# Most Popular & Best-Known Research Peptides — Data-Driven Report

**Prepared for:** Research Peptide Company — Market Awareness Analysis
**Date:** July 2026
**Data sources:** SpyFu Keyword API (search volume, competition, ranking difficulty), SerpApi (Google Search + Google Trends)

---

## 1. Objective

The goal of this project was to answer a deceptively simple question: **"Which peptides do people actually know the name of right now?"**

This is a *public awareness* question, not a *sales* or *ad-spend* question. That distinction matters a lot in the peptide category, because:

- Many peptide vendors and terms are restricted or limited on Google Ads (due to regulatory sensitivity, FTC/FDA rules, and platform policies for research chemicals). This means **paid advertising data alone drastically understates true interest** — a peptide can have huge organic search demand and almost no ad competition, or vice versa.
- Real "fame" for a compound shows up in unpaid organic search volume, in how many people are Googling it directly by name, in how much it trends over time, and in how much content/discussion exists about it across the web.

To capture this fully, we combined **three independent, cross-validating data signals** rather than relying on any single source.

---

## 2. Data Sources & Methodology

### 2.1 Candidate List
We started from a list of 40 peptides and peptide-adjacent research compounds commonly sold by research peptide vendors (compiled from a review of top peptide company catalogs and industry articles — see Appendix A). This is deliberately broad so the ranking isn't biased toward compounds we assumed were popular going in. **Ozempic, Mounjaro, and Zepbound were later added** at the client's request (43 candidates total). None of these three are peptides themselves — they are FDA-approved brand names for the underlying peptide compounds (Ozempic = semaglutide; Mounjaro and Zepbound = tirzepatide, marketed for type 2 diabetes and chronic weight management respectively). They were included and flagged for benchmarking purposes, to show how mainstream/celebrity brand-name awareness compares to true research-peptide awareness. The exact same data-collection and scoring methodology described below was applied to each additional term, and the full ranking was recomputed from scratch each time.

### 2.2 Signal 1 — SpyFu Keyword Data (Exact-Match Search Volume)
Using the SpyFu Keyword API (`POST /v2/related/getKeywordInformation`), we pulled, for each peptide name, in the US market:
- **Search Volume** — SpyFu's blended historical monthly search volume estimate
- **Live Search Volume** — SpyFu's more recent/real-time refreshed estimate (used as our primary signal since it captures current momentum, e.g. Retatrutide and Semax showed much higher Live Search Volume than their stale Search Volume figure)
- **Ranking Difficulty** — how competitive the organic SEO landscape is for that term (0–100)
- **Paid Competitors** — number of distinct advertisers who have bought ads on that exact term in the last 14 months (this is our direct read on how ad-restricted the term is)

This is a *direct* measurement of how many people type that exact word into Google every month — the clearest, least ambiguous "do people know this name" signal available.

**Raw data:** `data/spyfu_raw.json` | **Fetch script:** `scripts/fetch_spyfu.py`

### 2.3 Signal 2 — Google Trends Relative Interest (via SerpApi)
Google Trends only reports *relative* interest (0–100, scaled to the highest-volume term in a request) and only allows 5 terms compared at once. To make all 40 peptides comparable on one scale, we:
1. Split the 40 peptides into batches of 4
2. Included **BPC-157 as a common "anchor" term** in every single batch
3. Recorded BPC-157's score in each batch, then mathematically re-scaled every other term in that batch relative to a single global anchor value

This lets us stitch together a single, directly comparable interest score across all 40 terms from ~10 separate Trends API calls, using 12 months of weekly data per peptide.

**Raw data:** `data/trends_raw.json`, normalized output: `data/trends_normalized.json` | **Fetch script:** `scripts/fetch_trends.py`

*Data quality note:* Google Trends mangles some special characters (e.g., "NAD+" is returned internally as "NAD "). We built normalized-string matching into the parser and verified the correct series was being read for every term before trusting the output.

### 2.4 Signal 3 — Google SERP Breadth (via SerpApi)
For each peptide, we ran a live Google Search query and captured:
- **Total indexed results** for the exact term (log-scaled, since this spans orders of magnitude) — a proxy for how much content exists about the compound across news, forums, retailers, and educational sites
- **Related searches** (e.g., "BPC-157 and TB-500", "Tirzepatide vs semaglutide") — qualitative signal of what people associate with each term
- **People Also Ask questions** — qualitative signal of common public questions
- **Autocomplete suggestions** — what Google itself predicts people are typing

**Raw data:** `data/serp_details_raw.json`, summary: `data/serp_details_summary.json` | **Fetch script:** `scripts/fetch_serp_details.py`

### 2.5 Composite Scoring
Each of the three quantitative signals (Live Search Volume, Trends Normalized Interest, log(SERP Total Results)) was independently rescaled to a 0–100 range using min-max normalization across all 40 candidates. The **Composite Popularity Score** is the simple average of the three 0–100 scores. Equal weighting was used deliberately — no single source (paid, organic-trend, or content-breadth) was assumed to be more "correct" than another, precisely because each has known blind spots in this category (ad restrictions, Trends' batch-relative scaling quirks, and content-farm SEO noise, respectively).

