Using Machine Learning to Predict & Prevent Customer Churn

As businesses evolve, so does the need for smarter, more efficient solutions. One key issue many companies face, especially those with subscription-based models, is customer churn, which refers to the point at which customers discontinue their relationship with a business.

Tackling this issue can feel like searching for a needle in a haystack, and that's where Machine Learning (ML) can help. By analyzing large volumes of customer data, ML automates the prediction and prevention of customer churn, empowering businesses to understand their customer base better and foster improved retention and growth.

In this article, we’ll explore the power of ML in predicting and mitigating customer churn. We'll cover which questions you can answer with ML, its benefits over manual analysis, and the essential components of an ML customer churn prediction model. Finally, we'll introduce Akkio, a leading ML platform that simplifies this process, enabling businesses to leverage ML without needing a deep understanding of data science.

Understanding customer churn and its impact on business

Customer churn, also known as customer attrition or turnover, refers to how many people or businesses stop using a service or product within a specific time frame. It's a major concern in many industries, particularly those leveraging subscription-based models, such as telecommunications, online gaming, streaming services, financial services, and retail.

Customer churn can take different forms. Voluntary churn, for instance, occurs when a customer actively decides to end their relationship with a company—a situation that often stems from dissatisfaction with the product, customer service, or a change in personal financial circumstances. On the contrary, involuntary churn refers to instances where a customer's association is terminated due to uncontrollable factors, such as moving to a location where the service is unavailable.

A more subtle form of churn is passive churn, where customers may still pay for a subscription but not use the product or service. Lastly, deliberate churn occurs when customers sign up for a service or product knowing they will discontinue after a short-term period, which tends to happen at the end of a free trial.

Understanding the nuances of these types of churn is vital for any business, but voluntary churn is particularly significant. These customers initially intended to be long-term users, making their departure a more substantial loss, posing an important problem to address.

Churn is a critical metric for businesses reliant on recurring revenue streams—like subscription services, telecom companies, and software-as-a-service (SaaS) providers. A high churn rate may signal customer retention problems, potentially hindering revenue and growth. Understanding and managing churn can help a company optimize its customer acquisition strategies, improve its products and services, and ultimately increase revenue and customer satisfaction.

Key metrics to measure churn include:

• The churn rate, which calculates the number of customers lost during a specific period.
• Customer Lifetime Value (CLV), which measures the total revenue a business can reasonably expect from a single customer account.
• Customer Acquisition Cost (CAC), which quantifies the resources a company must allocate (in terms of marketing, sales, etc.) to acquire a new customer.

By monitoring these metrics, companies can strategize effective measures to minimize churn, ultimately enhancing their growth trajectory.

How Machine Learning helps with churn prediction and prevention

Understanding, predicting, and mitigating customer churn has traditionally relied on manual data analysis and statistical modeling techniques like regression and survival analysis. While these methods can offer valuable insights, they often involve laborious processes and require deep statistical expertise. Moreover, these techniques typically need a substantial dataset, a luxury not every startup or small business can afford.

Businesses are turning to ML solutions to simplify churn prediction and prevention and cut down manual effort while providing more accurate results. ML employs algorithms to autonomously analyze vast amounts of data, deciphering patterns and trends that help make informed predictions. It can even work effectively with smaller datasets, making it a viable choice for businesses just starting their journey.

ML's adaptability allows models to be transferable between use cases. For instance, a model trained on Google Analytics metrics could still perform reasonably well across different datasets. This flexibility enables businesses with fewer user interactions to build models and make predictions much sooner.

ML algorithms delve into diverse customer data, such as historical behavior, interactions, and demographics, identifying signals that might indicate a customer's potential departure. This detection level brings a higher degree of accuracy than traditional methods.

What sets ML apart is its ability to predict customer churn and explain why customers might leave. These explainable models can identify the key variables that influenced their predictions. For instance, a basic use of ML could reveal customers likely to churn, enabling you to entice them with attractive offers or discounts. However, a more nuanced approach could expose that location plays a critical role in churn rates. Then, you'll be able to tweak your product or service for a specific demographic—by translating your page to another language, for example.

