Every day, your hotel’s reservation team fields a nonstop stream of calls—each one a potential booking, each one a chance to hit your occupancy goals. Yet, for all that effort, far too many of those conversations end the same way: “I’ll call back,” “Let me check with my spouse,” or “That’s more than I was hoping to spend.”
For a Revenue Leader, those unbooked calls are more than missed numbers on a report. They’re the difference between a strong month and a not-so-good one. But your agents are busy and your supervisors are stretched thin. Nobody has time to comb through thousands of recordings to figure out why people don’t convert. Manual QA catches only a fraction of what’s really happening. The rest of the story (and the revenue tied to it) vanishes into thin air.
You can feel it in the daily firefight:
The consequence? Thousands—sometimes hundreds of thousands—of dollars in potential revenue quietly slip through the cracks every month. And it feels like there is nothing you can do about it.
At MiaRec, we have seen this pattern play out across some of the largest hospitality contact centers worldwide. That’s why our AI-driven platform was built to surface exactly what traditional QA misses: clear, structured insights into why bookings are lost, where the biggest opportunities lie, and how to recover them.
In this playbook, we’ll show you how leading hotel contact centers use MiaRec to uncover the root causes behind every unbooked call, prioritize follow-ups by revenue impact, and coach their teams to turn “I’ll think about it” into “Let’s book it.”
Every missed reservation represents a potentially empty room, which means lost revenue:
Lost Revenue = (Total Revenue if all rooms were occupied / Total Available Rooms) x Number of Empty Rooms
Hotel contact centers have always known this, but understanding why those reservations were lost has been nearly impossible. Traditional methods rely on spot-checking a handful of calls or maintaining manual Excel sheets. Even then, you might find out what happened—but not why.
The frustrating part is that you know you are sitting on a goldmine of data: every call is recorded. But until recently, there was no feasible, scalable, or trustworthy way to turn those recordings into structured insight.
Recent advancements in AI, particularly in large language models (LLMs), enable the analysis of every conversation with full context, eliminating the need for manual reviewers. Instead of listening to a small fraction (2-5%) of your calls, you can now extract accurate, actionable insights from 100% of your interactions. Once you have extracted those insights, you can aggregate them, perform trend or root cause analysis, as well as slice and dice the data to find the answers to your questions.
For hospitality contact centers, this means finally being able to answer critical revenue questions:
To set up your revenue recovery analysis, we will need to configure the AI Tasks to extract the data points and insights you need to make revenue recovery possible. We will show you below how you can do this with MiaRec Revenue Intelligence.
First, we go into the MiaRec Admin section to set up the AI Tasks. MiaRec AI Tasks are a set of prompts and custom instructions which tell the MiaRec AI what you are asking it to do and how you expect the results to look. Imagine the MiaRec AI Tasks as custom GPTs for MiaRec AI.
Each task has:
MiaRec is fully flexible; you can create your own AI Tasks and write your own prompts, and you can completely adjust the prompts and instructions to your specific business needs and show the AI what "good" looks like for your business.
During onboarding, MiaRec’s team configures the initial AI tasks and mappings with you. Over several meetings/emails, we will fine-tune the AI prompt that serves to achieve your hotel’s business goals.
For this hotel reservation recovery use case, we need five key AI tasks that power the Lost Revenue report and callback workflow:
Let’s go through them one by one so you know what is behind it all.
The first task we need to set up is using the AI to understand the preferred reservation start date, end date, and the number of nights the guest would like to book.
Image: Screenshot of how to create an AI Task to extract the reservation start and end date as well as the total nights the guest was going to book for in the MiaRec Admin Panel.
Purpose: Identify when the guest planned to begin their stay.
Purpose: Extract when the guest planned to check out.
Purpose: Automatically calculate how long the stay would have been.
If the AI does not properly extract the correct reservation dates, it will significantly affect the chance of success for an agent during a follow-up call. Many factors need to be taken into account for the AI to get the correct dates. For instance, the customer could be talking about a past booking, or one for this year, next year, or even talk about multiple dates as an option.
We customize this prompt to not make assumptions and analyze the call to be certain that it is for a new reservation, with the correct dates.
Purpose: Capture the total cost quoted for the stay.
Image: Screenshot of how to create an AI Task to extract the dollar value (or other currency) of the hotel reservation in the MiaRec Admin Panel.
To have the AI capture the cost of the stay, as opposed to other numbers mentioned in the call, together we will create a detailed prompt for it to recognize which numbers to categorize properly, knowing the difference between dollar amounts, loyalty numbers, and sensitive data such as credit card numbers (which, by the way, get automatically redacted).
