How might Earnix refine its banking product recommendation system to provide more tailored suggestions for individual customers?

Introduction

To refine Earnix's banking product recommendation system for more tailored suggestions, we need to dive deep into user behavior, data analysis, and personalization techniques. I'll outline a strategic approach to enhance the system's effectiveness and provide more value to individual customers.

Step 1

Clarifying Questions (5 mins)

• Looking at Earnix's position in the market, I'm thinking about the current state of their data infrastructure. Could you tell me more about the types and quality of data Earnix currently collects on customer behavior and preferences?

Why it matters: The depth and breadth of available data will significantly impact our ability to personalize recommendations. Expected answer: Earnix has access to basic transaction data and some demographic information. Impact on approach: Limited data would require us to focus on improving data collection before enhancing the recommendation algorithm.

• Considering the evolving financial landscape, I'm curious about the primary use cases for Earnix's recommendation system. What are the top 3 product categories that customers are typically recommended?

Why it matters: Understanding the most common recommendations helps us prioritize which areas to improve first. Expected answer: The top categories are personal loans, credit cards, and savings accounts. Impact on approach: We'd focus on refining recommendations for these key products initially.

• Given the importance of customer trust in banking, I'm wondering about the current customer satisfaction with Earnix's recommendations. Do we have any Net Promoter Score (NPS) or similar metrics specifically for the recommendation feature?

Why it matters: This will help us understand if we're solving the right problem and set a baseline for improvement. Expected answer: The NPS for the recommendation feature is currently at 30, which is average for the industry. Impact on approach: A moderate NPS suggests room for improvement but also indicates that the current system isn't failing entirely.

• Considering the competitive landscape, I'm thinking about Earnix's unique value proposition. How does Earnix's recommendation system currently differentiate itself from competitors?

Why it matters: This helps us understand what strengths to build upon and where we might need to catch up. Expected answer: Earnix's strength is in real-time pricing adjustments, but personalization lags behind some competitors. Impact on approach: We'd focus on leveraging the real-time capabilities while significantly enhancing personalization.