Feature
The Gist
- Technology understanding. AI relies on more than awareness of technological features.
- Marketing potential. Prompts open the door for marketers to leverage a number of insights and skills.
- Skill diversity. A variety of skills can help craft better answers from LLMs.
In the poker game of digital technology, many professionals consider syntax the ace in the deck. However, like a seasoned card player, a combination of skills forms the winning hand. Let’s take a look at the skills needed to be successful in using AI in marketing.
The ascendancy of AI has placed a spotlight on blending skills to make the most of the models available today.
So what skills are truly necessary for marketers when it comes to working with AI in marketing?
In today's vibrant and volatile marketplace, the required marketing AI skills have shifted from focusing on complex programming details to understanding the programmatic activity behind prompts. Prompts go beyond mere queries; they allow users to frame their questions based on their knowledge. This expertise paves the way for various skills marketers can leverage to enhance their prompt responses and AI workflow productivity.
Here is a look at how those skills can potentially play out in a workflow that includes ChatGPT, Gemini, Claude, or any other generative AI solutions.
1. Domain Skills
Large language models (LLMs) can generate vast amounts of content, yet assessing its quality and accuracy is crucial. Marketers must hone their critical thinking skills in relation to the specific domain topic for which their LLM is being utilized to evaluate effectively.
For instance, if I craft a ChatGPT prompt about car shoppers, I need to possess knowledge of the automotive industry. This involves applying content evaluation criteria to assess the output generated by LLMs and confirm that it aligns with brand standards.
2. Data Curation
Data curation is often guided by domain knowledge relevant to its application. As models evolve to become more multimodal, data can manifest in various forms, from metadata descriptions to diverse media types. Therefore, marketers must understand the potential of information curation through AI to identify the best workflow for using AI models.
LLMs are data-driven, so marketers need to be skilled at curating and preparing high-quality queries to optimize LLMs for specific marketing tasks.
Consider describing data sourced from SQL databases. Numerous tools enable you to map out data schema with minimal syntax, allowing you to grasp potential table relationships. The charting framework Mermaid can be utilized to map interrelated tables. Similarly, the solution DrawSQL can map interrelated tables and draft an SQL schema.
With AI in marketing, marketers have an opportunity to craft a prompt that can generate an outline for a schema, using the preview tools like DrawSQL for additional guidance. The resources imply that marketers must have a very good idea of what data will be frequently accessed and what queries are generally possible, then describe that data and structure in ways that the AI tool can understand.
Related Article: Top 5 ChatGPT Prompts for Customer Experience Professionals
3. Understanding LLM Fundamentals
Marketers need to understand how LLMs work at a level slightly better than the casual technology user. This means understanding what information was used in the datasets for a model, as well as what information would be in an Retrieval-Augmented Generation (RAG), the augmenting vectors used in a query. Doing so can help to craft useful prompts much faster in the first iterations.
Marketers should also appreciate the limitations of a model, such as the features of the different types of LLMs, how they are trained and the potential biases they may contain.
Related Article: Prompt Engineering Basics for Marketers, Advertisers and Content Producers
Learning Opportunities
Webinar
Apr
30
Supercharge Contact Center Efficiency with Generative AI
Learn practical strategies to reduce post-call documentation time by over 8 minutes.
Register
Conference
May
1
Qualtrics X4 Salt Lake City 2024
Register
Webinar
May
2
Unleash AI for Crisis Management: Transform Threats into Triumphs
Learn how Sprinklr Insights provides real-time insights on current events, trends, and brand crises to empower your team.
Register
Webinar
May
9
Driving Customer-Centricity in the Financial Services Industry
Discover how financial organizations are leveraging generative AI technologies.
Register
Conference
May
9
An Irish Roundtable in the Valley: The Economic Impact of AI on the World of Work Stanford 2024
Exploring the Impact of AI On the World of Work with Irish diaspora leaders and tech business champions from the North of Ireland
Register
Conference
May
13
Gartner Marketing Symposium/Xpo Conference London 2024
Register
4. Prompt Engineering
Crafting effective prompts is key to getting the desired output from LLMs. Moreover, the skills needed to craft a prompt have been changing rapidly. Researchers are discovering new performance insights such as variations of a chain of thought prompts and automation techniques. Marketers must hone their prompt engineering skills to communicate their goals and desired content clearly.
Mastering prompt engineering techniques lets marketers clearly communicate the expected outcomes and guide the LLM toward a desired response. My post on prompt engineering explains some of the prompt basics marketers should practice.
5. Visualization Skills
One of the benefits of AI in marketing is that it enables users to create visualizations without relying heavily on syntax or technical language. This allows users to edit content much more quickly.
For instance, in my article on ChatGPT ADA, I demonstrated how to correct a bar chart when an error in the data was discovered. Instead of revisiting Excel and reloading the data, I could instruct the AI prompt to ignore the error, and it recreated the bar chart perfectly.
Visualization skills extend beyond data; they can also involve shaping an image, as many models now have image creation capabilities. For instance, Midjourney prompts can include photogenic details like the type of lens and even the camera model to perfect an artistic image.
Ultimately, choosing the right visualization for your data or crafting an image with precision can result in a stunning visual outcome.
6. Data Analysis
In data management, curation and analysis are distinct workflow steps. Curation is essentially data cleaning, involving edits to formats, while analytics focuses on uncovering the data's meaning after it has been processed.
Analysis offers several advantages in understanding AI models beyond just interpreting model output. It also sheds light on the performance of an LLM and helps identify areas for improvement, from refining prompts to enhancing the RAG that supports the model.
Analysis involves breaking down complex information. Marketers need to dissect an LLM's output to scrutinize the generated data and evaluate the model's performance, ensuring the responses are well-aligned with the queries posed.
7. Human-in-the-Loop Insights Into an AI Workflow
LLMs are potent tools, yet they can't substitute human creativity in adjusting AI deployment 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, academic scholarships, and hiring, where approval decisions affect people's access, including a human-in-the-loop is essential.
8. Ethical Considerations That Impact Data Privacy & Security
Ethical considerations, such as bias and misuse, 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 addressed five key questions marketers should consider in an earlier post.
Marketers must be aware of these ethical considerations and use LLMs responsibly. By doing so, they can develop better strategies to mitigate potential issues regarding AI in marketing.
9. Storytelling With Information
Despite the assistance of LLMs, crafting compelling marketing narratives remains crucial. Marketers should refine their storytelling skills to clearly communicate how model outputs 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 grasp of LLM capabilities, limitations, and insights is vital for effective collaboration between marketers and content creators in shaping a story around a product, service or event.
10. Staying up-to-Date
This final skill encompasses the other nine. As the field of AI is in constant flux, marketers must keep abreast of the latest advancements in LLM technology to fully harness its potential.
Final Thoughts on Skills Needed for AI in Marketing
As the landscape of digital marketing continues to evolve, the integration of AI and LLMs becomes increasingly crucial. The skills outlined in this article form a comprehensive toolkit for marketers navigating this terrain. By mastering these competencies, from domain knowledge to storytelling, marketers can leverage AI in marketing to its fullest potential, ensuring their strategies remain relevant and effective in the ever-changing world of digital technology.
