One of the most common requests that we've received from clients since launching the live PPC Auction is the ability to get a forecast of potential clicks and coaching tips to maximize click volumes so we've been hard at working building just that and are excited to roll out the new missed clicks forecast.  Click here to learn how to use the missed clicks forecast to optimize your ppc campaigns

Next month we're going to be rolling out several new features to the Review Manager and since we're in the middle of the HotelTechAwards we wanted to make sure to announce these a few weeks in advance so companies can plan accordingly if you'd like to utilize any of these new features in your awards campaign.

1. 💌 Send from your Gmail to boost conversion rates on campaigns
2. 📝 Shared team templates to save time and simplify collaboration
3. 🇪🇸 Language specific review links
4. 🎨 Customizable branded footer to boost conversion
5. 🏷️ Tag updates to simplify list organization

1. Send from your Gmail to boost conversion rates on campaigns

• Before: All campaigns and review requests were sent from an alias email address that wasn't recognizable to recipient
• After: Ability to run review campaigns that are sent from your own email address to boost conversion on campaigns

Please note that this will be a Premium feature will be available to Gold and Platinum Members

2. Shared team templates to save time and simplify collaboration

• Before: Each team member had their own templates that only they could utilize and if you wanted to run campaigns with different templates (ex. running campaigns in different languages) you'd have to keep updating them constantly for each campaign.
• After: Now teams will have a library of shared templates

3. Language specific review links

• Before: The dynamic {{review_link}} inserted into all campaigns defaulted to english and relied on user's browser to switch the language upon arrival for all other languages
• After: Now in the new template library you'll be able to set the language for your template and it will insert the respective language native review link

4. Customizable branded footer to boost conversion

• Before: Users would have to manually create a branded email footer in design software like Canva
• After: Now in the new custom footer tab you can easily customize a professional branded footer to boost conversion on campaigns.

Also...you can use the custom footer to send campaigns on behalf of another team member.

5. Tag updates to simplify list organization

• Before: When a user imported a list of customers a tag would be automatically generated with the date of the import and importers first name.  As companies began to run more campaigns this became overwhelming in the UI and made it extremely difficult to filter by tag in the review manager (especially since all tags were sorted by least recent first).
• After: All auto-generated tags will be removed to de-clutter the work space and simplify filtering and organization so only intentionally created tags will remain.  Additionally, the list of tags will be sorted by most recent first making it quick and easy to find your most recent and relevant tags.

In an effort to create consistency add depth to company descriptions for users (as well as save time for vendors having to write them), we've infused AI into company 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)
• Company 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.

As we all know, there is no one best overall solution for all hotels in any category but rather there are better solutions (and worse solutions) for each individual hotel based on key attributes and buyer preferences.  

For this reason, HTR has been taking steps to continuously serve personalized results to users beginning with segment based rankings and Site Personalization is the next step in the evolution of providing a personalized buyer experience to each user as they browse the site based on attributes like their hotel size, location, PMS and more.

TL;DR

• HTR automatically identifies each hotelier's location based on IP address and stores this info via the new personalization settings which can be found on each category page (see below)
• Hoteliers can then add to their personalization settings additional info like their property size and PMS to further personalize their results
• Each hotelier's personalization attributes get saved and not only do the recommendations on the page they're on get personalized--but these settings get stored globally site wide so anywhere the user goes on the site they will automatically have results personalized to them
• Vendors can generate more (and better fit) leads by (a) improving segment based rankings and (b) configuring Lead Targeting settings (aka. Tell the HTR App what types of leads are best fit for you that you want to connect with)

Issues We Heard From Hoteliers

1. "It's tedious to continue applying the same filters on each new page"
2. "The default recommendations show vendors that sometimes don't even service my region"
3. "I reached out to a vendor that for a price quote or demo but they told me they don't service my region"

Issues We Heard From Vendors

1. "We're getting leads from regions that we don't service.  We don't want to ignore these requests but we also don't want to accept them because we don't service the region"

How This Feature Solves These Problems

The default sort is now set to personalization so that results show more relevant matches for users which not only takes segment based reputation into account but it also prioritizes vendors who are actively connecting with similar hotels  to improve the hotelier experience by reducing no response rates by increasing the probability that hoteliers reach out to vendors who are actively connecting with hotels in their region.

