Upgrade to market-intelligence dataset (real aggregate data, CC BY 4.0)
Browse files- .gitattributes +1 -60
- README.md +85 -89
- data/compliance.csv +5 -0
- data/industry_benchmarks.csv +15 -0
- data/market_overview.csv +8 -0
.gitattributes
CHANGED
|
@@ -1,60 +1 @@
|
|
| 1 |
-
*.
|
| 2 |
-
*.arrow filter=lfs diff=lfs merge=lfs -text
|
| 3 |
-
*.avro filter=lfs diff=lfs merge=lfs -text
|
| 4 |
-
*.bin filter=lfs diff=lfs merge=lfs -text
|
| 5 |
-
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
| 6 |
-
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
| 7 |
-
*.ftz filter=lfs diff=lfs merge=lfs -text
|
| 8 |
-
*.gz filter=lfs diff=lfs merge=lfs -text
|
| 9 |
-
*.h5 filter=lfs diff=lfs merge=lfs -text
|
| 10 |
-
*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 11 |
-
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 12 |
-
*.lz4 filter=lfs diff=lfs merge=lfs -text
|
| 13 |
-
*.mds filter=lfs diff=lfs merge=lfs -text
|
| 14 |
-
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
| 15 |
-
*.model filter=lfs diff=lfs merge=lfs -text
|
| 16 |
-
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 17 |
-
*.npy filter=lfs diff=lfs merge=lfs -text
|
| 18 |
-
*.npz filter=lfs diff=lfs merge=lfs -text
|
| 19 |
-
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 20 |
-
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 21 |
-
*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 22 |
-
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 23 |
-
*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 24 |
-
*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 25 |
-
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 26 |
-
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 27 |
-
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 28 |
-
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 29 |
-
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 30 |
-
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 31 |
-
*.tar filter=lfs diff=lfs merge=lfs -text
|
| 32 |
-
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 33 |
-
*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 34 |
-
*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 35 |
-
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 36 |
-
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 37 |
-
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 38 |
-
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 39 |
-
# Audio files - uncompressed
|
| 40 |
-
*.pcm filter=lfs diff=lfs merge=lfs -text
|
| 41 |
-
*.sam filter=lfs diff=lfs merge=lfs -text
|
| 42 |
-
*.raw filter=lfs diff=lfs merge=lfs -text
|
| 43 |
-
# Audio files - compressed
|
| 44 |
-
*.aac filter=lfs diff=lfs merge=lfs -text
|
| 45 |
-
*.flac filter=lfs diff=lfs merge=lfs -text
|
| 46 |
-
*.mp3 filter=lfs diff=lfs merge=lfs -text
|
| 47 |
-
*.ogg filter=lfs diff=lfs merge=lfs -text
|
| 48 |
-
*.wav filter=lfs diff=lfs merge=lfs -text
|
| 49 |
-
# Image files - uncompressed
|
| 50 |
-
*.bmp filter=lfs diff=lfs merge=lfs -text
|
| 51 |
-
*.gif filter=lfs diff=lfs merge=lfs -text
|
| 52 |
-
*.png filter=lfs diff=lfs merge=lfs -text
|
| 53 |
-
*.tiff filter=lfs diff=lfs merge=lfs -text
|
| 54 |
-
# Image files - compressed
|
| 55 |
-
*.jpg filter=lfs diff=lfs merge=lfs -text
|
| 56 |
-
*.jpeg filter=lfs diff=lfs merge=lfs -text
|
| 57 |
-
*.webp filter=lfs diff=lfs merge=lfs -text
|
| 58 |
-
# Video files - compressed
|
| 59 |
-
*.mp4 filter=lfs diff=lfs merge=lfs -text
|
| 60 |
-
*.webm filter=lfs diff=lfs merge=lfs -text
|
|
|
|
| 1 |
+
*.csv filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
README.md
CHANGED
|
@@ -1,89 +1,85 @@
|
|
| 1 |
-
---
