Retail Scanner Data consist of weekly pricing, volume, and store environment information generated by point-of-sale systems from more than 90 participating retail chains across all US markets.

Store Demographics: Includes store chain code, channel type, and area location. Retailer names are masked to protect identity.

Weekly Product Data: For each UPC code, participating stores report units, price, price multiplier, baseline units, baseline price, feature indicator, and display indicator. Products: 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 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 10 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.

Product Characteristics: All products include UPC code and description, brand, multipack, and size, as well as NielsenIQ codes for department, product group, and product module. Some products contain additional characteristics (e.g., flavor).

Geographies: Scanner Data 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. Data cover the entire United States, divided into 52 major markets, and include the same codes as those used in the consumer panel data.

Retail Channels: Food, drug, mass merchandise, convenience, and liquor.

The table 2004-2020 Stores is part of the dataset Nielsen Retail Scanner, available at https://columbia.redivis.com/datasets/eqcs-3z86jfqwb. It contains 96435 rows across 12 variables.

Context

Nielsen receives transaction level scanning data (POS Data) from its partner stores on a regular basis. Stores sharing POS data include bigger format store types such as supermarkets, hypermarkets as well as smaller traditional trade grocery stores (Kirana stores), medical stores etc. using a POS machine.

While in a bigger format store, all items for all transactions are scanned using a POS machine, smaller and more localized shops do not have a 100% compliance rate in terms of scanning and inputting information into the POS machine for all transactions.

A transaction involving a single packet of chips or a single piece of candy may not be scanned and recorded to spare customer the inconvenience or during rush hours when the store is crowded with customers.

Thus, the data received from such stores is often incomplete and lacks complete information of all transactions completed within a day.

Additionally, apart from incomplete transaction data in a day, it is observed that certain stores do not share data for all active days. Stores share data ranging from 2 to 28 days in a month. While it is possible to impute/extrapolate data for 2 days of a month using 28 days of actual historical data, the vice versa is not recommended.

Nielsen encourages you to create a model which can help impute/extrapolate data to fill in the missing data gaps in the store level POS data currently received.

Content

You are provided with the dataset that contains store level data by brands and categories for select stores-

Hackathon_ Ideal_Data - The file contains brand level data for 10 stores for the last 3 months. This can be referred to as the ideal data.

Hackathon_Working_Data - This contains data for selected stores which are missing and/or incomplete.

Hackathon_Mapping_File - This file is provided to help understand the column names in the data set.

Hackathon_Validation_Data - This file contains the data stores and product groups for which you have to predict the Total_VALUE.

Sample Submission - This file represents what needs to be uploaded as output by candidate in the same format. The sample data is provided in the file to help understand the columns and values required.

Acknowledgements

Nielsen Holdings plc (NYSE: NLSN) is a global measurement and data analytics company that provides the most complete and trusted view available of consumers and markets worldwide. Nielsen is divided into two business units. Nielsen Global Media, the arbiter of truth for media markets, provides media and advertising industries with unbiased and reliable metrics that create a shared understanding of the industry required for markets to function. Nielsen Global Connect provides consumer packaged goods manufacturers and retailers with accurate, actionable information and insights and a complete picture of the complex and changing marketplace that companies need to innovate and grow. Our approach marries proprietary Nielsen data with other data sources to help clients around the world understand what’s happening now, what’s happening next, and how to best act on this knowledge. An S&P 500 company, Nielsen has operations in over 100 countries, covering more than 90% of the world’s population.

Know more: https://www.nielsen.com/us/en/

Inspiration

Build an imputation and/or extrapolation model to fill the missing data gaps for select stores by analyzing the data and determine which factors/variables/features can help best predict the store sales.

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These data contain store-level information on UPC coded products, including store characteristics, sales, and detailed information on the products. There are three separate data series: the New England data, U.S. beverage data, and the WIC/Infant formula data. The New England data contain information on all food and beverage products sold in 8 major chains in New England. The U.S. beverage data include data on all non-alcoholic beverage products sold in grocery stores across the U.S. The WIC/Infant formula includes data on infant formula, ready-to-eat cereal, baby food, and peanut butter as well as demographics such as household size, income, race, and age and presence of children.

• The dataset consists of Excel (.xlsx) files with data on sales of plant-based food products between 2017 and 2020 in a number of European countries (i.e. Austria, Belgium, Denmark, France, Germany, Italy, the Netherlands, Poland, Romania, Spain and the UK.)

