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Decoding-Blockchain-Data

Code for the paper: "Decoding blockchain data for research in marketing: New insights through an analysis of share of wallet" (Hanneke, Skiera, Kraft, & Hinz, International Journal of Research in Marketing).

Public blockchains record every transaction openly, giving researchers a transparency traditional data sources don't offer. This project shows how to use that transparency to measure customer metrics that are normally unobservable: size of wallet (a customer's current spending with a firm), share of wallet, potential wallet, and total wallet.

Data: the entire Ethereum NFT trading market for 2022 — 22.7 million sales transactions from 1.3 million customers across 8 competing NFT platforms, totaling over US$500 million in fees.

Method: transactions are decoded from blockchain data (via Flipside Crypto), aggregated to the customer-platform level, and used to estimate each customer's share and size of wallet per platform, then related via regression to test whether current spending predicts growth potential.

Result

Distribution of share of wallet, across all customers and among customers who trade at more than one firm

Share of wallet is bimodal: most customers are either almost entirely loyal to one platform (share near 1.0) or barely engaged with it (share near 0), with few in between. This pattern holds even among customers who trade at more than one firm. The paper's key finding: a customer's current spending (size of wallet) has little correlation with their share of wallet or potential wallet size, so it is not a valid indicator of future growth potential.

Repository contents

  • df_sample_1percent.csv — 1% sample of the original transaction data, for reproducing the analysis without the full dataset
  • FlipSide-20241121a-TK.py — retrieves transaction data from Flipside Crypto
  • summary_statistics-20241119a-TK.py — wrangles the data (or runs directly on the sample file) and reproduces the summary tables and the headline figure
  • Regressions-all-2024-07-20a-TK.R — runs the regressions relating size of wallet to share/potential/total wallet
  • requirements.txt / requirements.R — Python and R package dependencies

Data

The full transaction dataset is not included; df_sample_1percent.csv (a 1% sample) is provided so the pipeline can be run end-to-end without access to the complete blockchain extract.

About

Decoding the entire 2022 Ethereum NFT market (22.7M transactions, 1.3M customers) to measure share of wallet, size of wallet, and potential wallet.

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var __re = new RegExp('^' + "github\\.com" + '

