Noah Ganter, Ph.D.

BMS MMM (2014)

Developed HCP-level marketing mix models for Bristol Myers Squibb, covering Eliquis, Yervoy, Creon, and Orencia, including an HCP call-plan optimization for Orencia. The aggressive timelines on this engagement drove the development of a fast, capable modeling application that became JMS. All brand deliverables were completed on time.

Abbvie MMM (2016 – 2020)

Delivered ongoing quarterly updates of marketing ROI and response curves for Humira, Rinvoq, Skyrizi, Ubrelvy, and Vraylar, working closely with Abbvie's internal technical team to deliver results in their required format and to their satisfaction.

Astellas MMM (2019 – 2025)

Helped Astellas understand the contribution of HCP and DTC advertising for Xtandi and Padcev using new-patient-starts data and media plans, working extensively with agency partners to get the most value from the data. Guided the development and launch of DTC ad campaigns for both brands over five years.

Alkermes Forecasting (2020)

Delivered a sales forecast for Aristada and Naltrexone covering January through December 2020 — a period that coincided exactly with the onset of the pandemic. Given the uncertainty, three scenarios were developed (worst case, best case, and most likely), along with an Excel-based forecasting tool. The most likely scenarios presented came within 5% and 10% of actual results for Aristada and Naltrexone, respectively.

Alkermes MMM (2021 – 2026)

Developed marketing mix ROI and response-curve analyses for Aristada, going through rigorous analytic review with the Alkermes science team. Successfully guided Aristada through its launch and growth trajectory over five years, balancing HCP and DTC advertising investment.

Pfizer (2023 – 2026)

Publicis won the Pfizer account — one of the largest in agency history — and Pfizer asked us to design a solution for deploying quarterly MMM analysis and eventually bringing the modeling function in-house. The brand portfolio included Eliquis, Nurtec, Comirnaty, Prevnar, Abrysvo, Cibinqo, Litfulo, Zavzpret, Braftovi/Mektovi, Lorbrena, Xeljanz, Ibrance, MyFembree, Premarin, Orgovyx, Zavicefta, Talzenna, and Ngenla across the US, Taiwan, Saudi Arabia, and Brazil. The engagement included numerous discussions with senior Pfizer leadership, including the CMO. Over the three-year partnership, Pfizer gained a complete understanding of the data and methods involved, and successfully took all MMM work in-house in June 2026.

Molson Coors (2014 – 2021)

Developed a full marketing mix modeling program for Molson Coors, working closely with the Collective/Connect agency in Chicago and presenting frequently to the client there. Used SKU-level national sales data and store-level shipment data combined with detailed media data, covering Miller Lite, Coors Light, Redd's, Blue Moon, Peroni, and Keystone across the US, Canada, UK, and Romania. Also developed solutions for quantifying the impact of sports and celebrity sponsorships. The program generated $1M–$2M in annual revenue with a team of three; the JMS modeling system was key to hitting tight delivery deadlines as scope expanded internationally.

National Vision Inc. MMM (2015 – 2019)

Delivered a store-level marketing mix analysis for National Vision Inc. (America's Best and Eyeglass World), helping the retailer transition from local spot marketing to national TV campaigns. Over the four-year engagement, National Vision's sales grew from $200 million to over $1 billion annually.

National Vision Inc. Doctor Survey Analysis (2016)

Used survey responses from optometrists to conduct a linguistic and sentiment analysis for America's Best and Eyeglass World, finding that time pressure was the leading concern among eye doctors at America's Best vision centers.

Comcast (2015 – 2022)

Delivered extensive marketing mix ROI analysis and forecasting for Comcast, including a custom forecasting module. For each time series, dozens of candidate forecasting techniques (ARIMA, ETS, neural networks, Prophet) were evaluated, with the most accurate and reliable model selected for each case.

Sanofi MMM (2020 – 2025)

Used the JMS modeling system to deliver ROI and response-curve analysis for Sanofi brands — Allegra, Dulcolax, Zantac, Gold Bond, and Unisom — on a biannual basis, helping the brands understand the shift from brick-and-mortar sales to e-commerce, including several Amazon-focused sales models.

Beam Suntory (2021 – 2025)

Used the JMS modeling system to build models at scale for Beam Suntory's spirits portfolio — Jim Beam, Maker's Mark, Hornitos, Tres Generaciones, Basil Hayden, Knob Creek, and On The Rocks — using IRI/Circana sales data. Helped Suntory understand how changes in profit margin affected brand ROI, particularly within the tequila category, and developed category analyses for the emerging ready-to-drink cocktail segment.

