Goal of the analysis:
Determine the rate at which commercial banking customers discontinue their relationships and identify drivers of churn to improve retention strategies.
Data required:
- Customer account tenure and closure data (e.g., the date each account was opened and closed)
- Transaction volumes and usage patterns (e.g., deposit activity, loan activity, treasury services)
- Customer profitability metrics (e.g., net revenue, cross-sell ratios)
- Demographic or firmographic details (e.g., company size, industry sector)
- Customer feedback or satisfaction scores (if available)
- Historical data on marketing campaigns, fee changes, or service enhancements
Detailed step-by-step instruction on how to conduct the analysis:
Step 1: Define what constitutes churn in your commercial banking context, such as account closures or a specified period of zero balance and usage, ensuring the definition is consistently applied across data.
Step 2: Extract and consolidate data on customers who have closed accounts or shown inactivity over a given timeframe, capturing relevant variables (e.g., industry, revenue contribution, product usage) for deeper analysis.
Step 3: Calculate your churn rate.
Churn Rate = (Number of Commercial Customers Who Discontinued / Average Number of Commercial Customers) x 100
Step 4: Segment the churned customers by key attributes (e.g., industry, company size, products used) to identify patterns or risk factors associated with higher churn.
Step 5: Analyze customer satisfaction indicators, usage frequency, and any recent service changes (e.g., pricing, branch closures) to see if they correlate with churn events.
Step 6: Compare current churn trends to historical benchmarks and industry averages. This helps determine if churn levels are stable, improving, or deteriorating, and how they stack up against peers.
Format of the output of analysis:
- Tables or dashboards summarizing churn rates by industry, product, or account size
- Charts illustrating trends in churn rates over multiple periods (monthly, quarterly, annually)
- Written commentary linking churn patterns to possible causes (e.g., fee changes, service issues)
How to interpret results:
- A rising churn rate can signal product dissatisfaction, increased competition, or misaligned service offerings for certain customer segments.
- Lower churn in certain industries or products may indicate successful retention efforts or strong market positioning.
- Deviations from historical churn patterns warrant closer investigation into external market shifts or internal policy changes.
Steps a company can take to improve on this measure:
- Develop targeted retention programs for high-value or at-risk customer segments based on churn analysis findings.
- Enhance customer onboarding and engagement processes (e.g., frequent relationship check-ins, usage tutorials).
- Conduct exit interviews or surveys to gather direct feedback from departing customers, using insights to address root causes.
- Use analytics and early-warning systems (e.g., sudden drop in transaction volume) to proactively intervene before customers exit.
- Align product and pricing strategies more closely with the evolving needs of commercial clients, leveraging ongoing market research and competitor insights.