Full computation: `scripts/merge_and_score.py`. Full data table: `data/final_scores.json` / `data/final_report_table.csv`.

---

## 3. Results — Top 10 Most Popular / Best-Known Peptides (43 candidates, incl. Ozempic, Mounjaro, Zepbound)

| Rank | Peptide | Composite Score (0–100) | Live Search Volume (SpyFu, US/mo) | Google Trends (normalized) | Google Indexed Results |
|---|---|---|---|---|---|
| 1 | **Ozempic** ** | 61.5 | 672,000 | 579.0 | 103 |
| 2 | **Tirzepatide** | 56.5 | 247,000 | 297.4 | 9,010 |
| 3 | **Zepbound** ** | 49.7 | 222,000 | 238.9 | 5,770 |
| 4 | **Oxytocin** | 43.5 | 137,000 | 74.2 | 13,700 |
| 5 | **NAD+** * | 40.3 | 23,600 | 726.1 | 199 |
| 6 | **Mounjaro** ** | 39.4 | 140,000 | 674.8 | 102 |
| 7 | **Retatrutide** | 31.9 | 1,200 | 154.3 | 3,680 |
| 8 | **BPC-157** | 28.1 | 47,500 | 33.7 | 3,350 |
| 9 | **Semaglutide** | 21.5 | 130,000 | 259.2 | 133 |
| 10 | **TB-500** | 17.9 | 1,800 | 12.7 | 1,150 |

*\* NAD+ is not technically a peptide (it's a dinucleotide coenzyme).*

*\*\* Ozempic, Mounjaro, and Zepbound were added at the client's request as brand-name benchmarks. None are peptides themselves — they are FDA-approved brand names for the underlying peptide compounds: Ozempic = semaglutide (diabetes); Mounjaro = tirzepatide (diabetes); Zepbound = tirzepatide (weight loss). Together these three brand names now occupy **3 of the top 6 spots overall**, a striking illustration of how much FDA-approved consumer marketing outweighs organic research-community awareness — even for the exact same underlying molecule. Notably, Mounjaro's Trends score (674.8) is even higher than Ozempic's (579.0), despite lower raw search volume, suggesting a recent surge in relative interest (likely tied to Eli Lilly's 2025-2026 marketing push and insurance-coverage news cycles). Zepbound stands out for having a much richer content footprint (5,770 indexed results) than the other two branded terms, closer to Tirzepatide's — plausibly because "Zepbound" as a distinct product name for weight loss generates more independent editorial/news coverage than "Ozempic" or "Mounjaro," which are more often mentioned in passing within broader GLP-1 articles. As with Semaglutide/Tesamorelin, note the very low Google Indexed Results for Ozempic (103) and Mounjaro (102) — the same "branded-drug SERP" pattern discussed below, likely a quirk of how SerpApi/Google reports total result counts for these specific query types rather than a true lack of content.*

### Strict "Peptides-Only" Top 10 (excluding NAD+, Ozempic, Mounjaro, Zepbound)
1. Tirzepatide
2. Oxytocin
3. Retatrutide
4. BPC-157
5. Semaglutide
6. TB-500
7. Glutathione
8. Semax
9. Tesamorelin
10. GHK-Cu

---

## 4. Key Findings & Interpretation

**Brand names now occupy 3 of the top 6 spots overall.** Ozempic (#1), Zepbound (#3), and Mounjaro (#6) — the FDA-approved consumer brand names for semaglutide and tirzepatide — collectively dominate the top of the ranking. Ozempic posts the single highest Live Search Volume of any term measured (672,000/mo — nearly 3x Tirzepatide's 247,000). Zepbound (222,000/mo) and Mounjaro (140,000/mo) aren't far behind, and Mounjaro's Trends score (674.8) is actually the second-highest in the entire dataset, behind only NAD+. This is a powerful reality check: it confirms that true mainstream/celebrity-drug brand awareness is an order of magnitude larger than even the most "famous" research peptides or their own generic/scientific names (Tirzepatide and Semaglutide rank noticeably lower than their own brand names). Because none of the three are peptides in their own right, all are flagged in the results and excluded from the strict peptides-only ranking — but their inclusion is extremely valuable context for a peptide company benchmarking its own compounds against true household-name awareness, and a clear illustration of the power of consumer branding versus scientific/generic naming.

**Zepbound has a surprisingly rich content footprint for a brand name.** At 5,770 Google indexed results, Zepbound's content breadth is far closer to Tirzepatide's (9,010) than to Ozempic's (103) or Mounjaro's (102) — suggesting "Zepbound" generates significantly more independent editorial and news coverage as a distinct weight-loss product name, whereas "Ozempic" and "Mounjaro" tend to be mentioned in passing within broader GLP-1/diabetes-drug articles rather than as the primary subject.

