A survey in the UK revealed that in 2019, only about ** percent of adult consumers intended on buying more from local shops. That number rose to ** percent in 2020.

In 2020, nearly 70 percent of consumers in both Canada and the United States primarily shopped locally in order to strengthen the local economy. In comparison, only about half of shoppers in the United Kingdom bought products locally for this reason. Other arguments, consumers across the globe used for buying locally, included helping support local job creation, as well as investing in the community.

As of January 2025, around ********* of shoppers visited local shopping areas once a week on average. About ** percent visited several times a week, while ***** percent stated they went shopping every day.

% of vacant shops - City Centre

When asked why they were more likely to purchase locally made products as of 2023, the majority of Canadian consumers responded that they wanted to support local businesses. About ** percent of consumers bought local because they believed that local products were more sustainable and better for the environment.

Comprehensive dataset containing 13 verified Local Shop locations in United Kingdom with complete contact information, ratings, reviews, and location data.

Dive into the world of time series forecasting with the Egg Sales Forecasting Challenge! This dataset originally made for a competition provides a rich dataset detailing 30 years of egg sales from a Sri Lankan shop. ,You'll uncover the nuances of seasonal fluctuations influenced by cultural festivities, global events, and even unexpected lockdowns. Beyond just raw numbers, this dataset tells a story of traditions, market dynamics, and the resilience of businesses. Your mission? Harness this wealth of information to predict the egg sales for 2022. Whether you're a newbie in forecasting or a seasoned data scientist, this challenge offers a unique opportunity to hone your skills."

What's expected? Utilizing the train.csv dataset, which contains three decades of egg sales, participants are tasked with predicting the daily sales for 2022 found in test.csv. Submissions should mirror the format of sample_submission.csv. The accuracy of forecasts will be evaluated using the Root Mean Square Error (RMSE) method."

**Note- This dataset was based on a real dataset presented by a shop, however, this is a simulation and not to be used as real world data.

The dataset includes 5 panoramic scans of the buildings, cultural resources, and other features within the National Park Service boundaries at Roger Williams National Memorial. The scans exist as point cloud files in Trimble Business Center. Each scan has at most 24 panoramic photographs associated with it, depending on whether it was a horizontal band or polygon scan. The points have been colorized based off the panoramic photographs and have been categorized into regions, such as buildings, ground, poles, and trees. There is a layer of points selected from the bottom of doorway thresholds to capture the finished floor elevation data. There is also a layer of points representing the elevation of first floor windows and basement windows. Each point in the layer has elevation data and latitude and longitude data associated with it. The layers are exportable into ArcGIS Pro as point layers, and the data associated with each point layer is exportable as a CSV file. The data was collected in NAD83 (2011) meters UTM Zone 19 and NAVD88. The controller used was a Trimble TSC7 data collector. Control points were set using the BOHA NTRIP base station, an R12i GNSS receiver, and a prism. Scans were completed using a Trimble SX12 scanner that was set so that points would be 2 cm apart at 20 m with a scanning distance of 200 m and a scanning radius of 1 m from the scanner. The scans were performed using the coarse resolution, stored in Trimble Access, and exported to Trimble Business Center.

In 2019, over **** of survey respondents in the United States shopped at pop-up shops in order to find unique products and because they offer unique experiences. Many consumers also went to such stores because they were curious and because they wished to support local and independent businesses. Let’s go to the mall While shopping for goods online is very convenient, many Americans still prefer going to the mall for certain products. In 2018, *** out of ten people preferred going to the mall when shopping for clothes. Many U.S. consumers also enjoyed going to the mall with family and friends, and making a full day of it, accompanied by dinner and entertainment. Shopping during the COVID-19 pandemic Shopping channels for groceries have been impacted severely by the COVID-19 outbreak in 2020: before the pandemic, about ** percent of consumers in the United States would typically shop at super centers and mass stores. Since the outbreak, roughly ** percent of consumers stated they now shop here. Many U.S. consumers even reported that their shopping behavior has permanently changed as a result of COVID-19. *** in five consumers now sanitizes carts and baskets before use, while roughly ** percent stated they now make less store trips in general.

Success.ai’s Retail Data for the Retail Sector in Asia enables businesses to navigate dynamic consumer markets, evolving retail landscapes, and rapidly changing consumer behavior across the region. Leveraging over 170 million verified professional profiles and 30 million company profiles, this dataset delivers comprehensive firmographic details, verified contact information, and decision-maker insights for retailers ranging from boutique shops and e-commerce platforms to large department store chains and multinational franchises.

