Reduce Booking Cancellations

Whether you run a legal, medical, or other service business, you know how important it is to minimize your no-show and cancellation rates. With Akkio, you can use past data to predict which customers are most likely to cancel their appointments, enabling you to take proactive measures.

Background

Hotel booking cancellation rates exceed 40%, and cancellation rates have been on the rise.

The negative impacts of a cancellation, and the loss of predictable bookings, can be immense for a business. Cancellations lead to lost revenue, lost time, and lost goodwill. Akkio's predictive analytics platform can help detect which customers are likely to cancel and take the necessary steps to avoid the problem.

The Akkio platform's AI and predictive analysis capabilities can help streamline your process by predicting which of your patients, clients, or customers are most likely to cancel their appointments. Once you know who is most at risk of cancelling, you can adjust your marketing and sales efforts accordingly, and make proactive adjustments to your business model.

An Akkio analysis can find trends within the data and recommend the best course of action to take in order to avoid and/or mitigate those trends, giving you the ability to set the right strategy for your company.

The goal of Akkio is to give marketing teams the insights they need to optimize their campaigns and reduce their losses, which will ultimately lead to more profits for their organisations.

Predict Time-to-Close With No-Code AI

To get started, sign up to Akkio for free. As with any machine learning task, the first step is getting historical data and picking a column to predict.

Historical Data

We’ll use a Kaggle dataset called hair salon no-show dataset, which includes data on around 2,000 bookings, including columns such as:

  • Booking time of day
  • Booking day of week
  • Booked service category
  • Booked staff member

Each of these columns are potentially indicative of customer behavior, and each will be given a corresponding weight in the model. For example, perhaps a no-show is more likely to occur on a Monday morning, or during the weekend. However, this pattern may only appear with certain staff members, or in certain categories of service. 

The model will then use this information to develop a prediction for each customer, based on the patterns it has seen in the training data.

Crucially, the file includes our desired target variable, or whether the customer showed up. We can use machine learning to predict booking cancellations, and then prioritize efforts accordingly.

There are many factors that could affect no-shows. With Akkio, you can build custom models to address the specific drivers of your business. And with our drag-and-drop interface, you can build any predictive model you want. First, we’ll just download the dataset as a CSV, and upload it to Akkio in a new model flow. You'll get a preview of the dataset, including modifiable data types, columns, and row count.

The Akkio Flow Editor showing an appointment no-show dataset.

Building the Model

Now, we can click on the second step in the AI flow, which is “Predict.” Under “predict fields” you can select the column to predict, named “target.”

You can add or remove as many columns as you’d like, and we'll build a model based on the selected columns. Then just hit “Create Predictive Model,” and you’re done.

The Akkio Flow Editor showing a model to predict no-shows.

You can select a longer or shorter training time—ranging from 10 seconds to 5 minutes—for potentially more accurate models. Keep in mind that longer training times will not always necessarily perform better.

Also note that you don’t pay for model training time, unlike with many typical automated machine learning tools, so feel free to build as many models as you’d like.

Analyzing the Model

After a model is created, you get a simple overview, which highlights the top fields, model prediction quality, sample predictions, and more. 

The Akkio Flow Editor showing a summary of a predictive model to predict no-shows. 

For instance, we can see that our total accuracy is around 92%, with solid predictive accuracy for both classes: Customers that show up, and customers that don’t. 

We can also see customer segments, including those with a particularly high or low likelihood of a no-show. For instance, customers who didn't set an appointment date are more likely to cancel, while customers who set an appointment are less likely to cancel. 

The Akkio Flow Editor showing a customer segment with a high probability of no-show.

We can also see which customer segments are unlikely to pull a no-show. For instance, customers who set an appointment with a certain staff member, JJ, are unlikely to cancel, as seen below.

The Akkio Flow Editor showing a customer segment with a low probability of no-show.

There’s also the option to “See Model Report,” which lets us easily collaborate with others and share these model details with anyone, even if they don’t have an Akkio account. In short, we’ve discovered that it’s very easy to build an accurate model to predict no-shows.

Deploying the Model

Now that we’ve built a model, it’s time to deploy it in the real world.

With Akkio, it becomes trivial to deploy complex machine learning models. Deployment via web is an easy way to instantly serve predictions. It’s also possible to deploy through Salesforce, Google Sheets, Snowflake, and the Akkio API, with many more methods coming soon.

The Akkio Flow Editor showing model deployment options. 

Further, you could deploy the model in practically any setting with Zapier, a no-code automation tool that connects tools with “Zaps.” Zapier allows you to link up thousands of different tools, so no matter where your data is coming from, it’s easy to connect it to Akkio to get a prediction, even if you don’t have any technical expertise.

If you’re looking for more technical power, you can also use Akkio’s API, which is formatted as a Curl command, allowing you to send a GET request that contains your flow key, API key, and input data, and get a prediction back.

Summary

We’ve explored how no-shows and cancellations can impact your business. Cancellation rates are on the rise, and can have a devastating effect on your business.

This article has shown how machine learning can be used to predict which customers are most at risk of canceling. By implementing the right machine learning model for this use case, you can reduce your no-show rates and increase profits. Akkio enables a wide range of other AI use-cases, such as scoring leads, predicting fraud risk, and predicting churn.

If you’d like to learn more about using Akkio's platform to improve your business operations through predictive analytics, sign up for a free trial.

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