Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases:

360-Degree Customer View:Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment:Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing:Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori Consumer Data graph you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

PurposeThis study examined the impacts of customer knowledge management and flow experience on customer value co-creation and the mediating role of flow experience in the context of fitness apps.Design/methodology/approachUsing the questionnaire star platform to edit the questionnaire and collect data(n = 450). A structural equation modeling test was conducted to examine the relationships between the variables.FindingsThe findings reveal that in a fitness app service scenario, customer knowledge management has a significant positive impact on customer flow experience, customer flow experience has a significant positive impact on customer value co-creation, and customer flow experience plays a partial mediating role in the path from customer knowledge management to customer value co-creation.Practical implicationsThe results could help fitness-app-related enterprises or service organizations understand the factors influencing and processes of customer participation in value co-creation and thus could help such enterprises and organizations formulate effective marketing strategies to realize customer value co-creation and ultimately to achieve their development goals.Originality/valueUsing value co-creation theory and customer-dominant logic, this study analyzed the effects of customer knowledge management, flow experience, and customer value co-creation in the context of fitness apps and examined the mediating role of flow experience. The findings fill a gap in the theoretical research regarding customer value co-creation in the context of fitness apps and expand the scope of research on customer knowledge management and flow experience.

Overview socio-demographics study sample of the control study (percentages).

Geodemographic Segmentation Data from Caliper Corporation contain demographic data in a way that is easy to visualize and interpret. We provide 8 segments and 32 subsegments for exploring the demographic makeup of neighborhoods across the country.

ObjectiveThis study aims to examine the impact of e-WOM on customer purchase intentions in Facebook fan pages using theories of trust, value co-creation and brand attitude. The present research has set out to explore this emerging domain of study and has thus developed & tested propositions which attempt to establish a relationship between e-WOM and customer‘s purchase intentions. A deeper understanding of this possible association is obtained by studying the mediating roles of Trust, Value Co-Creation, Brand Image and Brand Attitude.MethodologyThe context for exploring this phenomenon is chosen to be the fan pages of smartphone brands on Facebook. The study involved conducting a sample survey of 490 respondents, comprising of both male and female, who belong to 5 smartphone brands Facebook fan pages–Samsung, Moto G, Lenovo, MI and ASUS are considered for the study. Out of which sample of 100 each has been targeted individuallyFindingsThe findings suggested that e-WOM significantly predicts the purchase intentions of the customers of a specific product and considerable impacted on the purchase decision. The findings of the study also reveal that customer ‘s trust beliefs, perceived value co-creation, brand image and brand attitude partially mediate in between relationships of e-WOM and purchase intentionConclusionThe actual presence of different types of consumer electronics brands on the social media, more prominently, the smartphones, which undoubtedly are the most ubiquitous product of this segment. In fact, this indicates that presence on social media is a well- thought organizational strategy developed by companies to gain partial control over the customer ‘s decision- making process by establishing a close connect with the customers for a long period.ImplicationThis consequence will significantly impact the decision-making process of marketers or practitioners in relation to their marketing tactics. This research also indicates that marketers could devise more effective methods for distributing marketing content through social networking sites, while corporations can cultivate favorable electronic word-of-mouth for their products or services. Through the implementation of social media marketing strategies, companies can increase their sales volume and generate higher revenue. The study examined the role of trust, virtual community participation, and desire to purchase as mediators on smartphone brand fan sites on Facebook. It was observed that these factors had a partial influence on customer purchase intention.

Abstract

The EPC-Customer Satisfaction Survey 2014 collected information to obtain and establish a baseline for customer's satisfaction on the EPC services and to identify the areas of the corporation's services that need improvement. The CSS results are planned to provide updated information to design new strategies for improving the services of the corporation. The overall outcome of the CSS 2014 is to assist and recommend relevant strategies to improve and upgrade the service of the EPC to its clients.The Customer Satisfaction Survey 2014 was conducted on the domestic or household level as well as all the other types of customers registered with the corporation.

Geographic coverage

National Regional

Analysis unit

EPC customers such as domestic, commercial, school, religion, government, industrial, hotels

Universe

EPC users or customers

Kind of data

Sample survey data [ssd]

Sampling procedure

There were seven types of customers, namely: domestic, commercial, religion, school, government, hotel and industrial in the EPC frame or their list of population which was given to SBS for sampling selection. It took several months for both parties to sort the list of registered customers with the corporation especially the domestic clients, so that they can be easily searched and identified during the field work or data collection period, therefore the SBS offered it list of households as part of domestic customers for the EPC to avoid the delay with the survey timeframe.

The total number of households with SBS was 26,205 which were counted from the latest census of population and housing 2011. Out of that total households with SBS, 25,262 or 96percent of households were with electricity. The total number of customers proposed by the corporation was about 200 in which 100 from the domestic and 80 from the other types of customers, however to accommodate the non response cases, the SBS increased the sample size to 250 in which 150 were from household or domestic customers and 100 from other types of customers.

Household/domestic sample

The sample of domestic customers for the CSS 2014 was drawn from the master sample frame of the list of occupied households compiled in the most recent Population and Housing Census 2011. The sample size was based on a 95 percent confidence interval of ± 5 percent margin of error. This means that if the survey found that 50 percent of respondents were satisfied with induction meter services of EPC, we could be 95 percent sure of getting the same result had we interviewed everyone in the population give or take 5 percent. An 80 percent response rate and a design-effect of 1.2 was used to allow for clustering of the complex design. After taking into account all those features, it resulted in the required sample size of 150 selected households.