ML tends to generate two types of insights:

• Obscure Information: These are insights that work but often leave us puzzled as to why. Imagine A/B testing different UI button colors, only to find that users prefer one shade of blue over others, leading to increased engagement. The underlying reason might remain unknown, but the impact is clearly positive.
• Useful Information: These are insights that are intuitive, making sense and shedding light on your customer base's behavior. Although this type of information is more valuable, it might not surface as frequently as the obscure type.

Businesses can harness these insights to implement proactive measures aimed at customer retention, such as crafting personalized incentives or enhancing customer service quality. By leveraging ML for churn prediction, companies can better understand their customers, empowering them to enhance customer retention and drive sustainable growth.

Key questions about churn that ML can help answer

Unlocking insights about customer behavior, Machine Learning (ML) offers valuable answers to critical questions concerning customer churn. Let's delve into some of the pivotal questions ML can help answer:

Who is most likely to churn?

By sifting through vast data fields, such as demographics, purchase history, and customer engagement metrics, ML can pinpoint customers most likely to discontinue their relationship with your business. This targeted understanding can prompt proactive engagement with these customers to bolster their loyalty.

When are customers most likely to churn?

ML can identify telltale patterns in customer behavior, such as fluctuations in purchasing frequency or diminished engagement with your products or services. These patterns can indicate when customers are most likely to sever ties, enabling you to strategize timely retention initiatives.

What factors contribute to churn?

ML's powerful customer data analysis can reveal which factors are most closely linked with customer churn. This invaluable insight can help businesses identify potential shortcomings in their products or services that may be causing customers to leave, thereby informing improvements to curb churn rates.

What are the most effective retention strategies?

ML's robust customer data analysis and the ability to measure the success of various retention strategies help identify which tactics work best. 

Notably, these strategies can continuously improve.For instance, if an ML model suggests an ineffective retention strategy, this feedback can be utilized to refine the model and generate new strategies. This is known as active learning, wherein the model tests and learns from each strategy, paving the way for more effective retention efforts.

How can churn be predicted in real-time?

ML's ability to continuously and instantly analyze customer data allows it to pinpoint customers at risk of churning in real time. Real-time insight can prompt immediate recommendations for retaining at-risk customers, helping to mitigate potential churn before it happens.

Benefits of using ML for churn prediction

Harnessing the power of ML for customer churn prediction is a game-changer for businesses seeking to enhance customer retention. Here are some key benefits businesses can derive from leveraging ML prediction:

• Increased accuracy: ML algorithms excel in crunching vast amounts of customer data and identifying patterns and trends that might be missed in manual analysis. This can lead to highly accurate predictions of customer churn.
• Personalization: ML can delve deep into individual customer data, facilitating businesses to tailor their retention strategies to meet each customer's unique needs and preferences. Adding a personal touch can boost customer satisfaction and loyalty.
• Real-time insights: With its ability to analyze customer data in real time, ML provides businesses with up-to-the-minute insights into customer behavior and preferences, enabling proactive measures to prevent churn before it happens.
• Cost-effectiveness: By predicting churn, businesses can focus their retention efforts on customers most at risk of leaving and those most likely to be persuaded to stay. This targeted approach proves more cost-effective than blanket retention strategies.
• Scalability: ML models scale to handle large datasets effortlessly, making churn prediction accessible to businesses of all sizes.
• Automation: ML automates the churn prediction process, eliminating manual labor and freeing up resources for other business-critical tasks.
• Continuous improvement: ML models embody a self-learning mechanism that thrives on data. As more data becomes available, these models learn, adapt, and improve over time, resulting in increasingly accurate predictions and optimized business outcomes.

What do you need to get started with ML churn prediction?

Dipping your toes into ML for churn prediction might seem overwhelming at first glance. However, with the right resources, it becomes a less daunting venture. Here's what you'll need to jump-start your journey:

An ML platform

There are many ML platforms and software that can be used for churn prediction purposes. The best option for businesses that want a cost-effective and powerful solution is the predictive AI platform Akkio.

