Indian Protein & Creatine Calculator

Built on the ICMR-NIN Recommended Dietary Allowances for Indians, the ISSN position stands on protein and creatine, and the ACSM joint position stand — then translated into dal, roti, paneer, curd and eggs rather than abstract grams. Every number below is sourced. No email required.

Body weight

kglb

Sex Male Female

Used only for the ICMR-NIN reference comparison.

Activity level Sedentary — little or no exercise Recreational — 2–4 sessions a week Strength training — 4+ sessions a week Endurance — running, cycling, swimming

Primary goal Maintain Build muscle Fat loss, preserve muscle

Diet Non-vegetarian Vegetarian (lacto / lacto-ovo) Vegan

Affects the ICMR-NIN reference and the creatine guidance.

Meals per day 3456

Your daily protein target

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Per meal

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ICMR-NIN reference

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Creatine — loading (optional)

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0.3 g/kg/day for 5–7 days, split into 4 doses. Loading is optional.

Creatine — daily maintenance

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What that looks like on an Indian plate

Each row shows roughly how much of a single food would meet your midpoint target on its own. Nobody eats this way — the point is to show the relative contribution of foods you already eat.

Food	Protein per serving	Servings to hit target

You are on a cereal-dominant vegetarian or vegan diet ICMR-NIN sets a higher requirement of 1.0 g/kg/day, rather than 0.83, for Indians on cereal-based diets with lower-quality protein — a roughly 20% uplift written into India's own national guideline. The reason is amino acid quality, not quantity: lysine is the first limiting amino acid in cereal-dominant Indian diets. ICMR-NIN recommends a cereal : legume : milk ratio of 3 : 1 : 2.5 to correct for this.

Creatine and vegetarian diets Creatine occurs in meaningful amounts only in animal flesh — milk, curd, paneer and eggs are negligible sources. Vegetarians have been reported to carry muscle creatine stores roughly 20–30% lower than non-vegetarians, and in a controlled trial vegetarians gained more lean tissue from supplementation than non-vegetarians did (2.4 kg vs 1.9 kg over eight weeks). Same dose, larger expected effect.

What Indian guidelines actually say about supplements ICMR-NIN's Dietary Guidelines for Indians (2024) advises Indians to obtain good-quality protein through appropriate food combinations and to avoid protein supplements for building muscle mass. We sell protein supplements and we still think you should read that guideline. A supplement is a convenience for hitting a target you have already decided is right for you — it is not a requirement, and food-first is sound advice.

Cite or embed this calculator

This tool is free to cite, quote and embed, including commercially. We ask only for a visible link back. If you are a journalist or researcher and want the underlying formulas or a comment on the record, email support@imuscles.in.

Plain citation

iMuscles Nutrition (2026). Indian Protein & Creatine Calculator. https://imuscles.in/protein-calculator

Embed on your site

<iframe src="https://imuscles.in/protein-calculator?embed=1"
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<p>Calculator by <a href="https://imuscles.in/protein-calculator">iMuscles Nutrition</a></p>

