Vincent Granville

Current

• Founder and Principal at AnalyticBridge

Past

• Chief Science Officer at Authenticlick
• Web Mining, Web Analytics, Data Mining, and Click Fraud Expert at InfoSpace
• Principal at DataShaping.com

• User Behavior, Data Mining and Web Analytics Consultant at Wells Fargo
• Senior Fraud Consultant at Visa
• Senior Statistician at CNET
• Research Fellow at National Institute of Statistical Sciences

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Education

• Facultés universitaires 'Notre-Dame de la Paix'
• Cambridge University, England

Recommended

15 people have recommended Vincent

Connections

500+ connections

Industry

Internet

Vincent Granville’s Summary

Dr. Vincent Granville has successfully solved problems for 15 years in data mining, text mining, predictive modeling, business intelligence, technical analysis, keyword and web analytics. Vincent is widely recognized as the leading expert in click scoring and web traffic optimization. Over the last ten years, he has worked in real-time credit card fraud detection with Visa, advertising mix optimization with CNET, A/B testing with LowerMyBills, online user experience with Wells Fargo, search intelligence with InfoSpace, click fraud detection with major search engines and large advertising clients, as well as statistical litigation.

Vincent was formerly Chief Science Officer at Authenticlick, where he developed patent pending technology. Most recently, he successfully launched DataShaping and AnalyticBridge, the largest social network for analytic professionals, with 20,000 members. Vincent is a former post-doctorate of Cambridge University and the National Institute of Statistical Sciences. He was among the finalists at the Wharton School Business Plan Competition and at the Belgian Mathematical Olympiads. Vincent has published 40 papers in statistical journals and is an invited speaker at international conferences. He also developed a new data mining technology known as hidden decision trees.

+ Email: vincentg@datashaping.com

Vincent Granville’s Specialties:

data mining, six sigma, web analytics, text mining, pattern recognition, crm, sas, predictive modeling, splus, jmp, perl, web mining, machine learning, knowledge discovery, ai, sql, large data sets, data analysis, survey analysis, customer profiling, churn, user retention, search engine, query intelligence, credit card fraud, click fraud, scorecards, clustering, decision trees, logistic regression, advertising mix optimization, pareto analysis, business intelligence, design of experiments

Vincent Granville’s Experience

• Founder and Principal

  AnalyticBridge

  (Internet industry)

  February 2008 — Present (1 year 10 months)

  Social network for analytics professionals (20,000 members). Responsible for sales, viral marketing, ad campaigns, keyword optimization (SEO, SEM) and strategic partnerships (Rising Media, Arbita, Indeed, ACM, Money Sciences, Business Analytics Conference, Smart Data Collective). Increased revenue by 5% and profit by 50%. Advertisers include SAS, StatSoft, Zementis, Elsevier, recruiters, small companies and major ad agencies. KPI expert: user engagement, lift, conversion, revenue (ROI, CPA, RPC, ROAS) and web metrics.
  
  Consulting:
  
  • Developed Internet traffic scoring platform for ad networks, advertisers and publishers (rule engine, site scoring, keyword scoring, lift measurement, linkage analysis). Clients include eBay, Click Forensics, Cars.com, Turn.com, Microsoft.
  • Engineered the most popular real time analytics news feed: responsible for architecture (super-cluster topology), implementation (automated content selection) and distribution.
  • Identified major Botnets generating more than 10MM per year in fraudulent transactions.
  • Search engine analytics: prediction of keyword conversion for keywords with little or no historical data, increased lift by 20%.
  • Web crawling and text mining techniques to score referral domains, generate keyword taxonomies, and assess commercial value of bid keywords.
  • Collaborative filtering, social network analytics.
  • Developed new hybrid statistical and data mining technique known as hidden decision trees and hidden forests.
  • Invited speaker: Predictive Analytics World, SAS International Data Mining Conference.

• Chief Science Officer

  Authenticlick

  (Privately Held; Internet industry)

  June 2006 — May 2008 (2 years )

  Prototyped automated bidding and click scoring solutions for search engines, ad networks and advertisers. Worked with the engineering team to implement my algorithms. Design of Experiment. Score standardization. IP blacklist design and management. Patent-pending statistical technologies. Supported Sales and Production team. Presented at Ad-Tech and the American Statistical Association conferences. Helped win several deals against large competitors (including Fair Isaac). Identified major Botnet. Raised $1.5 MM (Venture Capital funding).