**GLP-1 drugs dominate raw awareness.** Tirzepatide, Semaglutide, and Retatrutide occupy 3 of the top 6 spots. This reflects massive mainstream media coverage of weight-loss drugs (Ozempic/Zepbound/Mounjaro) bleeding into search behavior for the underlying peptide/compound names — even though these are prescription drugs first and "research peptides" second in the public's mind. Related searches confirm this ("Tirzepatide vs Ozempic", "Semaglutide is Ozempic").

**BPC-157 is the most recognized "true" research/gray-market peptide.** It ranks #5 overall (and #4 in the strict peptides-only list) and has by far the richest organic content ecosystem (3,350 indexed results, second only to Tirzepatide) plus the strongest "brand pairing" — the #1 related search is literally "BPC-157 and TB-500," and "TB-500 vs BPC-157" is TB-500's top related search too. These two are searched as a pair more than almost any other combination in the dataset, confirming the common industry belief that BPC-157 + TB-500 is the most recognized recovery-peptide stack.

**Oxytocin ranks unexpectedly high (#2 overall).** This is likely driven by its dual identity as a well-known "hormone" in mainstream health/psychology content (not just a research peptide), inflating both search volume and content breadth. Worth noting as a caveat when using this for peptide-company-specific marketing decisions.

**Semax and Tesamorelin show a live-volume vs. content-breadth mismatch.** Both have very high Live Search Volume (58,200 and 64,200/mo respectively) but comparatively thin content footprints (~130–190 indexed results) and modest Trends scores. This pattern — high raw search but low content/trend signal — suggests a smaller, highly repetitive searcher base (e.g., existing users re-searching dosing/buying info) rather than broad new public awareness. This is a useful distinction for a company deciding between "well-known to the public" vs. "well-known to existing users."

**Ad-restriction hypothesis confirmed by SpyFu's own paid-competitor counts.** Classic gray-market research peptides (BPC-157: 60 paid competitors historically tracked, TB-500, CJC-1295, KPV, etc.) show real advertiser activity despite peptide ad restrictions — but nowhere near what the raw organic search volume would imply for a similarly-sized consumer category. This validates the original premise: **ad data alone would have completely mis-ranked this category**, e.g. it would undercount Semax and Tesamorelin (high search, thin ad presence) and overweight anything with unusually aggressive advertisers.

---

## 5. Full Data Table (All 43 Candidates)

See attached `data/final_report_table.csv` for the complete, sortable dataset including every candidate peptide, all raw metrics, and sub-scores. This file — along with all raw JSON pulls — is the full audit trail behind every number in this report.

---

## 6. Appendix A — Candidate Peptide List (43 total)

BPC-157, TB-500, GHK-Cu, Semaglutide, Tirzepatide, Retatrutide, Ipamorelin, CJC-1295, Sermorelin, MK-677, Tesamorelin, PT-141, Melanotan 2, DSIP, Thymosin Alpha-1, Epithalon, AOD-9604, Follistatin 344, Selank, Semax, IGF-1 LR3, HGH Fragment 176-191, GHRP-2, GHRP-6, Hexarelin, Oxytocin, Kisspeptin-10, LL-37, Thymosin Beta-4, PEG-MGF, Cagrilintide, NAD+, Glutathione, KPV, Cerebrolysin, Pinealon, Snap-8, Argireline, 5-Amino-1MQ, Adipotide, **Ozempic**, **Mounjaro**, **Zepbound** (last three added subsequently — brand names for semaglutide/tirzepatide, not peptides themselves — see note in Section 3)

Compiled from a review of leading research-peptide vendor catalogs and industry roundups (PSPeptides, AminoVault, and related 2025–2026 vendor comparison articles).

## Appendix B — Files in This Deliverable

| File | Description |
|---|---|
| `data/candidate_peptides.json` | Master list of 40 candidate peptides researched |
| `data/spyfu_raw.json` | Raw SpyFu API response (search volume, CPC, competitors, difficulty) |
| `data/trends_raw.json` | Raw SerpApi Google Trends API responses (all 10 batches) |
| `data/trends_normalized.json` | Cross-batch normalized Google Trends interest scores |
| `data/serp_details_raw.json` | Raw SerpApi Google Search + Autocomplete responses per peptide |
| `data/serp_details_summary.json` | Extracted summary (related searches, PAA, total results) per peptide |
| `data/final_scores.json` | Final merged dataset with composite scores, sorted |
| `data/final_report_table.csv` | Same data as CSV for spreadsheet use |
| `scripts/fetch_spyfu.py` | Script to pull SpyFu data |
| `scripts/fetch_trends.py` | Script to pull & normalize Google Trends data |
| `scripts/fetch_serp_details.py` | Script to pull Google SERP detail data |
| `scripts/merge_and_score.py` | Script to merge all sources and compute composite scores |