Sources

1. ICMR-NIN Expert Group (2020, rev. 2024). Nutrient Requirements for Indians: Recommended Dietary Allowances and Estimated Average Requirements. RDA 0.83 g/kg/day for adults; 1.0 g/kg/day for cereal-based diets with lower-quality protein; cereal : legume : milk ratio 3 : 1 : 2.5. nin.res.in
2. ICMR-NIN (2024). Dietary Guidelines for Indians. Guideline 8 on protein quality and supplements. nin.res.in
3. Jäger R, Kerksick CM, Campbell BI, et al. (2017). International Society of Sports Nutrition Position Stand: protein and exercise. J Int Soc Sports Nutr 14:20. 1.4–2.0 g/kg/day for exercising individuals; 0.25 g/kg or 20–40 g per serving, every 3–4 hours. doi:10.1186/s12970-017-0177-8
4. Kreider RB, Kalman DS, Antonio J, et al. (2017). ISSN position stand: safety and efficacy of creatine supplementation. J Int Soc Sports Nutr 14:18. Loading ~0.3 g/kg/day for 5–7 days; maintenance 3–5 g/day; alternative 3 g/day for 28 days without loading. doi:10.1186/s12970-017-0173-z
5. Thomas DT, Erdman KA, Burke LM (2016). Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and Athletic Performance. Med Sci Sports Exerc 48(3):543–568. 1.2–2.0 g/kg/day; 0.25–0.3 g/kg per dose. doi:10.1249/MSS.0000000000000852
6. Morton RW, Murphy KT, McKellar SR, et al. (2018). Meta-analysis of protein supplementation and resistance training. Br J Sports Med 52(6):376–384. Benefit plateaus at ~1.62 g/kg/day (95% CI 1.03–2.20). doi:10.1136/bjsports-2017-097608
7. Hector AJ, Phillips SM (2018). Protein recommendations for weight loss in elite athletes. Int J Sport Nutr Exerc Metab 28(2):170–177. 1.6–2.4 g/kg/day in a deficit. doi:10.1123/ijsnem.2017-0273
8. Burke DG, Chilibeck PD, Parise G, et al. (2003). Effect of creatine and weight training on muscle creatine and performance in vegetarians. Med Sci Sports Exerc 35(11):1946–1955. PMID 14600563.
9. Kreider RB, Stout JR (2021). Creatine in health and disease. Nutrients 13:447. Vegetarian muscle creatine stores 20–30% lower. doi:10.3390/nu13020447
10. ICMR-NIN (2017). Indian Food Composition Tables. Protein per 100 g for Indian foods. nin.res.in

Not medical advice. This calculator applies population-level guidelines and cannot account for kidney or liver conditions, pregnancy, diabetes, or medication. Speak to a doctor or a registered dietitian before making significant dietary changes.

Built and maintained by iMuscles Nutrition, New Delhi. Last reviewed August 2026. Found an error? Tell us at support@imuscles.in and we will correct it and credit you.

Detailed Guideline How I Created SEO ROI Calculator

 

1. Why this tool can earn links when most cannot

There are dozens of SEO ROI calculators online. Almost all of them share three flaws, and fixing those three flaws is the entire differentiation strategy for this page.

1.    They ignore AI Overviews. Pew Research measured that clicks fall from 15% to 8% of visits when an AI summary appears, and Ahrefs measured position-1 CTR down 58% on AI-Overview keywords. A traffic forecast built on 2019 CTR data is now materially wrong. This tool models the haircut as an adjustable input.

2.    They use CTR data that is six years old. The table almost everyone copies is either Backlinko 2019 or an agency meta-analysis that puts position 1 at 39.8% — a figure irreconcilable with every 2025–26 measurement. This tool offers three curves and labels the provenance of each.

3.    They assume instant results. A calculator that shows month-one revenue is selling something. This tool applies a ramp curve and states plainly that no published study establishes one.

The link-earning mechanism is the same as any tool: an obvious cite-and-embed box, no email gate, and sourcing good enough that a writer is willing to attach their own credibility to it.

2. Inputs

Group	Field	Default	Purpose
Search opportunity	Monthly searches across target keywords	20,000	Top of the funnel
	Target average position	3	Selects the CTR value
	CTR data source	Conservative	Three curves with different provenance
	Dominant search intent	Commercial	Sets the AI Overview default
	AI Overview click loss	10%	Adjustable slider, 0–60%
Conversion	Visitor → lead rate	2.5%	Traffic to enquiries
	Lead → customer close rate	20%	Required input, never a default benchmark
	Average revenue per customer	₹25,000	LTV or first-order value
	Gross margin	100%	Set below 100 to model on profit
Investment	Monthly SEO investment	₹40,000	Retainer, salary, content, tools
	One-off setup cost	₹0	Audit, migration, build
	Months to full effect	12	Ramp period
	Projection horizon	24 months	12 / 24 / 36
Paid comparison	Industry	Business Services	Sets benchmark CPC
	Cost per click	Auto-filled	Editable — Indian CPCs run below US medians

 

3. CTR curves — and why the default is the low one

Click-through rate by position is the most-cited and least-verified number in SEO. There is no independent primary source publishing a full position 1–10 table for 2025–26. The tool offers three curves and names the compromise in each.

