B2B Predictive Churn Analytics: Benefits, Models & Tools

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    Is there anything more frustrating than the potential of losing a customer? Perhaps they want to cancel their account because they aren’t using the tool enough to justify the costs. Maybe the key user has left the company and no one else has been trained to use it. Or it may be that the pricing structure doesn’t fit the company’s needs.

    What if you could detect these risky customers ahead of time? There’s no doubt that anticipating customer churn could significantly benefit your customer retention efforts as well as customer acquisition.

    In this article, we’ll guide you through how predictive churn analytics can help you boost customer retention and keep profits heading in the right direction.

    The Importance of Predictive Churn For B2B Businesses

    Customer churn can happen to the best of businesses. Say you’re a B2B company providing enterprise VoIP solutions: you’re delivering excellent service, communicating with clients well, and getting great feedback. 

    Despite all of these benefits, customers still pull out of trials, avoid upgrading from a freemium model, or cancel their accounts altogether.

    Customers often take time to research and choose your product. They want to solve problems, make their lives easier, and see maximum value. So chances are they don’t want to leave your company any more than you want to see them go.

    Predicting when and why customers might cancel empowers you to take action. And that’s where predictive churn analytics comes in.

    Using various data sets to detect potential dissatisfaction, this method takes a proactive approach to customer churn, allowing businesses to strengthen relationships before it’s too late.

    If your churn rate is high, the importance of predictive churn is probably obvious. If you consider it low for your industry, you might think that there isn’t much room for improvement. But even that small percentage of people who stop doing business with you can offer valuable insights.

    Predicting churn doesn’t only highlight at-risk customers. It also allows you to identify pain points in the customer journey, helping to improve aspects like onboarding and communications. This, in turn, boosts customer satisfaction but can also give you an edge in an increasingly competitive field.

    That said, predictive churn analytics should be implemented if you meet a couple of important criteria:

    1. you need to have sufficient high-quality data to work from,
    2. you need to be able to take action with the data you acquire.

    That implies that your company has been established for a few years, you have the resources to deal with historical data and use the results proactively. Meeting these criteria? You’re in the right place!

    💡 Pro tip: If you’re not quite there yet, our recommandation is to start with ensuring the quality of your data.

    The Different Models of Predictive Churn Analytics

    There’s not just one way to predict customer churn. Let’s go through the five different models:

    1. Logistic Regression Models

    blog logistic regression model
    Source: Saishruthi Swaminathan on Towards Data Science

    These models are considered linear and use a binary classification of prediction – that is, you’ll get a simple Yes/No or 1/0 type of result. Using personal data such as a customers’ income, age, or how long they’ve been a customer (known as ‘tenure’), the model guesses the probability of that customer churning. 

    2. Decision Trees

    blog decision tree model
    Source: Aunalytics

    These models look at historical factors like customer satisfaction and past issues. They then  break down data into a branch that represents a different question about the customer, providing a visual map of why they may decide to part ways with a business. 

    3. Random Forests

    blog random forest model
    Source: Will Koehrsen on Medium

    The random forest is a collection of decision trees that all work together. Each tree considers random data points to form an array of different predictions. All these predictions are then amalgamated to reach a general consensus. The advantage of this model is that it doesn’t just rely on one decision tree, but gets the most common answer from across the board.

    4. Support Vector Machines (SVM)

    This is the go-to model if you have intricate datasets and numerous variables. SVM excels at identifying the optimal boundary that separates these two groups so that complex data can be understood in the clearest way possible. 

    The model works by using a Machine Learning (ML) algorithm that analyzes different groups of relevant data and identifying hidden patterns to predict churn. Let’s take the example of a bank. Using the SVM model, you might look at binary categories such as whether the customer has a bank card or is an active member. You can also consider non-binary variables such as a customers’ credit score, geography, tenure, and bank balance. The model would cross-reference all of these data points to make its prediction.

    5. Survival Analysis Models

    blog predictive analytics survival model
    Source: JADBio

    These models concentrate on the timeframe leading up to a customer leaving. What sets them apart is that they don’t only consider if a customer will churn, but also when it is likely to happen. This gives you an insight into the most likely lifecycle of a customer, so you are prepared well in advance. 

