Almost Timely News: 🗞️ AI Use Cases for Ideal Customer Profiles

(2024-10-06)

Oct 06, 2024

Almost Timely News: 🗞️ AI Use Cases for Ideal Customer Profiles (2024-10-06) :: View in Browser

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👉 Watch my new 90-minute master class workshop (no slides!) from INBOUND 2024 on how to build a data-driven customer journey map using generative AI. (link fixed from last week)

👉 Watch my new 40-minute talk from MAICON 2024 about why open models are your answer to data privacy and AI.

Content Authenticity Statement

100% of this week's newsletter was generated by me, the human. You will see AI usage and outputs in the video version of the newsletter as I demo ICP use cases. Learn why this kind of disclosure is a good idea and might be required for anyone doing business in any capacity with the EU in the near future.

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What's On My Mind: AI Use Cases for Ideal Customer Profiles

Katie asked me this week to cover synthetic ideal customer profiles intended for use with AI. Now, we've talked at length about these, which you can see here, here, and here, and of course my company offers the creation of these as a service, which you can find here.

So since we've got all that covered, let's talk about what you DO with an ideal customer profile. All too often, people take a document like an ICP and... shelve it. It becomes attractive binder-ware, sitting next to your SWOT analysis and that expensive product-market fit analysis you hired that very expensive consulting firm to do.

This is a terrible fate for any useful document, but none moreso than an ideal customer profile. Let's dig into some examples of what you can DO with these things.

I strongly recommend you watch the video edition of this week's newsletter because I walk through many of these examples onscreen.

Part 1: What's Inside?

A good ideal customer profile is going to have these basics, which will vary based on your business to some degree but are pretty standard:

Demographics: classic 1990s IRC A/S/L (iykyk)
Psychographics: stuff like values, opinions, attitudes, activities
Firmographics: mainly for B2B, the corporate equivalent of demographics
Goals and motivations: what does the customer want to do?
Needs and pain points: what stands in the way of the customer's goals?
Buying behaviors: details about how the customer makes decisions

The difference between a buyer persona and an ICP is that ICPs are generally synthesized from actual data. When Katie builds them for clients, she gathers up a half dozen different data sources and uses generative AI to blend them together.

Here's the key to a good ICP. In general, for making use of an ICP with AI, it should contain as much relevant, specific information as possible. More is better as long as it's relevant and specific, because we'll be using them with generative AI. You don't have to worry about overwhelming a stakeholder's mere human brain with lots of details when you're using ICPs with AI - a key difference between the ICPs you're probably used to and a synthetic ICP intended for use with AI.

Part 2: Obvious Use Cases

Once you've got a solid ICP that's ready for generative AI, there's a universe of things you can do with them. Load into a generative AI system of choice and have it:

Validate your marketing copy
Brainstorm ideas for content by talking with your ICP
Test things like email subject lines, ad copy, etc.

How you do this is more important than the tasks themselves. A lot of people approach these tasks very naively as opposed to rigorously. What do I mean?

Naive use of an ICP is giving it to generative AI and then just asking it basic questions like "Hey, what do you think of this idea?". With a detailed ICP, that will still generate good results, but they're not going to be optimal results.

What should you do? There's a defined process I use with things like ICPs when it comes to evaluation:

Prime the model with background information, including the ICP
Summarize the information into best practices to reinforce its importance within the conversation (changing the in-conversation weights)
Build a scoring rubric for the evaluation process
Evaluate using the rubric and the best practices
Encapsulate the rubric and best practices into an app

Here's a simple example. Suppose I want to evaluate whether a blog post meets the needs of our ICP. I'd do the following:

Ask the model what good quality blog content is using the Trust Insights PARE framework
Load the ICP
Summarize high quality content
Build a scoring rubric for quality content that aligns with the ICP
Evaluate a piece of content to ensure the system works as intended
Put the rubric and quality outline into a Gem/GPT/artifact

For simple evaluation tasks, building apps like Gems, GPTs, and Artifacts is the easiest way to effectively scale the use of AI with an ICP at your company. Once you go through this process for a specific task, you can hand over the finished app to others and then they can benefit from your expert development.