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Decoding-Blockchain-Data
Code for the paper: "Decoding blockchain data for research in marketing: New insights through an analysis of share of wallet" (Hanneke, Skiera, Kraft, & Hinz, International Journal of Research in Marketing).
Public blockchains record every transaction openly, giving researchers a transparency traditional data sources don't offer. This project shows how to use that transparency to measure customer metrics that are normally unobservable: size of wallet (a customer's current spending with a firm), share of wallet, potential wallet, and total wallet.
Data: the entire Ethereum NFT trading market for 2022 — 22.7 million sales transactions from 1.3 million customers across 8 competing NFT platforms, totaling over US$500 million in fees.
Method: transactions are decoded from blockchain data (via Flipside Crypto), aggregated to the customer-platform level, and used to estimate each customer's share and size of wallet per platform, then related via regression to test whether current spending predicts growth potential.
Result
Distribution of share of wallet, across all customers and among customers who trade at more than one firm
Share of wallet is bimodal: most customers are either almost entirely loyal to one platform (share near 1.0) or barely engaged with it (share near 0), with few in between. This pattern holds even among customers who trade at more than one firm. The paper's key finding: a customer's current spending (size of wallet) has little correlation with their share of wallet or potential wallet size, so it is not a valid indicator of future growth potential.
Repository contents
  • df_sample_1percent.csv — 1% sample of the original transaction data, for reproducing the analysis without the full dataset
  • FlipSide-20241121a-TK.py — retrieves transaction data from Flipside Crypto
  • summary_statistics-20241119a-TK.py — wrangles the data (or runs directly on the sample file) and reproduces the summary tables and the headline figure
  • Regressions-all-2024-07-20a-TK.R — runs the regressions relating size of wallet to share/potential/total wallet
  • requirements.txt / requirements.R — Python and R package dependencies
Data
The full transaction dataset is not included; df_sample_1percent.csv (a 1% sample) is provided so the pipeline can be run end-to-end without access to the complete blockchain extract.
About
Decoding the entire 2022 Ethereum NFT market (22.7M transactions, 1.3M customers) to measure share of wallet, size of wallet, and potential wallet.
Resources
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MIT license
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Decoding-Blockchain-Data
Code for the paper: "Decoding blockchain data for research in marketing: New insights through an analysis of share of wallet" (Hanneke, Skiera, Kraft, & Hinz, International Journal of Research in Marketing).
Public blockchains record every transaction openly, giving researchers a transparency traditional data sources don't offer. This project shows how to use that transparency to measure customer metrics that are normally unobservable: size of wallet (a customer's current spending with a firm), share of wallet, potential wallet, and total wallet.
Data: the entire Ethereum NFT trading market for 2022 — 22.7 million sales transactions from 1.3 million customers across 8 competing NFT platforms, totaling over US$500 million in fees.
Method: transactions are decoded from blockchain data (via Flipside Crypto), aggregated to the customer-platform level, and used to estimate each customer's share and size of wallet per platform, then related via regression to test whether current spending predicts growth potential.
Result
Distribution of share of wallet, across all customers and among customers who trade at more than one firm
Share of wallet is bimodal: most customers are either almost entirely loyal to one platform (share near 1.0) or barely engaged with it (share near 0), with few in between. This pattern holds even among customers who trade at more than one firm. The paper's key finding: a customer's current spending (size of wallet) has little correlation with their share of wallet or potential wallet size, so it is not a valid indicator of future growth potential.
Repository contents
  • df_sample_1percent.csv — 1% sample of the original transaction data, for reproducing the analysis without the full dataset
  • FlipSide-20241121a-TK.py — retrieves transaction data from Flipside Crypto
  • summary_statistics-20241119a-TK.py — wrangles the data (or runs directly on the sample file) and reproduces the summary tables and the headline figure
  • Regressions-all-2024-07-20a-TK.R — runs the regressions relating size of wallet to share/potential/total wallet
  • requirements.txt / requirements.R — Python and R package dependencies
Data
The full transaction dataset is not included; df_sample_1percent.csv (a 1% sample) is provided so the pipeline can be run end-to-end without access to the complete blockchain extract.
About
Decoding the entire 2022 Ethereum NFT market (22.7M transactions, 1.3M customers) to measure share of wallet, size of wallet, and potential wallet.
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MIT license
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injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Decoding-Blockchain-Data
Code for the paper: "Decoding blockchain data for research in marketing: New insights through an analysis of share of wallet" (Hanneke, Skiera, Kraft, & Hinz, International Journal of Research in Marketing).
Public blockchains record every transaction openly, giving researchers a transparency traditional data sources don't offer. This project shows how to use that transparency to measure customer metrics that are normally unobservable: size of wallet (a customer's current spending with a firm), share of wallet, potential wallet, and total wallet.
Data: the entire Ethereum NFT trading market for 2022 — 22.7 million sales transactions from 1.3 million customers across 8 competing NFT platforms, totaling over US$500 million in fees.