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
-
|
| 7 |
-
|
| 8 |
-
-
|
| 9 |
-
-
|
| 10 |
-
-
|
| 11 |
-
-
|
| 12 |
-
size_categories:
|
| 13 |
-
-
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
#
|
| 20 |
-
|
| 21 |
-
This dataset
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
##
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
|
| 73 |
-
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
|
| 77 |
-
|
| 78 |
-
|
| 79 |
-
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
|
| 87 |
-
• Latin America Consumer Email List
|
| 88 |
-
• Sri Lanka Business Email List
|
| 89 |
-
• Arkansas Business Email Database
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-4.0
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
tags:
|
| 6 |
+
- market-intelligence
|
| 7 |
+
- b2b
|
| 8 |
+
- lead-generation
|
| 9 |
+
- email-marketing
|
| 10 |
+
- business-data
|
| 11 |
+
- business-email-list
|
| 12 |
+
size_categories:
|
| 13 |
+
- n<1K
|
| 14 |
+
task_categories:
|
| 15 |
+
- tabular-classification
|
| 16 |
+
- tabular-regression
|
| 17 |
+
pretty_name: Florida Business Email List — Market Intelligence Dataset
|
| 18 |
+
---
|
| 19 |
+
# Florida Business Email List — Market Intelligence Dataset
|
| 20 |
+
|
| 21 |
+
> **1,950,000 verified Florida, USA contacts are available from [LeadsBlue →](https://leadsblue.com/leads/usa-state-florida-business-email-leads-database/).** This open dataset provides the aggregate market intelligence behind that database — contact volume, benchmark open/reply rates, send timing, and compliance for the Florida, USA segment.
|
| 22 |
+
|
| 23 |
+
> **At a glance:** This dataset provides aggregated market-intelligence on the Florida Business Email List segment — real business-population statistics, cold-email performance benchmarks, and compliance context compiled by LeadsBlue Research.
|
| 24 |
+
|
| 25 |
+
## Key figures
|
| 26 |
+
|
| 27 |
+
| Metric | Value |
|
| 28 |
+
|---|---|
|
| 29 |
+
| Verified contacts in segment | **1,950,000** |
|
| 30 |
+
| Typical email open rate | **18-26%** |
|
| 31 |
+
| Typical email reply rate | **3-7%** |
|
| 32 |
+
| Best send days | Tuesday, Wednesday, Thursday |
|
| 33 |
+
| Best send time (local) | 08:00-10:00 ET |
|
| 34 |
+
| Geography | Florida, USA |
|
| 35 |
+
| Industry verticals benchmarked | 14 |
|
| 36 |
+
| License | CC BY 4.0 |
|
| 37 |
+
|
| 38 |
+
> 📥 **Get the full Florida Business Email List** — individual verified business records, ready to use: **[Download from LeadsBlue →](https://leadsblue.com/leads/usa-state-florida-business-email-leads-database/)**
|
| 39 |
+
|
| 40 |
+
## Market overview
|
| 41 |
+
|
| 42 |
+
Florida is part of the United States B2B market with 1,950,000 verified business contacts in LeadsBlue's database. Businesses in Florida operate under US federal CAN-SPAM rules, which permit B2B cold email without prior consent provided messages include accurate headers, a valid physical address, and a working opt-out. The figures below use US national benchmarks applied to the Florida segment.
|
| 43 |
+
|
| 44 |
+
The US B2B market is the largest and most data-dense in the world, with over 33 million registered businesses spanning every industry vertical. Technology dominates the coasts — Silicon Valley, Seattle, and New York City — while manufacturing holds the Midwest (Michigan, Ohio, Illinois), energy anchors Texas, healthcare clusters in Boston and Nashville, and financial services concentrate in New York and Charlotte. Decision-making speed is faster than any other major market: US executives are accustomed to cold outreach and respond within the same decision cycle that takes weeks in European or Asian equivalents.
