Success.ai delivers unparalleled access to Retail Store Data for Asia’s retail and e-commerce sectors, encompassing subcategories such as ecommerce data, ecommerce merchant data, ecommerce market data, and company data. Whether you’re targeting emerging markets or established players, our solutions provide the tools to connect with decision-makers, analyze market trends, and drive strategic growth. With continuously updated datasets and AI-validated accuracy, Success.ai ensures your data is always relevant and reliable.

Key Features of Success.ai's Retail Store Data for Retail & E-commerce in Asia:

Extensive Business Profiles: Access detailed profiles for 70M+ companies across Asia’s retail and e-commerce sectors. Profiles include firmographic data, revenue insights, employee counts, and operational scope.

Ecommerce Data: Gain insights into online marketplaces, customer demographics, and digital transaction patterns to refine your strategies.

Ecommerce Merchant Data: Understand vendor performance, supply chain metrics, and operational details to optimize partnerships.

Ecommerce Market Data: Analyze purchasing trends, regional preferences, and market demands to identify growth opportunities.

Contact Data for Decision-Makers: Reach key stakeholders, such as CEOs, marketing executives, and procurement managers. Verified contact details include work emails, phone numbers, and business addresses.

Real-Time Accuracy: AI-powered validation ensures a 99% accuracy rate, keeping your outreach efforts efficient and impactful.

Compliance and Ethics: All data is ethically sourced and fully compliant with GDPR and other regional data protection regulations.

Why Choose Success.ai for Retail Store Data?

Best Price Guarantee: We deliver industry-leading value with the most competitive pricing for comprehensive retail store data.

Customizable Solutions: Tailor your data to meet specific needs, such as targeting particular regions, industries, or company sizes.

Scalable Access: Our data solutions are built to grow with your business, supporting small startups to large-scale enterprises.

Seamless Integration: Effortlessly incorporate our data into your existing CRM, marketing, or analytics platforms.

Comprehensive Use Cases for Retail Store Data:

1. Market Entry and Expansion:

Identify potential partners, distributors, and clients to expand your footprint in Asia’s dynamic retail and e-commerce markets. Use detailed profiles to assess market opportunities and risks.

1. Personalized Marketing Campaigns:

Leverage ecommerce data and consumer insights to craft highly targeted campaigns. Connect directly with decision-makers for precise and effective communication.

1. Competitive Benchmarking:

Analyze competitors’ operations, market positioning, and consumer strategies to refine your business plans and gain a competitive edge.

1. Supplier and Vendor Selection:

Evaluate potential suppliers or vendors using ecommerce merchant data, including financial health, operational details, and contact data.

1. Customer Engagement and Retention:

Enhance customer loyalty programs and retention strategies by leveraging ecommerce market data and purchasing trends.

APIs to Amplify Your Results:

Enrichment API: Keep your CRM and analytics platforms up-to-date with real-time data enrichment, ensuring accurate and actionable company profiles.

Lead Generation API: Maximize your outreach with verified contact data for retail and e-commerce decision-makers. Ideal for driving targeted marketing and sales efforts.

Tailored Solutions for Industry Professionals:

Retailers: Expand your supply chain, identify new markets, and connect with key partners in the e-commerce ecosystem.

E-commerce Platforms: Optimize your vendor and partner selection with verified profiles and operational insights.

Marketing Agencies: Deliver highly personalized campaigns by leveraging detailed consumer data and decision-maker contacts.

Consultants: Provide data-driven recommendations to clients with access to comprehensive company data and market trends.

What Sets Success.ai Apart?

70M+ Business Profiles: Access an extensive and detailed database of companies across Asia’s retail and e-commerce sectors.

Global Compliance: All data is sourced ethically and adheres to international data privacy standards, including GDPR.

Real-Time Updates: Ensure your data remains accurate and relevant with our continuously updated datasets.

Dedicated Support: Our team of experts is available to help you maximize the value of our data solutions.

