Tiger Analytics Business Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Tiger Analytics hiring for Business Analytics roles
Tiger Analytics is an AI and analytics consulting company working with clients in consumer goods, retail, insurance, banking and healthcare. Its India offices handle data engineering, data science and ML engineering work. It hires for data analysts, data scientists, data engineers and ML engineers. Many projects run on major cloud data platforms.
For Business Analytics roles, this Tiger Analytics practice set focuses most on Case Studies and Guesstimates (7), SQL for Business Analysis (6), Requirements and Agile (5), Product Analytics (5). It has 7 easy, 29 medium, 4 hard questions, so you can start easy and work up to the harder ones.
Company type
Analytics
Headquarters
Santa Clara, California, USA
Main India offices
Chennai, Bengaluru, Hyderabad
Tiger Analytics Business Analytics interview process
Online assessment (SQL + Python + statistics)
Technical round 1
Technical round 2 or case round
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
A subscription app starts the month with 8,000 customers, gains 1,200 and loses 400. Calculate churn and net growth.
Churn rate uses customers at the start: 400 divided by 8,000 gives 5 percent. Ending customers are 8,000 plus 1,200 minus 400, which is 8,800. Net growth is 800 divided by 8,000, or 10 percent. Do not divide churned customers by the ending count, since new joiners who have not had a chance to churn would understate the churn rate.
02
Write a SQL query that groups customers into spend buckets using CASE WHEN.
SELECT
CASE
WHEN total_spend >= 50000 THEN 'High'
WHEN total_spend >= 10000 THEN 'Medium'
ELSE 'Low'
END AS segment,
COUNT(*) AS customers
FROM (
SELECT customer_id, SUM(amount) AS total_spend
FROM orders GROUP BY customer_id
) t
GROUP BY 1
ORDER BY customers DESC;
03
Managers want to click a region on a summary report page and jump to a detail page for only that region. How do you build it in Power BI?
Use drill-through. Create a detail page, add the Region field to the page's drill-through filter well, and design visuals for orders, products and trends. Power BI adds a back button automatically. On the summary page, users right-click a region and choose Drill through. Keep all filters on so date selections carry over. For guided navigation, you can also add buttons with bookmarks, but drill-through is the cleanest way to pass the selected region.
04
Explain exclusive, parallel and inclusive gateways in BPMN.
An exclusive gateway (diamond with X) sends the flow down exactly one path based on a condition, for example approved or rejected. A parallel gateway (diamond with +) splits into all paths at once and, when joining, waits for every path to finish. An inclusive gateway (diamond with O) activates one or more paths whose conditions are true and joins only those that were activated. Choosing the wrong one causes deadlocks or duplicated work in automated processes.
05
How do you handle NULL values when calculating averages?
Top-down: NCR population aged 18–30 × share of graduates × share wanting paid upskilling × average fee. Cross-check bottom-up with institutes × batches × students × fees. State your assumptions.
07
What is the profit equation and how do you use it in cases?
Build a discounted cash flow comparison over the vehicle life. For buying: purchase price, financing cost, maintenance, insurance, tax benefit of depreciation, and resale value. For leasing: lease rentals, what maintenance is included, tax treatment and end-of-term conditions. Discount both at the company's cost of borrowing and compare present value of costs. Then add qualitative factors: flexibility if volumes drop, technology risk such as a shift to electric vans, and impact on the balance sheet.
09
Split the epic 'Customers can track their order online' into user stories.
Possible stories: As a customer, I want to see my order status (placed, packed, shipped, delivered) so that I know its progress. As a customer, I want an SMS or email when the status changes so that I do not have to check. As a customer, I want the expected delivery date so that I can plan. As a customer, I want a courier tracking link once shipped. As support staff, I want to see the same timeline so that I can answer queries.
A model classifying features as basic, performance or delight attributes based on how they affect customer satisfaction, helping decide what to build first.
12
How would you structure a weekly project status update for stakeholders?
Use a short, fixed format. Start with an overall status of green, amber or red and one line explaining it. Then list what was completed this week, what is planned next week, key risks and issues with owners, and decisions needed from stakeholders with dates. Add a link to the detailed tracker. Keep it under one screen, and highlight anything that changed since last week.
