Unlocking Hanzo’s Growth: How a Global Beauty Company Mastered Churn and Boosted Satisfaction

A leading global beauty company, Hanzo, specializing in professional salon beauty tools offered through a product and service subscription model, faced challenges understanding their customer behavior. Customers purchased their tools and signed up for maintenance subscriptions, but would sometimes cancel their subscriptions without getting the full value of the offerings. They recognized a critical need for:

• Better understanding of why customers churn.
• Effective strategies to acquire new customers.

The Solution: Easy Answers to Complex Data Problems

To tackle these hurdles, the company knew they needed to unify data from their many sources, generate rich analytics, and then build and deploy a model that predicted churn. They implemented App Orchids’s conversation analytics, Easy Answers™ to accelerate their insight process:

• Unified View of Data: They used a unified semantic layer to unify data for product sales and subscriptions, creating a single, comprehensive view of their customer data. This eliminated data silos and provided a holistic understanding of customer behavior.‍
• Demand Forecasting: By integrating machine learning models, they gained the ability to accurately forecast demand and identify emerging trends. This proactive approach helped them optimize product, tailor offerings, and anticipate customer needs.‍
• Churn Prediction Optimization: Perhaps most critically, they developed robust customer churn prediction models. This allowed them to identify customers at risk of churning before they left, enabling timely interventions and personalized retention efforts.

The Remarkable Results

The impact of this strategic approach was significant and directly addressed their initial challenges:

• ‍Reduction in Customer Acquisition Cost (CAC): By understanding existing customers better and reducing churn, thecompany could optimize their marketing spend and focus on acquiring trulyvaluable new customers more efficiently.‍
• Reduction in Customer Churn: The churnprediction models and proactive retention strategies led to a substantialdecrease in customer attrition, keeping more subscribers engaged and loyal.‍
• Improvement in Customer Profitability:With reduced churn and more efficient acquisition, the lifetime value of theircustomers increased, directly leading to a significant improvement in overallc ustomer profitability.

This use case beautifully illustrates how a semantic layer can accelerate transforming business operations. By understanding their customers and leveraging predictive capabilities, thisglobal beauty company not only solved their churn problem but also unlocked new avenues for growth and profitability.