Since we rolled out AI Enhanced Company Descriptions a few months back we've had several vendors reach out asking how this is generated, what info it pulls from and how it works--which is why we added the new sources and citations element to help both vendors (and hoteliers) understand how this AI generated product description is being generated.

In order to make it easier and more secure for users to access their HTR accounts, as of today we have fully removed passwords from the HTR application and have replaced them with social login and magic link.

By default, if you use a social account that has your current HTR email associated with it you will automatically be linked to that account and logged in right away.  If you try to login using a social account that has a different email address associated than your current HTR account has, it will create a new account since the emails won't match but you can reach out to our team if you need assistance merging these.  Alternatively, you can also use the magic link option to login using your HTR account email.

Easily uncover the different ways that you can generate more visibility and leads via Hotel Tech Report.

Please note that as of February 1, 2025 HTR will be migrating its email campaigns including eblasts and newsletters from Mailjet to Beehiiv.  

Why are we making this change?

As of recent we have noticed inconsistencies in Mailjet's CTR reporting.  After several weeks of trying to get a clear answer and resolution from their support team with no luck, we decided that it was time to migrate to a different service that can better support our needs as HTR's newsletter now reaches more than 100k+ subscribers.

How does this impact you?

The main difference that some clients running eblast campaigns may notice is a difference in reporting specifically around click rate.  Each email service provider has their own reporting measurements for analytics as well as bot prevention which means that comparing analytics from a campaign from Mailjet to the same campaign in Beehiiv won't be an apples to apples comparison.  As always, we strongly recommend that clients running email campaigns with HTR provide us with a dedicated UTM tracking link for your campaign so that you also have the ability to track analytics and campaign performance in your own marketing analytics tools.  Having said that, Beehiiv is one of the market leaders when it comes to email which is why we have selected them as our new provider and believe 

Additionally: 

• Emails Sent from @Beehiiv.com domain: Prior to allowing companies to integrate and send from their own domain, Beehiiv has a warm up period on new accounts for several weeks.  We don't have an exact timeline for when we will be able to switch back over to @hoteltechreport.com domain for sending; however, we have run tests and have found that the @beehiiv.com deliverability and open rates align with our own domain sending so this shouldn't impact campaign performance for advertisers

Today we rolled out a full redesign of Hotel Tech Report's main blog.  The new design transforms the blog into a newsfeed style format that enhances the user experience making it easier to access and discover relevant content for users.

Check it out

To kickoff 2025 we're launching a new AI assistant, Bell.ai.  Instead of hoteliers having to spend hours reading through reviews trying to synthesize information about each product, the AI assistant does this for them.

So what questions can Bell.ai help answer for hoteliers?

1. Summarize review and partner recommendations content 👉 What do customers and partners say about the product?
2. Run topical sentiment analysis 👉 What do customers think about key buyer consideration topics like pricing and usability?
3. Frequent mention analysis 👉 What do customers and partners most frequently mention across reviews?
4. Install base customer segmentation analysis 👉 What types of hotels typically use this product?
5. Review search 👉 What do users say about [x]?

1. Summarize review and partner recommendations content

2. Run topical sentiment analysis

The AI assistant automatically analyzes all review content for key topics including: user interface, product functionality, value, automation, customer support and integrations to output sentiment analysis along with summarization and citations by topic.

3. Frequent mention analysis

The AI assistant automatically analyzes hotelier reviews and partner recommendations to identify the most frequently mentioned topics by hoteliers and partners and outputs citations and summaries for each along with sentiment analysis.

4. Install base customer segmentation analysis

The AI assistant analyzes install base by property size, region, country, property type and product capabilities to help users understand what types of hotels typically use a given solution to help them determine fit for their own property. 

5. Review search

From within the AI assistant component on profiles, if hoteliers are looking for something specific they can search review content. 

Feb 2025 Update: Citations & Sources for AI Generated Product Descriptions

In an effort to create consistency add depth to product descriptions for users (as well as save time for vendors having to write them), we've infused AI into product descriptions to automatically generate an in-depth description based on the following data sources:

• AI Generated Review Summaries (view example)
• AI Generated Partner Recommendation Summaries (view example)
• Product description field in the profile editor (view example)

More Context About This Update

After surveying our audience of hotel tech buyers we found that most company descriptions generally had two major problems:

1. Too vague: They didn't tell users exactly what companies products do
2. Too much hyperbole: Most company descriptions were filled with filler words and hyperbole (i.e. words like revolutionary, etc.) that reduced user's trust in them

Given that feedback from users, we decided to build this feature leveraging AI to ingest information from your company's self-written description as well as other profile information like reviews and output a clear, consistent and to the point description using a series of LLM prompts.

Today we rolled out an update to HTR's hotels database and hotel lookup on the site where we migrated from Google Places as the core search to Tripadvisor.

TL;DR

• At the core of HTR is our hotels database
• Since inception, we have used Google Places as our hotel lookup to find hotels
• PROBLEM #1. The issue with Google Places as our core lookup is that its not specialized for hotels which meant that the database was able to have hotels that weren't even hotels (ie. users could select their city or any other type of location in Google) and Google often doesn't have the best hotel specific content
• PROBLEM #2. In order to launch HTR's upcoming AI Tech Stack Advisor tool we needed a stronger foundation of a hotel lookup that could accurately on-the-fly build a model of a hotel with key attributes like location, size, ADR, chain scale, amenities and hotel type while ensuring that the only results in the hotel lookup field were real and valid hotels
• New Hotel Lookup Component. As such, we rebuilt our entire hotels db and hotel lookup throughout the site with a new component built on a foundation of Tripadvisor's API with custom logic and mappings for things like keywords, attributes and guest type to further enrich and segment the database and ensure accurate hotel lookup and model building in real time on-the-fly
• Historical Data Migration. A major undertaking in this project was to run the old hotels database of 100k+ hotels through the new hotel lookup component to verify, validate, enrich and de-dupe hotels which impacts historical data in the database 

How will this impact my company?

Migrating historical data is a massive undertaking and will be rolled out over the next several weeks gradually updating ~5,000-10,000 hotels per day.  During this time you will likely notice some changes to the flags on reviews on your profile as existing/legacy hotels in the database get mapped to Tripadvisor hotels and then enriched via the new hotel lookup.  If you have any concerns or see any data that you believe to be inaccurate please let us know.

Users can now easily search reviews to quickly find specific content or data points that they are most interested in.