TECHNICAL REPORT: ANALYSIS OF THE GLOBAL PRESENCE OF HIVE

Introduction

Objective of the report: This report aims to identify and analyze Hive's presence globally, focusing on the regions with the highest user activity and participation. Data related to posts, tags, languages, and user locations will be used to develop a hypothesis about the areas with the most Hive presence in the world.

Methodology: The data was obtained from the Hive blockchain and grouped into different categories: tags, countries, languages, and profile locations. Analysis includes visualizations and correlations to determine key trends.

Data Used

Posts and Tags

• Data on the number of publications grouped by tags.
• Analysis of the total payout in HBD for each tag.
  Unique users by Country (based on tags)
• Data showing the number of unique users per country.
  Use of languages ​​in publications
• Data on the use of different languages ​​in publications.
  Location of users in profiles
• Data about users who have specified their location in their profile.
  Data volume
  An approximate of more than 700,000 posts (not including comments) published in the last 8 months and a total of 30,102 accounts with a location profile in Hive were used.

Data Analysis

Posts and Tags

Tag	Total Payout (HBD)	Number of Posts
neoxian	735859.842	245296
spanish	492449.288	120038
proofofbrain	385142.061	129398
photography	341703.895	105453
palnet	282334.274	100165
leofinance	265713.120	89570

Unique users per country

Country	Users
Venezuela	910
Cuba	399
India	397
Mexico	227
Argentina	215
Italia	199
USA	160
Japan	145
España	128
Ukraine	126
Deutschland	115
Brazil	114

Use of languages ​​in publications Thanks to HiveSQL

_{For dual language posts, English has been removed to have a more realistic result based on the native language of the writers in Hive}

Language	Number of Publications	Total Payout (HBD)
en	439491	1063555
es	144652	575402
pl	27075	22936
de	23596	34103
nl	15219	6293

Location of users who have their location in the profile
_{More than 30,000 accounts analyzed - Starting from the premise that they have their location in their profile}

Country	% Users
A stranger	44
Venezuela	10
India	9
USA	8
Germany	6
Canada	2

Identification of trends

Countries with the greatest Hive presence

Analyzing the data on publications, unique users and language use, it is observed that the countries with the greatest presence of Hive are:

• Venezuela: With the largest number of unique users and high activity in publications in Spanish.
• India: High number of unique users and notable presence in publications.
• USA: Significant number of users and publications in English.

Global Distribution of Hive
Hive's user distribution shows a strong presence in Spanish-speaking countries (Venezuela, Cuba, Mexico, Argentina) and English-speaking countries (United States, India).

Trends in Latam and countries with the greatest growth trend

Trends in Latam
The Latin American (Latam) region shows a strong Hive presence, especially in countries such as Venezuela, Cuba, Mexico, and Argentina. These countries stand out not only for the number of users, but also for the high activity in publications.

Countries with Greater Growth Tendency (estimated who post on Hive social media)

Cuba: Cuba is experiencing significant growth in Hive adoption. Active growth initiatives on the part of these communities are driving this trend.
Peru: Peru is developing active initiatives to increase Hive adoption. Sustained growth is expected in the coming months.
Mexico: Mexico also shows growth potential due to its population and the growing adoption of blockchain technologies.
Colombia: With 85 unique users, Colombia is another Latam country that has significant growth potential.

Conclusions

Summary of Results

• Venezuela is the country with the largest Hive presence, followed by India and USA.
• The languages ​​most used in publications are English and Spanish, reflecting a strong presence in English-speaking and Spanish-speaking regions.
• Most users do not specify their location, suggesting that there is a need to improve location data capture.

Hypotheses and Trends
Hive's greater presence in Venezuela and Spanish-speaking countries may be influenced by the early adoption and active use of social media in these regions,

It is worth noting that during the last 2 years a Hive marketing budget has been allocated from ValuePlan in the Venezuela region, through:

• Support for sports activities.
• HBD adoption campaigns
• Local events

Although by searches and other data related to theoretical growth marketing KPIs, Hive does not have a large digital presence in Web2 social media, in the Venezuela region, we can assume that mostly Outbound marketing strategies are used, attached to these base initiatives.