The minimum detectable effect is the smallest lift worth detecting, such as a 2 percent relative increase in conversion. Smaller effects, lower baseline rates and higher required power need much larger samples, roughly four times the users to detect half the effect. Calculate sample size before starting using the baseline rate, MDE, significance level and power, usually 80 percent, then fix the test duration and avoid stopping early.
15
What is user activation and what is an 'aha moment'?
Activation is the point at which a new user first experiences the product's core value, such as completing a first lesson, sending a first payment or creating a first project. The aha moment is the specific early action that strongly predicts long-term retention, found by comparing retained and churned users. Teams then design onboarding to get more new users to that action quickly and track the activation rate.
16
Write an Excel formula for the weighted average selling price across orders.
// B2:B200 = price per unit, C2:C200 = units sold
=SUMPRODUCT(B2:B200, C2:C200) / SUM(C2:C200)
// a simple AVERAGE(B2:B200) would wrongly give a small order
// the same weight as a large one
17
Write a SQL query to find the percentage of orders delivered late in each city.
-- late = delivered after the promised date
SELECT
city,
COUNT(*) AS total_orders,
SUM(CASE WHEN delivered_at > promised_at THEN 1 ELSE 0 END) AS late_orders,
ROUND(100.0 * SUM(CASE WHEN delivered_at > promised_at THEN 1 ELSE 0 END)
/ COUNT(*), 2) AS late_pct
FROM orders
WHERE status = 'DELIVERED'
GROUP BY city
ORDER BY late_pct DESC;
18
What is user story mapping and how does it help plan releases?
User story mapping arranges stories in two dimensions. Across the top is the user journey as a sequence of activities, for example search, compare, buy and track. Below each activity, stories are stacked by priority. Drawing horizontal lines across the map creates release slices, where the first slice is a thin end-to-end MVP that covers every step at a basic level. It prevents a flat backlog where teams build one area deeply while the journey stays incomplete.
19
Guesstimate: how many cups of tea are sold in Delhi per day?
Population about 20 million, adult tea drinkers about 50%, cups bought outside per person around 1 on average. This gives roughly 10 million cups, then adjust for offices and stalls.
20
Daily active users dropped 10 percent week over week. How do you investigate?
Rule out data issues first: tracking changes, app release bugs, pipeline delays or bot filters. Check whether the drop is seasonal by comparing with the same week last year or a festival calendar. Then segment by platform, app version, country or city, acquisition channel and new versus returning users to locate it. Look at the funnel for the core action. Connect findings to releases, outages or marketing changes, then recommend fixes.
21
Write a SQL query to get each customer's first order date and first order amount.
WITH ordered AS (
SELECT customer_id, order_date, amount,
ROW_NUMBER() OVER (PARTITION BY customer_id
ORDER BY order_date, order_id) AS rn
FROM orders
)
SELECT customer_id, order_date AS first_order_date,
amount AS first_order_amount
FROM ordered
WHERE rn = 1;
22
Write DAX measures for Total Sales, Sales Last Year and Year-over-Year growth percent.
Situation: two days before the go-live of a loyalty points system, UAT showed points were doubled for refunded orders. Task: give a clear recommendation at the go or no-go meeting. Action: I presented the defect, estimated financial exposure at about 6 lakh rupees a month and showed the fix needed three days including regression. Result: the sponsor agreed to a one-week delay. The release went live cleanly, and the sponsor later cited the decision as an example of good risk management.
24
What is the difference between ROW_NUMBER, RANK and DENSE_RANK?
All three number rows within a window ordered by a column. ROW_NUMBER gives a unique sequence 1, 2, 3 even for ties. RANK gives ties the same number and then skips, so 1, 1, 3. DENSE_RANK gives ties the same number without gaps, so 1, 1, 2. Use ROW_NUMBER to pick exactly one row per group, such as the latest order, and DENSE_RANK for 'top 3 distinct salary levels' type questions.
Cohort analysis groups users by a shared starting point, usually their sign-up or first purchase month, and tracks their behaviour over time, such as the percentage still active in month 1, 2 and 3. It separates the effect of growth from the effect of retention, so you can see whether newer cohorts retain better after a product change. It is typically shown as a triangle table or retention curves.
26
A quick-commerce company wants to expand into Tier-2 cities. What would you analyse before recommending it?
Demand: population density, smartphone and digital payment penetration, average basket size and how often people buy groceries. Supply: dark store rent, rider availability and local sourcing. Unit economics: orders per store per day needed to break even, given lower average order values. Competition: local kirana delivery and other apps. Then propose a pilot in two or three cities with clear success metrics such as orders per day, contribution margin per order and repeat rate before scaling.
