In this final episode of our “Get Ready With Me” series, you’ll see how we pull everything together to create a compelling and insightful webinar tailored for the hospitality industry. We’ll use Google Trends data to predict travel demand and analyze Reddit forums to build detailed customer profiles. You’ll even get a glimpse into the future of travel booking as we experiment with voice-activated AI assistants and explore the implications for hotels. Join me for the grand finale and discover how to harness the full power of generative AI to transform your hospitality business!

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

Christopher Penn: In today’s episode, this is the fifth and final part of my “Get Ready With Me” series on how I prepare for a webinar using generative AI.

So let’s get right back into it and finish up the series. I’m going to keep the CRM example. I like that example. Hmm. Let’s build an ideal customer profile because I like where that’s going, and I [will] get rid of the — well, I [will] keep the predictive in — keep the predictive in because that’s something that — that’s something that real — that the hotel and hospitality management folks would be very, very interested in — in knowing, which is, “How do you take demand data and forecast it?”

So let’s go to Google Trends. Let’s see — let’s do “Boston”. Let’s take a look at “related queries”. “Hotel in Boston”, singular “hotel Boston”, and we want this to be of the United States. Let’s do the last five years. That looks good. All right, I’m going to export this data.

Okay, so let’s take our data that we’ve gotten from Google Trends and let’s get the keyword volumes for it and then feed that to some predictive software and hope it doesn’t blow up. We end up with — very nice — end up with a volume — keyword search volume — the numbers [are] kind of hard to read, aren’t they? They overlap a little — of when people will be searching for the — for a hotel room in Boston.

All right, so let’s take that and put that in the presentation. So take out this. So that’s the kind of thing that we talk about with non-generative AI.

But we want to use this to time our editorial calendars. For — for marketing purposes, we need the ideal customer profile. We can talk about — so we’ve got LinkedIn profiles as an example. Let’s go and get a screenshot of — go to Reddit — r/ — so one of the things that we can do is use tools, for example, like Reddit, where you have either customers or employees, or both, posting conversations about what their experiences are.

The nice thing about Reddit in particular is Reddit does have an API, and the API, if you use it according to the terms of service, does give you a lot of very useful information about what — what people are interested in or what people are talking about on. So let’s do — “subreddit is on the number of days to” — 14 days of posts. Let’s see what we come up with here. 997. This is a super busy subreddit. We’ve got a lot of data [to] process. Okay, it looks like we have 218. That’s actually still probably —

Go ahead and bring [it] up in our system here. This was sentiment analysis. I don’t need to —

Watch.

Start a new one. They were going to do some customer cohort analysis.

“Read through the following feedback from a forum about Hilton hotels, and infer the major types of customers that are posting to this forum. Return your analysis as an outline.”

Upload our data. We have 300,000 tokens. That’s about 220,000 words, give or take, which is plenty because we’ve got posts, and we’ve got the comments.

Let’s see. “We have loyal Hiltonists, business travelers, casual and frequent travelers, timeshare victims” — I don’t know why they’d be posting to the Hilton forum, but — “hotel employees, general commenters”, and, I would imagine, trolls.

All right. Let’s say, “Great. Inferring from the data we have supplied, build an ideal customer profile for the Hilton business traveler. Be sure to include demographics, if possible, inferred from the data, needs, pain points, goals, motivations, and challenges.”

Okay, so we’ve got a fantastic ideal customer profile here, just inferred from the [data]. Obviously, if you were Hilton, you would have way more data than this, but even just this is a great starting point. And, to be fair, you might want to do this separately from your own customer data because you might want to be able to see what people are saying when they’re not saying it to your face.

Go ahead and [put] this in the presentation here. We can remove this, remove this, remove this, [and put] this in place.

And then let’s do — so this is — this is the question as a synthesis. Let’s go to Hilton’s website, and let’s look at the newsroom here. “All-Inclusive Report” — let’s take this announcement here. This is their newest. Great.

“Score this news announcement from Hilton against the business traveler ICP. How well or poorly will — would the average Hilton business traveler perceive this announcement?”

Let’s put the announcement, and we’ll see what we get when I take a screengrab of the announcement itself. And we’re back to our model and see how it’s doing.

“Who would likely be poorly received by the average Hilton business traveler, scoring low on the ICP? Here’s why: no one cares, irrelevant, misaligned messaging.”

From a — if you were [a] hotel chain [and] had an announcement — an exciting new thing — you want to use your ID — you should even announce — or so how do we revise?

Okay, the rest of the talk is pretty templated in terms of the major points.

The last part, from the hospitality perspective, is the three major impacts. So hospitality — number one is AI, you know, AI agents. I think that’s worth keeping. The data is important. The org chart part is not important. But how people choose travel is going to radically change — how the customer chooses travel. This is back to that technological innovation.

Think here — let’s do this. I’m going to open up my phone. Let’s go to the ChatGPT app, and let’s see if they’ve turned on voice. Probably not. I don’t see “advanced voice mode” available yet here. See if it’s available in app language. “Voice mode, voices is Cove.” Who is Cove here?

“Hey there! I’ve got a really great feeling about us teaming up. I just want to share — hey, it’s great to meet you.