Introduction

Shopping Local Statistics: In 2024, according to the trend, shopping in the local market keeps growing momentum, giving priority to community, sustainability, and customised services. From farmers' markets to family-owned shops, in the U.S. and worldwide, renewed encouragement is put forth to small local businesses. Such online development has enabled these local shops to contend with large digital marketplaces.

Thus, this article deals with the latest shopping local statistics for 2025, along with consumer behaviour, and prevailing issues that small businesses must yet resolve.

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.

Local shopping has fluctuated over the past 12 years in the UK. In 2010, the sales revenue for local shops was measured at about 2.33 billion British pounds. Revenue peaked in 2020 but dropped to 2.58 billion British pounds in 2022.

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.

A survey in the UK revealed that during the COVID-19 lockdown in 2020, ** percent of UK consumers already purchased more from local shops in comparison to before lockdown. That number rose slightly to ** percent of consumers who want to buy more local products in the future. Only *** percent of UK consumers want to buy locally less often in the future.

This dataset provides comprehensive local business and point of interest (POI) data from Google Maps in real-time. It includes detailed business information such as addresses, websites, phone numbers, emails, ratings, reviews, business hours, and over 40 additional data points. Perfect for applications requiring local business data (b2b lead generation, b2b marketing), store locators, and business directories. The dataset is delivered in a JSON format via REST API.

Local Shopping Centres contain small scale facilities to meet local, day to day shopping needs. Areas are recorded as polygons

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

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...

The statistic depicts the results of a survey conducted in November 2012 by A.T. Kearney concerning the trust U.S. consumers have in different grocery formats to deliver local food, ranked on a 1-to-10 scale with 10 as most trustworthy. Farmers markets were the most trusted to deliver local foods, with a score of 8.2 out of 10.

The statistic depicts the results of a survey conducted in October 2015 by A.T. Kearney concerning the reasons why U.S. shoppers do not buy local groceries. The survey was conducted online among more than 1,500 U.S. shoppers who are primarily responsible for the food shopping or indicated to share the food shopping responsibility in their household. Some 27 percent of respondents said that local products are simply not available at their favorite retailer.

Abstract copyright UK Data Service and data collection copyright owner.

The Prices Survey Microdata include the underlying price data used by the Office for National Statistics (ONS) to produce the Consumer Prices Index (CPI), the Retail Prices Index (RPI) and associated price indices. The CPI has become the main domestic measure of inflation for macroeconomic purposes in the UK. Since December 2003 it has been used for the inflation target that the Bank of England is required to achieve. The RPI is the most long-standing measure of inflation in the UK, and its uses have included the indexation of pensions, state benefits and index-linked gilts. The study also includes the data underlying the Producer Prices Index.

There are four levels of sampling for local price collection: locations/shopping areas; outlets/shops within locations; representative items/goods and services; and products and varieties (price quotes).

There are two basic price collection methods: local and central. Local collection is used for most items; prices are obtained from outlets in about 150 locations around the country. Some 110,000 quotations are obtained by this method. Normally, collectors must visit the outlet, but prices for some items may be collected by telephone. Central collection is used for items where all the prices can be collected centrally by the ONS with no field work. These prices can be further sub-divided into two categories, depending on their subsequent use: 1) central shops, where the prices are combined with prices obtained locally, and 2) central items, where the prices are used on their own to construct centrally calculated indices. There are about 130 items for which the prices are collected centrally.

The retail price data include the locations containing the shopping outlets from which the price quotes were obtained. These locations are intended to be broadly representative of a central shopping area and the areas where the local shopping population tend to live. The data also include the regions in which those shopping areas are located.

Linking to other business studies
The producer prices data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest edition information
For the thirty-fifth edition (May 2024), monthly Item Indices and Price Quotes data files for January to March 2024 have been added to the study.

Main Topics:

The Prices Survey Microdata include both retail and producer prices. The retail data include the following files:

• 'backdata' or background information files
• locally collected files
• centrally collected item files
• item indices files
  
• price quote files
• Classification of Individual Consumption by Purpose (COICOP) level maps and weights files
  

The 'backdata' background information files include:

• COICOP descriptions and identification codes
• descriptions and identification codes for each item (goods and services)
• location descriptions and identification codes (and the region of the UK)
• shop codes for each item and location

The pre-2007 data also include postcodes for the shops.

The retail prices data span from 1996 to 2009 (centrally collected item indices), from 1996 to 2013 (annual item indices), from 1st quarter 1996 to 3rd quarter 2016 (quarterly item indices and quarterly price quote data), from October 2016 to July 2023 (monthly item indices and monthly price quote data), and from 1996 to 2013 (locally collected data).

The producer prices files span from 1998 to 2021 and include:

• item and index number codes
• Inter-Departmental Business Register Reporting Unit reference numbers, allowing the data to be matched to other ONS business survey data
• index descriptions
• prices for each item/index number

Additional producer prices files spanning 1996 to 2019 provide only the Producer Prices Indices and summary tables.

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.

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

Potential Applications of the Dataset:

1. Geospatial Information: Precise geographical coordinates for each Walgreens store, enabling accurate mapping and spatial analysis. State-wise and city-wise breakdown of store locations for a comprehensive overview.

2. Store Details: Store addresses, including street name, city, state, and zip code, facilitating easy identification and location-based analysis. Contact information, such as phone numbers, providing a direct link to store management.

3. Operational Attributes: Store opening and closing hours, aiding businesses in strategic planning and market analysis. Services and amenities are available at each location, offering insights into the diverse offerings of Walgreens stores.

4. Historical Data: Historical data on store openings and closures, providing a timeline perspective on Walgreens' expansion and market presence.

5. Demographic Insights: Demographic information of the areas surrounding each store, empowering users to understand the local customer base.

6. Comprehensive and Up-to-Date: Regularly updated to ensure the dataset reflects the latest information on Walgreens store locations and attributes. Detailed data quality checks and verification processes for accuracy and reliability.

The dataset is structured in a flexible format, allowing users to tailor their queries and analyses based on specific criteria and preferences.

China Shopping Mall Development Index: City Type data was reported at 68.600 % in Sep 2018. This records an increase from the previous number of 66.300 % for Jun 2018. China Shopping Mall Development Index: City Type data is updated quarterly, averaging 66.100 % from Dec 2016 (Median) to Sep 2018, with 8 observations. The data reached an all-time high of 69.300 % in Mar 2017 and a record low of 62.700 % in Dec 2016. China Shopping Mall Development Index: City Type data remains active status in CEIC and is reported by Ministry of Commerce. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HSA: Shopping Mall Development Index.

From the ************* to ************, a survey asked UK consumers whether they were actively trying to support local businesses more. The results show that fewer people were trying to support local businesses, with a decrease from ** percentage points in the first week from ************* to ** percent of respondents as of ************.

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

Information on CityStore, the products available, prices and product descriptions.

These statistics relate only to sales by local authorities under the Right to Buy scheme and exclude sales by Private Registered Providers (PRPs) under preserved Right to Buy. Sales by PRPs are recorded in Social Housing Sales