Haleon (2021 – 2025)

Used the JMS modeling system to build models at scale for Haleon's consumer health brands — Theraflu, Emergen-C, Advil, and Sensodyne. Retained Haleon as a client through several RFP rounds based on the quality of the work and close collaboration with the Haleon team.

Alcon (2024 – 2025)

Developed quasi-pharmaceutical marketing models for Systane eye drops — a category that spans both retail and HCP dynamics, being over-the-counter yet influenced by traditional pharmaceutical activities like sales-force detailing and sampling. Delivered budget allocation and optimization output to support brand growth.

JMS Modeling System (2014 – 2026)

Developed a web-based marketing mix modeling application spanning reporting, forecasting, data processing, and modeling capabilities. Traditional, manually executed MMM work typically requires a team of three to six people per brand and 8–12 weeks to load and process data, build models, and produce reports. JMS cut the required team down to two — a technical specialist and a client-facing reporter — and cut the delivery timeline roughly in half. It was used across more than a dozen clients, generating $2M–$5M in annual revenue.

freeloadr (2014 – 2026)

One of my proudest accomplishments: freeloadr is an automated data loading and processing application built in Python, using machine learning and rule-based approaches to make data processing point-and-click. As each client project is developed with freeloadr, the system is trained to recognize that client's data formats and steadily improves its ability to process them accurately over time.

Data Foundations Project (2026)

Worked with agency data professionals to build a next-generation data processing application.

Category Analysis of Snacks (2013)

Developed category share models (Multiplicative Competitive Interaction) for the Frito-Lay family of brands — Lay's, Ruffles, Tostitos, Doritos, and Fritos — using Nielsen Scantrack data. The analysis delivered an ROI assessment for Frito-Lay and clarified how new line extensions sourced volume from the broader category versus cannibalizing the existing portfolio.

Nature's Made MMM (2013)

Developed a marketing mix analysis for Nature's Made vitamins (client: Pharmavite), focused on identifying the best promotional strategies for the brand's line of vitamins, with particular attention to Costco promotions, using Nielsen Scantrack and Costco sales data. Presented the results to the executive team, demonstrating the health of Nature's Made's marketing program.

Retailer Sales Category Modeling for Procter & Gamble (2007 – 2009)

Developed category share models (Multiplicative Competitive Interaction) at the retail level to understand how brand promotions source volume from competitors, using Nielsen account- and store-level data spanning UPC-level detail up through promoted price groups and brand-level rollups. The resulting application spanned Pampers, Gain, Tide, Ariel, Bounce, Tampax, Braun, Gillette, Head & Shoulders, Herbal Essences, Pantene, Old Spice, Crest, Oral-B, Scope, Prilosec, Metamucil, Vicks, ZzzQuil, and Olay across CVS, Walgreens, Target, Kroger, and Walmart, covering baby care, health care, feminine care, personal health care, oral care, and skin care categories. It gave P&G a way to understand how in-category promotions might steal share from competitors while also cannibalizing other brands within their own portfolio.

Heinz Category Forecasting (2009)

During the 2008–2009 economic downturn, Heinz saw increased sales of staple foods — including Prego pasta sauce — as consumers cut back on eating out and tightened household budgets. Using Nielsen Scantrack and store-level sales data for the pasta category, this analysis linked the sales increase to rising unemployment and identified which categories were likely to grow versus decline during a recession.

Brand Sales Forecasting (2009 – 2010)

Worked with a team to develop a sales forecasting application that was successfully launched at Nielsen.

Store Switching Analysis (2009)

Using Nielsen's store-level data, clustered 80,000 stores into 3,000 retail local trading areas to understand how a promotion at one retailer drew sales away from other retailers in the same area, for client Procter & Gamble covering the Tide and Gain detergent brands. Drew on both Nielsen panel and Homescan panel data. Presented the analysis at the ART Forum in San Francisco, CA in 2009.

Sun Products Winners Study (2012)

Built a store profile for locations where Sun Products' laundry detergent brand held a high market share (the "winners") and compared them to low-share stores (the "losers"), using Nielsen store-level sales data. Found that Sun Products performed best in stores serving strong working-class communities, where budget-conscious consumers were unwilling to pay a premium for a product of equivalent quality, despite higher advertising spend on competing brands.

General Mills Downshifting (2011)

Studied how consumers shift their purchasing behavior with economic conditions — during downturns, shifting from premium products toward economy brands. For client General Mills, covering Cheerios, Betty Crocker, Nature Valley, and Progresso, the analysis demonstrated how the positioning of several brands in the General Mills portfolio actually helped them grow during the 2008–2009 economic contraction.