• The data are clearly labelled within each file. The key variables (common across datasets) are Value in Euros, Volume in KG/LIT and Volume in Selling Units for a number of meat and dairy substitute food products specific to the retail region.

• The data were originally collected by Nielsen Market Track. They were analysed on the Smart Protein project in 2021 and used to publish an extensive market data report and to host a public webinar, both entitled Plant-based foods in Europe: how big is the market?

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Contains annual sales values for all perishable and dry grocery categories for all retail channels in Alberta for the years 2013 to 2018 inclusive. Alberta retail grocery sales data is obtained from Nielsen Company of Canada.

The Billboard Hot 100 is a chart that ranks the best-performing singles of the United States. Its data, published by Billboard magazine and compiled by Nielsen Sound Scan, is based collectively on each single's weekly physical and digital sales, as well as airplay and streaming. At the end of a year, Billboard will publish an annual list of the 100 most successful songs throughout that year on the Hot 100 chart based on the information.

Billboard year end chart works These charts are a cumulative measure of a single or album's performance in the United States, based upon the Billboard magazine charts during any given chart year. Other factors including the total weeks a song spent on the chart and at its peak position were calculated into its year-end total.

Billboard Hot is determined The Hot 100 is ranked by radio airplay audience impressions as measured by Nielsen BDS, sales data compiled by Nielsen Sound scan (both at retail and digitally) and streaming activity provided by online music sources. There are several component charts that contribute to the overall calculation of the Hot 100.

The Billboard Global 200 is a weekly record chart published by Billboard magazine. The chart ranks the top songs globally and is based on digital sales and online streaming from over 200 territories worldwide.

Stories about the Billboard 200 albums chart generally post on Sunday afternoons, while stories about the Billboard Hot 100 generally post each Monday afternoon. Other stories, podcasts, videos and more covering our full menu of charts post throughout the week.

In the US, Billboard represents the cream of all the objective data. And their efforts to collect all the data from all these various sources to create an objective, final tally of each artist's popularity in a given week, still has merit. ... This is why the billboard chart is important.

So here we had collected list of Billboard Hot 100 singles from the year 1992 to 2014.

Contains annual sales values for all perishable and dry grocery (food) categories for all retail channels in Alberta from 2009 to 2014. Also included in this record is 2012 and 2013 sales data for major retail grocery categories for the cities of Edmonton and Calgary. Alberta retail data is purchased from The Nielsen Company of Canada.

The goal of sugar-sweetened beverage (SSB) taxes is to raise the prices of SSBs to decrease consumption. Price promotions play an important role in the sales of SSBs and could potentially be used by manufacturers to weaken the impact of such taxes. The purpose of this study is to determine how price promotions changed after the introduction of the 2017 Oakland SSB tax. A difference-in-differences study design was used to compare changes in prices and the prevalence and amount of price promotions for beverages in Oakland, California, relative to Sacramento, California, using two different datasets. Nielsen Retail Scanner data included price promotions for beverages sold and store audit data included price promotions offered by retailers. Changes were analyzed for SSBs, noncalorically sweetened beverages, and unsweetened beverages. After the implementation of the tax, the prevalence of price promotions for SSBs did not change significantly in Oakland relative to the comparison site of Sacramento. However, the depth of price promotions increased by an estimated 0.35 cents per ounce (P

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Unconditional and conditional means and standard deviations of household food and beverage expenditure by store type, Nielsen Panel data 2011 to 2015a.

Nielsen scanner data summary statistics pre- and post-tax implementation.

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North America Free From Food Market size was valued at USD 91.56 Billion in 2023 and is projected to reach USD 170.57 Billion by 2031 growing at a CAGR of 13.26% from 2024 to 2031.Key Market Drivers:Rising Food Allergies and Intolerances: According to FARE, around 32 million Americans suffer from food allergies, with 5.6 million of them being children under the age of 18. The CDC reports a 50% increase in food allergy prevalence between 1997 and 2021, fueling demand for allergen-free food options to accommodate expanding dietary restrictions.Growing Health and Wellness Consciousness: According to the International Food Information Council’s 2023 poll, 52% of Americans seek health-promoting foods, while 27% choose products free of artificial ingredients. Nielsen data reveal that “free-from” product sales increasing by 9.5% in 2022, showing expanding customer interest in natural, clean-label foods.

Contains annual sales values for all perishable and dry grocery categories for all retail channels in Alberta for the years 2013 to 2018 inclusive. Alberta retail grocery sales data is obtained from Nielsen Company of Canada.