|
| 45 |
+
|
| 46 |
+
**Primary business hubs:** New York City, San Francisco Bay Area, Austin, Chicago, Boston.
|
| 47 |
+
|
| 48 |
+
## Outreach benchmarks & strategy
|
| 49 |
+
|
| 50 |
+
US B2B cold email runs on volume and cadence. A 7-touch sequence over 21 days is standard — American SDR culture expects and accepts follow-ups in a way that European markets do not. Lead with a single, specific claim relevant to their role and company size, use social proof from recognizable US brands in their vertical, and make the ask clear in the first email rather than building to it. Subject lines with a direct question or specific number (stat, dollar amount, time frame) consistently outperform curiosity-gap or clever subject lines in US B2B contexts.
|
| 51 |
+
|
| 52 |
+
## Files in this dataset
|
| 53 |
+
|
| 54 |
+
| File | Contents |
|
| 55 |
+
|---|---|
|
| 56 |
+
| `data/market_overview.csv` | Segment-level metrics: contact volume, benchmark rates, send timing |
|
| 57 |
+
| `data/industry_benchmarks.csv` | Open/reply benchmarks across 14 industry verticals |
|
| 58 |
+
| `data/compliance.csv` | Regulatory framework, business culture, decision-maker profile, hubs |
|
| 59 |
+
|
| 60 |
+
## Methodology
|
| 61 |
+
|
| 62 |
+
Statistics are derived from LeadsBlue's verified database counts combined with published market baselines. This dataset contains aggregate figures only; it includes no individual contact records.
|
| 63 |
+
|
| 64 |
+
## How to cite
|
| 65 |
+
|
| 66 |
+
> LeadsBlue Research. (2026). *Florida Business Email List — Market Intelligence Dataset.* Derived from the B2B Cold Email Benchmark Report 2026 (https://doi.org/10.5281/zenodo.20136256). CC BY 4.0. Source: https://leadsblue.com
|
| 67 |
+
|
| 68 |
+
```bibtex
|
| 69 |
+
@dataset{leadsblue_florida_business_dataset_2026,
|
| 70 |
+
author = {Johnson, Luther},
|
| 71 |
+
title = {Florida Business Email List -- Market Intelligence Dataset},
|
| 72 |
+
year = {2026},
|
| 73 |
+
publisher = {LeadsBlue Research},
|
| 74 |
+
doi = {10.5281/zenodo.20136256},
|
| 75 |
+
url = {https://leadsblue.com/leads/usa-state-florida-business-email-leads-database/}
|
| 76 |
+
}
|
| 77 |
+
```
|
| 78 |
+
|
| 79 |
+
## Source & full dataset
|
| 80 |
+
|
| 81 |
+
This aggregate dataset is published by **[LeadsBlue Research](https://leadsblue.com)**. The full verified business email list — with individual business contact records — is available at **[Florida Business Email List](https://leadsblue.com/leads/usa-state-florida-business-email-leads-database/)**.
|
| 82 |
+
|
| 83 |
+
Related resources: [Benchmark report (Zenodo)](https://zenodo.org/records/20136256) · [Research hub](https://leadsblue-datasets.github.io/research/) · [Interactive tools](https://huggingface.co/emailmarketingdataset)
|
| 84 |
+
|
| 85 |
+
<script type="application/ld+json">{"@context": "https://schema.org", "@type": "Dataset", "name": "Florida Business Email List \u2014 Market Intelligence Dataset", "description": "This dataset provides aggregated market-intelligence on the Florida Business Email List segment \u2014 real business-population statistics, cold-email performance benchmarks, and compliance context compiled by LeadsBlue Research.", "license": "https://creativecommons.org/licenses/by/4.0/", "creator": {"@type": "Organization", "name": "LeadsBlue Research", "url": "https://leadsblue.com"}, "isBasedOn": "https://doi.org/10.5281/zenodo.20136256", "url": "https://leadsblue.com/leads/usa-state-florida-business-email-leads-database/", "sameAs": ["https://leadsblue.com", "https://b2bdataindex.com", "https://zenodo.org/records/20136256", "https://leadsblue-datasets.github.io/research/"]}</script>
|
|
|
|
|
|
|
|
|
|
|
|
data/compliance.csv
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
field,detail
|
| 2 |
+
compliance_framework,"CAN-SPAM requires no prior consent for B2B email — it is an opt-out law. Every commercial email must include a valid physical postal address, honest subject line, clear identification of the sender, and a functional opt-out mechanism honoured within 10 business days. California adds CCPA layer for consumer data but B2B email to business addresses is not classified as personal data sale under CCPA."
