5 Real World Examples Using Predictive Analytics to Reduce Customer Churn

It's a well-known marketing adage that it's more expensive to acquire new customers compared to retaining existing customers. However, customers have become more discerning and have many mechanisms to shop around for alternatives. At the same time, there has never been a more important time in business to reduce costs and improve the return on sales & marketing efforts. One way to do so is to reduce customer churn. The challenge is that it's not always apparent which levers will be best to reduce churn, as well as how those levers can be integrated into everyday business processes in an automated manner. This is where Predictive Analytics can play a crucial role, firstly by identifying the levers and secondly by mitigating the risk of human bias in our decision-making. The good news is that while machine learning and predictive analytics used to be the realm of data scientists and engineers, modern tools are providing the means for business to test and implement these models into their processes. 

Below, we have summarised 5 key considerations for using Predictive Analytics to reduce customer churn. The proof is always in the pudding though - if you would like to book a free Machine Learning workshop with us, contact us today. 

1. Use Predictive Analytics to Understand Customer Behavior

Predictive analytics can be applied to customer data to gain insights into their behaviour and preferences. By analysing historical data, businesses can identify patterns and trends in customer behaviour that are markers for a desired customer outcome (e.g. repurchasing). This analysis can help businesses understand why customers churn and what factors contribute to their behaviour. With this understanding, businesses can take proactive measures to address those issues and prevent customer churn.

Example: Harley Davidson harnesses the power of predictive analytics to identify potential buyers, attract leads, and successfully seal the deal. Harley Davidson relies on their AI program to identify individuals who have the highest propensity to make a high-value purchase.   From there, a sales representative takes charge, reaching out to these potential buyers and guiding them through the purchasing journey until they find their dream motorcycle. By directly targeting customers, they can ensure a highly customised experience which ultimately results in greater satisfaction. Predictive analytics helps provide a personalised service to customers when they're ready to buy, while allowing the business to concentrate their efforts on serious buyers. (Source: Forbes)

2. Leverage Predictive Analytics to Identify Churn Indicators

Predictive analytics can also be used to identify churn indicators. By analyzing various data points such as customer demographics, purchase history, and engagement metrics, businesses can build predictive models that can identify customers who are at a higher risk of churning. These models can help businesses take targeted actions to retain those customers, such as offering personalized discounts or reaching out with proactive customer support.

1. Focus on attributes that the business can change: It is important to identify and analyze factors that the business can actually control and influence. This will allow for effective interventions to be implemented in order to reduce customer churn. 
2. Choose only a handful of indicators to focus on: Instead of analyzing a large number of attributes, it is recommended to identify a few key indicators that have the highest impact on customer churn. This will help in simplifying the model and making it easier to interpret and act upon. It is crucial to prioritize the attributes that are most relevant and influential in determining customer churn.
3. Experiment with results so that you can measure impact: After identifying the key attributes, it is essential to conduct experiments and tests to measure the impact of each attribute on customer churn. This can involve running A/B tests or implementing targeted interventions to gauge the effectiveness of changes made to these attributes. By continuously experimenting, businesses can refine their predictive models and improve their ability to accurately predict and prevent customer churn.

Example: Hydrant, a Wellness brand based in the US,  has successfully leveraged Predictive Analytics to identify churn indicators and predict churn propensity with 83% accuracy, while increasing conversion rates and average customer spend by 2.7x and 3.1x respectively, when compared to control groups. The predictive model creates detailed forecasts for each customer's possibility of churn. With these accurate individual forecasts, Hydrant dynamically segments customers to receive tailored marketing messages and discounts that match their future buying power. (Source: Pecan.ai)

3. Use Predictive Analytics to Personalize Customer Experiences

Customer Experience is where the brand promise is delivered, and where expectations are either met, exceeded, or missed. Often, the challenge is understanding where the customer is in their customer journey and what initiatives would nudge them to a business goal based on their own individual context. Integrating predictive analytics models into the Customer Experience enables businesses to tailor their interactions and offerings to customers as a segment. By understanding customer preferences and behaviour, businesses can provide personalized recommendations and offers that are more likely to resonate with customers. This personalized approach can enhance the customer experience and increase customer loyalty, reducing the likelihood of churn.

