As of April 2020, ** percent of retailers anticipated that the COVID-19 pandemic would affect their retail operations for 3 to 12 months. Some ** percent believed the pandemic would affect their retail activity for *** to *** years.

Graph and download economic data for Monthly Supply of New Houses in the United States (MSACSR) from Jan 1963 to Aug 2025 about supplies, new, housing, and USA.

Abstract

Fruit and vegetable vendors in Bogota travel most days to a central market to purchase produce, incurring substantial costs. A social enterprise attempted to shorten the supply chain between farmers and vendors by aggregating orders from many small stores and delivering orders directly. We randomized the introduction of this service at the market-block level. Initial interest was high, and the service reduced travel time and costs, and increased work-life balance. Purchase costs fell 6 to 8 percent, there was incomplete pass-through into lower prices for consumers, and markups rose. However, stores reduced sales of products not offered by this new service, and their total sales and profits appear to have fallen in the short-run, with service usage falling over time. The results offer a window into the nature of competition among small retailers, and point to the challenges in achieving economies of scale when disrupting centralized markets for multi-product firms.

Geographic coverage

Southwest Bogota

Analysis unit

Firm

Kind of data

Sample survey data [ssd]

Sampling procedure

All neighborhoods in Bogota are classified by the government into one of six socio-economic strata, classified from 1 (poorest) to 6 (richest). Our focus is on poor neighborhoods (strata 1 to 3) in the South-West of Bogota, not immediately adjacent to Corabastos. Agruppa went door-to-door along streets in these neighborhoods in January and February 2016 (see Appendix 2 for a study timeline) to identify stores that sell fruit and vegetables, excluding the few large supermarkets and chain stores. Their aim was to map approximately 2,400 stores. Using larger streets as natural boundaries, these neighborhoods were then divided into 69 blocks, with a median block size of 36 retail shops per block. Six of these blocks were then dropped for safety reasons, leaving 63 blocks. Blocks were formed into matched pairs on the basis of geographic location and number of firms in the block, and then ordered according to the sequence in which Agruppa desired to expand operations. One block within each pair was then randomly assigned to treatment, and the other to control, for a total of 32 treatment blocks and 31 control blocks.

This yielded a sample of 1,620 firms, comprising 852 firms in treatment blocks and 768 firms in control blocks. On average, 69 percent of firms in treatment blocks and 70 percent of firms in control blocks expressed interest in Agruppa, giving us samples of 586 interested firms in treatment blocks, 266 uninterested firms in treatment blocks, 536 interested firms in control blocks, and 232 uninterested firms in control blocks.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Baseline and Follow-Up survey quetionnaires are published in Spanish and English, and provided under the Documentation tab.

Response rate

IPA Colombia conducted five rounds of high-frequency short-term follow-up surveys at 2, 4, 6, 10, and 14 weeks after the launch of Agruppa in a block. We would survey a treatment block and its corresponding control block in the same week, staggering the timing to match the staggered timing of the baseline surveys and introduction of Agruppa. The response rate averaged 79% for firms interested in Agruppa (81% in treatment blocks, 77% in control blocks), and 69% for not-interested firms (70% in treatment blocks, 68% in control blocks).

We then collected two longer surveys at six-months and twelve months after the launch of Agruppa in a block. In addition to the information collected in the high-frequency surveys, these questionnaires also asked about business opening hours, sales of some other products, pricing strategies, crime, record-keeping, and work-life balance. The response rates for interested firms were 78% at six months (80% in treatment blocks, 75% in control blocks), and 76% at twelve months (77% in treatment blocks, and 74% in control blocks), and were again lower for uninterested firms

Grocery Inventory and Sales Dataset

Dataset Overview:

This dataset provides detailed information on various grocery items, including product details, supplier information, stock levels, reorder data, pricing, and sales performance. The data covers 990 products across various categories such as Grains & Pulses, Beverages, Fruits & Vegetables, and more. The dataset is useful for inventory management, sales analysis, and supply chain optimization.