Part 3: Less Obvious Use Cases

Asking an AI model to evaluate something like content against an ICP is a great, simple, easy use case. A less obvious but similar use case is turning your ICP into buyer personas and having those buyer personas have a conversation with you for things like product market fit.

Here's an example. Katie and the Trust Insights team built an ICP for me as a keynote speaker, to help better guide my marketing efforts at being hired as a keynote speaker by events and conferences. That's a solid, straightforward use case. We have no shortage of data about who event planners are or the things that are important to them.

I could just hand a talk or talk summary to that ICP and ask it to evaluate and score how well the talk would meet their needs as an event planner. That's useful. But what if I took a step back and had several different event planners at a table, and had a conversation with them about an upcoming talk?

Using the ICP, I'll direct my AI tool to split it into three synthetic customers - an event planner for a major marketing conference, an event planner for an industry conference in a vertical like healthcare, and an event planner for an individual corporation.

Why do this? Because an ICP is so rich and robust with real data, assembled from real data, generative AI tools can infer a great deal from that data, extrapolating it into personas that have differing goals and motivations. The needs of a corporate event planner needing a speaker for a sales kickoff are somewhat aligned but somewhat different from a speaking planner for a major marketing conference.

Unlike talking to a monolithic profile, using the ICP as the basis for personas also allows you to create adversarial variants, personas that might not be as receptive to your sales and marketing. This is a great way to see how different segments of the market might react; telling the model to generate a skeptic or an outright critic of your company can give you a much wider perspective of how the market might see you.

Part 4: Challenging Use Cases

Now, let's kick things up a notch, as the cooking show aphorism goes. What are some more technically challenging use cases for ICPs?

Well, for one thing you can talk to them directly, as though they were real people. I have an example using OpenAI's ChatGPT Advanced Voice Mode in the video edition. This week, OpenAI announced we can use those same voices via their API, so you can build entire apps around them.

I did an example this week using Google's synthetic voices for a live, phone call-style focus group in which I talk to two variants of our Ideal Customer Profile. You'll see a snippet of that in the video edition as well.

Beyond this, you can build an entire synthetic advisory service. For example, you could take one of your personas, a pile of marketing news for the week, and have your ICP-based persona email you asking questions about what the news means for their business. Or, since everyone's in love with podcasts made by AI, have one or two of your personas discussing marketing news of the week and then interviewing you as the expert on the topic.

Want to kick it up even more? Take the ideal customer profile and use it with a CRM agent as an advanced form of lead scoring. Every lead that comes in, a generative AI model can use the ICP to score how similar the new lead is to the ICP. Think of it instead of product market fit as lead fit, and use it as an additional scoring mechanism to prioritize and sort your prospects and leads in your marketing. (more on building AI agents here)

Part 5: Wrapping Up

As with so many other things, people have a tendency to treat ideal customer profiles as just another document on the pile, when in reality they're the basis for some of the most powerful generative AI work you can accomplish. You know who your customer is, or who you want your customer to be. Now bind that knowledge to as many aspects of your operations as possible, and you'll turn shelfware into real revenue.

Blatant pitch: if you'd like one of our AI-powered synthetic ICPs, just visit here and book some time with Katie to get one.

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Events I'll Be At

Here are the public events where I'm speaking and attending. Say hi if you're at an event also:

MarketingProfs B2B Forum, Boston, November 2024
AMA Houston, Online, November 2024
Social Media Marketing World, San Diego, April 2025
Content Jam, Chicago, April 2025
SMPS, Columbus, August 2025

There are also private events that aren't open to the public.

If you're an event organizer, let me help your event shine. Visit my speaking page for more details.

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Required Disclosures

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My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.

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See you next week,

Christopher S. Penn