Method: transactions are decoded from blockchain data (via Flipside Crypto), aggregated to the customer-platform level, and used to estimate each customer's share and size of wallet per platform, then related via regression to test whether current spending predicts growth potential.
Result
Distribution of share of wallet, across all customers and among customers who trade at more than one firm
Share of wallet is bimodal: most customers are either almost entirely loyal to one platform (share near 1.0) or barely engaged with it (share near 0), with few in between. This pattern holds even among customers who trade at more than one firm. The paper's key finding: a customer's current spending (size of wallet) has little correlation with their share of wallet or potential wallet size, so it is not a valid indicator of future growth potential.
Repository contents
  • df_sample_1percent.csv — 1% sample of the original transaction data, for reproducing the analysis without the full dataset
  • FlipSide-20241121a-TK.py — retrieves transaction data from Flipside Crypto
  • summary_statistics-20241119a-TK.py — wrangles the data (or runs directly on the sample file) and reproduces the summary tables and the headline figure
  • Regressions-all-2024-07-20a-TK.R — runs the regressions relating size of wallet to share/potential/total wallet
  • requirements.txt / requirements.R — Python and R package dependencies
Data
The full transaction dataset is not included; df_sample_1percent.csv (a 1% sample) is provided so the pipeline can be run end-to-end without access to the complete blockchain extract.
About
Decoding the entire 2022 Ethereum NFT market (22.7M transactions, 1.3M customers) to measure share of wallet, size of wallet, and potential wallet.
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MIT license
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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
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Decoding-Blockchain-Data
Code for the paper: "Decoding blockchain data for research in marketing: New insights through an analysis of share of wallet" (Hanneke, Skiera, Kraft, & Hinz, International Journal of Research in Marketing).
Public blockchains record every transaction openly, giving researchers a transparency traditional data sources don't offer. This project shows how to use that transparency to measure customer metrics that are normally unobservable: size of wallet (a customer's current spending with a firm), share of wallet, potential wallet, and total wallet.
Data: the entire Ethereum NFT trading market for 2022 — 22.7 million sales transactions from 1.3 million customers across 8 competing NFT platforms, totaling over US$500 million in fees.
Method: transactions are decoded from blockchain data (via Flipside Crypto), aggregated to the customer-platform level, and used to estimate each customer's share and size of wallet per platform, then related via regression to test whether current spending predicts growth potential.
Result
Distribution of share of wallet, across all customers and among customers who trade at more than one firm
Share of wallet is bimodal: most customers are either almost entirely loyal to one platform (share near 1.0) or barely engaged with it (share near 0), with few in between. This pattern holds even among customers who trade at more than one firm. The paper's key finding: a customer's current spending (size of wallet) has little correlation with their share of wallet or potential wallet size, so it is not a valid indicator of future growth potential.
Repository contents
  • df_sample_1percent.csv — 1% sample of the original transaction data, for reproducing the analysis without the full dataset
  • FlipSide-20241121a-TK.py — retrieves transaction data from Flipside Crypto
  • summary_statistics-20241119a-TK.py — wrangles the data (or runs directly on the sample file) and reproduces the summary tables and the headline figure
  • Regressions-all-2024-07-20a-TK.R — runs the regressions relating size of wallet to share/potential/total wallet
  • requirements.txt / requirements.R — Python and R package dependencies
Data
The full transaction dataset is not included; df_sample_1percent.csv (a 1% sample) is provided so the pipeline can be run end-to-end without access to the complete blockchain extract.
About
Decoding the entire 2022 Ethereum NFT market (22.7M transactions, 1.3M customers) to measure share of wallet, size of wallet, and potential wallet.
Resources
Readme
MIT license
Activity
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injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
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Decoding-Blockchain-Data
Code for the paper: "Decoding blockchain data for research in marketing: New insights through an analysis of share of wallet" (Hanneke, Skiera, Kraft, & Hinz, International Journal of Research in Marketing).
Public blockchains record every transaction openly, giving researchers a transparency traditional data sources don't offer. This project shows how to use that transparency to measure customer metrics that are normally unobservable: size of wallet (a customer's current spending with a firm), share of wallet, potential wallet, and total wallet.
Data: the entire Ethereum NFT trading market for 2022 — 22.7 million sales transactions from 1.3 million customers across 8 competing NFT platforms, totaling over US$500 million in fees.
Method: transactions are decoded from blockchain data (via Flipside Crypto), aggregated to the customer-platform level, and used to estimate each customer's share and size of wallet per platform, then related via regression to test whether current spending predicts growth potential.
Result
Distribution of share of wallet, across all customers and among customers who trade at more than one firm
Share of wallet is bimodal: most customers are either almost entirely loyal to one platform (share near 1.0) or barely engaged with it (share near 0), with few in between. This pattern holds even among customers who trade at more than one firm. The paper's key finding: a customer's current spending (size of wallet) has little correlation with their share of wallet or potential wallet size, so it is not a valid indicator of future growth potential.
Repository contents
  • df_sample_1percent.csv — 1% sample of the original transaction data, for reproducing the analysis without the full dataset
