill encompasses the other nine. As the field of AI is constantly evolving, marketers need to stay abreast of the latest advancements in LLM technology to realize its full potential.
Final Thoughts on the Skills Needed for AI in Marketing
As the digital marketing landscape continues to evolve, the integration of AI and LLMs becomes increasingly crucial. The skills outlined in this article provide a comprehensive toolkit for marketers navigating this terrain. By mastering these skills, from domain knowledge to storytelling, marketers can harness the full potential of AI in marketing, ensuring their strategies remain relevant and effective in the ever-changing world of digital technology .
