Get Ready With Me: Generative AI Webinar Prep, Part 4 of 5

In today’s episode, we’ll transform raw data into actionable insights, exploring how generative AI can enhance employee engagement and technological innovation in the hospitality industry. You’ll witness the power of AI in crafting practical checklists and personalized travel itineraries that cater to specific guest preferences. We’ll even delve into real-world datasets, like TripAdvisor reviews, uncovering the potential for AI-powered analysis to optimize guest experiences. Join me as we unlock the transformative potential of AI for hospitality!

Get Ready With Me: Generative AI Webinar Prep, Part 4 of 5

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

One of the things that is challenging about the consumer, generative AI web interfaces is they often can’t handle large amounts of data in a prompt itself. The developer versions of software can; [they] will do typically a much better job of this.

So we see here now, it’s extracting out the key themes from this dataset. So we’re going to go ahead and take a screenshot of this. Now, let’s duplicate this slide and change the opacity of this. This becomes more prominent.

All right. Another example: hotels have property management software. Let’s see if we can find some — what is hotel property management software? Let’s take a look at some images. Ah, look — we have — have we got here? With this is an image of — of property management, front desk operations interface from Clock PMS, and this is room — this is room stays.

So let’s open this image in this new tab. For any kind of extraction, we want to work with a model — we want to work with something where we can extract useful information from visual [to] identity information. So let’s go ahead and save this. Let’s save our notes, create a new prompt.

“I have attached an image of the front desk operations for our hotel, from our PMS, or property management software. The image shows our capacity, which guests are booked for which rooms. Provide an analysis of our property based on the image. How are we doing in terms of keeping our rooms full?”

Now we’re going to take that image that we saved — get “run calendar view style, room occupancy, dates, high occupancy, possible trends, several rooms are locked with maintenance.”

So this is super helpful for our example. Let’s go ahead and put the navigation out so that we can — because, yeah, we’re not necessarily trying to say that we are experts necessarily in — in hotel and property management. We do want to show our attendees we’ve done our homework, and we can — and we can give them examples of, “Here’s the software that you use in your industry, and here’s how generative AI” — even if it doesn’t plug into that software — “could be made to work with that software by taking things [like] screenshots and turning [them] into useful and actionable insights.”

Let’s move on to our next section of the deck, which is going to be summarization. I like this. That’s fine. This is, “Summarized call, customer history.” I’m going to leave this in here. I’m going to swap this out. This is our example of monitoring YouTube channels. And we’re actually going to — should we do an example? We [will] do a new example of how a hotel manager might — yeah, we might want to.

So let’s do this. Let’s go to YouTube. Let’s — and let’s [search] for “hotel travel hacks”. All right. Let me go ahead and close all these other tabs because we’re going to need a list of URLs.

So we’re going to look [at] — pull out — “Cheap Hotel Hacks”, “Hotel Hacks For Smart Traveler” — all right.

So we’ve gotten a good selection of the major hacks. We’re going to take that list of videos, clean it up just a little bit here, and now, as we did earlier, let’s take out the closed captions. Okay. So, as we did before, we now have our data file of the hotel hacks that people have come up with from YouTube. So let’s go ahead and get this in here.

“Today, we’re going to summarize a collection of transcripts about hotel hacks — ways travelers can make the most of their hotel stays, save money, and do unexpected — take advantage of unexpected loopholes. Outline the hacks discussed in this collection of transcripts in outline format, ordered by priority. In descending order, priority is determined by the positive or negative impact — actually, this is the negative impact — by the negative impact on a hotel property’s revenues.”

Since our audience is obviously hotel folks, they would want to know, “What are people saying online?” And what are they saying online? “What tips are the audience being given that could negatively impact your business?”

Okay, so let’s see what [it’s] come up with for hotel hacks — hacks that directly cost hotels money in booking: cancellation, rebooking at lower rates, bidding for upgrades to first-class, using bereavement rates when you’re not going to a funeral — that’s kind of scummy. So I would definitely say that’s — that’s worth — Yeah, people are awful.

