Sprinklr Business Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Sprinklr hiring for Business Analytics roles
Sprinklr is an enterprise software company that offers a unified customer experience management platform covering social media, customer service, marketing and research. Large brands use it to manage customer conversations across many digital channels. It was founded by Ragy Thomas and has a large engineering presence in India, where it hires software engineers, product analysts, support engineers and implementation consultants.
For Business Analytics roles, this Sprinklr practice set focuses most on Case Studies and Guesstimates (8), Excel for Business Analysts (6), Process Modelling and BPMN (4), Product Analytics (4). It has 30 medium, 10 hard questions, so you can start easy and work up to the harder ones.
Company type
Product
Headquarters
New York, USA
Main India offices
Gurugram, Bengaluru
Sprinklr Business Analytics interview process
Online coding assessment
Technical round 1 (DSA)
Technical round 2 (design or CS fundamentals)
Hiring manager round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Write an Excel formula for month-to-date sales up to today.
// Dates in Sales[Date], amounts in Sales[Amount]
=SUMIFS(Sales[Amount],
Sales[Date], ">=" & EOMONTH(TODAY(), -1) + 1,
Sales[Date], "<=" & TODAY())
// EOMONTH(TODAY(),-1)+1 gives the first day of the current month
A data object (page shape with a folded corner) represents information that an activity needs or produces during one process instance, such as an invoice or an application form. It is linked with dotted associations. A data store (cylinder) represents information that persists beyond the process instance, such as the customer database or ERP. Showing them helps developers see which systems are read or updated at each step and helps spot missing integrations.
Start from the questions to answer: how many users discover it, use it and later buy saved items. Define events such as save_clicked, saved_item_viewed, saved_item_removed and saved_item_purchased, with properties like product ID, price, source screen and platform. Follow the existing naming convention, assign owners, review with developers, and test events in a staging build. Build the dashboard before launch so data is checked on day one.
06
Write a SQL query that shows revenue per region with one column per quarter.
SELECT region,
SUM(CASE WHEN EXTRACT(QUARTER FROM order_date) = 1 THEN amount ELSE 0 END) AS q1,
SUM(CASE WHEN EXTRACT(QUARTER FROM order_date) = 2 THEN amount ELSE 0 END) AS q2,
SUM(CASE WHEN EXTRACT(QUARTER FROM order_date) = 3 THEN amount ELSE 0 END) AS q3,
SUM(CASE WHEN EXTRACT(QUARTER FROM order_date) = 4 THEN amount ELSE 0 END) AS q4
FROM orders
WHERE EXTRACT(YEAR FROM order_date) = 2026
GROUP BY region;
07
Your AS-IS map of a loan approval process shows 14 handoffs between teams. How do you identify improvements?
First measure each handoff: waiting time, rework rate and volume, using system timestamps where possible. Then classify steps as value-adding, required for compliance or waste. Look for patterns: repeated approvals, information re-entered in two systems, work bouncing back for missing documents. Typical fixes are merging roles, adding a checklist at intake so applications arrive complete, parallelising checks and automating rule-based approvals. Quantify the expected reduction in cycle time for each option.
08
What checklist do you apply to a BPMN diagram before sharing it with stakeholders?
Every diagram has clear start and end events and a title with version and owner. Activities use verb-noun names such as Approve invoice. Each gateway is labelled with a question and its outgoing flows with answers. Splits and joins use matching gateway types. Message flows only connect separate pools. No dangling or crossing arrows where avoidable. The happy path reads left to right. Exceptions are handled, and complex rules are referenced to decision tables rather than drawn as gateways.
09
How would you size the market for IT courses in Delhi NCR?
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.
10
Write a DAX measure that shows each category's percent share of total sales.
The p-value is the probability of seeing a difference at least as large as observed if the change truly had no effect. If it is below the chosen threshold, often 0.05, the result is called statistically significant. It does not mean there is a 95 percent chance the variant is better, and significance is not the same as business importance. Always report the confidence interval of the lift as well.
13
Write a DAX measure for the product return rate that avoids divide-by-zero errors.
Return Rate % =
VAR ReturnedQty = SUM ( Returns[Quantity] )
VAR SoldQty = SUM ( Sales[Quantity] )
RETURN
DIVIDE ( ReturnedQty, SoldQty, 0 )
// DIVIDE returns the third argument (0) when SoldQty is zero or blank.
// Format the measure as a percentage in the model.
