Using No-Code AI to Predict Booking Cancellations

Use Cases

This article will walk you through how to use the Obviously AI to predict booking cancellations.

Predicting booking cancellations can decrease uncertainty and increase revenue for hotels. Hotels, resorts, airlines, and the like need to be able to forecast revenue to plan for the future, but this is made difficult by cancellations.

As a result, many hotels implement strict cancellation policies, but this comes with the severe consequence of hampering customer satisfaction. Instead, hotels can use AI to predict booking cancellations, allowing them to accurately predict demand and revenue, besides improving cancellation policies.

That said, gaining value is about more than just building AI models. Hotels need to implement those models and predictions in the real-world to ultimately decrease uncertainty. What good is a prediction if it’s not acted upon? In this guide, we’ll explore how to deploy our model via Zapier, a no-code automation tool.

While we’re going to build and deploy a model to predict hotel booking cancellations, the same steps can be used for airlines, resorts, doctor’s offices, or even barbershops.

1. Building a booking cancellations prediction model

To start, simply make a Pro account on Obviously.AI — you get a 14-day free trial, with no credit card needed to sign up.

Once you log-in, you’ll see a dashboard with an “Add Dataset” icon on the left. For this example, upload a simple tabular dataset from Kaggle, which features 32 attributes across nearly 120,000 bookings.

Our goal is to predict a KPI called is_canceled, which is a value indicating if the booking was canceled (1) or not (0).

After clicking to upload a CSV file, you’ll need to verify the dataset, and simply check off that the first row is the column names and that all requirements are met.

Now, we select the column we want to predict, which is called “is_canceled.” In the background, a series of machine learning models compete to create the most accurate predictions for is_canceled. You can see that the dataset is somewhat imbalanced, in that fewer bookings were canceled than not, but it's overall a high quality dataset.

And we’re done! Now, we can also see how various attributes impact the booking cancellations KPI. The main drivers discovered by the automated machine learning models were country and market segment.

Let’s move on to building a Zapier sequence, so you can deploy predictions in the real world, and predict booking cancellations on new data.

2. Building a Zapier Flow

Now, make a free Zapier account, if you haven’t already. We’ll start by connecting Zapier to a dataset in Google Sheets. We’ll select “New Spreadsheet Row in Google Sheets” as the trigger to activate a Zapier sequence.

After selecting that trigger, I simply located my demo Google Sheet, which I titled “New Data.”

This sheet should have the exact same column names and data types as the file I used to make the predictive model (minus the actual target column of “is_canceled”).

For instance, since my training data had columns like lead_time and arrival_date_month, and so on, I use those exact columns in this Google Sheet.

Next, we’ll select “Webhook,” and then “Custom Request.” This is how we’ll integrate with Obviously AI.

To set up the Custom Request, add in these details:

The data field is the most important, which requires matching up the column names in your data with the column names used in your model. Check the documentation if you’re unsure!

To set it all up, you’ll also need your Report ID and API key. Your API key can be found in your account, which gets added as a “Header” in Zapier. To get the Report ID, head to the “Export Predictions” tab in Obviously AI, and click on the shareable report link.

The Report ID is the ID you see in that URL. In the example above, it’s the string “cff9...1fd7.” As you can see in the documentation, this gets added in the bottom of the “Data” field in Zapier.

Now, we can test our Zap, which sends data from our new data sheet to our booking cancellations prediction model, and we’ll get the booking cancellations probability as an output. With these predictions in Zapier, we can now do anything we want with them: Send them to a Slack channel, send an email to our team, add the prediction to our sheet, or anything else.

For now, let’s just update our sheet with the estimated booking cancellations. This can be done with the action titled “Update Spreadsheet Row in Google Sheets.”

Then, simply add the probability created in the aforementioned webhook.

Now, let’s take it a step further. We can create a “filter” in Zapier to send an email notification when the predicted booking cancellations is above a threshold that we set. I set up a filter that gets activated when the booking cancellations probability is higher than .7.

If the booking cancellations probability is higher than .7, we can automatically send a notification email so that we’re aware of the situation.


Zapier is a powerful way to automate practically anything in your workflow. With Obviously AI, you can integrate the power of machine learning to create AI-driven workflows, including to predict booking cancellations and to decrease uncertainty and increase revenue for hotels.

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