Almost Timely News: 💡 Step By Step Building an Ideal Customer Profile From Scratch With Generative AI

(2024-03-31)

Mar 31, 2024

Almost Timely News: Step By Step Building an Ideal Customer Profile From Scratch With Generative AI (2024-03-31) :: View in Browser

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What's On My Mind: Step By Step Building an Ideal Customer Profile From Scratch With Generative AI

This week, let's get down to brass tacks. We've talked a lot at a high level about building an ideal customer profile, and the very best ideal customer profiles are built on a foundation of lots of high quality data. That's still the gold standard, the way you want to do things if you can.

But let's acknowledge reality. A lot of companies... don't have that data. Either because the data is a mess, or because the company just doesn't have it. There's a reason why social networks have ALL locked down their data, cancelled their APIs or raised prices to make them unaffordable, and put up walls to interoperability as fast as possible. They recognize just how valuable that data is.

The good news is, you can still build a strong ideal customer profile with relatively little data. It will not be as good as having lots of high quality data, to be clear. But it will be good enough to at least start using generative AI more powerfully than basic starter "you're a Pulitzer Prize winning reporter" prompts.

For today's use case, I'm going to pretend I'm back at the public relations agency I used to work at - but this technique will work for just about any kind of role where you need an ideal customer profile, from sales to marketing to advertising.

First, we need to be clear on who even is in our ideal customer profile. Let's say I work at a B2B PR agency. I know that I need to get press coverage for my client - we'll say Trust Insights, my company, is the client. Trust Insights has a fancy generative AI course (shameless plug) and wants to get the word out about it.

We'll need a media list, or at least the start of a media list, of people who even cover the generative AI space. So our first stop will be any AI-enabled search engine such as Microsoft Bing, Google Gemini, or Perplexity. We'll ask it for a few names of prolific journalists in the generative AI space.

At the top of the list is author and influencer Bernard Marr. That's as good a place as any to start. Next, we should get a sense of what kinds of things Bernard writes about. Let's download and store his last 20 or so articles in plain text format; you can automate this step or do it by hand, so long as you end up with plain text articles when you're done.

After that, let's grab the contents of Bernard's LinkedIn profile. The easiest way to do this is to simply download the PDF version of it.

Great. Now we have quite a lot of raw materials to work with. Let's start building Synthetic Bernard. Open the generative AI tool of your choice that can handle large datasets, such as the paid versions of Google Gemini, ChatGPT, or Anthropic Claude.

Start your session by asking the model what it knows on the topic, like so:

"Today, we're going to build an ideal customer profile. What do you know about best practices for this kind of thing?"

Every model will behave differently, but this will give you a starting point for framing out the ICP and more important, help the model give structure to all the data we're about to give it. Once the model has returned its answer, tell it you're going to give it some raw data to start inferring a profile, a persona:

"Great. I'm going to provide you with a large body of data of a person who is our ideal customer. This person, Bernard Marr, represents the ideal customer for my company, Trust Insights. From this information, and the best practices you've outlined, I'd like you to infer an ideal customer persona using the key elements you've highlighted and the information I've provided from Bernard's publicly available information."

After it has a good long think, it'll come back with a profile that looks like this:

Based on the provided information and best practices for creating ideal customer profiles, here's an inferred persona for Bernard Marr:

Demographics:

Age: Mid-50s (estimated)
Gender: Male
Location: Milton Keynes, England
Education: Highly educated, with degrees from Cambridge University and Cranfield School of Management

Professional Profile:

Occupation: Internationally best-selling author, keynote speaker, futurist, business, tech & strategy advisor
Industry: Technology, Business, Management Consulting
Company: Founder and CEO of Bernard & Co. and Bernard's Wine Cellar
Expertise: Business performance, digital transformation, data strategy, AI, big data, blockchain, IoT

Psychographics:

Values: Innovation, efficiency, data-driven decision making, continuous learning, human-centric technology
Interests: Emerging technologies, business trends, future of work, social impact of technology
Motivations: Helping organizations improve performance, understand and leverage new technologies, make better decisions
Challenges: Keeping up with the rapid pace of technological change, educating businesses on the potential and risks of AI, ensuring ethical and responsible use of AI

Behaviors:

Regularly reads and writes about technology and business trends
Attends industry conferences and events
Actively engages on social media and shares content with a large following
Consults with businesses and governments on strategic initiatives
Seeks out and uses innovative tools and technologies

Media Consumption:

Forbes (regular columnist)
World Economic Forum (frequent contributor)
LinkedIn (highly active)
Other social media platforms
Online publications and research reports

Inferences:

Bernard is a thought leader and influencer in the technology and business space. He is highly knowledgeable and passionate about emerging technologies, particularly AI, and their impact on businesses and society.
He is likely an early adopter of new technologies and seeks out solutions that can improve efficiency and effectiveness.
He values data-driven decision making and is a strong advocate for ethical and responsible use of AI.
He is a prolific writer and speaker, reaching a large audience through various channels.

This is a great start for this kind of persona. If the people I'm pitching for my PR agency are like Bernard, this persona will be super helpful. Let's now put it to work. Suppose I have a pitch like this (yes, generated by AI):

Subject: New Course Exposes Marketing Potential and Pitfalls of Generative AI

Dear [Journalist's Name],

With generative AI tools like ChatGPT booming, many marketers are scrambling to grasp their potential and manage the risks. A new course by Trust Insights, "Generative AI for Marketers," aims to bridge this knowledge gap. I saw your recent piece on [topic related to AI in marketing] and believe this course would be of interest to your readers.

This 5-hour intensive course goes beyond basic prompt engineering to tackle topics crucial for marketers: AI governance, scaling, risk mitigation, and career impacts. Could an interview with course creator Chris Penn [or a lead instructor] offer valuable insights to your audience navigating this rapidly changing landscape?

Key Benefits for Marketers:

Deep dive into AI foundations
Practical skills for enhanced marketing strategies
Real-world use cases and guidance
Who is it for? Marketers interested in, or currently using, generative AI seeking to maximize its benefits while mitigating risks.

Please let me know if you'd like to learn more or schedule an interview.

Best regards,
[Your Name]
[Your Contact Info]

Now, I can say without hesitation, this isn't a super great pitch to start with. (and yet I've gotten hundreds of pitches just like this). So how do we use our Ideal Customer Profile, our synthetic Bernard? From the profile generated... we ask him.

In other words, Synthetic Bernard has basically said... pass. As someone who receives a lot of pitches like this, I can say that Gemini's predicted reaction is much kinder than what usually goes through my mind. So, now that we know our pitch isn't going to land, what do we do about it? I'll leave the rest as an exercise for you to complete, but it's not a great leap of imagination for you to have an ongoing conversation with Synthetic Bernard to ask how to fix this pitch.

The key takeaways from this exercise are that ideal customer profiles can be built with minimal data. In this case, I had the contents of a LinkedIn profile plus 16 articles from the web. That's it - no multi-year sales data, no deep dive into a person's background, no secret agent stuff, just a pile of data from publicly available sources.

If you were doing this for your PR agency, for your marketing agency, for your sales team inside your company, for your corporate communications team, you'd want to broaden this beyond just a single person. Maybe you'd have 5 or 10 people's publicly available data as a starting point and create a rollup synthetic persona or ideal customer profile from that data.

If you're a job seeker, this technique applies to you. Synthesize a persona for the hiring managers you're working with - especially the ones that have rejected you - and then use that persona to critique your materials, your interview responses, etc.

No matter what route you take, building an ideal customer profile is within reach for any organization, big or small, wealthy or poor. Leverage the data you do have, plus the best models available to you today, and you can get started making use of generative AI with ideal customer profiles to answer questions you have about your marketing (or anything else you want to ask it).

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ICYMI: In Case You Missed it

Besides the new Generative AI for Marketers course I'm relentlessly flogging, I did a piece this week on how to use generative AI to head off criticism.

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Australian Food and Grocery Council, Melbourne, May 2024
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See you next week,

Christopher S. Penn

Tony Lopes

Apr 1, 2024

Chris! You are a legend, I just put this into practice for my own consultancy and the Ideal Client Personas I am coming up with are spot on. Just a few minor tweaks, as you mentioned, but these are definitely the personas of people my consultancy should be working with.

Expand full comment

Sharon Machlis

This is where LLM training data gender and racial bias is likely to be an issue. Would instructing it, for example, not to automatically assume profiles are men for technology and women for household goods help?

1 reply by Christopher S. Penn

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