Predictive analytics can be used to personalize customer experiences by analyzing customer data and making predictions about each individual customer's preferences, behaviors, and needs. Here are some steps to use predictive analytics for personalization:

1. Collect and integrate customer data: Gather data from various sources such as customer profiles, purchase history, website interactions, social media activities, and customer feedback. Ensure that the data is accurate, up-to-date, and properly integrated.

2. Clean and preprocess the data: Cleanse the data to fix any errors, remove duplicates, and handle missing values. Preprocess the data to transform it into a suitable format for analysis.

3. Define customer segments: Use clustering techniques or customer segmentation algorithms to group customers into different segments based on their similar characteristics, preferences, and behaviors. This helps in understanding different types of customers and tailoring experiences accordingly. Pro-tip: Leverage Text Analysis to identify key themes in unstructured customer feedback in order to build richer customer segments. 

4. Analyze and model customer behavior: Use predictive modeling techniques like regression, classification, or recommendation algorithms to understand customer behavior patterns. This can help predict future actions, preferences, and likelihood of certain events, such as purchases or churn.

5. Develop personalized recommendations: Based on the predictive models, make personalized recommendations to customers. For example, suggest relevant products, promotions, or content based on their past behaviors or similar users' actions. This can be done through targeted advertising, on-site recommendations, or personalized emails.

6. Real-time personalization: Implement systems that use real-time analytics to personalize the customer experience in the moment. For example, show personalized product recommendations as soon as a customer visits a website, or tailor the website content based on the customer's browsing behaviour.

7. Measure and optimize: Continuously monitor customer engagement, conversion rates, and customer satisfaction to assess the effectiveness of personalized experiences. Use A/B testing to compare different personalization strategies and fine-tune the models and recommendations based on the results.

Example: Having a truck breakdown is not only a bad experience for the driver and business operations, it also costs the business money.  Using connected devices and machine learning models, Volvo is able to predict when a truck is likely to breakdown, before the event has occurred.  The essence of connected services and proactive maintenance lies in the fact that, thanks to wireless technology and sensors, Volvo can gather copious amounts of real-time data from a vehicle. By analysing this data and identifying patterns, they can effectively forecast and preempt any potential malfunctions. This allows customer to plan a workshop visit at their convenience, and promptly address the issue before it results in an unforeseen breakdown. (Source: Volvo Trucks)

4. Measure the ROI of Predictive Analytics

Measuring the return on investment (ROI) of predictive analytics is crucial to assess its effectiveness and justify its implementation. Businesses can track key metrics such as customer retention rates, revenue generated from retained customers, and cost savings from reducing churn.

By comparing these metrics with the costs associated with implementing predictive analytics, businesses can determine the ROI and make informed decisions about the use of predictive analytics for customer churn reduction. Using A/B testing is a critical component of measuring the success of Predictive Analytics programs, and can be especially effective if tangible metrics such as churn rates and revenue are attributed in the model.

What are predictive models?

Predictive models are algorithms that use predictive analytics to forecast future outcomes based on historical data. These models use statistical techniques, such as regression analysis and time series models, to identify patterns and relationships within the data. By analyzing factors that have influenced customer churn in the past, predictive models can predict future churn and help businesses take proactive actions to retain their customers.

Reducing customer churn is a crucial objective for businesses. Machine learning and predictive analytics can play a significant role in achieving this objective. By using predictive analytics to understand customer behavior, identify churn indicators, personalize customer experiences, engage in proactive customer retention measures, and measure the ROI, businesses can effectively reduce customer churn and improve their overall profitability and growth.

At Advance Business Consulting, we're passionate about helping our customers get the best value out of their data and technology. Predictive Analytics and machine modelling are some of the techniques that our data geeks love to work with our customers on. If you're interested in exploring how these can enhance your business, book a free workshop. 

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