|
| 3 |
+
business_culture,"American business culture is direct, ROI-first, and time-compressed. Executives make purchasing decisions faster than European counterparts but switch vendors more easily too. Social proof from peers and competitors matters enormously — case studies naming specific US companies in the same vertical are the single most effective trust signal in American B2B cold email."
|
| 4 |
+
primary_business_hubs,New York City; San Francisco Bay Area; Austin; Chicago; Boston; Los Angeles; Seattle; Dallas; Atlanta; Miami
|
| 5 |
+
decision_maker_profile,"US B2B purchasing is typically driven by department heads and VPs with delegated budget authority. In SMEs, the CEO or founder often controls all purchases above $5,000. Enterprise deals involve a buying committee of 5-7 stakeholders — economic buyer (CFO/VP), champion (department head), and technical evaluator. Titles to target: VP of Sales, Head of Marketing, Director of Operations, CTO for tech"
|
data/industry_benchmarks.csv
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
industry,open_rate,reply_rate,best_send_time
|
| 2 |
+
Technology & Software,22-31%,3.5-6.5%,"Tuesday-Thursday, 8-10am local"
|
| 3 |
+
Healthcare & Medical,18-26%,2.0-4.5%,"Tuesday-Wednesday, 6:30-8am (before rounds)"
|
| 4 |
+
Manufacturing & Engineering,16-23%,1.8-3.5%,"Tuesday-Thursday, 7-9am (before floor opens)"
|
| 5 |
+
Finance & Banking,15-22%,1.5-3.2%,"Tuesday-Wednesday, 7:30-9am"
|
| 6 |
+
Professional Services,20-28%,2.5-5.0%,"Tuesday-Thursday, 8-10am"
|
| 7 |
+
Real Estate,18-26%,2-6%,
|
| 8 |
+
Construction & Real Estate,17-24%,2.0-3.8%,"Monday, Wednesday, 6-8am (before site visits)"
|
| 9 |
+
Legal,19-27%,2.2-4.5%,"Tuesday-Thursday, 8-10am"
|
| 10 |
+
E-commerce,24-33%,3.5-6.5%,"Tuesday-Thursday, 9-11am"
|
| 11 |
+
Oil & Energy,14-20%,2-4%,
|
| 12 |
+
Education & Training,16-24%,2-5%,
|
| 13 |
+
Retail & Commerce,17-24%,2-5%,
|
| 14 |
+
Agriculture & Food,14-22%,2-4%,
|
| 15 |
+
Transportation & Logistics,16-22%,2-5%,
|
data/market_overview.csv
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
metric,value,source
|
| 2 |
+
verified_contacts,"1,950,000",LeadsBlue verified database
|
| 3 |
+
segment,business email list,LeadsBlue catalog
|
| 4 |
+
geography,"Florida, USA",LeadsBlue catalog
|
| 5 |
+
email_open_rate_pct,18-26,LeadsBlue benchmark
|
| 6 |
+
email_reply_rate_pct,3-7,LeadsBlue benchmark
|
| 7 |
+
best_send_days,Tuesday; Wednesday; Thursday,LeadsBlue benchmark
|
| 8 |
+
best_send_time_local,08:00-10:00 ET,LeadsBlue benchmark
|