27
A ride-hailing app's ride cancellation rate rose from 8 to 14 percent. How would you structure the problem?
First split by who cancels: riders or drivers. For rider cancellations, check wait times, pickup accuracy, price surges and changes in ETA. For driver cancellations, check trip distance, payment mode, low fares, destination visibility and incentive changes. Then segment by city, time of day and area. Link to recent changes like a new pricing rule. Recommend fixes for the largest driver, such as better matching or adjusted incentives.
28
The scheduled refresh failed overnight and leadership reviews the dashboard at 10 am. What do you do?
Check the refresh history error first: expired credentials, gateway offline, source schema change or timeout. Fix the quick cause and trigger a manual refresh. If it cannot be fixed in time, inform the stakeholders early, state the last refreshed date clearly on the report and share the key numbers from a direct query if needed. Afterwards add refresh failure alerts and a data-as-of label so it never surprises anyone.
SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
-- or with a window function
SELECT DISTINCT salary FROM (
SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rk
FROM employees
) t WHERE rk = 2;
Situation: a new developer built a GST calculation from an old FRD version, and the error was caught only in UAT. Task: fix the defect and prevent repeats. Action: I moved all requirement documents into one shared space with version history, added a change log and status labels such as draft or approved, and linked each user story to the latest approved section. Result: in the next two releases we had no defects caused by outdated specifications.
An issue tree breaks the main question into smaller, MECE sub-questions until each can be answered with data, such as 'why are profits down?' into revenue and cost branches. A hypothesis-driven approach starts with a likely answer, for example 'profit fell because delivery costs rose', and uses the tree to decide which data proves or disproves it first. It focuses effort and keeps the case structured and fast.
A model assessing industry attractiveness through supplier power, buyer power, threat of new entrants, threat of substitutes and rivalry among competitors.
38
Inventory days rose from 45 to 70 over two quarters. What could be the causes and what would you recommend?
Possible causes include falling sales against unchanged purchasing, poor demand forecasts, bulk buying for discounts, slow-moving or obsolete SKUs, or deliberate stock-building ahead of a festive season or expected price rise. Analyse by SKU, warehouse and age bucket to find where stock is stuck. Recommend clearing aged items, adjusting reorder points using recent demand, and reviewing supplier minimum order quantities. Also quantify the cash locked up and the carrying cost to show urgency.
39
Write Excel formulas for IRR with yearly cash flows and XIRR with irregular dates.
# Yearly cash flows in B2:B7, year 0 investment negative
=IRR(B2:B7)
# Irregular dates: cash flows in B2:B7, dates in C2:C7
=XIRR(B2:B7, C2:C7)
# XIRR returns an annualised rate based on actual dates,
# which suits SIP returns or projects with uneven payments.
40
Write a DAX measure to count distinct active customers in the last 30 days.
Active Customers 30D =
CALCULATE(
DISTINCTCOUNT(Sales[CustomerID]),
DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -30, DAY)
)
-- MAX('Date'[Date]) makes it relative to the date in the current filter
Tips to crack the Tiger Analytics Business Analytics interview
Revise machine learning algorithms, feature engineering and evaluation metrics, as technical rounds often go into depth.
Practise PySpark and SQL for data engineering roles, including data pipeline design on cloud platforms.
Prepare analytics case questions on demand forecasting or customer segmentation for retail and consumer goods clients.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; Tiger Analytics interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Tiger Analytics Business Analytics interview?
Candidates usually go through 5 stages: Online assessment (SQL + Python + statistics); Technical round 1; Technical round 2 or case round; Managerial round; HR round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Tiger Analytics Business Analytics interview?
This practice set leans on Case Studies and Guesstimates (7), SQL for Business Analysis (6), Requirements and Agile (5), Product Analytics (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Tiger Analytics interviewers ask you to walk through real work.
Are these the exact questions asked at Tiger Analytics?
No. These are curated practice questions that match the skills and difficulty usually tested in analytics interviews like Tiger Analytics's. They are not a leaked or verified list of past questions.
Where does Tiger Analytics hire Business Analytics professionals in India?
Tiger Analytics has large teams in Chennai, Bengaluru, Hyderabad. Openings and locations change often, so check the Tiger Analytics careers page and filter by city and experience.
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