Empower Your Business with Success.ai:

Success.ai’s Retail Store Data for the retail and e-commerce sectors in Asia provides the insights and connections needed to thrive in this competitive market. Whether you’re entering a new region, launching a targeted campaign, or analyzing market trends, our data solutions ensure measurable success.

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BestPlace is an innovative retail data and analytics tool created explicitly for medium and enterprise-level CPG/FMCG companies. It's designed to revolutionize your retail data analysis approach by adding a strategic location-based perspective to your existing database. This perspective enriches your data landscape and allows your business to understand better and cater to shopping behavior. An In-Depth Approach to Retail Analytics Unlike conventional analytics tools, BestPlace delves deep into each store location details, providing a comprehensive analysis of your retail database. We leverage unique tools and methodologies to extract, analyze, and compile data. Our processes have been accurately designed to provide a holistic view of your business, equipping you with the information you need to make data-driven data-backed decisions. Amplifying Your Database with BestPlace At BestPlace, we understand the importance of a robust and informative retail database design. We don't just add new stores to your database; we enrich each store with vital characteristics and factors. These enhancements come from open cartographic sources such as Google Maps and our proprietary GIS database, all carefully collected and curated by our experienced data analysts. Store Features We enrich your retail database with an array of store features, which include but are not limited to: Number of reviews Average ratings Operational hours Categories relevant to each point Our attention to detail ensures your retail database becomes a powerful tool for understanding customer interactions and preferences. Geo-Analytical Factors Each store in your database is further enhanced with geo-analytical data. We analyze: Maximum pedestrian and vehicle traffic within a defined radius Number of households and average income within the catchment area vicinity Number of schools, hospitals, universities, competitors, stores, bars, clubs, and restaurants in the surrounding area Point attendance based on mobile device location data (ensuring GDPR compliance) Our refined retail data collection and analysis provides detailed shopping behavior insights, leading to in-depth shopper analytics and retail foot traffic data that support strategic planning and execution. The Power of Points of Interest (POI) Data At BestPlace, we harness the power of Point of Interest (POI) data (to bring you the most complete retail data set.) to bring your retail data to life. Our POI data collection process involves analyzing and categorizing foot traffic data, providing a comprehensive foot traffic dataset as a result. This data allows you to understand the ebb and flow of individuals around your store locations, suggesting invaluable insights for strategic planning and operational efficiency. Leveraging GIS Data Our GIS data collection process is meticulous and comprehensive. We tap into multiple GIS data sources, providing a wealth of data to enhance your retail analytics. This process allows us to equip your database with a broad range of geospatial features, including demographic and socioeconomic information from various census data for GIS applications. By including GIS data in your analysis, you gain a multi-dimensional perspective of your retail landscape, allowing for more strategic decision-making. The Advantages of Census Data BestPlace grants you direct access to a wealth of census data sets. This transforms your retail database into a more potent tool for decision-making, providing a deeper understanding of the demographics and socioeconomic factors surrounding your store locations. With the ability to download census data directly, you can enrich your retail data analysis with valuable insights about potential customers, giving you the upper hand in your strategic planning. Extensive Use Cases BestPlace's capabilities stretch across various applications, offering value in areas such as: Competition Analysis: Identify your competitors, analyze their performance, and understand your standing in the market with our extensive POI database and retail data analytics capabilities. New Location Search: Use our rich retail store database to identify ideal locations for store expansions based on foot traffic data, proximity to key points, and potential customer demographics. Location Comparison: Compare multiple store locations based on numerous factors and make informed decisions about where to focus your resources. Distribution Optimization: Leverage our FMCG data analytics and retail traffic analytics to optimize your distribution strategy and maximize ROI. Building Machine Learning Models: Integrate our all-purpose machine learning models into your business decision processes to enable more efficient and effective decision-making. (Integrate our all-purpose machine learning models to build your own in-house solutions with the help of our data.) Comprehensive Deliverables As a BestPlace client, you receive a comprehensive produc...

The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

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.

Graph and download economic data for Retailers Inventories (RETAILIMSA) from Jan 1992 to Jan 2025 about inventories, retail, and USA.

