Mind Readings: Most Analytics Data is Wasted

In today's episode, let's talk about analytics. Because this past week I've been on the road talking a lot about analytics. And here's the uncomfortable reality. A lot of analytics data serves no purpose. Doesn't matter.

This is something that my my CEO and co-founder Katie Robert and I have been discussing for years. And that's actually the origin of the title of our live stream, our Thursday live stream called So What, The Marketing Analytics of Insights Live Show. Katie routinely asks me, so what? Whenever I present something like, hey, look at this cool new chart or this API that I wrote or this, that, or the other thing. She's like yeah, so what?

What am I supposed to do with this? Not in a mean way, not in a disrespectful way, but in a in a very realistic way. Like, what is the value? What problem does this solve? And a lot of analytics doesn't solve a problem.

A lot of analytics, analysis in general, is almost a solution in search of a problem because you've got data and you need to make something with that data. So you make something that no one asked for. Right. I was putting together a presentation. In fact, I'm gonna be giving the presentation as I record this the next day on building a data-driven customer journey.

Now, this is a talk that I did in 2016 at Inbound. I updated it for a talk I gave in Poland, and now I've revamped the entire thing because hello, generative AI changed everything. And when I did the revamp of the 2019 talk, I realized that there was so much emphasis on how predictive analytics works and how data-driven analytics works, and all this stuff. There was no so what. It's like, okay, at the end of this, you've got your data-driven customer journey, and what?

What are we supposed to do with it? It's great, it looks great. The data flows from stage to stage, you can see exactly where in the funnel things have gone wrong, but it's pointless. Now, to be fair, there are situations where just having this data alone has a function, right? In say risk-averse corporate cultures, there is tremendous value in having data that shows, hey, here's everything that's happening with the data.

And you can see how this is what's happening with the lower sales number ain't my fault, right? It is a cover your backside piece of data. And so that slide is like a shield in the manager's hiding behind, uh, you know, deflecting blame for poor performance. And that is a fair and valid use case for analytics. But generally, what we say, what I say a lot is analytics without action is distraction.

Analytics without action is distraction. You've got the analysis and you don't do anything with it, it really didn't do anything, right? It really didn't help. Maybe you you found it insightful, maybe you found it a interesting to look at, but if you don't change what you're gonna do, it doesn't have a point. Seth Godin used to say years and years ago, uh, if you're not gonna change uh what you eat or how you exercise, don't bother getting on a scale.

You're not gonna change anything. So what's what's the point? And and there's a lot of truth to that. So I sat there with my deck and I was like, okay, well, what am I gonna do then? How can I make this data-driven customer journey more actionable, more useful?

And then in a flash of the blindingly obvious, I realized the answer, well, an answer, is generative AI. Right? Generative AI can provide a lot of those answers and recommended actions. So let's say your data-driven customer journey says that you've got your weakest point of convergence between prospects and marketing qualified leads. You just can't get prospects to become marketing qualified leads, right?

You've got your requested demo page up and it's just not working. What do you do? Well, you don't just show your stakeholder the chart, you take a screenshot of your requested demo page and feed it into Googlebard or Chat GPT or whatever, and say, you are a UI UX expert. You know what makes people convert. You know, page layout, design, color theory, psychology of conversion.

And here's the page, critique it. Tell me what I've done wrong. And it will spit out a long list of everything that you've done wrong with that page. Now you've got a plan of action. Now there's a so what.

The so what is this could be better, right? This sucks, and it could be better. The suppose that you've got you know a customer retention metric, right? Retention of customers and how loyal they are, and you don't know why it's going down. What do you do?

Go in your call center, go in your customer service inbox, pull all the customer feedback out, condense it down into a large file that can be analyzed by a language model and say, give me the top five reasons that people love us, give me the top five people reasons that people hate us, give me three things that we need to fix, and it will do that. It will crunch the data and spit out recommendations based on what you've given it to summarize, and you can take action on that, right? You can bring it to life. You can answer the so what. What does this mean?

Hey, our our our customer service ratings are down. Okay, well, what are we going to do about it? We are going to fix X, Y, and Z. That will that kick starts the process of getting people to take action, getting people to do something with their data. You can have reams of data, right?

Google Analytics generates enough data to fill a library by itself. What do you do with it? The answer is you feed the relevant data points into generative AI and say, help me understand some possible options. Give me some options. Give me some ideas about how to fix this problem.

And that gets you away from the blank page of what do I do to okay? Well, we can work with this. Or we can adapt this idea. Well, that idea won't work with our company, but it gives me an idea to do this. It jump starts action.

So it converts analytics into action. So the key takeaway here is doing the data-driven customer journey and all the governance that comes with that is important. You should do it. But it should be paired with generative AI to better know what you're going to do with the findings. If things are good, how do you make them better?

If things are bad, how do you keep it from getting worse? That's the power of a data-driven customer journey paired with generative AI as your expert advisor on your marketing strategy. Thanks for tuning in. We'll talk to you next time. If you enjoyed this video, please hit the like button.

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