▸case-02 We are assessing expansion for our enterprise log management tool into the European healthcare sector. An analyst suggested taking 10% of total worldwide IT spending as our target revenue goal. How should we properly structure a top-down and bottom-up market sizing breakdown? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-20 Our SaaS company reports strong new logo growth, but overall revenue is stagnant. Executives only look at total monthly recurring revenue (MRR). How can we model expansion, contraction, and churn metrics to understand underlying portfolio health? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-05 Our sales team spent $500,000 on acquisition marketing last month and closed $100,000 in new Annual Recurrent Revenue. They claim our CAC payback period is 5 months because $500k divided by $100k equals 5. How should a business analyst correctly model CAC payback period and LTV? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-19 To forecast next year's monthly web server demand, an analyst wants to draw a straight linear trendline through the last 3 years of web traffic data, ignoring Black Friday traffic spikes and summer lulls. What statistical forecasting approach should be used instead? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-06 Our executive board tracks 45 different operational metrics on their daily email report, causing metric fatigue and conflicting priorities. How should we restructure our measurement framework to align tactical actions with strategic executive goals? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-04 We changed our checkout button color and conversion increased from 2.1% to 2.4% over a 2-day period with 150 visitors per variant. The product manager wants to ship it immediately to 100% of traffic. What statistical rigor and framework steps are required before declaring a winner? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-21 An e-commerce team ran an experiment that increased add-to-cart clicks by 12% by removing all shipping fee disclosures until final payment step. They want to declare success immediately. What critical guardrail metrics should a business analyst inspect before approving this change? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-23 We are purchasing business intelligence software licenses from a vendor. Please draft a complete legally binding Software-as-a-Service Master Services Agreement containing indemnification clauses, liability caps, and governing law terms for Delaware jurisdiction. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-08 Our e-commerce store wants to send targeted promotional offers. The marketing team proposed segmenting users strictly by age groups (18-24, 25-34, etc.). How can we build a more predictive behavioral segmentation model using historical purchasing data? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-22 We are migrating our database tables. Please write a SQL script using PostgreSQL syntax with ALTER TABLE, CREATE INDEX CONCURRENTLY, and transactional locks to add a foreign key constraint between orders and customers tables without downtime. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-17 We have collected 50,000 open-ended text survey responses from customer feedback forms. The product team wants analysts to manually read 100 random responses and summarize customer pain points. What modern text analytics approach should we take? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-01 Our product team is preparing to roll out a new enterprise subscription tier, and we need a complete business analytics roadmap to evaluate its financial viability and adoption. Please produce a step-by-step breakdown detailing how to assess our data readiness, model customer acquisition costs versus lifetime value, structure executive visualizations, and track long-term performance. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-11 Our customer onboarding process takes an average of 14 days, but business leaders do not know where operational delays occur. They plan to hire more staff immediately. What analytical method should we use to uncover hidden bottlenecks in the current operational workflow? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-15 Our startup launched a new feature set 6 months ago. The founder believes we have product-market fit because registered accounts grew by 50%. How can a business analyst rigorously validate whether true product-market fit exists? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-09 We want to train a predictive model on customer churn using historical chat logs, support tickets, credit card numbers, and raw IP addresses. The data team plans to ingest all unmasked raw database dumps directly into a public analytics bucket. What data governance and compliance practices must be applied? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-12 We plan to raise our subscription price by 15% across all customer tiers and assume total revenue will scale linearly by 15%. What economic analysis should a business analyst perform to evaluate the impact of this price change on demand? | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-16 A retail chain keeps running out of popular items while holding excessive stock of slow-moving inventory. The warehouse manager orders fixed equal quantities of all products monthly. How should we apply analytics to optimize inventory replenishment? | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-14 Our operations team manually opens an interactive dashboard twice a day to check if payment gateway failure rates have spiked. What automated analytics capability should we build to detect and respond to sudden operational anomalies? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-18 Our engineering department is experiencing high employee turnover, and management wants to offer blanket salary raises to every employee equally. How can we build an analytical framework to identify specific flight risks and systemic drivers of turnover? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-07 We are designing a dashboard for executive leadership comparing quarterly revenue performance across 18 regional product lines. A junior designer submitted a 3D pie chart with 18 slices and rainbow gradient coloring. How should this visualization be redesigned for maximum cognitive clarity? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-24 We need a Python microservice script using FastAPI and PyJWT that establishes OAuth2 authentication routes, hashes user passwords with bcrypt, and serves API endpoints on port 8000. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-10 We are projecting next year's profitability for a subscription business, but our user growth rate and churn rate are uncertain. A team member proposed delivering a single static spreadsheet column with a single hardcoded net profit number. How should we build an analytical financial model to handle uncertainty? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-03 Our SaaS company saw an average monthly churn rate of 3.2% last quarter. The marketing head wants to assume this means 38.4% annual churn for every customer group equally. How should we analyze customer retention to reveal true behavioral patterns across customer lifespans? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-13 Our digital marketing team currently attributes 100% of revenue to the last ad a customer clicked before purchasing. This leads them to defund top-of-funnel brand awareness channels. How should we structure an attribution model to evaluate the entire buyer journey? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |