In what ways can we improve Akulaku's product recommendation algorithm to increase customer satisfaction?

Introduction

To improve Akulaku's product recommendation algorithm and increase customer satisfaction, we need to take a comprehensive approach that considers user behavior, data analysis, and technological advancements. I'll outline a strategic plan to enhance the algorithm's effectiveness and ultimately drive customer satisfaction.

Step 1

Clarifying Questions (5 mins)

• Looking at Akulaku's position as a fintech platform, I'm thinking about the diversity of financial products offered. Could you provide more information about the range of products currently recommended by the algorithm?

Why it matters: Determines the complexity of the recommendation system and potential areas for improvement. Expected answer: A wide range of products including loans, credit cards, and investment options. Impact on approach: Would focus on cross-product recommendations and personalization.

• Considering user engagement patterns, I'm curious about the frequency of user interactions with the platform. How often do users typically engage with Akulaku's recommendations?

Why it matters: Helps understand the window of opportunity for making impactful recommendations. Expected answer: Users check recommendations weekly on average. Impact on approach: Would prioritize strategies to increase engagement frequency and improve recommendation relevance.

• Given the importance of data in recommendation algorithms, I'm wondering about the types and quality of data currently available. What user data points are we currently leveraging for recommendations?

Why it matters: Identifies potential gaps in data collection and areas for enrichment. Expected answer: Basic demographic data and transaction history. Impact on approach: Would explore ways to ethically gather more behavioral and contextual data to improve recommendations.

• Considering the competitive landscape in fintech, I'm interested in understanding our current performance. How does our recommendation algorithm's performance compare to industry benchmarks or competitors?

Why it matters: Helps identify specific areas where we're lagging or leading. Expected answer: Slightly below industry average in terms of click-through and conversion rates. Impact on approach: Would focus on quick wins to boost performance while planning for long-term algorithmic improvements.