Restricted-access consumer and retailer data for business and marketing research available to Cornell researchers.
The Kilts Center at the University of Chicago is an institution which focuses on a multidisciplinary approach to marketing. Through a partnership with the Nielsen Company, Kilts makes various restricted-access datasets available that are useful in analyzing household purchasing, product data, and advertising trends. Cornell researchers who register with the Kilts Center through the Office of Sponsored Programs have access to these large datasets, which allow for robust subsamples by demographics, geographic locations, and retail channels, among others. These data's size, scope, breadth, and longitudinal time frame make them unique. The data can be used for analysis of many varied research topics. For example, Cornell researchers have used this data to analyze GMO and Non-GMO Labeling Effects, Sugar-sweetened Beverage Taxes, and how marijuana legalization has affected household spending on food and alcohol. See the Cornell-Authored articles and dissertations below for additional examples.
Consumer Panel Data
A representative panel of households that continually provide information about their purchases in a longitudinal study in which panelists stay on as long as they continue to meet NielsenIQ's criteria. NielsenIQ consumer panelists use in-home scanners to record their purchases (from any outlet) intended for personal, in-home use. Consumers provide information about their households, what products they buy, and when and where they make purchases.
PanelView Surveys:
- Complementary to the Consumer Panel Data, these surveys contain additional data about households and their members.
- Tax Rebate: This survey shows how households spent the 2008 federal government tax rebate.
- Tell Us More About You: These surveys, one from 2008 and the other from 2011, contain data about where household members were born, where they live now, additional information about education (e.g. college major), occupation, and purchase behavior in specific categories.
Key Features:
- Annual surveys starting with 2004.
- United States divided into 52 major markets.
- Panel size between 40,000 - 60,000 active panelists, projectable to the full United States by household projection factor.
- Variables include household demographic, geographic, product ownership, plus selected demographics for heads of households and other members.
- Data covers all 10 NielsenIQ food and nonfood departments (~ 1.4 million UPC codes), dry grocery, frozen foods, dairy, deli, packaged meat, fresh produce, nonfood grocery, alcohol, general merchandise, and health and beauty aids.
Retail Scanner Data
Weekly pricing, volume, and store environment information generated by point-of-sale systems from more than 90 participating retail chains across all US markets. Researchers can integrate the consumer-panel and retail-scanner datasets to enable additional types of research. By integrating these two datasets, researchers can determine not only the items purchased by panelists but also the availability, prices, and promotions associated with other products on the shelf simultaneously.
Key Features:
- Available data starts with 2006, including annual updates.
- Covers the entire United States, divided into 52 major markets
- Includes the same codes as those used in the consumer panel data.
- Come from 35,000-50,000 participating grocery, drug, mass merchandiser, and other stores, covering more than half the total sales volume of US grocery and drug stores and more than 30 percent of all US mass merchandiser sales volume.
- Weekly product data for 2.6-4.5 million UPCs, including food, nonfood grocery items, health and beauty aids, and select general merchandise.
- Aggregated into 1,100 product categories and store environment variables (i.e., feature and display indicators) from a subset of stores. The 1,100 product categories are categorized into 125 product groups and ten departments.
- The structure matches that of the consumer panel data.
- All private-label goods have a masked UPC to protect the identity of the retailers.
- For each UPC, participating stores report units, price, price multiplier, baseline units, baseline price, feature indicator, and display indicator.
Ad Intel Data
Covers advertising occurrences for various media types across the United States, starting in 2010, including annual updates. The dataset includes ad impressions for TV and radio and, most recently, social media advertising data. These data can be broken down by Market Code (i.e., ~200 Designated Market Areas (DMAs), which can be matched to DMAs in the Consumer Panel and Retail Scanner datasets).
Key Features:
- Includes advertisement occurrences from the following media types: National TV, local TV, radio, magazine, newspaper, Free Standing Insert (FSI) Coupon, outdoor (e.g., billboards), internet, digital, and cinema.
- Advertisement impression and universe estimates are also available for national TV, local TV, and radio to calculate Gross Rating Points.
- Advertisement impressions are further broken down by age and gender.
- National TV impressions can be broken down into 50 demographic classifications, called Market Breaks.
- Categories range from the types of beverages a family consumes to household income to the presence of computers or the internet in the home.
- Reference data files include advertisers, brands, product categories, creative descriptions, TV programs, distributors (e.g., TV stations), and publishers.
How to Access Kilts-Neilsen Marketing Datasets
How to Apply for Access to Kilts-Nielsen Marketing Datasets
The Kilts-Nielsen data is available to researchers at Cornell who have registered with the Kilts Center and have been approved by the Cornell Office of Sponsored Programs.