Columns:

• Product_ID: Unique identifier for each product.
• Product_Name: Name of the product.
• Category: The product category (e.g., Grains & Pulses, Beverages, Fruits & Vegetables).
• Supplier_ID: Unique identifier for the product supplier.
• Supplier_Name: Name of the supplier.
• Stock_Quantity: The current stock level of the product in the warehouse.
• Reorder_Level: The stock level at which new stock should be ordered.
• Reorder_Quantity: The quantity of product to order when the stock reaches the reorder level.
• Unit_Price: Price per unit of the product.
• Date_Received: The date the product was received into the warehouse.
• Last_Order_Date: The last date the product was ordered.
• Expiration_Date: The expiration date of the product, if applicable.
• Warehouse_Location: The warehouse address where the product is stored.
• Sales_Volume: The total number of units sold.
• Inventory_Turnover_Rate: The rate at which the product sells and is replenished.
• Status: Current status of the product (e.g., Active, Discontinued, Backordered).

Dataset Usage:

• Inventory Management: Analyze stock levels and reorder strategies to optimize product availability and reduce stockouts or overstock.
• Sales Performance: Track sales volume and inventory turnover rate to understand product demand and profitability.
• Supplier Analysis: Evaluate suppliers based on product availability, pricing, and delivery frequency.
• Product Lifecycle: Identify discontinued or backordered products and analyze expiration dates for perishable goods.

How to Use:

This dataset can be used for various tasks such as: - Predicting reorder quantities using machine learning. - Analyzing inventory turnover to optimize stock levels. - Conducting sales trend analysis to identify popular or slow-moving items. - Improving supply chain efficiency by analyzing supplier performance.

Notes:

• This dataset is fictional and used for educational or demonstration purposes only.
• The expiration dates and last order dates should be considered for time-sensitive or perishable items.
• Some products have been marked as discontinued or backordered, indicating their current status in the inventory system.

License:

This dataset is released under the Creative Commons Attribution 4.0 International License. You are free to share, adapt, and use the data, provided proper attribution is given.

Business or organization change in supply chain challenges over the last three months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2025.

Wholesalers registered to trade alcohol in the Northern Territory provide the Department of Industry, Tourism and Trade with data on the volume of alcohol supplied to licensed retailers by product type (cask wine, bottled wine, fortified wine, cider, standard spirits, pre mixed spirits, full strength beer, mid strength beer and low strength beer). The reference period and the actual date of release can have can have a substantial lag. Data is collated and analysed in a chronological fashion. Always produced 3 months in arrears. Due to resourcing and competing priorities there is often a backlog of data to be processed.

Graph and download economic data for Producer Price Index by Industry: Building Material and Supplies Dealers (PCU44414441) from Dec 2003 to Sep 2025 about dealers, materials, supplies, buildings, PPI, industry, inflation, price index, indexes, price, and USA.

Supply chain analytics is a valuable part of data-driven decision-making in various industries such as manufacturing, retail, healthcare, and logistics. It is the process of collecting, analyzing and interpreting data related to the movement of products and services from suppliers to customers.

The Digital Skills Indicator 2.0 (DSI) is a composite indicator which is based on selected activities related to internet or software use that individuals aged 16-74 perform in five specific areas (Information and data literacy, Communication and collaboration, Digital content creation, Safety, and Problem solving). It is assumed that individuals having performed certain activities have the corresponding skills. Therefore, the indicators can be considered as proxy of individuals’ digital skills.

According to the variety of activities performed, two levels of skills are computed for each of the five areas ("basic" and "above basic"). Finally, based on the component indicators for each area, an overall digital skills indicator is calculated as a proxy of the digital skills of individuals ("no skills", "limited", "narrow", "low", "basic", "above basic" or "at least basic skills").

1. Information and data literacy skills

Definition in Digital Competence Framework 2.0: To articulate information needs, to locate and retrieve digital data, information and content. To judge the relevance of the source and its content. To store, manage, organize digital data, information and content.

Activities used for calculating the information and data literacy skills:

• Finding information about goods or services (IUIF);
• Seeking health-related information (IHIF);
• Reading online news sites, newspapers or news magazines (IUNW1);
• Activities related to fact-checking online information and its sources (TICCSFOI, TICIDIS, TICNIDIS, TICXND).

Levels of information skills:

• Basic: one activity (I_DSK2_IL_B);
• Above basic: more than one activity (I_DSK2_IL_AB);
• At least basic: basic or above basic skills (I_DSK2_IL_BAB).