ROI (return on investment) is the gain from a specific investment divided by its cost and is used to judge projects or campaigns. ROE (return on equity) is net profit divided by shareholders' equity and shows the return to owners; high debt can inflate it. ROCE (return on capital employed) is EBIT divided by equity plus long-term debt and shows how efficiently all long-term capital is used. ROCE is better for comparing companies with different debt levels.
15
How can LET and LAMBDA make complex Excel formulas easier to maintain? Give an example.
// LET names intermediate values so they are calculated once
=LET(rev, SUM(C2:C100), cost, SUM(D2:D100),
IF(rev=0, 0, (rev - cost) / rev))
// LAMBDA saved in Name Manager as MARGIN
=LAMBDA(rev, cost, IF(rev=0, 0, (rev - cost) / rev))
// then use =MARGIN(C2, D2) anywhere in the workbook
16
Finance asks you to build a monthly cohort retention table in Excel from raw order data. How do you approach it?
Add a helper column for each customer's first order month using MINIFS on customer ID, and another for the order month. Compute the month index as the difference in months between them. Then build a PivotTable or COUNTIFS grid with cohort month as rows, month index as columns and distinct customers as values. Divide each cell by the month-zero count to get retention percentages and add a colour scale to show decay.
17
Revenue grew but profit fell. Why could that happen?
Look at the cost side: heavier discounts, higher CAC, logistics costs, product mix shifting to low-margin items, one-time expenses or pricing changes. Break contribution margin down by segment.
18
A project's business case promised 30 percent cost savings, but after go-live only 10 percent is realised. What do you do?
Run a benefits review against the original baseline and assumptions. Check whether savings depended on actions that did not happen, such as reducing manual roles, retiring the old system or full user adoption. Check whether the baseline or volumes changed. Separate delayed benefits from lost ones. Then produce a recovery plan with owners, for example decommissioning the legacy licence or driving adoption. Also feed the lessons into how future business cases estimate benefits.
19
How do you write an effective executive summary for an analysis report?
Put it first and keep it to one page or five lines on a slide. Start with the answer or recommendation, then the two or three key findings that support it, each with a number, then the impact in rupees or percentage, and finally the decision or action needed and by when. Avoid methodology details, which go in the appendix. Test it by asking whether a busy leader could act after reading only this section.
Start from the test cases and list the data each needs: customer types, product variants, edge values and historical records. Decide whether to use masked copies of production data, synthetic data or both, because real personal data must be anonymised under data protection rules such as the DPDP Act. Prepare data for negative and boundary cases, keep a refresh plan for repeated cycles, and assign owners. Poor test data is one of the most common causes of blocked UAT days.
21
Write a SQL query that shows revenue, cost and gross margin percent by product category.
SELECT
p.category,
SUM(oi.quantity * oi.unit_price) AS revenue,
SUM(oi.quantity * p.unit_cost) AS cost,
ROUND(100.0 * (SUM(oi.quantity * oi.unit_price)
- SUM(oi.quantity * p.unit_cost))
/ NULLIF(SUM(oi.quantity * oi.unit_price), 0), 2) AS gross_margin_pct
FROM order_items oi
JOIN products p ON p.product_id = oi.product_id
GROUP BY p.category
ORDER BY gross_margin_pct DESC;
22
What is a star schema and why do BI tools prefer it?
A star schema has one central fact table holding numeric events (sales, orders) linked to surrounding dimension tables (date, product, customer, region). BI tools prefer it because filters flow cleanly from dimensions to facts, DAX and Tableau calculations stay simple, and compression and query performance improve. A flat single table repeats text values and gets slow, while a snowflake with many chained lookups makes filtering and maintenance harder.
Drive it from vehicles. Assume about 2 crore new two-wheelers are sold a year, each with an average of 1.5 helmets bought (rider plus pillion), giving 3 crore. The existing fleet is around 25 crore vehicles; assume 60 percent are active and each active vehicle replaces one helmet every 3 years: 15 crore / 3 = 5 crore. Total is about 8 crore helmets a year. Sanity check: enforcement varies by state, so give a range of 6 to 9 crore.
25
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
A short requirement written as 'As a user, I want X so that Y', backed by acceptance criteria. Good stories follow INVEST: independent, negotiable, valuable, estimable, small and testable.
27
Your LEFT JOIN report should list all customers, but customers without orders are missing. Why?
A condition on the right table was probably placed in the WHERE clause, for example WHERE o.order_date >= '2026-01-01'. For customers with no orders, o.order_date is NULL, so WHERE removes them and the query behaves like an INNER JOIN. Move the condition into the ON clause, as in LEFT JOIN orders o ON o.customer_id = c.id AND o.order_date >= '2026-01-01', or explicitly allow NULLs.