Tip to Generate More Leads from Your Target Segments

When you are logged into your vendor account you can now see your Lead Targeting Settings on category pages, make sure your Lead Targeting Settings match the types of leads that you want to connect with to generate more (and better fit) leads.

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

Earlier this week HTR officially rolled out AI Review Summaries.  We will be staging the rollout of this feature over the coming weeks with new products being analyzed by AI each day so keep an eye out for the new 'AI Summaries' badge on your profile.  

This feature will help hoteliers extract key topics, keywords and insights from thousands of reviews in a matter of seconds helping them make better decisions faster.

• Want to check out some examples of products that have already run? Check out: Duve, Oaky and Mews. 
• Want your product to be analyzed sooner? Reach out via the live chat to request to fast track your profile

Things to Keep in Mind

• Eligibility: Only products with a trustworthy profile (25-reviews) will have access to this feature.
• Content Control & Curation: HTR does not control the content generated from the AI Summaries feature.  We use OpenAI's API to analyze reviews and this is automated and executed programmatically based on the content of your product's reviews and partner recommendations.
  
• Updating for Freshness: Content is updated for summaries automatically for both hotelier reviews and partner recommendations when a given product generates an incremental 25-reviews (ex. if you have 50 reviews today and AI runs, then it will re-run when you reach 75-reviews, etc)

On average HTR buyers guides generate between 100-400 organic downloads/year in each category.  As such, when clients want to generate leads via downloads HTR invests to drive additional visibility and downloads by running multi-channel promotional campaigns driving traffic to the landing page to generate more downloads, faster and be able to service client demand in a given category. 

As such, HTR is subject to changes in demand and pricing in the digital marketing ecosystem which is why these campaigns fluctuate in pricing.  The below change reflects the change in advertising and acquisition costs and goes into effect for campaigns purchased individually or requested as part of a membership plan after June 1, 2024.

Since HTR launched the lead program back in 2019 we've heard one question time and time again which is, why are all leads priced the same? Today we're excited to announce that the lead program is moving to a demand based pricing model.

What is Demand Based Pricing?

Demand based pricing is a strategy where prices for products or services are adjusted in real-time based on various factors such as demand, availability, and market conditions. This model allows for more flexibility and responsiveness, ensuring that vendors have access to the most competitive pricing at any given moment.

How Does it Work?

With the new demand based pricing model, the cost of leads will be determined based on demand. For example, a 500+ room opportunity in the United States may cost more than a similar opportunity in India (assuming that there is higher demand for opportunities in the United States).  Lead pricing will gradually evolve over time to better fit market demand and reach equilibrium pricing where leads from segments that are less in demand will decrease in price and leads from segments in high demand will increase.

Access Real-Time Pricing

Vendors can access real-time pricing information directly from their vendor dashboard. This allows for greater transparency and enables vendors to make informed decisions about which leads to pursue based on demand and market pricing.

Stability and Predictability

To ensure stability and predictability, lead pricing is capped so that it will not change by more than 5% week over week for each segment. This constraint helps ensure pricing transparency as well as prevent rapid fluctuations in pricing giving vendors time to adjust their strategies to find the most cost-effective market segments based on real-time market conditions.

Benefits of Demand Based Pricing

• Competitive Pricing: Vendors can take advantage of real-time pricing to secure leads at the most competitive rates.  Low demand segments will get leads at large discounts encouraging more vendors to connect with these hotel tech buyers and improve the marketplace experience.
• Flexibility: The demand based pricing model allows for greater flexibility, enabling vendors to adapt to changing market conditions.
• Transparency: Vendors have access to real-time pricing information, promoting transparency and trust in the lead procurement process.
• Stability: With pricing changes limited to 5% week over week, vendors can expect a level of stability and predictability in lead pricing.

Conclusion

The shift to a demand based pricing model will enhance the overall experience for vendors over time by makings leads in low demand segments more accessible via decreased pricing and leads in high demand segments more accurately aligned to lead value.  This will in turn improve marketplace dynamics by encouraging more connections to hotel tech buyers that may be neglected in the market in a flat priced world. If you have any questions or need assistance navigating the new pricing model, please don't hesitate to reach out.