Churn prediction and retention managemetnt

the Power of Churn Prediction

In the fast-paced world of business, customer retention is a key determinant of success. As companies strive to stay ahead of the competition, understanding and mitigating customer churn has become a top priority. One powerful tool that is revolutionizing this effort is churn prediction.

What is Churn Prediction?

Churn prediction involves the use of advanced analytics and machine learning algorithms to forecast which customers are likely to discontinue their relationship with a business. By identifying early signs of potential churn, companies can take proactive measures to retain customers, thereby safeguarding revenue and fostering long-term loyalty.

Reduce customer churn by 15%

How Does Churn Prediction Work?

Churn prediction relies on the analysis of historical customer data, encompassing various touchpoints such as interactions, transactions, and engagement metrics. Machine learning models are then developed/trained to recognize patterns and correlations within this data, enabling them to make predictions about future customer behavior.

Key components of Churn Prediction:

  1. Data Collection and Integration

  2. Feature Engineering

  3. Model Training

  4. Evaluation and Validation

  5. Insights and Retention Strategies

  6. Implementation, Deployment and Monitoring

The Future of Churn Prediction

As technology continues to advance, the future of churn prediction holds exciting possibilities. The integration of artificial intelligence, predictive analytics, and big data will further refine models, providing businesses with even more accurate insights into customer behavior.

The 4 most important benefits from adding churn prediction to your retention strategy:

Resource Optimisation

Allocating resources more efficiently by focusing efforts on customers with a higher likelihood of churning.

1

Proactive Retention Strategies

Anticipating customer churn allows businesses to implement targeted retention strategies, such as personalized offers, loyalty programs, or enhanced customer support.

2

Causal explanation

What were the reasons behind your sales results? All events and activities are described.

3

4

Customer Satisfaction

Addressing issues and concerns before customers decide to leave enhances overall satisfaction and loyalty.

HOW MAGISTRENES A-KASSE WORK WITH CHURN PREDICTION

CHALLENGE

A favorable job market with low employment reduce risk and value from unemployment insurance.

Recruitment and retention of membership is showing reduced performance.

SOLUTION

Magistrenes A-Kasse requested a early warning churn-risk and retention tool from Predictify.

Predictify implemented a combination of ML models to predict member churn risk 3-6 months before actual churn.

Magistrenes A-Kasse are provided with a recommended retention activity based on member profile.

IMPLEMENTATION

Churn risk scores are implemented throughout the member service channels.

Retention initiatives are implemented in CRM-channels where dialogue and activity are fitted each member profile.

RESULTS

Members are giving very positive feedback towards retention activities and dialogue.

Retention KPIs are significantly improved in high value, high risk segments.

Our team on Churn Prediction

  • Kristian Vejborg

    Experince: I have worked with ML and retention management since 2016.

    My role: My key contribution is to lead the project, manage the scoping and delivery phase.

  • Dan Storm

    Experince: I’ve worked with churn prediction for the past couple of years.

    My Role: I manage data and build machine learning models that predict churn. I also measure retention impact.

  • Mads Vibe Ringsted

    Experience: I’ve worked with churn prediction for a couple of years.

    My role: I help to speed things up by setting up automation solutions. And I also work with Dan on building models.