Position	Conservative	Optimistic	Legacy 2019
1	19.0%	39.8%	27.6%
2	12.6%	18.7%	13.1%
3	6.0%	10.2%	8.7%
4	4.4%	7.2%	6.4%
5	3.4%	5.1%	5.0%
6	2.8%	4.4%	4.1%
7	2.3%	3.0%	3.4%
8	2.0%	2.1%	2.9%
9	1.8%	1.9%	2.6%
10	1.7%	1.6%	2.4%

 

Provenance of each curve

•     Conservative (default) — positions 1 and 2 are measured: GrowthSRC (2025) recorded position 1 falling from 28% to 19% year on year, and position 2 from 20.83% to 12.60%, across 200,000+ keywords of Search Console data. Positions 3–10 are interpolated from the Backlinko 2019 shape scaled by 0.688. The tool says so on screen. Sistrix independently measured position 1 at 27% without AI Overviews and 11% with, across 100M+ keywords.

•     Optimistic — First Page Sage 2025, a meta-analysis with no disclosed sample size. Position 1 at 39.8% cannot be reconciled with any 2025–26 measurement. Included because clients will have seen it, not because it should be trusted.

•     Legacy — Backlinko 2019, a real measurement of 1.3 million pages. Frequently republished as current; the data predates AI Overviews entirely.

4. The AI Overview haircut

This is the input that makes the tool current, and it is deliberately exposed as a slider rather than baked in, because the effect varies enormously by query type.

Intent	Default haircut	Evidence
Informational	45%	Ahrefs: position-1 CTR −58% on AIO keywords. Pew: clicks 15% → 8% of visits
Commercial	10%	Ahrefs: 99.2% of AIO-triggering keywords are informational intent
Branded / navigational	0%	Amsive: branded queries gained 18.68% where AIO appeared

 

The nuance most coverage misses

Headlines report AI Overviews destroying organic traffic, citing the 58% position-1 drop. But Ahrefs also found that 99.2% of keywords triggering an AI Overview are informational, and Amsive found branded queries actually gained clicks. For a business chasing commercial-intent keywords, the honest haircut is much smaller than the headline. Applying a 58% cut to a transactional keyword set would understate SEO value as badly as ignoring AI Overviews overstates it.

5. Formulas

Output	Formula
Monthly sessions	Search volume × CTR at position × (1 − AI Overview loss)
Leads	Sessions × conversion rate
Customers	Leads × close rate
Gross profit	Customers × average revenue × gross margin
Ramp factor, month n	Smoothstep: t² × (3 − 2t) where t = n ÷ ramp months, capped at 1
Cumulative net	Σ gross profit − (setup + Σ monthly spend)
ROI	(Σ gross profit − Σ cost) ÷ Σ cost × 100
Break-even month	First month where cumulative net turns positive
Cost per lead	Σ cost ÷ Σ leads over the horizon
Cost per acquisition	Σ cost ÷ Σ customers over the horizon
Paid equivalent	Sessions × cost per click

 

A smoothstep curve is used for the ramp rather than a straight line because organic growth is genuinely S-shaped — slow while pages are indexed and gain trust, faster in the middle, then flattening as the target position is reached. It is a defensible shape, not a measured one, and the tool says so.

6. Verification — worked examples

These were run in a headless browser against the delivered HTML.

Scenario	Sessions	Leads	Customers	Revenue	ROI	Break-even
Default: 20k searches, pos 3, conservative, 10% AIO	1,080	27	5.4	₹1.35 L/mo	+160%	Month 8
Pos 1, optimistic curve, branded (0% AIO)	7,960	199	39.8	₹9.95 L/mo	+1,817%	Month 2
500 searches, ₹2L/mo spend, 12-month horizon	199	5	1	₹24,867/mo	−93%	Never

 

Hand-check of the default: 20,000 × 6.0% = 1,200 clicks, less the 10% AI Overview haircut = 1,080 sessions. 1,080 × 2.5% = 27 leads. 27 × 20% = 5.4 customers. 5.4 × ₹25,000 = ₹1,35,000. Matches.

7. What the tool refuses to do, and why that is the point

The 14.6% close-rate statistic is fabricated

"SEO leads close at 14.6% versus 1.7% for outbound" appears in thousands of agency decks and is routinely attributed to HubSpot. It traces to a 2012 blog post that cites no source whatsoever, and the HubSpot attribution is simply wrong. It is fourteen years old and unsourced. The calculator makes close rate a required user input rather than shipping this number, and says so on the page. Pointing this out publicly is itself a link-earning move — it is the kind of thing other marketers cite.

