How to Use Generative AI in Marketing.pdf
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Generative AI, exemplified by tools like ChatGPT and DALL-E 2, is revolutionizing marketing by enabling the creation of content, data analysis, and process optimization. It offers applications such as automation of routine tasks, personalized advertising, and efficient customer insights, thereby allowing marketers to save time and reduce costs. However, it is crucial for marketers to review and vet the output of these tools to ensure accuracy and effectiveness before implementation.
How to Use Generative AI in Marketing.pdf
- 1. How to Use Generative AI in Marketing Overview Generative AI, aka tools like ChatGPT and Google Bard, are the latest shiny object in the tech space. As such, they’re getting a lot of attention right now, so the potential exists for a lot of great ways to use them. This article will discuss one area, marketing, and how marketing professionals might be able to use generative AI technology in their jobs. Generative Artificial Intelligence 101 Generative artificial intelligence (generative AI) is a subset of the larger artificial intelligence (AI) field (see https://community.hpe.com/t5/hpe-ezmeral-uncut/what-is- machine-learning/ba-p/7164922), in which computer algorithms are used to create (or generate, hence the name) some sort of output that is similar to content that’s created by a human. This output can take many forms, including text, pictures and images, music, computer code, graphics, or many other types of output.
- 2. Generative AI uses machine learning (ML) algorithms to mimic how humans learn (see https://community.hpe.com/t5/hpe-ezmeral-uncut/what-is-machine-learning/ba- p/7164922). The ML process uses different types of learning models that are applied to a set of data to be evaluated, with a predefined goal as an output. An example of this is a machine that sorts “good” product from “bad” as it moves down an assembly line. As the accuracy of the model improves, the computer “learns.” Generative AI effectively takes this another step further by adding more structure to the learning models, so that the algorithms can generate output that is not predefined. This is where the “creative” aspect comes in. Numerous generative AI tools have already been released, across a variety of categories. These include tools such as ChatGPT that can generate text, DALL-E 2 that can generate images, and Soundraw that can generate music. There are many other categories where generative AI is also being applied, such as information summary, video creation, and computer code development, and more categories continue to be added. Generative AI in Marketing As a relatively new technology, generative AI has already shown how it can be used in many different ways. Generating new art, new music, and new written content are just a few examples that have already made headlines. But this new technology also has the potential to significantly impact how marketing is done. Because generative AI can analyze massive amounts of data extremely quickly, and then deliver an output, marketing can take advantage of that capability in a variety of new and different ways. Here are just a few ideas of how generative AI can be applied to some typical marketing tasks: Business Process Improvement: analyze existing marketing processes and process flows to help reduce extraneous or redundant steps, reorder the process flows, streamline and optimize them, or suggest new ways to accomplish the same tasks, making them more efficient, take less time, etc. Data Summary: analyze massive amounts of data in a short period of time and provide a summary that marketing can then use, saving time and allowing the marketer to focus on other tasks instead. An example of this would be a new product is being developed, so existing competitive products on the market need to be analyzed to compare and contrast features and capabilities between those existing solutions and the new product being developed, so that messaging, positioning and content can clearly differentiate the new product from the competition.
- 3. Content Creation: new assets (such as white papers, use cases, brochures, blogs, etc.) can quickly and easily be developed or updated via generative AI. Ad development: instead of spending days or weeks creating new advertising mockups, generative AI can be used to create storyboards and drafts to create and revise ads in a much shorter period of time. Video Creation: working with agencies on video development takes a long time, so generative AI can be used to significantly reduce that by taking ideas and developing videos that can be reviewed to determine if a video concept works or not. It can also be used to quickly and easily make edits to the layout, flow, images, graphics, colors, fonts, music, text, etc. in a much shorter period of time than typically is involved going back and forth with an agency. Personalization: new or existing ads and content can be targeted or personalized to a specific individual (aka, hyper-personalization), a specific target audience, a specific business, a specific vertical, or a specific industry using generative AI. A/B Testing: generative AI can develop different versions of the same ad, blog, content, etc. to see how effective they are with a target audience. This would include monitoring the results, summarizing the result data, and generating a report on which is most effective. Analyze Channel Effectiveness: automate and track the effectiveness of the various channels used by marketing to determine which had the most reach, the best level of engagement, the most downloads, the most requests for more information or to be contacted by sales, etc. and generate a recurring report to compare and contrast the differences. Social Media Monitoring: monitor social media channels to determine the relevant key words for your product or industry, as well as determine which social media channels have the most activity tied to those key words. Generate New Customer Insights: analyze massive amounts of customer data to generate new insights on topics such as buying behavior, buying cycles, biases, purchase patterns, preferences, etc. Customer Segmentation: analyze data to determine customer segmentation, such as by geographical location, by wealth distribution, or by industry. Automation: use generative AI to help automate routine and repetitive tasks, such as updating weekly reports or collating data. Website Management: generative AI tools can quickly crawl through an entire website and find grammatical errors (spelling, sentence structure), broken links, images that aren’t working, etc. and generate a report summarizing areas that need to be fixed. It could then also fix them. It can also be used to automate recurring updates or changes. As you can see by these examples, there are numerous different ways generative AI can be used in the field of marketing. This short list is just a start, but I know there are numerous additional areas in which it will be used to help marketing professionals. Using generative AI in this manner saves the marketing department time, allows marketers to focus on other tasks, and helps reduce costs.
- 4. Caveat Emptor However, when you begin using generative AI (and you will at some point, trust me), there is one key caveat that must be followed: Don’t accept the output of any generative AI tool without having it reviewed, vetted and approved by someone on the marketing team. Generative AI is good, but it’s still just a tool; it’s not infallible. It must be reviewed and approved, because the output of the generative AI tool depends strictly on the input. There’s an old acronym that’s been used in computer programming for decades: GIGO. That stands for “Garbage In, Garbage Out.” This means that the output of the program depends on the data that was input into it. If you put in bad data, you get bad results. The same holds true for generative AI. If your input parameters are not clear, if they’re not accurate, the output will reflect that lack of precision and provide a less than optimal result. In the same way that generative AI uses machine learning techniques to improve the accuracy of the output, marketers must do the same. Review the output of the generative AI tool to ensure the results are what you expected. If they are, great, move forward. But if they’re not, review what the inputs were that you provided to the tool, change them as needed, and run the tool again and see how the output changes. This is an iterative process and may take some time at first, but just like the self-learning iterative process that AI uses, this process helps you learn what level of specificity is required to derive an output that is usable. And then, review and approve the output just like any other piece of content or asset that is developed. If it’s acceptable and usable, great. You’ve just taken advantage of a new technology that can help you do much more with less time, money, and resources. Summary Generative AI is here to stay, and using it in marketing is already being done, with more uses likely coming. Don’t be afraid of it. Instead, investigate it, download it, play with it, learn it, and then use it in whatever way makes the most sense for your business and your role. After all, generative AI, while it has very impressive features and abilities, is still just another tool. Using it in these and other ways means you’ve expanded your marketing capabilities and added another new tool to your marketing toolbox, making yourself more valuable and a more well-rounded marketing professional. © 2023 Richard Hatheway / Catalyst Strategic Marketing