In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to leverage large language models like ChatGPT with your business analytics data. We talk through real examples of using AI to analyze CRM data for buyer personas, extract tables from PDFs, summarize PowerPoints into executive briefs, transform your data into actionable insights, classify social media comments, and answer questions about your data. We explain that while generative AI is exciting, your analytics data should still be the backbone of business decisions. Tune in to learn creative ways analytics and AI can work together to enhance your marketing strategy.

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In this week’s In-Ear Insights, we have been talking and everyone has been talking about large language models, the tools that power things like ChatGPT, for ever and ever.

And one of the things that we want to be cognizant of, is not losing sight of the fact that the workhorse of machine learning and AI is typically around good old, almost boring analytics data.

How do we use these these newfangled fashionable tools with data that still runs our businesses? So Katie, when you think about analytics, and what’s the what are the kinds of data that you might want to be able to ask a language model about? And then we can talk through some of the use cases, I did a talk recently with our friends over at martec martec.org, about using language models with analytics data?

Well, I’m glad we’re talking about analytics data, because I feel like large language models and generative AI have overtaken the conversations in a way that we have been distracted and almost forgotten.

That things like our analytics data are what power the business are what help us make decisions, and that without it, these other pieces, the shiny objects are just that.

So when I think about analytics data, you know, obviously, I think about my website data, things like a Google Analytics, or Adobe analytics or a matomo, telling me what’s happening within my website in that ecosystem.

But then I also think about like my CRM data, what are people opening and what are people ignoring and what’s causing someone to unsubscribe or decide that they want to stick around.

And then the third piece is my sales and revenue data.