Sam's Club Store Profiling (2012)

Created a store profile for 500 Sam's Club locations based on consumer survey data, sales data, local area conditions, and employee and store-manager ratings, drawing on Walmart's internal data systems combined with consumer surveys. Found that stores with stronger profiles achieved higher sales, though local economic conditions and area characteristics needed to be controlled for in the analysis.

Nestlé Drinks Category Analysis (2013)

Analyzed the bottled water category and the position of San Pellegrino within it for client Nestlé, finding that San Pellegrino served as a premium driver of category sales.

DiGiorno Sourcing Analysis (2013)

Using Homescan panel data, conducted a source-of-volume analysis for Nestlé's DiGiorno frozen pizza brand, identifying which competing products and categories DiGiorno's sales were being drawn from.

Custom Analytics Group (2011 – 2013)

Led the Custom Analytics Group at Nielsen, managing a team of five that developed custom analyses to answer clients' specific business questions. The group generated $4 million in annual revenue, allowing Nielsen to service in-house client requests and keep that revenue internal rather than losing it to outside vendors — a highly profitable operation relative to its team size.

Consumer Needs Analysis — Developed a structural equation model to understand the underlying need states driving consumers' soft drink choices, covering Coca-Cola products. Presented the work in a poster session at the ART Forum in Chicago in 2013.

Price and Product Attribute Study — Developed a CART (Classification and Regression Tree) analysis to determine which product attributes matter most to consumers, for client Mondelez covering Kraft cheese brands. Found that, across most CPG categories, price sits at the top of the decision tree, followed by other product attributes.

Pharmaceutical Test and Control Design (2002 – 2003)

Matched healthcare providers (HCPs) on key attributes to create clean test-control designs for analysis, and developed an identity graph for HCPs based on DEA number, medical registration number, and NPI identifiers. Built a web-based application for creating matched HCP pairs and running the resulting analysis. For client Bristol Myers Squibb — covering Coumadin, Plavix, and Abilify — the work drew on IQVIA (formerly HMS) sales data at the HCP level combined with speaker and event details, plus anonymized patient-level data, and produced robust test-control matching procedures for point-and-click event analysis.

Sales Forecasting (2002 – 2004)

Forecasted sales for Excedrin (an OTC brand owned by Bristol Myers Squibb) using Nielsen Scantrack data and media plans, applying ARIMA and ETS forecasting methods within a custom-built forecasting application that let the team review different forecast scenarios. The resulting Excedrin sales forecast came within 5% accuracy annually and correctly anticipated the impact of the Excedrin Tension Headache campaign.

Pharmaceutical Response Modeling (2003 – 2007)

Developed numerous marketing mix models for a variety of medications for client Bristol Myers Squibb, spanning the cardiovascular, antipsychotic, and oncology categories — including Abilify, Plavix, Erbitux, and Enbrel — across the US, UK, and France. Used IQVIA sales data combined with agency-based media impressions and spending, and built HCP-level response models along with a web-based application for model development and reporting. The work moved BMS's analysis and reporting forward significantly and delivered numerous insights into media effectiveness and sales response.

Mars MMM

Developed marketing mix models and forecasts for Mars Foods using a software-based MMM approach, drawing on Nielsen Scantrack data and media plans. Covered the confections category, including Snickers, Milky Way, Skittles, Starburst, and Three Musketeers, and delivered a monthly response model and forecast.

T-Mobile MMM

Developed a marketing mix model for T-Mobile in the telecom category, using T-Mobile's internal sales database, and guided T-Mobile through its annual media planning and budget allocation process.

McCormick Foods

Developed a marketing mix model for McCormick Foods and Spices, a leading CPG spice and seasoning brand.

Product Feature Design, Ford Motor Company (2001)

Developed a conjoint analysis to determine the optimal feature set for a new SUV, using survey data collected specifically for the study. The results guided Ford's consideration of the optimal feature set for its new Explorer model.

Product Feature Design, Gillette (2001)

Developed a conjoint analysis to determine the optimal feature set for Gillette's Mach3 razor, using survey data collected specifically for the study. Made Gillette aware of additional features worth offering on the Mach3.

iPanel (2002)

Developed an online panel to collect survey responses for Summitry, Inc., successfully gathering data from over 10,000 respondents.

Beverage Market Structure (2002)

Developed a market structure analysis to understand how beverage consumption segments by day part and activity, using SIP Panel survey data for client PepsiCo. Confirmed that carbonated soft drink consumption clusters into discrete dayparts, and showed that the new line of drinks PepsiCo was developing — spanning Pepsi, Mountain Dew, Mug, Starry, and Aquafina — was best positioned in non-competing dayparts. Covered the carbonated soft drinks, energy drinks, and bottled water categories.