There is no independent SEO ROI dataset

The only structured SEO ROI figures in existence come from a single SEO agency publishing returns on its own campaigns, with no sample size, no control group and no failed campaigns included. The calculator therefore computes ROI from user inputs and never asserts a benchmark multiple.

No study establishes a month-by-month SEO ramp curve

Every "month 3 = 20%, month 6 = 50%" chart online is agency marketing with no underlying data. The only real measurement adjacent to it is Ahrefs finding that just 1.74% of newly published pages reach the top 10 within a year, and that 72.9% of top-10 pages are more than three years old. On that evidence a 12-month ramp is the optimistic case, not the cautious one. The tool makes the ramp a user input and states the limitation.

8. Implementation checklist

•     Publish at a clean permanent URL such as /seo-roi-calculator

•     No email gate, no exit popup, no newsletter interstitial — a gated tool does not get linked

•     Add SoftwareApplication or WebApplication schema markup

•     Support ?embed=1 to render without site navigation, so the iframe embed works cleanly

•     Swap the placeholder contact email and citation URL before publishing

•     Set the USD→INR rate default to a current figure and review it quarterly

•     Re-check the CTR curves and AI Overview figures every six months — this data is moving fast

•     Add a visible "last reviewed" date and honour it

•     Track referring domains to this URL monthly

 

Distribution beats the build

The tool itself is maybe a third of the work. The rest is telling people it exists: answer SEO ROI questions on Reddit and Quora with it, pitch the AI-Overview modelling angle to marketing publications, and use the fabricated-14.6%-statistic finding as a standalone post. That finding is more linkable than the calculator itself.

9. Sources

4.    Pew Research Center (2025). Google users are less likely to click on links when an AI summary appears. 900 US adults, 68,879 searches. INDEPENDENT. pewresearch.org

5.    Ahrefs (2026). AI Overviews reduce clicks — position-1 CTR down 58%, 300k keywords. VENDOR STUDY. ahrefs.com

6.    Sistrix (2026). Position-1 CTR 27% without AI Overviews vs 11% with, 100M+ keywords. VENDOR STUDY. via Search Engine Journal

7.    Amsive (2025). AI Overview click drop-off: non-branded −19.98%, branded +18.68%. AGENCY STUDY. amsive.com

8.    GrowthSRC Media (2025). Organic CTR study, 200k+ keywords from Search Console. AGENCY STUDY. growthsrc.com

9.    Backlinko (2019). CTR by position, 1.3M pages. VENDOR STUDY, DATED. backlinko.com

10. First Page Sage (2025). CTR by ranking position — meta-analysis, no sample size. AGENCY MARKETING. firstpagesage.com

11. Ruler Analytics (2026). Conversion rate by channel — organic search 4.9%, 110M+ sessions. VENDOR STUDY. ruleranalytics.com

12. WordStream / LocaliQ (2026). Google Ads benchmarks, 13,474 US campaigns. Average CPC $5.42, CPL $66.69. VENDOR STUDY. wordstream.com

13. Ahrefs (2023). How long it takes to rank — 1.74% of new pages reach top 10 within a year. VENDOR STUDY. ahrefs.com

14. Ahrefs (2024). SEO pricing survey, 439 providers. Average retainer $2,917/month; 76% of Indian providers under $1,000/month. VENDOR STUDY. ahrefs.com

15. SparkToro / Similarweb (2026). 68.01% of US Google searches end without a click. INDEPENDENT PANEL. via Search Engine Land

10. Honest limitations to keep on the page

These are printed in the footer of the live tool. Removing them would make it more persuasive and less credible, which is the wrong trade for an asset whose entire job is to be cited.

•     It cannot tell you whether you will actually reach the target position — the single biggest variable in the model

•     It assumes the conversion rate holds as traffic scales, which it usually does not

•     It models one keyword set at one average position, when real organic performance is a long-tail distribution

•     It compares gross traffic cost against Google Ads, ignoring that paid arrives instantly and stops when you stop paying, while organic ramps slowly and persists

 

Go to the page -> SEO ROI Calculator  

Read the detailed SEO Glossary Playbook.