2. Communication and collaboration skills

Definition in Digital Competence Framework 2.0: To interact, communicate and collaborate through digital technologies while being aware of cultural and generational diversity. To participate in society through public and private digital services and participatory citizenship. To manage one’s digital identity and reputation.

Activities used for calculating the communication and collaboration skills:

• Sending/receiving emails (IUEM);
• Telephoning/video calls over the internet (IUPH1);
• Instant messaging (IUCHAT1);
• Participating in social networks (IUSNET);
• Expressing opinions on civic or political issues on websites or in social media (IUPOL2);
• Taking part in online consultations or voting to define civic or political issues (IUVOTE).

Levels of communication and collaboration skills:

• Basic: one activity (I_DSK2_CC_B);
• Above basic: more than one activity (I_DSK2_CC_AB);
• At least basic: basic or above basic skills (I_DSK2_CC_BAB).

3. Digital content creation skills

Definition in Digital Competence Framework 2.0: To create and edit digital content. To improve and integrate information and content into an existing body of knowledge while understanding how copyright and licences are to be applied. To know how to give understandable instructions for a computer system.

Activities used for calculating the digital content creation skills:

• Using word processing software (CWRD1);
• Using spreadsheet software (CXLS1);
• Editing photos, video or audio files (CEPVA1);
• Copying or moving files (such as documents, data, images, video) between folders, devices (via e-mail, instant messaging, USB, cable) or on the cloud (CXFER1);
• Creating files (such as documents, image, videos) incorporating several elements such as text, picture, table, chart, animation or sound (CPRES2);
• Using advanced features of spreadsheet software (functions, formulas, macros and other developer functions) to organize, analyse, structure or modify data (CXLSADV1);
• Writing code in a programming language (CPRG2).

Levels of digital content creation skills:

• Basic: one or two activities (I_DSK2_DCC_B);
• Above basic: 3 or more activities (I_DSK2_DCC_AB);
• At least basic: basic or above basic skills (I_DSK2_DCC_BAB).

4. Safety skills

Definition in Digital Competence Framework 2.0: To protect devices, content, personal data and privacy in digital environments. To protect physical and psychological health, and to be aware of digital technologies for social well-being and social inclusion. To be aware of the environmental impact of digital technologies and their use.

Activities used for calculating the safety:

• Managing access to own personal data by checking that the website where the respondent provided personal data was secure (MAPS_CWSC);
• Managing access to own personal data by reading privacy statements before providing personal data (MAPS_RPS);
• Managing access to own personal data by restricting or refusing access to own geographical location (MAPS_RRGL);
• Managing access to own personal data by limiting access to profile or content on social networking sites or shared online storage (MAPS_LAP);
• Managing access to own personal data by refusing allowing use of personal data for advertising purposes (MAPS_RAAD);
• Changing settings in own internet browser to prevent or limit cookies on any of the respondent devices (PCOOK1).

Levels of digital content creation skills:

• Basic: one or two activities (I_DSK2_SF_B);
• Above basic: 3 or more activities (I_DSK2_SF_AB);
• At least basic: basic or above basic skills (I_DSK2_SF_BAB).

5. Problem solving skills

Definition in Digital Competence Framework 2.0: To identify needs and problems, and to resolve conceptual problems and problem situations in digital environments. To use digital tools to innovate processes and products. To keep up-to-date with the digital evolution.

Activities used for calculating the problem solving skills:

• Downloading or installing software or apps (CINSAPP1);
• Changing settings of software, app or device (CCONF1);
• Online purchases (in the last 12 months) (IBUY=1 or IBUY=2);
• Selling online (IUSELL);
• Used online learning resources (IUOLC or IUOLM);
• Internet banking (IUBK);
• Looking for a job or sending a job application (IUJOB).

Levels of problem solving skills:

• Basic: one or two activities (I_DSK2_PS_B);
• Above basic: 3 or more activities (I_DSK2_PS_AB);
• At least basic: basic or above basic skills (I_DSK2_PS_BAB).

OVERALL DIGITAL SKILL INDICATOR

• Individuals with “above basic” (I_DSK2_AB) level of skills:

“above basic” in all 5 areas.

