You Ask, I Answer: Customer Personas for Generative AI?

In today's episode, Charlie asks, what belongs in a customer persona file that you use for generative AI? Okay, so this is a good question. It's a challenging question because it depends on the kind of business you have and what you're going to be doing with the customer persona file. So this is uh developing an ideal customer profile with generative AI. A big part of what goes into it needs to be focused on what's going to be coming out of it.

So if you're going to use it for things like proofreading text, etc., um, you would probably want to have a lot of data about what the customer reads, what the customer is expecting to see, as well as the usual stuff like demographics, psychographics, firmographics, et cetera. Who is this person? Uh, what kind of company do they work at? What are their needs and pain points that whatever your customer is. If your customer is, you know, a 35 to 44 year old uh female uh mom, then you're gonna have uh different needs than a 45 to 54 year old Korean male dad, right?

So there's there's different needs that and pain points that each persona is gonna have. And then the second part is really dependent on the use case. So some of the use cases for ideal customer profiles are doing things like vetting ideas, having you know, brainstorming, uh validating your content, validating an offer, etc. And depending on what the output is, depends on what you're gonna put on the input. So if you want to have a conversation with this kind of person, uh synthetic person, you need to have what kind of personality they have.

Who are they as a person? And this can be a synthesis of different people. This could be doing things like taking uh customer writing that from your customer service inbox, etc., um, and turning it into a personality analysis. You might say, What's what's develop a big five personality analysis for this so that we can have a conversation and this person will sound like the kind of person that they're supposed to represent. For example, if you're working with the persona of a 45 to 54 year old Korean man, uh Korean American man, uh, they're probably unlikely to use Gen Z slang.

It's not impossible, but uh that's probably you're probably gonna get more 90s references, right? Now, I would presume that uh people like that, aka me probably aren't going to be using terms like tubular uh anymore that we did in the 90s, but um the point is that you want the dialogue and the personality to reflect who that person is, likewise, you would want to have uh a more complete list of things like that person's interests above and beyond just work, because those are personality traits that will influence how somebody thinks. What are that person's hobbies? Um, what do they value in life? Are they married?

Are they divorced? Are they parents? Are they uh double income no kids? Are they uh dog owners, cat owners? All those personality traits that would be in a real human being for that purpose should be in that profile if you're gonna use it conversationally.

If you're just gonna be validating uh content, then of course you'd have more things like uh the needs and the pain points, how that person shops, what they value, what they they think about all throughout the customer journey. How do they become aware of problems? How do they become aware of solutions? How do they shortlist and select vendors? B2B, B2C doesn't matter.

You want to know those things from your market research and from the data that you have access to to assemble what that looks like, assemble who that persona is. And then those are all the pieces that go into an ideal customer profile. You will break them out by sections, such as demographics, photographics, uh economics, etc. In a probably markdown file format, since language models seem particularly predisposed to coding style formats. So markdown is a very popular one.

Uh most of GitHub uses it, and GitHub, of course, is a huge repository of data. And so the language model understands markdown all probably better than it understands most other uh text markup, except maybe HTML. So that's what belongs in a customer persona file that you use for generative AI. It's a it's a good idea to have one, but you want to follow the 5P process uh from Trust Insights, right? That is purpose, people, process, platform, performance.

You want to be clear about the purpose of how you're going to use this thing because that dictates all the rest of the parts. If you're unclear about that, and you're just trying to make a generic customer profile, it's probably not, it won't perform as well as you want it to for those purposes. That's gonna do it for this episode. Thanks for the question. I'll talk to you soon.

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