Here are the steps:
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Users must first register with the Kilts Center here.
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Then contact the Cornell University Office of Sponsored Programs, responsible for negotiating and authorizing contracts for academic purposes at Cornell, for authorization here.
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In this email, indicate that you have registered with the Kilts Center for access to the Nielsen data and request that OSP begin the process of authorizing a data use agreement for your project.
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Lastly, submit a new CCSS Regulated Research Environment project request here. You can submit this request before or after receiving approval from the Kilts Center.
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No IRB is required for Kilts-Nielsen data access, and the Data Provider can be listed as Kilts-Nielsen.
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A project cannot be activated until CCSS receives the agreement from OSP in Step 2.
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PIs with active projects cover a share of the annual subscription fee. The share amount is prorated depending on the number of PIs. It has been in the $800-900 range, which is significantly less than an individual access fee.
How to Cite Kilts-Neilsen Marketing Datasets
Below are the citations for each of the individual data collections:
Nielsen Ad Intel Dataset
A.C. Nielsen Company. Nielsen Ad Intel Dataset. Catalog no. 2875. Ithaca, NY: Cornell University. Cornell Center for Social Sciences | Research Support. Chicago, IL: Kilts Center for Marketing [distributor]. Version 1.
Nielsen Retail Scanner Dataset
A.C. Nielsen Company. Nielsen Retail Scanner Dataset. Catalog no. 2877. Ithaca, NY: Cornell University. Cornell Center for Social Sciences | Research Support. Chicago, IL: Kilts Center for Marketing [distributor]. Version 1.
Nielsen Consumer Panel Dataset
A.C. Nielsen Company. Nielsen Consumer Panel Datasets. Catalog no. 2876. Ithaca, NY: Cornell University. Cornell Center for Social Sciences | Research Support. Chicago, IL: Kilts Center for Marketing [distributor]. Version 1.
Questions and Resources
Where Do I Find Additional Marketing Data Resources?
The CCSS Data and Reproduction Archive also includes a small subset of Nielsen Retail Scanner Data (Retail sales of specific packaged goods (coffee, laundry detergent, shampoo) broken out by U.S. region, brand, size, packaging material, UPC, and price in 2019) for use by all Cornell researchers here.
Also, see this Marketing Research Library Guide created by Mann Library staff.
Have Additional Questions About This Data?
Please get in touch with us at socialsciences@cornell.edu for assistance.
How Do I Get Cited When I Use Kilts-Nielsen Data?
Please inform us of your publication that used this data so we can add it to our bibliography of related articles, as seen below.
Cornell-Authored Journal Articles and Dissertations
Retail Scanner Data:
Adalja, Aaron, Liaukonyte, Jura, Wang, Emily, and Xinrong Zhu. "GMO and Non-GMO Labeling Effects: Evidence from a Quasi-Natural Experiment." Marketing Science Volume 42 Issue 2 (2023-03): 213 https://doi.org/10.1287/mksc.2022.1375.
Cawley, John H., Frisvold, David E., Hill, Anna, and David Jones. "The Impact of the Philadelphia Beverage Tax on Prices and Product Availability." Journal of Policy Analysis and Management Volume 39 Issue 3 (2020): 605-628 https://doi.org/10.1002/pam.22201.
Cawley, John H., Frisvold, David E., and David Jones. "The impact of sugar-sweetened beverage taxes on purchases: Evidence from four city-level taxes in the United States." Health Economics Volume 29 Issue 10 (2020-10): 1289-1306 https://doi.org/10.1002/hec.4141.
Cawley, John H., Frisvold, David E., Jones, David, and Chelsea Lensing. "The Pass-Through of a Tax on Sugar-Sweetened Beverages in Boulder, Colorado." American Journal of Agricultural Economics Volume 103 Issue 3 (2021-05): 987-1005 https://doi.org/10.1111/ajae.12191.
Chen, Jialie and Vithala R. Rao. "A Dynamic Model of Rational Addiction with Stockpiling and Learning: An Empirical Examination of E-cigarettes." Management Science Volume 66 Issue 12 (2020-12): 5886-5905 https://doi.org/10.1287/mnsc.2019.3490.
Green, Isaac Nelson. "Essays in Macro-finance and Political Economy." Cornell Theses and Dissertations (2022-05) http://doi.org/10.7298/nth4-dj82.
Jin, Lawrence, Kenkel, Donald Scott, Lovenheim, Michael, Mathios, Alan D., and Hua Wang. "Misinformation, Consumer Risk Perceptions, and Markets: The Impact of an Information Shock on Vaping and Smoking Cessation." National Bureau of Economic Research Working Paper Series Issue 30255 (2022-07) https://doi.org/10.3386/w30255.