(Venezuela) I used Google Trends to analyze in which region the word Hive is most searched for.

Keyword: HIVE

Keyword: Web3

Web traffic analysis (similarweb.com)
Venezuela as the country that most enters hive.io

Based on the popular social networks of Hive and according to similarweb.com Venezuela is the country with the highest growth.

India shows a growing trend in Hive usage, possibly due to the large population and interest in blockchain technologies.

Latam is emerging as a key region for Hive's growth, with active initiatives in countries such as Cuba and Peru.

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Recommendations

• Improve location data capture: Implement strategies to encourage users to specify their location in their profiles or target across communities.
• Promote usage in new regions: Develop marketing campaigns targeting regions with low Hive presence to expand global adoption.
• Monitor trends: Continue to analyze post and user data to identify new trends and adjust growth strategies.

Approach as a Growth Marketing Expert

To maximize Hive's growth and adoption in key regions, the following marketing strategies should be implemented:

Growth Strategies

• Support marketing campaigns: Create specific marketing campaigns for regions with growth potential, such as Cuba, Colombia, Mexico, and Peru, highlighting the advantages and opportunities of using Hive.

• Local collaborations: Establish collaborations with local influencers, Web3 sector leaders, and technology communities in the target countries to increase Hive's visibility and credibility, this can be accompanied by a reputation campaign.

For Latam: Implement SEO campaigns, and create different conversion funnels to holahive.com so that new users can create accounts, as well as create activation campaigns.

Education and training: Develop education and training programs on blockchain and Hive simplified for new users in growth regions, facilitating their adoption and effective use of the blockchain.

Incentives and rewards: Implement incentive and reward programs for users that promote the adoption of Hive and have educational programs established, give bonuses for activities and awards for significant contributions.

Develop Hive Builders programs: to strengthen knowledge about Hive and how we can focus adoption strategies thinking about Hive's business and development objectives.

Among other activities that can be done.

Contribute

If you have data analysis skills, you can complement and validate this information and if you wish you can leave your analysis in the comments.

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Data requested by @starkerz

Disclaimer: The information in this report was gathered from HiveSQL and processed through Python scripts. Despite striving for accuracy, some discrepancies might exist. The marketing evaluations are based on subjective observations and should not be taken as definitive representations.