  • FlipSide-20241121a-TK.py — retrieves transaction data from Flipside Crypto
  • summary_statistics-20241119a-TK.py — wrangles the data (or runs directly on the sample file) and reproduces the summary tables and the headline figure
  • Regressions-all-2024-07-20a-TK.R — runs the regressions relating size of wallet to share/potential/total wallet
  • requirements.txt / requirements.R — Python and R package dependencies
Data
The full transaction dataset is not included; df_sample_1percent.csv (a 1% sample) is provided so the pipeline can be run end-to-end without access to the complete blockchain extract.
About
Decoding the entire 2022 Ethereum NFT market (22.7M transactions, 1.3M customers) to measure share of wallet, size of wallet, and potential wallet.
Resources
Readme
MIT license
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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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Decoding-Blockchain-Data
Code for the paper: "Decoding blockchain data for research in marketing: New insights through an analysis of share of wallet" (Hanneke, Skiera, Kraft, & Hinz, International Journal of Research in Marketing).
Public blockchains record every transaction openly, giving researchers a transparency traditional data sources don't offer. This project shows how to use that transparency to measure customer metrics that are normally unobservable: size of wallet (a customer's current spending with a firm), share of wallet, potential wallet, and total wallet.
Data: the entire Ethereum NFT trading market for 2022 — 22.7 million sales transactions from 1.3 million customers across 8 competing NFT platforms, totaling over US$500 million in fees.
Method: transactions are decoded from blockchain data (via Flipside Crypto), aggregated to the customer-platform level, and used to estimate each customer's share and size of wallet per platform, then related via regression to test whether current spending predicts growth potential.
Result
Distribution of share of wallet, across all customers and among customers who trade at more than one firm
Share of wallet is bimodal: most customers are either almost entirely loyal to one platform (share near 1.0) or barely engaged with it (share near 0), with few in between. This pattern holds even among customers who trade at more than one firm. The paper's key finding: a customer's current spending (size of wallet) has little correlation with their share of wallet or potential wallet size, so it is not a valid indicator of future growth potential.
Repository contents
  • df_sample_1percent.csv — 1% sample of the original transaction data, for reproducing the analysis without the full dataset
  • FlipSide-20241121a-TK.py — retrieves transaction data from Flipside Crypto
  • summary_statistics-20241119a-TK.py — wrangles the data (or runs directly on the sample file) and reproduces the summary tables and the headline figure
  • Regressions-all-2024-07-20a-TK.R — runs the regressions relating size of wallet to share/potential/total wallet
  • requirements.txt / requirements.R — Python and R package dependencies
Data
The full transaction dataset is not included; df_sample_1percent.csv (a 1% sample) is provided so the pipeline can be run end-to-end without access to the complete blockchain extract.
About
Decoding the entire 2022 Ethereum NFT market (22.7M transactions, 1.3M customers) to measure share of wallet, size of wallet, and potential wallet.
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MIT license
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if (__m === '*' || __re.test(location.href)) {
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Decoding-Blockchain-Data
Code for the paper: "Decoding blockchain data for research in marketing: New insights through an analysis of share of wallet" (Hanneke, Skiera, Kraft, & Hinz, International Journal of Research in Marketing).
Public blockchains record every transaction openly, giving researchers a transparency traditional data sources don't offer. This project shows how to use that transparency to measure customer metrics that are normally unobservable: size of wallet (a customer's current spending with a firm), share of wallet, potential wallet, and total wallet.
Data: the entire Ethereum NFT trading market for 2022 — 22.7 million sales transactions from 1.3 million customers across 8 competing NFT platforms, totaling over US$500 million in fees.
Method: transactions are decoded from blockchain data (via Flipside Crypto), aggregated to the customer-platform level, and used to estimate each customer's share and size of wallet per platform, then related via regression to test whether current spending predicts growth potential.
Result
Distribution of share of wallet, across all customers and among customers who trade at more than one firm
Share of wallet is bimodal: most customers are either almost entirely loyal to one platform (share near 1.0) or barely engaged with it (share near 0), with few in between. This pattern holds even among customers who trade at more than one firm. The paper's key finding: a customer's current spending (size of wallet) has little correlation with their share of wallet or potential wallet size, so it is not a valid indicator of future growth potential.
Repository contents
  • df_sample_1percent.csv — 1% sample of the original transaction data, for reproducing the analysis without the full dataset
  • FlipSide-20241121a-TK.py — retrieves transaction data from Flipside Crypto
  • summary_statistics-20241119a-TK.py — wrangles the data (or runs directly on the sample file) and reproduces the summary tables and the headline figure
  • Regressions-all-2024-07-20a-TK.R — runs the regressions relating size of wallet to share/potential/total wallet
  • requirements.txt / requirements.R — Python and R package dependencies
Data
The full transaction dataset is not included; df_sample_1percent.csv (a 1% sample) is provided so the pipeline can be run end-to-end without access to the complete blockchain extract.
About
Decoding the entire 2022 Ethereum NFT market (22.7M transactions, 1.3M customers) to measure share of wallet, size of wallet, and potential wallet.
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