Price matching with third-party sites, finding owner-direct rentals, using corporate rates, claiming corporate rates — right — not being an employee of the company with a negotiated rate, booking mobile-only discounts, using free amenities, etc.

So yeah, this is useful for — if you were a hotel property manager, this is the kind of thing that you would want to know what people are being told about, what people — being told — what’s in their media diet that could impact your hotel chain.

Remember, we are — if we go right back to our — our key priorities list, our key priorities list is recruiting issues, brand differentiation, playing your experience kind of — technological innovation. We’re using — this falls into technological innovation and that very core [of], “Hey, we need to make more money.”

And remember, in the key priorities for hotels, [it’s] “We need to make more money.” Well, this is how you’re being cost money, and there, you may or may not want to close some of those loopholes.

Let’s see. I’m going to get rid of this slide because we don’t need it for this example. Let’s go into our fourth category: rewriting.

So NDA examples — good. The training and professional development is good. That — that one’s good. I want to do one for — to address recruiting because I think that’s important.

So let’s go to — let’s go to our — our consumer model here. Start ourselves a new chat, and say, “Let’s get our expert prompt rolling for what the Gen Z employee cares about.” So we’re essentially having the model export out its knowledge of Gen Z and the things that they care about within the hospitality industry.

Right, so we have all of these — these key areas are the things that Gen Z would care about.

Now let’s go to indeed.com. Let’s go into “hotel”. Go away. And let’s go into the Boston, Massachusetts, mark — market. We have here The Eliot Hotel. Let’s see if we can actually — “Guest Service Agent, Hilton Boston Park Plaza” — Great! Let’s take this text.

I’m going to open up a text document here. Now, rewriting from our preloaded prompt area, we’re going to make a few little changes here.

“Here’s the job description — description from Hilton Boston Park Plaza for a guest service agent. The job description is written for a generic employee. Examine the job description and then outline how well or poorly the job description addresses the specific needs and pain points of Gen Z employees.”

So that’s going to be our prompt. Let’s go ahead and feed this into Gemini and hope it doesn’t choke.

It says, “Hey, here’s things that are good: emphasis on benefits, growth opportunities, diversity, inclusion. As [for] improvement, [there’s a] lack of purpose and impact, flexibility — lack of flexibility — traditional tone, which is a big deal. [And it needs a] big career path.”

So it’s great using the information provided and the facts in the existing job description. “Rewrite the job description to be more tonally appropriate for a Gen Z employee in the hospitality industry. Confine your writing to the facts in the original job description” — because we don’t want [it] hallucinating things like benefits [that] don’t exist.

That’s not — that’s — I would say that’s an improvement. If we had more information, we could certainly have this do a better job. I would say that’s the advice that I would give to any employer is to say, “Let’s take the existing job descriptions, take the known things that the audience cares about, and have these tools do a better job of writing your stuff.”

If you look at this original text here — full job description — “one of the most historic hotels in the city” — so from old to bold. That’s a nice rewrite.

Okay. Our fifth category: classification. So this is sentiment analysis. Hmm, we have those hotel reviews, let’s do — and they’re in JSON format, which is even better. Okay, so let’s go and take our — go back to Gemini. Save this. X.

Okay, so we now have — we’ve taken our JSON content set — here’s 20 reviews. “Perform a sentiment analysis, minus five — minus five to plus five and a large data set.”

And now we have a very nice sentiment analysis. So this will go into our slide deck as an example of how to classify text, which again, will be super helpful for hotel managers to know.

Our last category is question answering. Let’s see, how do we want to tackle this? Because there’s a lot of really good examples here, but these are all very, very real estate examples of how do you answer [a] question. I’m going to keep the agent in place.

That is it for part four of my “Get Ready With Me” series. Stay tuned for the fifth and final part! See you on the next one!

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Get Ready With Me: Generative AI Webinar Prep, Part 4 of 5

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