As a data analyst hired by a bank to understand customer spending and banking habits, leveraging various data sources is crucial. My initial task involves reviewing the bank’s income statements, but a complete picture will necessitate integrating other data.

Identifying an Area of the Bank’s Income Statement Related to Customer Spending

While a bank’s income statement primarily reflects its revenues and expenses, certain line items indirectly relate to customer spending and habits. The most relevant area would be Service Charges on Deposit Accounts and Card-Related Income (e.g., Credit Card Fees, Debit Card Interchange Fees).

• Service Charges on Deposit Accounts: This includes fees charged to customers for various banking services, such as overdraft fees, non-sufficient funds (NSF) fees, monthly maintenance fees (if applicable based on account activity/balances), and ATM usage fees (especially if out-of-network). Changes in these fees over time can indicate shifts in how customers manage their accounts, their liquidity, and their reliance on certain bank services. For example, a rise in overdraft fees might suggest customers are experiencing more financial instability or are less diligent in managing their balances, potentially leading to increased short-term borrowing or reliance on credit.
• Card-Related Income: This encompasses income generated from credit card interest and fees (annual fees, late fees, foreign transaction fees) as well as interchange fees earned from debit card transactions. Changes in this income can reflect overall customer spending volume (debit card usage), credit utilization, and credit card payment behaviors. An increase in credit card interest income could indicate higher revolving balances, while a rise in debit card interchange fees might suggest increased transaction volume.

Data Points for a Complete Picture of Customer Spending (Last Six Months)

To get a complete picture of customer spending patterns over the last six months, beyond the aggregated figures on the income statement, I would focus on granular, transactional data.

1. Transaction Data (Categorized):
   • Debit Card Transactions (Amount, Merchant Category, Date/Time): This shows actual spending from checking accounts. Categorization (e.g., groceries, dining, entertainment, utilities, online shopping) provides insights into where and how customers spend their disposable income.
   • Credit Card Transactions (Amount, Merchant Category, Date/Time): Similar to debit, but also indicates reliance on credit for purchases.
   • ACH/Bill Pay Transactions (Amount, Recipient, Frequency): Reveals recurring payments for rent/mortgage, utilities, subscriptions, and other regular expenses.
   • ATM Withdrawals (Amount, Frequency, Location): Indicates cash usage patterns, which might be linked to small businesses, services, or personal spending not captured by card transactions.
2. Account Activity Data:
   • Average Daily Balance (Checking & Savings): Reflects liquidity and savings habits. Low or fluctuating balances might correlate with higher spending pressure.
   • Number of Overdrafts/NSF Occurrences: Directly linked to the “Service Charges on Deposit Accounts” and indicates financial stress or poor money management.
   • Number of Transactions per Account: Reflects account activity level.
   • Digital Banking Engagement (Login Frequency, Feature Usage): Indicates reliance on online/mobile banking for managing finances and making payments.
3. Credit Card Specific Data:
   • Average Credit Utilization Ratio: The percentage of available credit a customer is using. High utilization suggests reliance on credit for spending.
   • Payment Behavior (Payment Amount vs. Statement Balance, On-time Payments): Indicates financial discipline or potential struggles.
   • Interest Paid on Credit Cards: Directly related to revolving balances and spending habits.
4. Loan/Borrowing Activity:
   • Personal Loan Applications/Approvals/Balances: Indicates short-term borrowing needs outside of credit cards, potentially for larger spending items or to cover gaps.

Other Data Sources to Understand Customer Spending Patterns

Beyond the income statement and internal transactional data, several other data sources would be crucial for a holistic understanding:

1. Customer Demographics and Segmentation Data:

   • Internal Data: Age, gender, geographic location (zip code), marital status, occupation (if provided), income level (if estimated or verified for loans).
   • External Data (with consent/anonymized): Lifestyle indicators, household composition, life stage.
   • Benefit: Allows for segmentation of spending patterns (e.g., Millennials vs. Gen X, urban vs. rural, high-income vs. low-income) and identification of needs specific to different customer groups.

2. Customer Relationship Management (CRM) Data:

   • Customer Interactions: Records of calls, chats, branch visits, complaints, inquiries.
   • Product Holdings: What other products the customer has with the bank (e.g., mortgages, investments, auto loans).
   • Benefit: Provides context for spending behavior (e.g., is a sudden change in spending linked to a recent life event discussed with a representative? Are customers with certain product combinations spending more or less in specific categories?). It can also reveal customer satisfaction levels related to banking services that facilitate spending.