    How to Get Started With Predictive Churn Analytics?

    Churn analytics are an integral part of customer retention. Here are the best tactics to use when you’re just starting out.

    Clearly define what constitutes churn for your business

    First, you need to establish what churn means for your company. It may mean they stop using a product or service altogether, but it could also mean significantly reduced engagement. You need to set the criteria to improve the accuracy of predictive churn. 

    Gather relevant customer demographic and transactional data

    Assemble the right data to get the most precise results. Transactional, behavioral, and demographic information are all essential to building a comprehensive understanding of future churn possibilities. To gather this data, look at existing systems such as your CRM, customer service platforms, email marketing list, call center database, and so on. 

    Split data into training and testing sets

    You need to find which of the aforementioned models are right for your needs, and that means assessing their capabilities. Start by dividing your data into two different sets: training (which will teach the algorithm the patterns within the data) and testing (which is a benchmark to test how the model works). 

    There is no definitive answer as to how to split data into training and testing. However, a good rule of thumb is to use the 80:20 model: 80% training data and 20% test data.

    Choose a suitable predictive churn model

    The model you choose will depend on the nature of your business and your priorities. Do you have large and complicated datasets? Then, Random Forests or SVMs would be the best choice. Or, if you need a high level of accuracy, start with a Logistic Regression Model.

    Evaluate model performance using testing data and key metrics

    Use your testing data to assess the accuracy of the model. The metrics you look at should include:

    • Recall: Also known as ‘sensitivity’, this measures the ability of your chosen model to correctly flag all positive cases, i.e. people who churned. High recall indicates the model is correctly identifying most people who churn.
    • Precision: Measures the ability of the model to avoid incorrectly flagging negative cases (people who didn’t churn) as positive ones. High precision shows the model is able to correctly flag customers who do actually churn.
    • F1-score: A ‘mean’ metric, the F1-score balances recall and precision by capturing both the goals of maximizing true positives while minimizing false positives. A high F1-score indicates a model with both good recall and precision.

    You’re aiming for a high recall to identify as many customers likely to churn as possible. But you also want solid precision to avoid wasting resources on contacting customers that won’t actually churn. 

    This stage will help you to determine how effective the model is in accurately predicting customer churn.

    Fine-tune the model to optimize accuracy and address issues

    It may take some work to get the model to the highest possible level of accuracy. With the help of your evaluation results, keep refining the model and correct any issues that come up during testing. Commit to continuous improvement to get the most accurate predictions.

    Making improvements starts with overcoming common data management challenges, like data quality and integration. It also means continually using the insights gained over time to keep the model up-to-date and precise. 

    Interpret results to understand factors influencing churn predictions

    Next, you’ll look at model outcomes to get insight into factors influencing churn rates. For example, safety stock planning can impact customer service and satisfaction and, therefore, churn rates. Likewise, price increases can cause customers to leave, whereas quick customer response times can decrease the likelihood of churn.

    How to Use Predictive Churn Analytics to Retain Your Customers

    Now you’ve got all your data, it’s time to put it to good use. 

    Identify at-risk customers using predictive churn analytics

    Your data analytics are telling you what is causing churn in your business. Use that information to identify those customers who are showing warning signs of churn. You now have the opportunity to proactively intervene.

    To streamline this process, create a list of at-risk customers in a centralized data analytics platform like ClicData. Use the dashboard to identify patterns, visualize the data, and generally stay organized.

    Implement targeted retention strategies to prevent churn

    Create a strategy to handle churn-risk customers. Your approach will be guided by the information your prediction analytics has provided on why they are likely to churn. Customer service may need to improve, product training could be provided, or a more personalized level of communication may be required. 

    Build Stronger Customer Relationships With Predictive Churn Analytics

    Predictive churn analytics is one of the most effective tools to prevent lost customers. 

    Rather than having to react to customer churn when the ship has already sailed, prediction analytics give you the chance to solve issues before they become a serious problem. 

    Your customer relationships will subsequently become stronger to help secure long-term growth.

    Good luck!