• Ease of use: Akkio's interface is intuitive, making it easy to build and deploy ML models. It automates complex processes and even selects the right ML model for you, sparing you the need for an in-depth understanding of ML.
• Integration: Akkio seamlessly integrates with popular tools like Hubspot, Google Sheets, Snowflake, Big Query, Salesforce, and Zapier. These integrations make creating scalable ML models that improve with larger datasets a breeze. Akkio even facilitates real-time churn prediction and lets you set up churn alerts.
• Data preparation: Akkio's Chat Data Prep functionality allows you to easily transform your data. Specify the changes needed, and Akkio prepares and cleans your data automatically.

• Data exploration: With its Chat Explore feature, powered by GPT-4, Akkio enables you to ask questions about your data and receive instant answers, providing quick insights on customer churn patterns.

Customer data

You'll need datasets containing historical data to train your ML model. Ideally, These datasets should include customer segmentation data, which could enhance the accuracy of your churn prediction model.

If you haven't defined your customer segmentation yet, Akkio can assist in detecting data patterns using clustering techniques.

A model

An ML model is a program trained on a dataset to recognize patterns and make predictions or decisions. For customer churn prediction, various types of ML models like logistic regression, decision trees, random forests, and neural networks can be utilized.

As we mentioned, Akkio chooses the optimal model for each scenario. So, as an Akkio user, all you need to do is:

1. Head to the Akkio homepage and sign up for an account—you can choose from three subscription tiers tailored to small businesses and larger enterprises, starting from $50 per month. Input your information, and be sure to verify your email address.
2. Once you’ve logged in, select ‘Create New Flow’ to get started.

3. You’ll need to connect your datasets. You can do this directly or integrate Akkio with a data collection tool.

4. Next, it’s time to prepare your data with help from Akkio’s automations. Click ‘Data Cleaning’ and configure your choices, then select ‘Preview’ > ‘Apply Transform’. The ‘Chat Data Prep’ option can provide additional assistance and transform your tables based on written instructions.

5. To train your model, click the plus icon on the left-hand side of the screen and selec to train a predicition modelt'. You can also tailor your training settings here; select which numerical or categorical fields to predict and ignore, and click 'Train Model' when you're done.

6. Once you've trained your model, click the plus icon again. Under 'Deploy Flow', you'll be prompted to select your deployment method. Choose from an API, web app, or integrated tool, and dig into your data!

7. Once training is complete, select the plus icon again, then ‘Deploy Flow’. Select your deployment method—API, web app, or integrated tool—and hit ‘Deploy’.

A target variable

This is the variable your model is designed to predict. In a customer churn prediction model, a binary variable is often used as the target variable to train the model to predict customer churn based on various input variables or features. The variable takes one of two values: "Churn" or "Not Churn".

Most models will provide predictions and probabilities of these predictions. Leveraging these probabilities allows for strategic decision-making—like focusing discounts on users with a medium likelihood of churn while letting high-risk customers go due to high acquisition costs. This can then be combined with customer segmentation algorithms.

The input variables or features used in the model can shed light on the factors associated with customer churn. For instance, if customers who haven't made a purchase in the last 30 days are more likely to churn, improving purchase frequency might be an effective churn reduction strategy.

Test a customer churn prediction model in real-time

Ready to see how ML can predict customer churn in real time? Check out the iFrame below, where you can upload datasets and try Akkio's powerful predictive AI platform for yourself.

Predict and prevent churn with Akkio

Leveraging ML for customer churn prediction opens up a world of benefits for businesses. These include increased accuracy, enhanced personalization, real-time insights, cost-effectiveness, scalability, automation, and the potential for continuous improvement.

By harnessing the power of a comprehensive tool like Akkio, businesses can take customer retention to the next level. Akkio's intuitive, cost-effective platform enables businesses to predict and prevent customer churn in a few simple steps. The solution also equips businesses with tailored insights that can enhance customer satisfaction and loyalty.

Now, it's time to turn insights into action. Explore Akkio's pricing plans and discover how machine learning can power your business to new heights. Don't just predict the future of your customer base; shape it!