country_keywords ={
    "Venezuela": ["ven", "vzla", "venezuela", "hivevenezuela", "barquisimeto", "caracas", "maracaibo", "valencia", "merida", "maracay", "maturin", "guayana", "anzoategui", "aragua", "barinas", "bolivar", "carabobo", "cojedes", "delta amacuro", "falcon", "guarico", "lara", "monagas", "nuevo esparta", "portuguesa", "sucre", "tachira", "trujillo", "vargas", "yaracuy", "zulia"],
    "Mexico": ["mex", "mexico", "hivemexico", "mexicana", "mexicano", "mexican", "mexicans", "mdx", "cdmx", "guadalajara", "monterrey", "puebla", "tijuana", "merida", "cancun", "acapulco", "chihuahua", "juarez", "zacatecas", "queretaro", "toluca", "morelia", "cuernavaca", "mazatlan", "veracruz", "oaxaca", "chiapas"],
    "Colombia": ["col", "colombia", "hivecolombia", "bogota", "medellin", "cali", "barranquilla", "cartagena", "bucaramanga", "pereira", "manizales", "cucuta", "neiva", "ibague", "villavicencio", "santa marta", "popayan", "pasto", "monteria", "valledupar"],
    "Cuba": ["cub", "cuba", "hivecuba", "habana", "lahabana", "cuban", "cubano", "santiagodecuba", "camaguey", "holguin", "guantanamo", "bayamo", "matanzas", "cienfuegos", "pinar del rio"],
    "Argentina": ["arg", "argentina", "hiveargentina", "buenosaires", "cordoba", "rosario", "mendoza", "la plata", "tucuman", "mar del plata", "salta", "santa fe", "neuquen", "san juan", "resistencia", "san salvador de jujuy", "rio gallegos"],
    "Chile": ["chi", "chile", "hivechile", "santiago", "valparaiso", "concepcion", "la serena", "antofagasta", "temuco", "iquique", "puerto montt", "valdivia", "copiapo", "osorno", "arica", "rancagua"],
    "Peru": ["per", "peru", "hiveperu", "lima", "arequipa", "trujillo", "chiclayo", "cusco", "huancayo", "piura", "iquitos", "pucallpa", "juliaca", "ayacucho", "chimbote"],
    "Poland": ["pol", "poland", "polonia", "hivepoland", "warsaw", "krakow", "lodz", "wroclaw", "poznan", "gdansk", "szczecin", "bydgoszcz", "lublin", "katowice", "bialystok", "gdynia", "czestochowa"],
    "Brazil": ["bra", "brasil", "brazil", "hivebrasil", "sao paulo", "rio de janeiro", "brasilia", "salvador", "fortaleza", "belo horizonte", "manaus", "curitiba", "recife", "porto alegre", "goiania", "belem", "sao luis", "maceio", "natal", "terezina"],
    "Spain": ["esp", "spain", "españa", "hivespain", "madrid", "barcelona", "valencia", "sevilla", "zaragoza", "malaga", "murcia", "palma", "las palmas", "bilbao", "alicante", "cordoba", "valladolid", "vigo", "gijon", "hospitalet", "la coruña", "granada", "vitoria"],
    "United States": ["usa", "us", "united states", "hiveusa", "eeuu", "new york", "los angeles", "chicago", "houston", "phoenix", "philadelphia", "san antonio", "san diego", "dallas", "san jose", "austin", "jacksonville", "fort worth", "columbus", "charlotte", "san francisco", "indianapolis", "seattle", "denver", "washington dc"],
    "Canada": ["can", "canada", "hivecanada", "toronto", "montreal", "vancouver", "calgary", "edmonton", "ottawa", "quebec city", "winnipeg", "hamilton", "kitchener", "london", "victoria"],
    "Ecuador": ["ecu", "ecuador", "hiveecuador", "quito", "guayaquil", "cuenca", "santo domingo", "machala", "manta", "portoviejo", "loja", "ambato", "riobamba", "quevedo", "ibarra", "babahoyo", "latacunga"],
    "Uruguay": ["uru", "uruguay", "hiveuruguay", "montevideo", "salto", "paysandu", "maldonado", "rivera", "tacuarembo", "artigas", "durazno"],
    "Paraguay": ["par", "paraguay", "hiveparaguay", "asuncion", "ciudad del este", "encarnacion", "luque", "san lorenzo", "capiata", "lambare", "fernando de la mora"],
    "Bolivia": ["bol", "bolivia", "hivebolivia", "la paz", "santa cruz", "cochabamba", "sucre", "oruro", "potosi", "tarija", "trinidad"],
    "Panama": ["pan", "panama", "hivepanama", "panama city", "colon", "david", "santiago", "chitre", "la chorrera", "penonome"],