• Individuals with a “basic” (I_DSK2_B) level of skills:

if all 5 areas are at least basic level (some can be “basic” and some can be “above basic”, but not all 5 areas are “above basic”).

• Individuals with “at least basic” level of skills:

if individuals fall either into “above basic” or “basic” category of skills (I_DSK2_BAB).

• Individuals with “low” (I_DSK2_LW) level of skills:

if individuals have “basic” or “above basic” level in 4 areas and “no skills” in 1 area (4 out of 5).

• Individuals with “narrow” (I_DSK2_N) level of skills:

if individuals have “basic” or “above basic” level in 3 areas and “no skills” in 2 areas (3 out of 5).

• Individuals with “limited” (I_DSK2_LM) level of skills:

if individuals have “basic” or “above basic” level in 2 areas and “no skills” in 3 areas (2 out of 5).

• Individuals with “no skills” (I_DSK2_X):

if individuals have “no skills” in 4 areas or “no skills” in all 5 areas despite declaring having used the internet at least once during last 3 months.

• Individuals for whom the digital skills could not be assessed (I_DSK2_NA):

individuals that have not used the internet in the last 3 months.

(For formula and references to original variables collected by the survey on the use of ICT in households and by individuals, please see in the Annex, 'A worked example of how the DSI is computed').

As of 2021, the dataset encompasses an additional indicator (not included in DESI):

Online Information and Communication Skills

The dataset encompasses individuals who performed some activities from both INFORMATION AND DATA LITERACY and COMMUNICATION AND COLLABORATION areas (at “basic” or “above basic” level)

Individuals with online information and communication skills (I_DSK2_IC_S):

1. At least one variable from the following list:

• Finding information about goods or services (IUIF);
• Seeking health-related information

This is a case study for the company to improve sales

Business Goal
Date: 2023/09/15
Dataset: Sales quantity of a certain brand from January to December 2022 and from January to September 2023.

Please describe what you observe (no specific presentation format required). Among your observations, identify at least three valuable insights and explain why you consider them valuable.
If more resources were available to you (including time, information, etc.), what would you need, and what more could you achieve?

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Sample question & answer 1. Product insights: identify the product sales analysis, such as BCG matrix 2. Store insights: identify the sales performance of the sales 3. Supply chain insights: identify the demand 4. Time series forecasting: identify tread, seasonality

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

This dataset provides synthetic yet realistic data for analyzing and forecasting retail store inventory demand. It contains over 73000 rows of daily data across multiple stores and products, including attributes like sales, inventory levels, pricing, weather, promotions, and holidays.

The dataset is ideal for practicing machine learning tasks such as demand forecasting, dynamic pricing, and inventory optimization. It allows data scientists to explore time series forecasting techniques, study the impact of external factors like weather and holidays on sales, and build advanced models to optimize supply chain performance.

Challenges for Data Scientists:

Challenge 1: Time Series Demand Forecasting Predict daily product demand across stores using historical sales and inventory data. Can you build an LSTM-based forecasting model that outperforms classical methods like ARIMA?

Challenge 2: Inventory Optimization Optimize inventory levels by analyzing sales trends and minimizing stockouts while reducing overstock situations.

Challenge 3: Dynamic Pricing Develop a pricing strategy based on demand, competitor pricing, and discounts to maximize revenue.

Key Data Features:

Date: Daily records from [start_date] to [end_date]. Store ID & Product ID: Unique identifiers for stores and products. Category: Product categories like Electronics, Clothing, Groceries, etc. Region: Geographic region of the store. Inventory Level: Stock available at the beginning of the day. Units Sold: Units sold during the day. Demand Forecast: Predicted demand based on past trends. Weather Condition: Daily weather impacting sales. Holiday/Promotion: Indicators for holidays or promotions.

Example Notebook Ideas

Exploratory Data Analysis (EDA): Analyze sales trends, visualize data, and identify patterns. Time Series Forecasting: Train models like ARIMA, Prophet, or LSTM to predict future demand. Pricing Analysis: Study how discounts and competitor pricing affect sales.