Whether you’re launching new products, entering emerging markets, or optimizing supply chain strategies, Success.ai’s continuously updated and AI-validated data ensures you engage the right stakeholders at the right time, all backed by our Best Price Guarantee.

Why Choose Success.ai’s Retail Data in Asia?

1. Comprehensive Company Information

   • Access verified work emails, phone numbers, and LinkedIn profiles of retail decision-makers, buyers, and merchandising managers across Asia.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and minimizing wasted outreach efforts.

2. Regional Focus on Asian Markets

   • Includes profiles of small specialty retailers, large department stores, convenience chains, online marketplaces, and luxury brands spanning regions like East Asia, Southeast Asia, and South Asia.
   • Understand region-specific consumer preferences, product trends, and competitive dynamics to guide targeted campaigns and market entries.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, store expansions, new franchise agreements, and shifts in inventory sourcing.
   • Stay aligned with evolving market conditions, shopper behaviors, and regulatory environments impacting the Asian retail sector.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and global privacy regulations, ensuring that your data usage remains compliant and your outreach respects personal boundaries.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, buyers, store managers, and e-commerce directors shaping retail landscapes in Asia.
• 30M Company Profiles: Gain insights into brand portfolios, store counts, revenue ranges, and distribution networks.
• Firmographic & Demographic Data: Understand retail categories, merchandising strategies, supply chain partners, and consumer demographics influencing local markets.
• Verified Decision-Maker Contacts: Connect directly with key stakeholders responsible for purchasing decisions, vendor selection, category management, and brand partnerships.

Key Features of the Dataset:

1. Retail Decision-Maker Profiles
   • Identify and connect with CEOs, CFOs, category buyers, inventory planners, marketing directors, and store operations leaders.

2. Target professionals who determine product assortments, vendor negotiations, store layouts, pricing strategies, and promotional campaigns.

3. Advanced Filters for Precision Targeting

   • Filter by retail segment (fashion, electronics, groceries, cosmetics), country of operation, store format, or omnichannel strategies.
   • Tailor campaigns to align with unique cultural preferences, local consumer spending habits, and regulatory frameworks.

4. AI-Driven Enrichment

   • Profiles are enriched with actionable data, enabling personalized messaging, highlighting market-entry value propositions, and improving engagement outcomes in diverse Asian markets.

Strategic Use Cases:

1. Market Entry & Expansion

   • Identify suitable partners, franchisees, or distribution channels when entering new Asian markets.
   • Benchmark against established players, adapt offerings to local tastes, and secure placements in prime retail locations.

2. Supplier and Vendor Relations

3. Connect with procurement managers and inventory planners evaluating new suppliers or seeking innovative products.

4. Present packaging solutions, POS technology, or loyalty programs to retailers aiming to enhance the shopping experience.

5. Omnichannel and E-Commerce Growth

   • Engage e-commerce managers and digital marketing teams embracing online retail, click-and-collect services, and mobile payment integrations.
   • Align technology solutions with growing demand for contactless shopping, personalized recommendations, and seamless customer journeys.

6. Seasonal and Cultural Campaigns

   • Leverage local holidays, shopping festivals, and cultural events by reaching marketing directors and store managers who coordinate merchandise rotations, promotional deals, and experiential activations.
   • Adapt messaging to align with regional festivities and peak shopping periods.

Why Choose Success.ai?

1. Best Price Guarantee

2. Access top-quality verified data at competitive prices, ensuring strong ROI for product launches, brand expansions, and supply chain optimizations.

3. Sea...

Comprehensive dataset containing 69 verified City Shop locations in United States with complete contact information, ratings, reviews, and location data.

Designated local shopping centres for Hackney. The next level of town centre below District Town Centre category.

Hierarchy of local centres across the city providing a range of shops, services and other related facilities to meet the needs of local communities

Dataset

This dataset was created by Adeyinka Akanbi

Released under CC0: Public Domain

Contents

Replication data for: "Shops and the City: Evidence on Local Externalities and Local Government Policy from Big-Box Bankruptcies"

Financial overview and grant giving statistics of City Thrift Shop

Comprehensive dataset containing 7 verified Do-it-yourself shop businesses in West District, Chiayi City, Taiwan with complete contact information, ratings, reviews, and location data.

Financial overview and grant giving statistics of Iron Workers Local 580 Shop Apprenticeship Training Upgrading Fund

Content

Information on City Store – the Official Story of the City of New York

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Andre Benz on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Financial overview and grant giving statistics of Dane Buy Local Inc