Build with Catalyst: Risk churn analysis model using Catalyst QuickML

For our next use case in our Build with Catalyst series, we'll look at how you can build a robust model to predict who among your customers could possibly churn and why using Catalyst QuickML.

Check out our previous posts in the series to learn how you can:

• Build an AI-based lead-scoring engine using Catalyst Quick ML
• Improve healthcare service workflows using Catalyst SmartBrowz
• Migrate your app from another platform to AppSail
• Automate your sales incentive flow using CRM Event Listeners

Now, let's get started!

What is Catalyst QuickML?

Catalyst QuickML is an end-to-end, no-code ML platform by Catalyst to build, train, and optimize ML models, facilitating data-driven decision-making. We want to empower developers—with or without ML expertise—to create powerful, custom ML models and harness the power of AI.

Build a risk churn analysis model using Catalyst QuickML

Imagine this: You run a subscription based business. Customer churn is an essential metric to monitor. All the efforts you invest into perfecting the product, service, and marketing strategies would be meaningless if you do not understand the factors behind the churn.

Using QuickML, you can develop a model that predicts the likelihood of customers unsubscribing or downgrading their product, service, or subscription in near future. The model can provide insights for the sales team so they can take proactive measures to prevent at-risk accounts from churning.

The same model can also be used to predict the likelihood of a prospect becoming a customer, identify the types of products most likely to be bought by a customer, and many more.

Catalyst components needed to achieve this:
Catalyst QuickML
Catalyst Functions

To create a high-quality ML model in QuickML and make the most of your customer data, follow these steps:

1. Feed the historical customer data to Catalyst QuickML. This would include data like last visit to the product, socio-demographic data, ecommerce behavior, customer information, usage information, complaints data, and requests. QuickML can help collate this data coming from different sources and as separate files into one.
2. Build a pipeline to produce a model that identifies trends based on customer and behavioral data. Once models are created, QuickML can figure out the data points and factors that lead to a customer churning on its own.
3. Establish an endpoint using QuickML when the model is ready. This endpoint will provide a REST API to share prediction information to the right database or customer management tool.
4. Query the created model each month with recent customer data to ensure it adapts to changes in customer behavior.
5. Use Catalyst Functions to push predictions back to your CRM. This could help you filter the data accordingly to take required actions.
6. Since you could also figure out what factor is leading to churn for an individual customer, you can take pointed actions to retain the customer.

You can follow a similar workflow for multiple use cases. Get creative, tap into your data goldmine, and make informed business decisions.
We'd love to hear about your journey with Catalyst QuickML. Feel free to share your thoughts in the comments section or get a detailed 1:1 session. We will come back next week with another compelling use case. Stay tuned to this space to see how you can take full advantage of Catalyst and its capabilities.