How to Build an AI Customer Research Agent in Claude Code

Greg Greiner (0:00)

So back when I was a CPO at Bolt, I would have been happy to talk to seven or eight customers a month. And now with AI, you have the opportunity to 1000X or even more how many customers you're able to engage with on a regular basis.

Aakash Gupta (0:12)

Meet Greg Greiner, former CPO at Bolt and head of product at Uber for Business, now CEO at AI company Wolf.

Greg Greiner (0:19)

In the modern world, you should be using AI to create a scaled representation of yourself to talk to your customers for you. It should also come back with what it's learned and share insights with you on a regular basis. As PMs, we really need to get close to customers, and this is how we do it. Essentially, it's scaled research with AI.

Aakash Gupta (0:35)

How has AI changed what PMs need to be able to do?

Greg Greiner (0:38)

I think it's now possible to actually have real back and forth conversations with thousands or even hundreds of thousands of customers every month in a way that would previously require an army of researchers, an army of support reps.

Aakash Gupta (0:52)

You cooked it up in just 30 minutes. This is something I haven't seen somebody build before. What is the groundedness? What is the true data source?

Before we get into today's show, please take a second to check that you're subscribed on YouTube and following on Apple and Spotify podcasts. If you want access to all of my favorite AI tools, I've gotten them to give you an entire year of their paid plans. Check out bundle.akashg.com for an entire year of gold.new, Airtable, Speechify, Descript, Magic Patterns, Linear, Dovetail, Arise, and Mobin. Now, into today's show.

Engineering is getting faster with AI. Is product management really getting faster? One of the ways I would argue it is, is by helping you talk to more and more of your customers. Now, of course, you've been using Claude to help you synthesize transcripts, but have you been using it to drive automated proactive research with every single one of your customers? How might you actually do so? If you really want to get to that next level of customer research with AI, today's episode is for you. Greg, welcome to the podcast.

Greg Greiner (2:16)

Thanks for having me.

Aakash Gupta (2:18)

Greg, you told me something pretty interesting. When you were Chief Product Officer of Bolt, you would be happy to talk to five to ten customers a month.

That has really changed with AI. What is this new level of AI research report that you can build?

Greg Greiner (2:38)

Yeah, I think it's now possible to actually have real back and forth conversations with thousands or even hundreds of thousands of customers every month in a way that would previously require an army of researchers, an army of support reps to facilitate all of those conversations.

When you are able to have all of those conversations at scale, you're able to produce really interesting insights. This is an example for the company ancestry.com of what one of these reports might look like if you're able to have these skilled conversations with customers. One of the first things that we always report on is obviously the impact in the customer journey from a business metric standpoint. We found that having this type of personal conversation with customers doesn't only capture insight, but it also allows you to convert a broader set of this audience. In this case, this is people who've abandoned the process of purchasing a DNA kit with ancestry.com. Beyond that conversion insight, you're also learning for these people who specifically dropped off in that funnel, what are their biggest concerns? What are they bringing up most frequently? What is actually driving them to abandon?

Then in addition to that, when you layer on the conversion data about what people actually do after these conversations, whether they've finished their purchase or not, you can double-click a layer deeper into what are the relative conversion rates of these buckets. Not only how often are they specifically happening, but when they do happen, how do these people behave and how does that compare against the baseline of all the people who engage in conversations? You can see whether something is an issue or a hesitation or a concern that is easily addressed with just messaging, the AI responding back to the customer and sharing, specifically answering the question around shipping and delivery time. That's something where this can really just be solved by updating your messaging, maybe more proactively talking about shipping and delivery time, versus something like subscription confusion that even when the AI has the chance to respond to the customer and address that concern, these people are still not converting at a high clip. That opens up a broader opportunity for you to dive into that problem and try and address it in a more holistic way with your product.