Customers are any business’ most prized possession. In fact, it’s safe to say that they help companies become what they are.
But what happens when customers churn? Besides the obvious heartbreak, of course.
Well, customer churn takes a toll on the revenue, customer lifetime value, and brand reputation. And, if you want to avoid all this happening to your company and increase your customer retention rate, it’s high time that you invested in churn prediction.
Churn prediction enables businesses to identify, with some confidence, the likelihood of customer churn and enable appropriate, timely retention strategies.
So, let’s discuss in detail what churn prediction is, why it is important, and how businesses can leverage BlueConic’s AI Workbench for successful churn prediction.
What Is Churn Prediction?
A customer churn means that a customer decides that they no longer want to use your product or service.
And churn prediction is the process of using data to identify customers who are at risk of churning. By predicting churn, businesses can take steps to prevent it, such as offering discounts, providing more personalized service, or making changes to their product or service.
The good news is that now you can leverage BlueConic’s AI Workbench for a seamless and efficient churn prediction. Let’s discuss it in more detail.
What Is BlueConic’s AI Workbench, and How Can It Help in Churn Prediction?
Source: BlueConic
BlueConic is a Customer Data Platform (CDP) that enables businesses to collect, unify, and activate customer data across all channels.
The platform provides a single view of the customer, which businesses can use to improve customer engagement, personalize marketing campaigns, and make better business decisions.
And BlueConic’s AI Workbench is a machine learning platform integrated with the BlueConic CDP.
AI Workbench provides businesses with a variety of tools for data analysis, machine learning, and artificial intelligence. As a business, you can use these tools to improve customer insights, automate marketing campaigns, and predict customer behavior. Marketing teams can use machine learning to analyze their data, gain new insights, and further enrich individual profiles.
How Does AI Workbench Help with Churn Prediction?
AI Workbench provides businesses with a variety of tools that can be used to predict churn.
These tools are:
Predict Propensity to Churn Notebook
This notebook uses a random forest classifier to predict the probability that a customer will churn. The notebook can be used to predict churn for individual customers or for groups of customers.
Analyze Propensity to Churn Notebook
A notebook that analyzes churn rates for different segments of customers. It uses the churn scores from the Predict Propensity to Churn notebook to calculate churn rates for different customer segments.
Churn Prediction API
The Churn Prediction API allows businesses to predict churn for individual customers or for groups of customers programmatically.
Now let’s talk about how incredible BlueConic’s AI Workbench is.
How to Leverage BlueConic’s AI Workbench for Customer Churn Prediction?
BlueConic’s AI Workbench is a machine learning platform that allows businesses to build and deploy machine learning models, including churn prediction models.
The following features provided by BlueConic’s AI Workbench can be used to build efficient churn prediction models.
Data integration
BlueConic’s AI Workbench can integrate with a variety of data sources, including CRM systems, marketing automation platforms, and web analytics platforms. This allows businesses to use all customer data to build a more accurate churn prediction model.
Model templates
You can use several model templates to build churn prediction models. These templates are pre-configured with the most common machine learning algorithms and settings, making it easy to get started with churn prediction.
Model evaluation
Many tools for evaluating churn prediction models allow businesses to assess their accuracy and identify areas where they can be improved.
Model deployment
Once a churn prediction model is built, it can be deployed to BlueConic’s customer engagement platform. This allows businesses to use the model to target customers with retention campaigns or to make changes to the product or service to reduce churn.
6 Things to Consider While Building a Churn Prediction Model
Creating a successful churn prediction model is no walk in the park. But you can increase the accuracy of your model’s results by choosing the right factors in the creation process.
To build a churn prediction model that gives you the desired results, you need to include the following:
- Segments
Identify the segments of customers that you want to target. For example, you might want to build a model for customers who have been with your company for less than a year or for customers who have purchased a particular product or service.
- Parameter Selection
Once you have identified your target segments, select the parameters that you want to use in your model. These parameters can include customer demographics, purchase history, and engagement data.
- Order and Expiry Date
Specify the date range that you want to use. This is important because customer behavior can change over time, so you need to make sure that you are using data that is relevant to your current business needs.
- Built-in Churn Template Model
BlueConic’s AI Workbench includes a built-in churn template model that can be used to build churn prediction models. This model is pre-configured with the most common machine learning algorithms and settings, making it easy to get started with churn prediction.
- BlueConic’s Library of Python Functions
BlueConic’s AI Workbench also includes a library of Python functions that can be used to build and deploy churn prediction models.
This library provides a number of functions that make it easy to clean, transform, and analyze data, as well as to build and evaluate machine-learning models.
- Scikit-learn Library
In addition to the built-in churn template model and the BlueConic library, you can also use the Scikit-learn library to build churn prediction models.
Scikit-learn is a popular open-source machine-learning library that provides a wide range of machine-learning algorithms.
Once you have built a churn prediction model, you can integrate it with BlueConic’s customer engagement platform. This enables you to use the model to target customers with retention campaigns or to make changes to the product or service to reduce churn.
What Makes a Customer Churn?
The first step to solving any problem is to identify what causes it in the first place.
That said, we identified several reasons why customers churn, and we’d like to share them with you so you can avoid pitfalls.
Customers churn because:
- Their user onboarding process wasn’t up to the mark.
- They had poor customer experience, such as friction in using the application or inadequate results.
- Their pricing plan had gray areas that were discovered later.
- They weren’t satisfied with the customer support on facing an issue.
- There was a profound delay in getting the results they wanted.
If any of this is happening with your business, you must address these concerns immediately.
Your customer support can be a great help in identifying what troubles your customers are facing.
This doesn’t end here.
Churn prediction may be a significant step to reduce churn, but it can only be reduced if you take the necessary measures to reduce it.
Data Pilot specializes in the use of BlueConic and leveraging its robust workbenches to present your business with accurate customer churn prediction models and pinpointing their root causes giving your company actionable steps to diagnose and remedy said causes.
Join us as we continue to navigate the data landscape, turning data into actionable insights.
Written by: Hafiz Usman and Rida Ali Khan