During the COVID-19 pandemic, the number of house sales in the UK spiked, followed by a period of decline. In 2023 and 2024, the housing market slowed notably, and in January 2025, transaction volumes fell to 46,774. House sales volumes are impacted by a number of factors, including mortgage rates, house prices, supply, demand, as well as the overall health of the market. The economic uncertainty and rising unemployment rates has also affected the homebuyer sentiment of Brits. How have UK house prices developed over the past 10 years? House prices in the UK have increased year-on-year since 2015, except for a brief period of decline in the second half of 2023 and the beginning of 2024. That is based on the 12-month percentage change of the UK house price index. At the peak of the housing boom in 2022, prices soared by nearly 14 percent. The decline that followed was mild, at under three percent. The cooling in the market was more pronounced in England and Wales, where the average house price declined in 2023. Conversely, growth in Scotland and Northern Ireland continued. What is the impact of mortgage rates on house sales? For a long period, mortgage rates were at record-low, allowing prospective homebuyers to take out a 10-year loan at a mortgage rate of less than three percent. In the last quarter of 2021, this period came to an end as the Bank of England rose the bank lending rate to contain the spike in inflation. Naturally, the higher borrowing costs affected consumer sentiment, urging many homebuyers to place their plans on hold and leading to a decline in sales.

View daily updates and historical trends for Bitcoin Supply. Source: Blockchain.com. Track economic data with YCharts analytics.

How high is the brand awareness of Gardener's Supply Company in the United States?When it comes to DIY and garden online shop users, brand awareness of Gardener's Supply Company is at **% in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Gardener's Supply Company in the United States?In total, *% of U.S. DIY and garden online shop users say they like Gardener's Supply Company. However, in actuality, among the **% of U.S. respondents who know Gardener's Supply Company, **% of people like the brand.What is the usage share of Gardener's Supply Company in the United States?All in all, *% of DIY and garden online shop users in the United States use Gardener's Supply Company. That means, of the **% who know the brand, **% use them.How loyal are the customers of Gardener's Supply Company?Around *% of DIY and garden online shop users in the United States say they are likely to use Gardener's Supply Company again. Set in relation to the *% usage share of the brand, this means that **% of their customers show loyalty to the brand.What's the buzz around Gardener's Supply Company in the United States?In July 2022, about *% of U.S. DIY and garden online shop users had heard about Gardener's Supply Company in the media, on social media, or in advertising over the past three months. Of the **% who know the brand, that's **%, meaning at the time of the survey there's little buzz around Gardener's Supply Company in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

How high is the brand awareness of Chewy in the United States?When it comes to pet supply online shop users, brand awareness of Chewy is at ** percent in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Chewy in the United States?In total, ** percent of U.S. pet supply online shop users say they like Chewy. However, in actuality, among the ** percent of U.S. respondents who know Chewy, ** percent of people like the brand.What is the usage share of Chewy in the United States?All in all, ** percent of pet supply online shop users in the United States use Chewy. That means, of the ** percent who know the brand, ** percent use them.How loyal are the customers of Chewy?Around ** percent of pet supply online shop users in the United States say they are likely to use Chewy again. Set in relation to the ** percent usage share of the brand, this means that ** percent of their customers show loyalty to the brand.What's the buzz around Chewy in the United States?In March 2024, about ** percent of U.S. pet supply online shop users had heard about Chewy in the media, on social media, or in advertising over the past three months. Of the ** percent who know the brand, that's ** percent, meaning at the time of the survey there's considerable buzz around Chewy in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

How high is the brand awareness of Just for Pets in the UK?When it comes to pet supply online shop users, brand awareness of Just for Pets is at *** in the UK. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Just for Pets in the UK?In total, *** of UK pet supply online shop users say they like Just for Pets. However, in actuality, among the *** of UK respondents who know Just for Pets, *** of people like the brand.What is the usage share of Just for Pets in the UK?All in all, *** of pet supply online shop users in the UK use Just for Pets. That means, of the *** who know the brand, *** use them.How loyal are the customers of Just for Pets?Around ** of pet supply online shop users in the UK say they are likely to use Just for Pets again. Set in relation to the *** usage share of the brand, this means that *** of their customers show loyalty to the brand.What's the buzz around Just for Pets in the UK?In July 2022, about ** of UK pet supply online shop users had heard about Just for Pets in the media, on social media, or in advertising over the past three months. Of the *** who know the brand, that's ***, meaning at the time of the survey there's little buzz around Just for Pets in the UK.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.