INBOX INSIGHTS, April 24, 2024: Downside of Shortcuts, AI Use Case Identification

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👉 Catch the replay of yesterday’s webinar, Generative AI for Agencies!

The Downside of Short Cuts

There are no short cuts in life.

That’s a lie.

There are plenty of short cuts. The question a short cut raises when presented is whether or not you should take it.

Taylor Swift’s trainer has commented that she worked out six days a week and her routine would make most people sit down and cry. When she performs 3.5 hours three nights in a row, she makes it look effortless. She did not take any short cuts.

Stephen King writes for 4 hours a day, every single day. He has written over 70 books and sold over 400 million of those books world wide. His name is synonymous with horror. He did not take any short cuts.

Simone Biles trains up to 7 hours a day six days a week. She has 37 World and Olympic medals. She is the most decorated gymnast in history. She did not take any short cuts.

Why am I telling you this? Because you (and I) need to manage our expectations when it comes to success.

You cannot sneeze without some one mentioning generative AI. There are lots of real (and fake) experts out there claiming that they have the magic solution, the short cuts. Here are 5 tips, 10 prompts, 30 steps, to master generative AI today!

Here’s the thing. Generative AI is software. And like any new software, there is a learning curve. Some will climb that hill faster than others. With generative AI, there are a vast number of use cases. Because of that, there are a lot of moving pieces to learn and master.

We like to pick on Chris Penn and say that he tinkers all day and night with generative AI. The truth is, he is! He’s putting in the time, the hours, the practice. He’s honing his skills. He’s turning over every rock that generative AI puts down. When you ask him just about anything about generative AI he knows the answer.

I, on the other hand, never stop trying to understand people. I’m constantly alert to how people act. I am researching behaviors. I am learning about body language. I am finding new ways to solve old communication problems. If I really think about it, it might be why I’m so obsessed with true crime. I’m trying to understand human nature. The more I understand, the more effective I can be at my job.

When we step on stage to give a talk about AI, marketing, or something else, know that we’ve been working on it for months. We’re building, refining, practicing, practicing more, and then recording. The goal is to step on stage and deliver a high value experience. What happens if the slides don’t work? No problem. The mics aren’t functional? Not an issue. We’ve rehearsed these scenarios so that if they happen, we’re ready.

Imagine if Taylor Swift decided to step on stage without putting on all the hours she did at the gym ahead of her tour. Could she put on a show? Sure. Would it be great? Maybe the first time. But she would not have the stamina to do it over and over every night and deliver a spectacular experience. She wouldn’t be able to recover between shows. She would let her fans down.

The point is that to really master something, like generative AI, there are no short cuts. If you take the hundreds of short cuts that people are offering, you’re missing out on actually learning the system. You’ll scrape the surface, but never really understand how it works.

This kind of discipline is a quality you want in a subject matter expert. If you’re looking to bring someone into your business, to collaborate with your team, you want the best. You want someone who lives and breathes the tools. You want someone who hasn’t taken short cuts.

If you’re looking for someone to help your team get up to speed on generative AI, give us a shout!

What are you doing to skill up? Reply to this email to tell me or come join the conversation in our Free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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Binge Watch and Listen

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to build an AI strategy. Discover why focusing on AI as a strategy is misguided and what you should do instead to achieve meaningful business outcomes. You’ll learn how to identify the actual problems you need to solve and how AI tools like generative AI can fit into your existing processes and strategies. Gain insights into the six broad use cases of generative AI and explore how to leverage these capabilities to enhance your business operations. Finally, understand how to effectively communicate and collaborate with stakeholders who may have unrealistic expectations about AI.

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we walked through the prompt engineering lifecycle. Catch the episode replay here!

On this week’s So What? The Marketing Analytics and Insights Live show, we’ll be digging into how to use AI for SEO. Are you following our YouTube channel? If not, click/tap here to follow us!

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Data Diaries: Interesting Data We Found

In this week’s Data Diaries, let’s talk about identifying AI use cases. In case you missed it, yesterday’s Generative AI for Agencies recapped the major use cases of generative AI and large language models:

  • Generation: the most commonly thought of, making stuff
  • Extraction: pull data out of a source and reformat it
  • Summarization: turn big data into small data
  • Rewriting: turn one kind of data into another
  • Classification: organize your data
  • Question Answering: answer questions about data

In this context, data is any form of text.

The challenge here is that generative AI and language models in particular are so versatile, they can tackle any language-related task. That means any task within your company that uses language is a candidate use case. So how do you identify use cases when everything could be a use case?

This goes back to the 5P framework: purpose, people, process, platform, and performance. Like the golden delicious toasted ciabatta you use for a BLT, purpose and performance are the book-ends for people, process, and platform. What purposes are you trying to accomplish in your marketing? What performance are you looking to improve?

Sketch out your marketing operations funnel. What are the major stages? Most companies have something that looks more or less like this:

  • Awareness: people know we exist. Measured in stuff like website traffic or social followers. No commercial intent.
  • Consideration: people know we solve a general category of problems. Measured in things like newsletter subscribers or downloads. Very tiny commercial intent, not even enough to be considered leads.
  • Evaluation: people are asking if we can solve their specific problem. Measured in things like marketing qualified leads.

There may be granular stages, but these are the broad categories. Now, which of these categories is your area of weakness? Which phase transition (awareness -> consideration, for example) is your weakest layer of conversion?

Once you identify that, then look at your current processes in that layer, and identify which of the six use case categories that process incorporates. Let’s say your weakest area is turning prospects like newsletter subscribers into marketing qualified leads. The purpose is clear – generate more leads to eventually turn into sales and revenue. The performance is equally clear – marketing qualified leads. What processes do you currently use to do this?

Suppose one of those processes is nurture emails. You might look at those emails and decide they’re not performing well. Which of the six use case categories might you apply? You might create an ideal customer profile from your existing data – summarization – and then ask that ICP to evaluate your email – question answering – and then rewrite the emails to be more appealing – rewriting. Then you’d test and see if the results were better.

The use cases for generative AI are infinite, but the problems we face in our marketing are specific. Rather than try to invent use cases and justifications for using generative AI, identify the most broken things in your own marketing operations and candidate use cases for generative AI will become apparent quickly.

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