The New WMD’s

The New WMD’s

WMD? Is Cathy O’Neil creating a hysterical media frenzy so she can sell more copies of “Weapons of Math Destruction“, or is she sincerely onto something?

Well, if the word “math” in your book title, it’s best to do anything you can to get some other relevant attention! Anything! Foment hysteria? Well, worth a try… But we’re into something more than just fomenting hysteria here. It’s a radical book, but fair. And well written….

Predictive Models I’ve Built

While at Epsilon, from 2000-2016, and at Time Inc. prior to that, my primary use of data was to create analyses and predictive models for client applications.  I also created predictive models that became data products in their own right, many of which are still sold to Epsilon clients today.

Generally working with customer file data using SAS and R, I created the following types of modeling solutions:

  1. Likelihood to Pay on a Marketing Offer – Predict the likelihood of payment and non-payment using ensemble modeling techniques
  2. Response Optimization – Enable optimal business results in client marketing campaigns through response models
  3. Segmentation – Used nearest neighbor and hierarchical segmentation techniques to segment the population into categories, or personas
  4. Prospect Models – Predict prospects that look most likely to exhibit a specific behavior, such as, response, pay, or product purchase
  5. Cross-Sell Models – Response models using customer transactional data to predict likelihood to respond to a new product offer
  6. Product Optimization – Given the likelihood of response, predict which product will be most profitable to offer to an existing customer.
  7. Area-Level Data Imputation – Impute missing data values using geographic roll-ups to enable complete data set coverage
  8. Propensity Models – Use census-balanced weighting to model look-alikes to self-reported consumer behaviors
  9. Econometric Models – Predict estimates for household characteristics such as income, net worth, and home value, applied as census-balanced scores across the US market
  10. Area-level Models – Predict geographies with populations most likeyl to exhibit an interest or purchase behavior

 

Data I’ve Worked With

Most recently I was responsible for Epsilon’s Analytical Data Assets team.  In this role I was tasked with analytic product development based on both Epsilon and 3rd party data.  While at Epsilon, from 2000-2016, my primary use of data has been to create analyses and predictive models for client applications.

I’ve worked with the following types of data:

  1. Compiled Consumer Data – Household characteristics such as age, income, home ownership, and purchase transactions primarily on Epsilon’s TotalSource Plus file containing thousands of data points on 160MM US households.
  2. Consumer Self-Reported Data – Self-reported warranty and survey data relating to interests, purchases, and ailments (for example, interest in gardening, shops at Walmart, and suffers from diabetes), primarily from Epsilon’s Target Source database of over 40MM US households.
  3. Consumer Transactional Data – Bank data relating to credit and debit card line items summarized by merchant and merchant category by month, primarily Epsilon’s Market View data.
  4. Credit Data – Consumer credit line and credit inquiry data from Credit Bureaus
  5. Web Data – Email and digital data streams for web analytic analysis
  6. Syndicated Market Research Data – Census-balanced self-reported consumer research data, primarily the MRI syndicated survey research file.
  7. Business Firmagraphic Data – Characteristics and contacts associated with business entities
  8. Aggregated Credit Data – Credit bureau information aggregated at a zip+4 level
  9. US Census Data – Typically compiled and resold by companies such as, Epsilon and others
  10. Customer Relationship Management Data – Client specific transactional data in various industries (publishing, travel, financial).

About our logo

cropped-Wheel.pngIllustration of 10 congruent equilateral triangles that have the same center. Each triangle has been rotated 12° in relation to the one next to it. The outer vertices are connected with a smoother curve to form a circle. Hence, the circle is circumscribed about the triangles.  ref. http://etc.usf.edu/clipart/42900/42974/tri2_42974.htm  Copyright © 2004–2016 Florida Center for Instructional Technology. ClipArt ETC is a part of the Educational Technology Clearinghouse and is produced by the Florida Center for Instructional Technology, College of Education, University of South Florida.