    "Costa Rica": ["crc", "costa rica", "hivecostarica", "san jose", "alajuela", "cartago", "heredia", "puntarenas", "limon", "liberia", "quesada"],
    "Guatemala": ["gua", "guatemala", "hiveguatemala", "guatemala city", "mixco", "villa nueva", "quetzaltenango", "escuintla", "san juan sacatepequez", "villa canales"],
    "Honduras": ["hon", "honduras", "hivehonduras", "tegucigalpa", "san pedro sula", "choloma", "la ceiba", "el progreso", "choluteca", "comayagua"],
    "El Salvador": ["esa", "elsalvador", "hivesalvador", "san salvador", "santa ana", "san miguel", "soyapango", "santa tecla", "mejicanos", "apopa"],
    "Nicaragua": ["nic", "nicaragua", "hivenicaragua", "managua", "leon", "masaya", "tipitapa", "chinandega", "matagalpa", "granada"],
    "Dominican Republic": ["dom", "dominicanrepublic", "hivedominican", "dominicana", "santo domingo", "santiago", "la romana", "san cristobal", "puerto plata", "san pedro de macoris", "higuey"],
    "Ucrania": ["ukr", "ucrania", "ukraine", "hiveukraine", "kiev", "kharkiv", "odessa", "dnipro", "donetsk", "zaporizhia", "lviv"],
    "Rusia": ["rus", "russia", "rusia", "hiverussia", "moscow", "saint petersburg", "novosibirsk", "yekaterinburg", "nizhny novgorod", "kazan", "chelyabinsk"],
    "China": ["chn", "china", "hivechina", "beijing", "shanghai", "guangzhou", "shenzhen", "chengdu", "nanjing", "wuhan"],
    "India": ["ind", "india", "hiveindia", "mumbai", "delhi", "bangalore", "hyderabad", "ahmedabad", "chennai", "kolkata", "surat", "pune", "jaipur", "lucknow"],
    "Japan": ["jpn", "japan", "hivejapan", "tokyo", "osaka", "nagoya", "sapporo", "fukuoka", "kyoto", "kobe"],
    "South Korea": ["kor", "southkorea", "korea", "hivekorea", "seoul", "busan", "incheon", "daegu", "daejeon", "gwangju", "suwon"],
    "Australia": ["aus", "australia", "hiveaustralia", "sydney", "melbourne", "brisbane", "perth", "adelaide", "gold coast", "canberra", "hobart"],
    "Deutschland": ["ger", "germany", "alemania", "hivedeutschland", "berlin", "hamburg", "munich", "cologne", "frankfurt", "stuttgart", "dusseldorf", "dortmund"],
    "France": ["fra", "france", "francia", "hivefrance", "paris", "marseille", "lyon", "toulouse", "nice", "nantes", "strasbourg", "montpellier", "bordeaux", "lille"],
    "Italia": ["ita", "italy", "italia", "hiveitaly", "rome", "milan", "naples", "turin", "palermo", "genoa", "bologna", "florence", "bari"],
    "United Kingdom": ["uk", "unitedkingdom", "reinounido", "hiveuk", "london", "birmingham", "glasgow", "liverpool", "bristol", "manchester", "edinburgh", "leeds"],
    "Portugal": ["por", "portugal", "hiveportugal", "lisbon", "porto", "amadora", "braga", "coimbra", "funchal", "setubal"],
    "Netherlands": ["netherlands", "holanda", "hivenetherlands", "amsterdam", "rotterdam", "the hague", "utrecht", "eindhoven", "tilburg", "groningen"],
    "Croacia": ["cro", "croatia", "croacia", "hivecroatia", "zagreb", "split", "rijeka", "osijek", "zadar", "pula", "sibenik"],
    "Greece": ["gre", "greece", "grecia", "hivegreece", "athens", "thessaloniki", "patras", "heraklion", "larissa", "volos", "ioannina"]
}

Query SQL

        query = """
        SELECT 
            Comments.title,
            Comments.created,
            Comments.author,
            Comments.body_language,
            Comments.permlink,
            Comments.category,
            Comments.total_payout_value,
            STRING_AGG(Tags.tag, ',') AS tags
        FROM Tags
        INNER JOIN Comments ON Tags.comment_id = Comments.ID
        WHERE Comments.depth = 0 
        AND Comments.created >= %s
        AND Comments.created < %s
        GROUP BY Comments.ID, Comments.title, Comments.created, Comments.author, Comments.body_language, Comments.permlink, Comments.category, Comments.total_payout_value
        """