Also, as part of the prompt, we have the AI recognize common colloquial terms for numbers so they don’t get ignored or misinterpreted, i.e., "two fifty-seven forty-five" means $257.45. So the AI knows that "two fifty" a night for 8 nights is $2,000.00, and if this call didn’t end in a booking, it warrants follow-up.
Purpose: Determine why the caller did not book.
Image: Screenshot of how to create an AI Task to extract the reason the reservation was lost.
Typical loss‑reason categories (customized to your property/brand):
Knowing if the room was booked or not on the call is crucial in order for the AI to get it correct for timely follow-up on the no-sale calls. Traditionally, hotel contact center agents record all of this manually, and even get 10-15 cents per call when they properly classify it as successfully booked. Even with this incentive, a lot of data gets missed due to human error, call volume, etc.
Image: Screenshot of how to create an AI Task to extract the call outcome in the MiaRec Admin Panel.
The AI automatically goes through each call and records the outcome in the database, a task that would require an unfeasible number of agents to do the same thing.
The AI prompt is scripted to not only look for a successful booking (e.g., "Booking is confirmed. See you on the 25th of November.") but also to check to see if:
Hotel contact center managers can now create reports of “No Sale” call outcomes and filter them by reservation date and dollar amount so their agents can contact high-value prospective guests to increase the hotel’s occupancy rate and revenue.
Hotel call centers field calls for multiple reasons, such as:
Image: Screenshot of how to create an AI Task to extract the call reason in the MiaRec Admin Panel.
As part of trying to recapture missed reservations, only attempted new reservation calls are required. Using the AI to classify the calls into categories, you can filter out the noise of non-reservation calls.
If your hotel contact center has divisions outside of reservations, i.e., support, those calls can automatically be filtered out since they won’t be relevant. For reservation calls, our team, together with you, will script the AI to look for key phrases to determine if this is a new reservation call or not.
Once configured, these AI tasks work together automatically. Each call transcript flows through the pipeline, the AI extracts these data points, and MiaRec populates structured fields for every record—ready for filtering, charting, or exporting.
Good to know: During onboarding workshops, we co‑create the list, write short definitions, and add guardrails (“classify only when explicit; no guessing”). We calibrate with real call samples, and then lock the taxonomy for reliable reporting.
Also, you can ask MiaRec to only apply the above AI Tasks to a specific set of calls. For example, you can choose to ignore calls under approximately two minutes or without usable transcripts (less than 200 words) and not include customer service calls.
We then test each prompt line by line to ensure accuracy. You can clone and edit tasks as you scale to new use cases—without re-engineering the foundation. The testing is crucial since agents and callers say the same things in different ways, and we codify them so the AI can classify consistently.
Now that we have all the data automatically populating, we need to turn our attention to reporting. Together, we will build a Lost Revenue report that aggregates no‑book calls by sales‑loss reason and estimated dollar value. This includes:
Image: MiaRec Revenue Insights Dashboard indicating the revenue lost per day. It specifically breaks down why the reservations were lost (by the number of calls and actual revenue).
In addition, you can create aggregated views in visual dashboards, like these two examples below:
Image: Screenshot of a dashboard within MiaRec indicating the average total nights booked per call as well as the number of calls requesting 1, 2, 3, 4, and 5+ nights.
Image: Screenshot of another dashboard within MiaRec indicating the dollar value bands per call.
This will give you information like:
All of this is done automatically, so your agent can focus on helping the customer, making the reservation, increasing revenue, and staying compliant with data-sensitivity regulations.
MiaRec Revenue Intelligence analyzes all of the relevant (within the scope we set above) calls for the insights we asked it to look for. It will analyze each call automatically and aggregate the data for you.
Now, you do daily triage. Depending on your contact center’s needs, this is what it could look like:
Agent workflow:
Image: Screenshot of single call view showing the AI-generated call summary.
Manager workflow:
In addition to calling back those lost revenue opportunities, you can also leverage this information and move upstream from recovery to prevention. Consider running a trend for loss reasons by intended stay date to see when "Price too high" or "Availability" spikes. You can also partner with revenue management to test price adjustments, packages, or inventory changes (e.g., pet‑friendly allocation) for those periods.
Note: You can also layer in MiaRec CX Intelligence to understand friction drivers behind each category.
Last, but not least, we want to ensure we track the success of this revenue recovery initiative closely by defining KPIs, such as:
Cadence: Weekly ops review for triage and trends; monthly revenue optimization review for pricing/policy tests.
With a one‑time, guided setup, MiaRec surfaces exactly why bookings are lost, how much revenue is at stake, and where to focus callbacks and coaching. The result is a tighter feedback loop, more brilliant pricing moves, and more rooms booked without adding headcount.
See MiaRec in action. Ask us to walk you through the AI Admin setup, the Lost Revenue report, and the callback workflow using your data—and start recovering the revenue you are already earning.