28
Tell me about a time UAT revealed a major issue that earlier testing missed.
Situation: a new claims system passed QA, but in UAT a claims officer tried a real scenario with two policies on one claim. Task: as BA I had to assess and resolve it quickly. Action: I confirmed the requirement never covered multi-policy claims, quantified that 8 percent of claims were affected, and agreed a manual workaround while the change was built. Result: we went live on time, delivered the fix in the next release, and I added real case samples to future test design.
29
Finance and sales blame each other for wrong numbers in a monthly report. How do you resolve it as the analyst?
Stay neutral and move the discussion from blame to facts. Trace the number end to end: source system, extraction, transformation and report logic, and find exactly where the values diverge. Share the findings with both teams together, agree on the fix and on a single owner for the definition. Then add automated reconciliation checks and a data dictionary, so the process, not people, prevents the next issue.
Situation: our support team handled order-status queries manually, about 9,000 a month. Task: I was asked to evaluate a self-service tracking page. Action: I measured handling time from ticket data, estimated deflection conservatively at 40 percent, worked with IT on build and run costs and showed best, base and worst cases with payback. Result: leadership approved the base case. Six months after launch, deflection reached 46 percent, and I presented the benefits review to the same committee.
32
Tell me about a time your analysis changed a product decision.
Situation: the team planned to add more filters to the search page to fix low conversion. Task: I was asked to size the opportunity. Action: funnel analysis showed most drop-off happened after users opened a product page with no delivery date shown, not in search. I shared the data with session recordings. Result: the team prioritised showing delivery dates on the product page, conversion rose about 8 percent, and the filter work was deprioritised.
33
A feature launched two months ago is used by only 4 percent of users. What would you do?
Find out whether it is a discovery problem or a value problem. Check how many users saw the entry point, how many tried it and how many returned. If few saw it, test better placement or onboarding prompts. If many tried and few returned, interview users and review session recordings to understand why. Also check if the 4 percent are high-value users. Then decide to improve, reposition or remove it.
34
How would you analyse the declining sales of a retail store?
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.
37
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.
38
Guesstimate: how many smartphones are sold in India each year?
Use a replacement plus new-buyer approach. India has about 144 crore people; assume around 60 crore active smartphone users. If people replace phones every 4 years on average, replacements are about 15 crore a year. Add first-time buyers, say 2 to 3 percent of the remaining adult population, roughly 1.5 crore. The estimate is about 16 to 17 crore phones a year, which is close to reported industry numbers of around 15 crore.
39
Describe a time when stakeholders resisted a process change you proposed. How did you handle it?
Situation: I proposed removing a manual second approval for low-value expense claims, and team leads resisted, fearing fraud. Task: get agreement without weakening control. Action: I analysed six months of claims and showed the second approver rejected less than 0.5 percent below 5,000 rupees. I suggested a pilot with random audits of 10 percent of claims and agreed a reversal trigger. Result: the pilot ran for two months with no extra fraud, approval time fell by 3 days and the change was adopted.
40
After many sprints, project documentation is out of date and new team members are confused. What do you do?
Identify which documents are actually used: usually a product overview, process maps, business rules, data dictionary and integration details. Update those first rather than everything. Make documentation part of the Definition of Done, so a story is not complete until related docs are updated. Keep living documents in a shared wiki with owners and last-reviewed dates, and archive outdated files clearly. Test the result by having a new joiner use it during onboarding.
Tips to crack the Sprinklr Business Analytics interview
Practise medium-level DSA problems on arrays, trees, graphs and dynamic programming, as coding rounds can be fairly demanding.
Revise operating systems, DBMS and basic system design, and explain trade-offs clearly when discussing your project architecture.
Learn about customer experience management and social media tools to explain how Sprinklr's platform helps large enterprises.
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; Sprinklr interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Sprinklr Business Analytics interview?
Candidates usually go through 5 stages: Online coding assessment; Technical round 1 (DSA); Technical round 2 (design or CS fundamentals); Hiring manager 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 Sprinklr Business Analytics interview?
This practice set leans on Case Studies and Guesstimates (8), Excel for Business Analysts (6), Process Modelling and BPMN (4), Product Analytics (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Sprinklr interviewers ask you to walk through real work.
Are these the exact questions asked at Sprinklr?
No. These are curated practice questions that match the skills and difficulty usually tested in product interviews like Sprinklr's. They are not a leaked or verified list of past questions.
Where does Sprinklr hire Business Analytics professionals in India?
Sprinklr has large teams in Gurugram, Bengaluru. Openings and locations change often, so check the Sprinklr careers page and filter by city and experience.
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