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Read More →Download the business analytics case studies PDF from Uncodemy: worked scenarios with the question, data, method, findings and lessons learned.
This page offers Uncodemy's data analytics business case studies as a PDF download. The cases are illustrative scenarios that show how an analyst moves from a business question to data, method, findings and a recommendation across retail, marketing, subscription, finance and operations settings. The numbers in this overview are invented for teaching, not real company results.
Download the complete set of worked business case studies as a PDF. Fill in the short form and the file will download automatically.
Worked scenarios across retail, subscription, marketing, finance and operations — each with the question, data, method, findings and recommendation.
A case study teaches judgement, not just technique. When you read one, ask the same five questions each time. What was the business problem? What data was needed and where did it come from? What method was used and why? What did the analysis find? What action followed, and how would success be measured?
Reading cases this way trains you to approach new problems systematically, which is the skill interviewees and employers look for. The scenarios here are teaching examples with invented figures, so focus on the reasoning rather than the numbers.
| Stage | What the analyst does |
|---|---|
| Question | Revenue in the north region dropped compared with the previous quarter. Why? |
| Data | Orders, products, stock levels and calendar for both quarters |
| Method | Break revenue down by product, week and store; compare against stock availability |
| Finding | The drop is concentrated in two best-selling products that were out of stock for several weeks |
| Recommendation | Add a reorder alert and review supplier lead times; track stock-out days weekly |
| Stage | What the analyst does |
|---|---|
| Question | Which customer groups have the highest cancellation rate? |
| Data | Subscription start and end dates, plan, support tickets and usage |
| Method | Compute churn by plan, signup month and usage band; compare early-usage patterns of customers who stayed and left |
| Finding | Customers who did not use a key feature in the first two weeks cancelled far more often |
| Recommendation | Add onboarding prompts that guide new customers to the key feature; measure repeat usage |
| Stage | What the analyst does |
|---|---|
| Question | Where should next quarter's marketing budget go? |
| Data | Spend and leads by channel, lead-to-customer conversion and revenue per customer |
| Method | Compute cost per lead, cost per customer and revenue per customer by channel; compare over time |
| Finding | A channel with cheap leads converts poorly, so its cost per customer is higher than a channel with costlier leads |
| Recommendation | Shift part of the budget gradually and test; judge by cost per customer, not cost per lead |
Cost per lead and cost per customer by channel. Table and column names are illustrative.
SELECT channel,
SUM(spend) AS spend,
SUM(leads) AS leads,
SUM(customers) AS customers,
ROUND(SUM(spend) * 1.0 / SUM(leads), 2) AS cost_per_lead,
ROUND(SUM(spend) * 1.0 / SUM(customers), 2) AS cost_per_customer
FROM marketing_performance
GROUP BY channel
ORDER BY cost_per_customer;| Stage | What the analyst does |
|---|---|
| Question | Why is cash tight even though sales are growing? |
| Data | Invoices, payment dates, customer terms and inventory balances |
| Method | Measure days sales outstanding and invoice ageing by customer; compare agreed and actual payment days |
| Finding | A few large customers pay far later than their agreed terms |
| Recommendation | Introduce reminders and early-payment incentives for those accounts; monitor ageing weekly |
| Stage | What the analyst does |
|---|---|
| Question | Which orders are delivered late and what do they share? |
| Data | Order, dispatch and delivery timestamps, warehouse, carrier and route |
| Method | Compute on-time rate by carrier, warehouse and weekday; check for patterns |
Each case starts with a clear business question rather than a dataset. Each breaks a big number into parts, which is the most reliable way to find the cause of a change: by product, by time, by customer group, by channel.
Each ends in a specific action with a way to measure the result. The method is rarely complicated; the value comes from asking the right question and checking the explanation against the data. Notice also that the best answer in Case 3 was the opposite of the obvious one, because the analyst looked at cost per customer rather than cost per lead.
Choose a public dataset that fits the scenario and state clearly that it is a practice exercise. Write the business question first and the plan before any code. Show your data cleaning and your reasoning, not just the final chart. Finish with a recommendation and what you would measure next. Be honest about limits, such as missing data or assumptions.
The cases above are teaching scenarios written for learning. They do not describe any specific company, and any numbers are invented to make the reasoning clear. Do not quote them as industry benchmarks.
Ready to work through real business cases? Explore Uncodemy's Data Analytics course and learn from industry trainers.