• Web Mining, Web Analytics, Data Mining, and Click Fraud Expert

  InfoSpace

  (Public Company; INSP; Internet industry)

  January 2005 — June 2006 (1 year 6 months)

  Click scoring technology, query intelligence, web analytics, business intelligence related to mobile search. Designed click fraud detection algorithms to process billions of clicks. Created rule selection and rule discovery system for fraud detection, based on machine learning (unsupervised clustering), design of experiments, robust cross validation and linkage with external data sources (Google search results) to discover additional fraud patterns. Enhanced keyword taxonomies using data driven algorithms to detect keyword associations. Defined and tested metrics for keyword correlations. Developed multi-threaded web crawler to feed text mining algorithms with rich, targeted data sources related to local search and / or yellow pages.
  
  Keywords: data mining, text mining, query intelligence, keyword taxonomy, clustering algorithms, impression fraud, click fraud detection, click quality, search engine, web analytics, web crawler, design of experiments

• Principal

  DataShaping.com

  (Internet industry)

  May 2002 — December 2004 (2 years 8 months)

  • Real time credit card fraud detection, with Visa: improved speed of feature selection algorithm by a factor 200.
  • Online user behavior analysis with Wells Fargo: significantly reduced churn and improved site navigation.
  • LowerMyBills.com – 3 months contract, saved $80K by improving A/B testing methodology.
  • Statistical litigation. Automated copyright infringement detection. Web robot technology. Click fraud detection.

• User Behavior, Data Mining and Web Analytics Consultant

  Wells Fargo

  (Public Company; WFC; Financial Services industry)

  June 2004 — November 2004 (6 months)

  Analyse online traffic on B2B platform to optimize user experience.
  
  Keyword: data mining, web analytics, wells fargo, toad, oracle

• Senior Fraud Consultant

  Visa

  (Public Company; 10,001 or more employees; Financial Services industry)

  February 2003 — May 2004 (1 year 4 months)

  Develop proprietary feature selection system (200x faster than SAS Enterprise Miner) to detect first instances of fraud and horizontal (single ping) fraud in real time. Production of US zip code maps showing fraud simultaneously in the time, location, recency and volume dimensions. SAS, Perl, C, R, Splus.
  
  Keywords: decision trees, SAS Enterprise Miner, fraud detection, data mining, web analytics

• Senior Statistician

  CNET

  (Public Company; 1001-5000 employees; CNET; Internet industry)

  June 1996 — May 2002 (6 years )

  Web analytics. CRM. Advertising reach and frequency: provided mathematical formula. Inventory forecasting. Price elasticity modeling. Web robot. Advertising mix optimization. Customer profiling. User retention, churn. Data Warehousing. Web traffic forecast (with automated alarm system to notify product managers of traffic abnormalities). Automating production of various reports (dashboard, quarterly reports for financial analysts). Perl, Sybase, SQL, SAS, CGI, C.
  
  Keywords: web analytics, data mining, metrics, market research, market intelligence, business intelligence, competitive intelligence, SAS

• Research Fellow

  National Institute of Statistical Sciences

  (Government Agency; 51-200 employees; Research industry)

  July 1995 — June 1996 (1 year )

  Environmental statistics. MCMC. Hierarchichal Bayesian models. Clustering. Storm modeling (time series, spatial processes). Hanford nuclear reservation: risk analysis (leakage) using space / time models. Simulation of bivariate exponential distributions.
  
  Keywords: data mining, hierarchical clustering, Fortran, C, general linear models

Vincent Granville’s Education

• Facultés universitaires 'Notre-Dame de la Paix'

  Ph.D. , Statistics, Mathematics, Science , 1983 — 1993

• Cambridge University, England

  Post doctorate , Statistics

Additional Information

Vincent Granville’s Interests:

web analytics, web mining, data mining, fraud detection, writing a book entitled "Data Mining with the Naked Eye"

Vincent Granville’s Groups:

Royal Statistical Society,
American Statistical Association,
SEMPO,
Web Analytics Association,
Northwest Recruiters Association,
TDWI - The Data Warehouse Institute

•    DataShaping Advanced Analytics
•    Web Analytics Association
•    Analytic Bridge

Vincent Granville’s Honors:

Finalist, Wharton School Business Plan Competition
Finalist, Belgian Mathematical Olympiads
Ph.D. Summa Cum Laude
Publications in top scientific journals including IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Royal Statistical Society (series B), Journal of Number Theory, Computational Statistics and Data Analysis
Invited speaker at Ad:Tech, The American Statistical Association Conference, Duke University, CWI (Amsterdam), Imperial College (London), National Institute of Statistical Sciences

Vincent Granville’s Contact Settings

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• new ventures
• job inquiries
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• getting back in touch