Data analyst at a retail bank

 Situation: Sales Decline for Premium Credit Cards

"In my previous role as a data analyst at a retail bank, we noticed a decline in premium credit card sales. Despite strong marketing efforts, conversions from applications to approvals were lower than expected."

Step 1: Data Collection and Funnel Analysis

• Data Gathering: Analysed sales data segmented by region, demographics, and channels (in-branch, online, mobile).
• Funnel Analysis: Focused on conversion rates across the sales funnel:

Lead → Application → Approval.

Step 2: Identifying the Bottleneck

• Drop-Offs: Found a high drop-off rate between application and approval—especially from online applicants.
• Root Cause: Identified manual approval processes as the issue, leading to delays.

It became clear that manual verification processes were the key issue. During the approval stage, the process required manual data entry and document verification for many online applicants. The system was not as automated as it should be, and these manual processes created delays that frustrated customers."

Step 3: Solution Implementation

• Automation: Worked with the team to implement AI-based document verification and system upgrades to reduce manual data entry.
• Communication: Collaborated with marketing to inform customers about faster approval times.

·       ·  Automation of Document Verification: We implemented an AI-based document verification tool that automatically scanned and verified customer documents during the application process, cutting down the need for manual intervention.

·       ·  System Upgrades: We upgraded the approval system to reduce the time spent on manual data entry, integrating it with the CRM and other backend systems to automate much of the data population.

·       ·  Process Re-engineering: We redesigned the approval workflow to prioritize faster, automated processing for online applicants and created a priority escalation path for high-value customers.

Step 4: Results

Sales Rebound: Sales increased by 25%, and approval rates improved by 15%.

Customer Satisfaction: Positive feedback led to a higher Net Promoter Score (NPS).

 

"By identifying process bottlenecks and automating workflows

, I was able to significantly improve performance and customer satisfaction, demonstrating the importance of understanding the entire customer journey, not just the numbers.

"When communicating complex data findings to non-technical stakeholders?

My goal is always clarity, relevance, and impact.

 I use a few key strategies:

1. Understand the audience: I first identify what the stakeholder cares about—whether it's revenue, customer behaviour, or operational efficiency. This helps me tailor the message to what's most meaningful to them.
2. Simplify without dumbing down: I avoid jargon and use analogies or real-world comparisons to make complex concepts relatable. For example, I might compare a predictive model to a weather forecast—it's not always perfect, but it helps you plan better.
3. Visual storytelling: I use clean, intuitive visuals—charts, dashboards, or infographics—to highlight trends, outliers, and insights. Tools like Power BI or Tableau allow me to make interactive dashboards so stakeholders can explore the data themselves.
4. Action-oriented communication: I focus on what the data means for them. Instead of saying 'The model accuracy is 87%', I say 'This model can help reduce churn by identifying 8 out of 10 customers at risk before they leave.'
5. Two-way communication: I always invite questions and feedback. Sometimes, what seems clear to me as an analyst may still be unclear to them. Being open to discussion helps refine the message and build trust."**