Market Research ROI: Break-Even Worksheet with Rupee Examples

• The useful number is break-even risk reduction, r*, the share of launch risk a study must remove to pay for itself. If your honest estimate of the risk it removes is above r*, the study pays for itself in expected terms.
• Write your failure chance and your expected risk reduction down before fieldwork. A figure fitted after the results are in will not survive a CFO's questions.
• If r* comes out above 100%, no study at that price can pay back, however good it is. Shrink the study or question the launch size.
• Our calculation shows expected value, not a forecast. It cannot prove a return after the fact, because research gives direction and the business still has to act on it.

Enter four inputs, all in rupees or percentages, and every default below is an example to overwrite.

If you want ROI in the usual form, divide the net expected value by the study cost: (L × p × r − S) ÷ S. In the example that is ₹1,00,000 ÷ ₹4,00,000 = 25%. It also equals r ÷ r* − 1, so ROI is simply how far your estimate of r sits above the break-even.

Take a food brand planning a ₹50,00,000 launch. It rates its chance of failure at 40% and gets a quote of ₹4,00,000 for a qualitative study.

1. Expected loss without the study: ₹50,00,000 × 40% = ₹20,00,000.
2. The team believes the study will remove a quarter of that risk, so r = 25%. That is 10 percentage points: the failure chance falls from 40% to 30%.
3. Expected loss avoided: ₹20,00,000 × 25% = ₹5,00,000.
4. Net expected value: ₹5,00,000 − ₹4,00,000 = ₹1,00,000, so ROI is 25%.
5. Break-even: ₹4,00,000 ÷ ₹20,00,000 = 20%. The study needs to remove at least a fifth of the risk to cover itself.
6. Break-even launch size: ₹4,00,000 ÷ (40% × 25%) = ₹40,00,000. Below that launch size, the same study at the same belief does not pay.

The margin is thin. If the team's honest estimate were r = 15%, loss avoided would be ₹3,00,000 and the net would be −₹1,00,000. That is the point of the worksheet: it shows which assumption the decision hangs on.

A study has to remove r* = S ÷ (L × p) of the launch risk to pay for itself. Both tables below are our own arithmetic, using the formula above with the example launch and the stated changes.

Changing the failure chance (S fixed at ₹4,00,000, L at ₹50,00,000):

A study that is cheap relative to what is at stake clears the bar easily. An expensive study on a launch you are already confident in does not. In decision theory, the most any information can be worth is the expected value of perfect information, which is the expected loss from deciding without it (Howard, "Information Value Theory", 1966). In this worksheet that ceiling is L × p. When S is above L × p, r* is above 100% and nothing you buy at that price can pay for itself.

Read break-even by comparing r with r*, then stress-testing r. If r is comfortably above r*, the study is justified on money alone. If it is close, the decision rests on a guess, and you should say so.

Our recommendation, in order:

• Stress-test p. Lower your failure chance by 10 points and recompute r*. In the example, p = 30% gives L × p = ₹15,00,000 and r* = 26.7%, which is above the 25% you hoped for. If a small change in p flips the verdict, the study is a judgement call, not a certainty.
• Treat r above about half as a warning. Believing a study will remove more than half of a launch's risk is rarely credible. Shrink the study, or look at whether the launch is too large for the evidence you have.
• Set r to zero if no result could change the decision. If you will launch whatever the findings say, the study has no expected value in this model.
• Use L* for small launches. If your launch money at stake is below L*, a cheaper design may fit better. Our pages on sample size in qualitative research and market research for startups show how far a study can be trimmed.

They fall apart on attribution, on implementation and on the meaning of "failure". Each of these is an input in the worksheet, which is why it asks for them up front.

Attribution. A return counts only if something happened because of the research that would not have happened otherwise, and the financial value of that difference can be stated (Quirk's). A launch that succeeds does not prove the study mattered. Break-even sidesteps this before the study, by asking what difference you need it to make.

Implementation. A study gives direction, and the business must act on it. Findings can be ignored or arrive after the decision window has closed. Build that into r: if you think there is a one-in-three chance the findings are set aside, your r is two-thirds of what the study could deliver.

What "failure" means. Published launch figures cannot stand in for your p. NielsenIQ (NIQ), the retail measurement company, covers more than 60,000 new SKUs (stock-keeping units, meaning individual product variants) launched in Europe, mostly between 2011 and 2013. It found that 55% reached 26 weeks and 24% reached a full year (NIQ, 23 June 2015). That is European data more than a decade old, not an Indian figure and not your plan. Define failure against your own plan, such as "below 60% of year-one volume target", and set p from that.

A clear market research brief helps here, because it forces you to write down the decision, the options and what would change your mind. Pre-testing a single idea is a narrower case, covered in concept testing.

The study cost is the sum of fieldwork, recruitment, participant incentives, moderation, GST, internal time and any cost of delay, and a quote should itemise each line. For a line-by-line check of what a quote should itemise, use our focus group cost quote checker.

At Qualfacto we recruit participants for qualitative research in India and can also supply moderators, depending on the project, across formats including in-depth interviews, focus groups, digital diary studies, ethnography, usability and app testing, online bulletin boards, shop-alongs and product testing. Recruitment can be by city, in languages other than English, with identity checks before invitation. Deliverables depend on the scope you agree.

We have no fixed rate card. We price each project from its brief, and the main drivers are the method, the audience and participant profile, sample size, geography, recruitment difficulty and overall scope. After we review a brief for feasibility, we send a tailored proposal and quote, and we offer a free pilot.

That quote is your S. Our page on how to choose a qualitative research agency in India covers the choice. If you want to take part in studies instead, register as a research participant, where invitations depend on each study's requirements and your profile.

Email your brief to support@qualfacto.io and ask for the free pilot. We reply during business hours, Monday to Friday, 10:00 AM to 6:00 PM Indian Standard Time (IST), and aim to respond within one business day.