3. Third-Party Data (Aggregated/Anonymized):

   • Consumer Spending Indices: Economic data from governmental or research organizations that track overall consumer spending trends at a national or regional level.
   • Market Research Reports: Reports on specific consumer segments, spending habits in certain industries (e.g., retail, travel), or adoption of payment technologies.
   • Benefit: Provides external benchmarks and context to compare the bank’s customer spending patterns against broader economic trends and industry averages. This helps determine if observed trends are unique to the bank’s customer base or part of a larger economic shift.

4. Digital Engagement Analytics Data:

   • Website and Mobile App Usage Data: Pages visited, features used, time spent, common navigation paths.
   • Benefit: Shows how customers interact with the bank’s digital channels to manage their finances and potentially influence spending (e.g., using budgeting tools, setting spending alerts, applying for new credit products).

5. Surveys and Customer Feedback:

   • Direct Surveys: Asking customers about their financial goals, spending priorities, satisfaction with banking services, and pain points.
   • Focus Groups: In-depth qualitative insights into motivations behind spending decisions.
   • Benefit: Provides qualitative context and direct voice-of-customer insights that quantitative data alone cannot capture. It helps understand the why behind spending behaviors.

Steps to Prepare All Data Sources for Clear and Accurate Information

Preparing these diverse data sources is critical for accurate analysis. I would follow these steps:

1. Data Acquisition and Extraction:

   • Income Statement: Download the last five years’ annual income statements from Mergent Online. Ensure data is consistent in currency and reporting standards.
   • Internal Transactional Data: Extract raw transaction logs (debit, credit, ACH, ATM) and account activity from core banking systems. Specify the six-month period.
   • CRM Data: Extract relevant customer interaction logs and product holdings from the CRM system.
   • Digital Analytics Data: Export usage metrics from web and mobile analytics platforms.
   • Demographic Data: Extract from customer profiles within the bank’s database.
   • Third-Party Data: Acquire relevant reports or datasets.

2. Data Cleaning:

   • Handle Missing Values: Identify and address missing data points (e.g., using imputation, removal, or flagging).
   • Correct Inconsistencies: Standardize data formats (dates, currencies, text fields). Correct typos or erroneous entries (e.g., incorrect merchant codes).
   • Remove Duplicates: Ensure unique records for transactions and customer IDs.
   • Outlier Detection and Treatment: Identify and investigate extreme values that could skew analysis (e.g., unusually large transactions, extreme number of overdrafts). Decide whether to remove, transform, or cap them based on context.

3. Data Transformation and Normalization:

   • Standardize Naming Conventions: Ensure consistent naming for categories (e.g., “Grocery Stores,” “Groceries,” “Supermarket” should all be unified to “Groceries”).
   • Aggregate to Relevant Levels: Aggregate transactional data to customer-level (e.g., total monthly spending per category per customer) or segment-level.
   • Create Derived Variables: Calculate new metrics like average daily balance, credit utilization ratio, transaction frequency, or personalized spending categories (e.g., “Discretionary Spending”).
   • Time-Series Alignment: Ensure all data sources are aligned by time period (e.g., monthly, quarterly) for trend analysis.
   • Data Type Conversion: Ensure variables are in the correct data types (e.g., numeric for amounts, date/time for timestamps, categorical for merchant types).

4. Data Integration:

   • Identify Common Keys: Establish unique identifiers (e.g., Customer ID, Account ID) across all internal datasets to link them effectively.
   • Merge/Join Datasets: Combine data from different sources into a unified analytical dataset (e.g., join transactional data with demographic data by Customer ID).
   • Anonymization/Pseudonymization: For privacy and compliance, particularly with external datasets or when sharing results, ensure customer-identifiable information is anonymized or pseudonymized where appropriate.

5. Data Validation and Quality Assurance:

   • Cross-Verification: Compare summarized data points across different sources to ensure consistency (e.g., do the total service charges from the income statement broadly align with the sum of individual service charge transactions?).
   • Run Basic Statistics: Check descriptive statistics (min, max, mean, median, standard deviation) to ensure data distributions are sensible.
   • Create Data Dictionary: Document all variables, their definitions, sources, and any transformations applied.
   • Regular Audits: Establish procedures for ongoing data quality checks, especially for real-time data feeds.

By meticulously following these steps, I can ensure that the gathered data is not only comprehensive but also clean, accurate, and ready for advanced analytical techniques to uncover meaningful trends in customer spending and banking habits. This robust data foundation will enable the bank to make informed decisions for improvement.