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must take its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

This dataset shows the location of retailers that are allowed to sell alcoholic beverages in Ontario. It is based on licences issued by the Alcohol and Gaming Commission of Ontario.

Variables included in the dataset include:

• name of store
• address
• city/town
• postal code
• GPS coordinates (latitude and longitude)
• products the store is authorized to sell (for example, beer, wine, cider)

Graph and download economic data for Retail Sales: Pharmacies and Drug Stores (MRTSSM44611USN) from Jan 1992 to Jan 2025 about medicines, retail trade, sales, retail, and USA.

At Echo, our dedication to data curation is unmatched; we focus on providing our clients with an in-depth picture of a physical location based on activity in and around the point of interest (POI) over time. Our dataset empowers you to explore the cross-shopping patterns from your visitors by allowing you to dig deeper into consumer profiles, eliminate gaps in your trade area and discover untapped sites of action.

This sample of our Market Analysis solution helps you determine the geographical reach of your store or facility based on the brands or categories most visited by consumers who visit your specific POI. This empowers your location strategy. This particular dataset is for Europe.

Additional Information:

• Understand the actual movement patterns of consumers without using PII data, gaining a 360-degree consumer view. Complement your online behavior knowledge with actual offline actions, and better attribute intent based on real-world behaviors.
• Echo collects, cleans and updates its footfall on a daily basis. Normalization of the data occurs on a monthly basis.
• We provide data aggregation on a weekly, monthly and quarterly basis.

• Information about our country offering and data schema can be found here:

  1) Data Schema: https://docs.echo-analytics.com/activity/data-schema 2) Country Availability: https://docs.echo-analytics.com/activity/country-coverage 3) Methodology: https://docs.echo-analytics.com/activity/methodology

  Echo's commitment to customer service is evident in our exceptional data quality and dedicated team, providing 360° support throughout your location intelligence journey. We handle the complex tasks to deliver analysis-ready datasets to you.

Business Needs: - Site Selection and Lease Renegotiation: Leverage foot traffic data for optimal site selection and advantageous lease renegotiations. This approach enables you to pinpoint ideal store locations and secure lease terms that align with business objectives, optimizing operational efficiency and cost-effectiveness.

-Market Intelligence: Outsmart your competition by understanding competitor foot traffic trends, allowing you to identify growth opportunities and gain a competitive advantage. Analyze regional consumer behaviors and preferences to pinpoint new markets and assess the competitive landscape for strategic expansion.

Graph and download economic data for Retail Sales: Beer, Wine, and Liquor Stores (MRTSSM4453USN) from Jan 1992 to Jan 2025 about spirits, wine, beer, retail trade, sales, retail, and USA.

In the fast-paced world of hospitality, data is essential for success. Our Global Bar & Restaurant POI database offers in-depth information on the locations of the world's top bars and restaurants, providing businesses with a powerful tool for strategic decision-making. Whether you're a restaurant chain, a marketing agency, or a hospitality researcher, our Global Bar & Restaurant database is a valuable resource for making informed decisions.

What You'll Find in the Database:

-Precise Location Data: Full address, geographic coordinates, and neighborhood details for each establishment.

-Establishment Information: Official name, unique identifier, and type of establishment (bar, restaurant, café, fast-food chain, etc.).

-Operational Status: Whether the establishment is currently open or closed.

-Date Established: Historical context for trend analysis.

-Data Confidence Level: A rating indicating the accuracy of the information.

How You Can Use This Database:

-Market Analysis: Assess the distribution and density of bars and restaurants globally.

-Site Selection: Identify promising locations for new establishments based on demographics and competition.

-Targeted Marketing: Reach customers near specific establishments with personalized offers.

-Competitive Intelligence: Understand the landscape and identify rivals' strategies.

-Supply Chain Optimization: Streamline logistics based on the distribution of your target establishments.

Abstract

This research was conducted in Vietnam between June 2009 and January 2010 as part of the Enterprise Survey initiative.

