Tracking customer credit journeys

Oakmark Bank runs promotions offering on-the-spot loan application reviews (& free coffee) when visitors sign up to be a member of the bank. Some of those members' applications were rejected and they still don’t have an active loan with Oakmark.

The Chief Loan Officer would like to see whether any of these customers have improved their credit score since their rejected application, and offer them another application review.

Data to identify these customers lives across multiple tables; build a table leveraging multiple lookups to identify who needs follow up.

Deze oefening maakt deel uit van de cursus

Calculations in Sigma

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Deze oefening maakt deel uit van de cursus

Calculations in Sigma

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Begin gratis met de cursus

This chapter introduces the core building blocks that make Sigma calculations accurate and dependable. You’ll learn how syntax cues, data types, and logical expressions shape the results you compute. Then you’ll progress from IF and SWITCH logic to conditional aggregations and window functions to build calculations that answer real business questions.

Exercise 1: Sigma data and column types Exercise 2: Understanding Sigma function rules Exercise 3: Troubleshooting function errors and warnings Exercise 4: Basic IF functions in Sigma Exercise 5: Flagging customer groups with If functions Exercise 6: Assigning categories with nested If functions Exercise 7: Standardizing naming conventions with Switch Exercise 8: Aggregate IF functions Exercise 9: Building conditional KPIs with CountIf and AvgIf Exercise 10: Leveraging logical columns in calculations Exercise 11: Window functions Exercise 12: Recognizing window functions in Sigma Exercise 13: Calculating rolling interest rates with MovingAvg Exercise 14: Calculating cumulative sums on un-aggregated data

This chapter focuses on how Sigma streamlines period-over-period analysis for real business reporting. You’ll learn how to set up valid date fields, choose the right time granularity, and use Sigma’s automated MoM, QoQ, and YoY comparison tools. You’ll then expand into flexible techniques like DateLookBack and InDateRange to support rolling windows and custom time periods.

Exercise 1: What is Period-over-Period analysis?Exercise 2: Understanding the purpose of PoP analysis Exercise 3: Understanding what PoP analysis needs to work Exercise 4: Adding a quick PoP comparison to a grouped table Exercise 5: Sigma’s PoP (Period-over-Period) comparison menu Exercise 6: Calculating new loans approved since last quarter Exercise 7: Comparing growth of loan types year-over-year Exercise 8: InDateRange formula for custom PoP analysis Exercise 9: Identifying upcoming loan maturities Exercise 10: Trailing 12-month account openings

This chapter explores how Sigma’s Lookup tools let you enrich tables by bringing in data from other sources without writing SQL. You’ll learn when to use lookups instead of joins, how to configure them correctly, and how to return aggregated values through the Lookup wizard. You’ll also learn to troubleshoot multiple-match issues and perform multi-key lookups for complex datasets.

Exercise 1: Add Lookup modal Exercise 2: Understanding lookup requirements in Sigma Exercise 3: Configuring a Lookup in Sigma Exercise 4: Looking up account balances by customer Exercise 5: Labeling transactions with account type Exercise 6: Aggregating lookup results Exercise 7: Averaging credit scores for customer groups Exercise 8: Evaluating loan officer performance Exercise 9: Lookups with multiple key columns Exercise 10: Calculating monthly loan approval rates by type Exercise 11: Tracking customer credit journeys

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Exercise 12: Congratulations