 

LinkedIn Profile and Work Experience

Peter Zajonc on LinkedIn

Specialties: Framing vision, analytic consulting, analytic leadership, strategic analytics, business value, communications, self-motivated leader, team player, customer segmentation, profiling, prospecting analytics, retention analytics, loyalty analytics, contact strategies, offer optimization, multi-channel marketing, multi-product marketing, behavioral profiling, market research, multi-variate regression, multi-variate testing, exploratory data analysis, factor analysis, demographic and lifestyle data expert, creating marketing, data fusion, data mining, database marketing, online display marketing, machine learning, gradient boosting, random forest, SAS, R, SQL, Oracle Data Miner, Teradata, FOCUS, Excel

Experience

Principal at Pinpoint Targeting, LLC
October 2015 – Present (11 months)
Pinpoint Targeting is a consulting and analytical services company whose sole mission is to create business value and strategic insight from data analytics.  Accomplishments:

  • Implemented a machine learning modeling process for a financial services marketer, reducing costs by 30% and improving results by 10%.
  • Created a propensity modeling program to grow digital ad exchange revenue by $1.5MM, or 10%.
  • Created a recommendation engine that optimized data enrichment proposals through machine learning ensembles.

Pinpoint Targeting clients look to expand application and monetization of their marketing data through improved customer acquisition and retention.

 

Sr. Director, Data Assets at Epsilon
January 2014 – July 2016 (2 years 7 months)

Developed analytical and modeling data services to retain business and drive new revenue.  Led effort to develop ensemble modeling and launch three data dimensions of Epsilon’s flagship data product, TotalSource Plus™:

  • Financial – Estimates of income, net worth, home value, and ability to pay (Value Score)
  • Market Trends – Over 250 data propensities predicting consumer lifestyles and behaviors
  • Market View – Transactional targeting data across 62 categories and 900 merchants.  Hilton and United deployed transactional data, growing their data spend by 10%.

 

Sr. Director, Analytical Consulting Services at Epsilon Targeting
July 2010 – December 2013 (3 years 6 months)

Defined the vision and led a merged analytic organization formed after the sale of Equifax’s Direct Marketing Solutions (July, 2010).

  • Led 12 analysts and consultants responsible for over $25MM in annual revenue generated from custom analytical solutions. Retained all staff through the company integration period.
  • Led $100k+ projects for Travelers (segmentation), Verizon (prospect modeling), and Synchrony (profitability uplift).

 

Director Analytic Consulting at Equifax
October 2000 – June 2010 (9 years 9 months)

  • Led analytical marketing services, including a team of 11 analysts tasked with providing analytical consulting solutions to Equifax clients.
    • Grew $8MM in revenue and over 300 client solutions annually across various industries, and 10% annually
    • Co-led cross-functional team tasked with designing and implementing aggregated credit data product
    • Led efforts to create and deploy the Household Income Identifier and Niches, a segmentation of the US market
    • Delivered custom segmentation, predictive models, optimum contact analysis, and campaign analytic solutions to Equifax clients and grew results 10% year-over-year.

Assoc Dir, Customer Acquisition Analysis at Time Inc.
1996 – 2000

Designed and developed analytical solutions.  Led team of four whose work produced $3MM in profitability gains from response and payment models, as well CHAID analyses, segmentation analyses, and profiling.

  • Managed the development, maintenance, and design of the company predictive modeling system.
  • Grew TIME magazine database acquisition mailings over 300%, from 6 million to 20 million pieces, over three years through the use of corporate marketing database segmentation and predictive modeling
  • Improved outside list rental profitability by 10% through offer optimization modeling
  • Led cross-departmental team in the development and implementation of multivariate tests of a large number of carefully controlled creative executions, greatly improving testing efficiency and cycle time

 

Prior Experience (Information Systems, Consulting):

  • Grew Time Warner Corporate Database by 25% to 50 million households and reduced build costs by 15% through efficiencies.
  • Developed and implemented database systems for inventory control, sales costs, and sales contacts.
  • Designed and implemented systems for international subscription fulfillment, newsstand and agent accounts receivable, general ledger, executive information reporting.
  • Designed and implemented property management system for Exxon/Mobil.

 

Education and Training

MBA, Finance, Stern School of Business, New York University, NY

BA, Mathematics and Linguistics, University of Toronto, Toronto, Ontario

Multivariable Testing Techniques for Service Processes, QualPro

Master R Developer Workshop, RStudio, New York

Data Science Specialization, Coursera