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Vietnam was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into 6 manufacturing industries, 1 services industry -retail -, and two residual sectors. Each manufacturing industry had a target of 160 interviews. The services industry and the two residual sectors had a target of 120 interviews. For the manufacturing industries sample sizes were inflated by about 33% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. An additional 85 interviews were added to the survey half way through the fieldwork. Targets were adjusted such that the manufacturing sectors' targets were increased to 160-180 interviews.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in five regions containing 14 provinces: Red River Delta (Hanoi, Ha Tay, Hai Duong, and Hai Phong), the North Centre Coast (Thanh Hoa, Nghe An), Mekong River Delta (Can Tho, Long An, Tien Giang), South Centre Coast (Khanh Hoa, Da Nang) and South East (Ho Chi Minh City, Binh Duong, Dong Nai).

Two frames were used for Vietnam. The sample frame containing fresh contacts used in the Vietnam was obtained from the 2008 Vietnam General Statistics Office. A frame containing firms that had participated in the 2005 survey constituted a second frame of panel contacts. Each database contained the following information: -Name of the firm -Location -Contact details -ISIC code -Number of employees.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 23% (734 out of 3131 establishments). Breaking down by industry, the following numbers of establishments were surveyed: 15 (Food) - 127, 17 (Textiles) -120, 18 (Garments) - 120, 26 (Non-metallic mineral products) - 123, 28 (Metal & Fabrication) - 122, Other manufacturing - 196, Retail & IT - 128, Other services - 117.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The “Core Questionnaire” is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments- the “Core Questionnaire + Manufacturing Module” and the “Core Questionnaire + Retail Module.” The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Vietnam Implementation 2009" in "Technical Documents" folder.

SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 52M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

Supermarkets and grocery store outcomes have been a tale of dealing with volatile prices at their purchase and sales points. The continued expansion of Aldi and Amazon has forced the two established industry giants, Woolworths and Coles, to remain price-competitive on both the physical store and online service fronts. To differentiate themselves from low-cost supermarkets, Coles and Woolworths have leant into attracting customers with convenient locations and expanded online shopping capabilities. These supermarket giants also rely on loyalty programs and promotions. Coles and Woolworths have displayed interest in data analytics, strengthening their relationships with analytics firms like Palantir to optimise their marketing and operational processes. The ACCC and Treasury have taken the lead on addressing supplier and customer concerns relating to deceptive discounting practices and supplier contract bargaining exploitation. Supermarket and grocer revenue rose significantly following the COVID-19 outbreak. Household expenditure shifted towards retail industries amid restrictions on many services industries, with this imbalance remaining as high costs limit eating out. A combination of panic buying, along with the suspension of many specials and promotions in supermarkets, boosted grocery turnover at the beginning of the period, spiking revenue for 2019-20. This high benchmark at the start of the period has resulted in an industry correction and an annualised revenue decline of 0.6% to $148.7 billion over the five years to 2024-25. However, stores have largely managed to pass on upstream costs to customers, steadying their profit margins while suppliers and consumers bear the brunt of inflation-driven costs. Revenue is estimated to climb by 0.2% in 2024-25, reflecting the price-driven industry growth more indicative of the overall revenue trend that was drowned out by the pandemic revenue spike and correction. Supermarkets and grocery stores are set to continue performing well with industry revenue slated to climb at an annualised 0.4% over the five years through 2029-30 to $142.8 billion. Population growth and stubborn inflationary pressures, despite rate hikes, are set to keep store prices inching upwards. The results of the Treasury and the ACCC's investigations will shine a light on new regulations and potential penalties in store for large supermarkets. Eventually, when inflationary pressures subside and consumer sentiment returns to a positive level, supermarkets and grocers will be well-positioned to take advantage of consumer appetite for value-added and premium goods. Strong growth in online sales is set to continue.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

The Vector Database Market is projected to grow at 23.7% CAGR, reaching $7.13 Billion by 2029. Where is the industry heading next? Get the sample report now!