Kim, Sungjin, Lee, Clarence, and Sachin Gupta. "Bayesian Synthetic Control Methods." Journal of Marketing Research Volume 57 Issue 5 (2020-10): 831-852 https://doi.org/10.1177/0022243720936230.
Lu, Thanh. "Marijuana legalization and household spending on food and alcohol." Health Economics Volume 30 Issue 7 (2021-07): 1687-1696 https://doi.org/10.1002/hec.4266.
Richards, Timothy J. and Bradley Rickard. "Dynamic model of beer pricing and buyouts." Agribusiness Volume 37 Issue 4 (2021-03-31): 685-712 doi.org/10.1002/agr.21698.
Willage, Barton. "Essays in Health Economics." Cornell Theses and Dissertations (2018-05-30) https://doi.org/10.7298/X4M61HH7.
Willage, Barton. "Unintended consequences of health insurance: Affordable Care Act's free contraception mandate and risky sex." Health Economics Volume 29 Issue 1 (2020-01): 30-45 https://doi.org/10.1002/hec.3967.
Yonezawa, Koichi, Gómez, Miguel I., and Timothy J. Richards. "The Robinson–Patman Act and Vertical Relationships." American Journal of Agricultural Economics Volume 102 Issue 1 (2020-01): 329-352 https://doi.org/10.1093/ajae/aaz049.
Zheng, Yuqing, Dong, Diansheng, Burney, Shaheer, and Harry M. Kaiser. "Eat at Home or Away from Home? The Role of Grocery and Restaurant Food Sales Taxes." Journal of Agricultural and Resource Economics Volume 44 Issue 1 (2019-01): 98-116 https://www.jstor.org/stable/10.2307/26797545.
Consumer Panel Data:
Cawley, John H., Thow, Anne Marie, Wen, Katherine, and David Frisvold . "The Economics of Taxes on Sugar-Sweetened Beverages: A Review of the Effects on Prices, Sales, Cross-Border Shopping, and Consumption." Annual Review of Nutrition Volume 39 (2019-08): 317-227 https://doi.org/10.1146/annurev-nutr-082018-124603.
Debnam, Jakina. "Selection Effects and Heterogeneous Demand Responses to the Berkeley Soda Tax Vote." American Journal of Agricultural Economics Volume 99 Issue 5 (2017-10): 1172-1187 https://doi.org/10.1093/ajae/aax056.
Debnam, Jakina. "Essays on the Role of Social Influence in Behavioral Economics." Cornell Theses and Dissertations (2018-12-30) https://doi.org/10.7298/0ht4-as95.
DeCicca, Philip, Kenkel, Donald Scott, and Feng Liu. . "Reservation Prices: An Economic Analysis of Cigarette Purchases on Indian Reservations." National Tax Journal Volume 68 Issue 1 (2015-03): 93-118 https://doi.org/10.17310/ntj.2015.1.04.
DeCicca, Philip, Kenkel, Donald Scott, and Michael Lovenheim. "The Economics of Tobacco Regulation: A Comprehensive Review." Journal of Economic Literature Volume 60 Issue 3 (2022-09): 883-970 https://doi.org/10.1257/jel.20201482.
Feldman, Naomi and Ori Heffetz. "A Grant to Every Citizen: Survey Evidence of the Impact of a Direct Government Payment in Israel." National Tax Journal Volume 75 Issue 2 (2022-06): 225-444 https://doi.org/10.1086/719449.
Ho, Shuay-Tsyr. "Essays on the Economics of Policy and Regulation in Agricultural and Food Markets." Cornell Theses and Dissertations (2019-08-30) https://doi.org/10.7298/emgh-8268.
Kaçamak, Yeliz, Velayudhan, Tejaswi, and Eleanor Wilking. "Retailer Remittance Matters: Evidence from Voluntary Collection Agreements." National Tax Journal Volume 76 Issue 2 (2023-06): 233-473 https://doi.org/10.1086/724590.
Yonezawa, Koichi, Gómez, Miguel I., and Timothy J. Richards. "The Robinson–Patman Act and Vertical Relationships." American Journal of Agricultural Economics Volume 102 Issue 1 (2020-01): 329-352 https://doi.org/10.1093/ajae/aaz049.
Zhao, Jason. "Putting Grocery Food Taxes on the Table: Evidence for Food Security Policy-Makers." Cornell Theses and Dissertations https://doi.org/10.7298/tyjt-vc54.