SiteMinder says hotels have been limited by tools that surface pricing recommendations only when someone asks. On September 22, the company unveiled the Dynamic Commerce Engine, which adds a second layer to those recommendations by covering the distribution setup underneath the price, including broken channel connections and unmapped room types. SiteMinder says the engine will surface optimization recommendations on its own and execute them once a hotelier approves, and that it will go live over the coming months.
What SiteMinder announced
According to SiteMinder’s release, hotels have been limited by technologies that surface pricing recommendations only on request. The Dynamic Commerce Engine uses machine learning and AI to find and execute optimization recommendations across every commercial lever for a hotel, at the pricing layer and, SiteMinder says for the first time, at the distribution layer. The distribution problems it names are broken distribution channel connections and unmapped room types, which the company says often go unnoticed and contribute to what it calls a multi-billion-dollar opportunity cost for hotels.
The engine draws on SiteMinder’s platform scale. The company says it has been used for 20 years by hoteliers at 56,000 individual properties and powers 2.6 million hotel rooms, 140 million reservations and 300 million room nights every year. Its machine learning model will use nearly 4 billion availability, rates and inventory signals to score optimization opportunities for each property and then surface the most valuable recommendations. Users approve every recommended change before the engine executes it.
SiteMinder paired the launch with traveler research. In a survey of more than 12,000 travelers across 14 countries, 51.4% said they had abandoned an online hotel booking because of a bad experience, and 26.5% said the cause was reviews, photos or details that did not feel trustworthy. The release does not describe how the survey was fielded.
Sankar Narayan, CEO and Managing Director of SiteMinder, said, “Hotels want to keep up with today’s changing travel landscape, but they don’t have the time or resources to do it.”
Why it matters to revenue and distribution leaders
The release puts pricing and distribution in one recommendation process, so a revenue manager’s list of priorities could include an unmapped room type next to a rate change. A VP of revenue evaluating the engine can ask how SiteMinder ranks those two kinds of recommendation against each other, and what the scoring is based on beyond the signal count the company cites.
The approval step is the second item to test. SiteMinder says every change needs a person’s sign-off before execution. For a multi-property group, that raises operating questions the release does not answer: who holds approval rights at property level versus corporate, how quickly a pending recommendation expires, and whether approvals can be set in bulk for low-risk change types. Those answers will decide whether the engine saves time or adds a queue.
The third is data scope. SiteMinder says the model will use signals from across its platform, and individual groups will want to know how much of their own property history it uses. Buyers can request a written description of what inputs feed each property’s score.
What is not yet known
The engine is not live. SiteMinder says it will go live over the coming months and has opened an interest registration. The release gives no pricing, no list of supported property management or revenue systems and no performance results from hotels using it. A group already running a separate revenue management system will want to see how the engine sits beside it before assuming it replaces any part of that stack.
SiteMinder Limited (ASX:SDR) is headquartered in Sydney, with offices in Dallas and London among others.
Source: SiteMinder newsroom
AI Editor gate notes
Gate 0: D1 source is a primary: SiteMinder newsroom (vendor primary). The article page returns a Cloudflare block to automation from this network; the full release text was read from SiteMinder’s own newsroom feed (https://www.siteminder.com/news/feed/, item dated 22 Sep 2026) and the gates were run against that text saved as a local file. Re-check the live page in a browser before posting. D3 excerpt 120-200 characters and distinct from body. D4 body 500-800 words. D5 zero em dashes, en dashes, entities or mojibake (scanned). D6 headline, body, source and topic cover one event. D7 one topic and 4 tags. D9-D12: neutral coverage, no negative verdict on a named company, no “reportedly”, every named-company claim traced to the cited source, no takedown-register hit. D13a headline_check PASS (see LAUNCH-POSTS.md). D-QUOTE quote_check PASS.
Notes: Run headline_check with –source-file and quote_check with a file:// URL because the live page is blocked.
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