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Alternative Data Market Size 2025-2029

The alternative data market size is forecast to increase by USD 60.32 billion at a CAGR of 52.5% between 2024 and 2029.

The market is experiencing significant growth due to the increased availability and diversity of data sources. This trend is driven by the rise of alternative data-driven investment strategies, which offer unique insights and opportunities for businesses and investors. However, challenges persist in the form of issues related to data quality and standardization. big data analytics and machine learning help businesses gain insights from vast amounts of data, enabling data-driven innovation and competitive advantage. Data governance, data security, and data ethics are crucial aspects of managing alternative data.
As more data becomes available, ensuring its accuracy and consistency is crucial for effective decision-making. The market analysis report provides an in-depth examination of these factors and their impact on the growth of the market. With the increasing importance of data-driven strategies, staying informed about the latest trends and challenges is essential for businesses looking to remain competitive in today's data-driven economy.

What will be the Size of the Alternative Data Market During the Forecast Period?

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Alternative data, the non-traditional information sourced from various industries and domains, is revolutionizing business landscapes by offering new opportunities for data monetization. This trend is driven by the increasing availability of data from various sources such as credit card transactions, IoT devices, satellite data, social media, and more. Data privacy is a critical consideration in the market. With the increasing focus on data protection regulations, businesses must ensure they comply with stringent data privacy standards. Data storytelling and data-driven financial analysis are essential applications of alternative data, providing valuable insights for businesses to make informed decisions. Data-driven product development and sales prediction are other significant areas where alternative data plays a pivotal role.
Moreover, data management platforms and analytics tools facilitate data integration, data quality, and data visualization, ensuring data accuracy and consistency. Predictive analytics and data-driven risk management help businesses anticipate trends and mitigate risks. Data enrichment and data-as-a-service are emerging business models that enable businesses to access and utilize alternative data. Economic indicators and data-driven operations are other areas where alternative data is transforming business processes.

How is the Alternative Data Market Segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Credit and debit card transactions
  Social media
  Mobile application usage
  Web scrapped data
  Others


End-user

  BFSI
  IT and telecommunication
  Retail
  Others


Geography

  North America

    Canada
    Mexico
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    India
    Japan


  South America



  Middle East and Africa

By Type Insights

The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.

Alternative data derived from card and debit card transactions offers valuable insights into consumer spending behaviors and lifestyle choices. This data is essential for market analysts, financial institutions, and businesses seeking to enhance their strategies and customer experiences. The two primary categories of card transactions are credit and debit. Credit card transactions provide information on discretionary spending, luxury purchases, and credit management skills. In contrast, debit card transactions reveal essential spending habits, budgeting strategies, and daily expenses. By analyzing this data using advanced methods, businesses can gain a competitive advantage, understand market trends, and cater to consumer needs effectively. IT & telecommunications companies, hedge funds, and other organizations rely on web scraped data, social and sentiment analysis, and public data to supplement their internal data sources. Adhering to GDPR regulations ensures ethical data usage and compliance.

Get a glance at the market report of share of various segments. Request Free Sample

The credit and debit card transactions segment was valued at USD 228.40 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 56% to the growth of the global market during the forecast period.

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Philippines BC: Wholesale & Retail Trade; Repair of Motor Vehicles & Motorcycles data was reported at 203.000 Unit in 2015. This records a decrease from the previous number of 254.000 Unit for 2014. Philippines BC: Wholesale & Retail Trade; Repair of Motor Vehicles & Motorcycles data is updated yearly, averaging 254.000 Unit from Dec 2013 (Median) to 2015, with 3 observations. The data reached an all-time high of 390.000 Unit in 2013 and a record low of 203.000 Unit in 2015. Philippines BC: Wholesale & Retail Trade; Repair of Motor Vehicles & Motorcycles data remains active status in CEIC and is reported by Department of Labor and Employment - Bureau of Local Employment. The data is categorized under Global Database’s Philippines – Table PH.O007: Business Closure/Retrenchment.