In national statistical surveys, the region of Apia Urban Area (AUA) represented the urban population while the regions of North West Upolu (NWU), Rest of Upolu (ROU) and Savaii represented the rural population. Therefore in order to achieve the sample size of 150 for the domestic customers, a representative probability sample of households was selected in two stages.

The first stage involved the selection of clusters or enumeration area (EAs) from the master sample frame using stratified systematic sampling with probability proportional to size. A total of 30 primary sampling units or clusters were selected in which 6 clusters were from the urban areas and 24 clusters were selected from the rural areas. The design did not allow for replacement of clusters or households.

In the second stage, a total of 5 households were selected from each cluster using systematic equal probability selection. Normally an updated household listing from selected clusters could have been done to select 5 households. However, due to the delay in sorting of customers list and it was towards the end of the year, and the fact that the census 2011 was just completed in the previous three years, it was seen not necessary to conduct a fresh household listing which would have taken SBS another two months to carry out causing delay to the survey.

Other Types of Customers

The sample for the CSS other types of customers such as commercial, religion, school, government, hotel and industrial was drawn from the master sample frame of the list of all the 3767 customers registered with the EPC . The commercial type has 2587 customers, religion with 751customers, school with 229 customers, governments with 118 customers, hotels with 75 customers and industries with 47 customers .The sample size was based on a 95 percent confidence interval of ±5 percent margin of error, assuming an 80 percent response rate. To achieve a representative probability sample, the systematic method was used to select the 100 customers of other 6 types apart from the domestic customers.

Mode of data collection

Face-to-face [f2f]

Research instrument

A structured English questionnaire was prepared by the EPC team to collect the feedback from the corporation's customers. However, SBS made some improvements in terms of instructions between questionnaire sections in order to make the interviewing flow properly from beginning to end. The questionnaire was also translated into the Samoan language to complement the English questionnaire so that the interpretation of questions by the field enumerators was consistent in the field. A cover page of the questionnaire was also developed so that selected customer's identifications were clearly noted. The options for the survey status were also listed to account for non-coverage of EPC customers during the fieldwork. The Survey Questionnaire consists of four sections with a cover page in the beginning for the Identification of selected households and other types of customers. Section A has seven questions about the type of meters the customers used and the service provided by EPC to pay bills and buy cash power units, and open questions to state some reasons why the customers were not satisfied with the service given by the EPC, areas of paying of electricity bills and selling cash power units. Section B contains five questions on the management of complaints lodged to the corporation and the satisfaction of service provided. Section C asked two types of questions in which one was a rating question on the perception of the customers of the EPC service, and the second was a ranking question of the mediums that the public used to get EPC public awareness. Section D was open for the customers to list any of their comments about the service of EPC for improvement.

Cleaning operations

After coding, the computer data program was created using CSPro 5 software for data entry. After testing the program, the data entry was conducted in one week (March 24th-28th). The data editing, cleaning and weighting of the data took another two weeks (April 1st-11th) to complete, leaving three weeks (April 14th - May 2nd) to analyse and write the analysis report to meet the deadline.

Data editing was done using writing option in CSPro 5.0.

Response rate

A total of 150 households were selected to represent the domestic customers and 139 households were occupied during the field work period. Of the occupied households only 133 were successfully interviewed resulting in a household response rate of 95.7 percent. The 6 households which were selected but not able to answer the questionnaire because of they had no access to electricity during the survey period; most of them were in the island of Savaii.

For other types of EPC customers in which 100 were selected, only 97 customers were found during the survey period. From these customers, 94 were able to complete the survey while the others were no longer operating due to the following reasons: one was destroyed by tsunami, another changed its customer type, and the last was not in the location previously identified in the list of sample respondents.

This is explained in the final analytical report.

Sampling error estimates

Any survey will be affected by sampling errors and non-sampling errors. The latter is difficult to measure but can be greatly reduced by the application of high quality survey management, efficient field supervisions, skilful enumerators, good control of data coding and data processing, sufficient resources, etc. Sampling errors are usually calculated using relevant sampling estimation formulae and computer programs. For the CSS 2014, the variance formula for complex design was used to calculate sampling errors. Dr Ren Ruilin of ICF Macro developed specific sampling error estimation templates in Excel for use by developing countries like Samoa where expensive computer programs like SAS could not be purchased. The Excel templates used the Taylor linearization method of variance estimation for survey estimates like means and proportions. The design effect (DEFT) for each estimate was also calculated whereby a DEFT value of 1.0 indicates that the complex design used was just as efficient as the simple random sampling and a value more than 1 indicates an increase in sampling error due to the design and vice versa. In addition, the confidence limits of 95 percent can also be estimated for each variable which provides the range of values for which the true value falls.

Details of sampling errors are presented in the sampling errors appendix of the report.

Demographic characteristics, health and functional status of home care clients comparing those with and without DSI across multiple countries.

The American Customer Satisfaction Index (ACSI) score of the e-commerce website of Amazon.com has fluctuated since 2000. In 2024, the customer satisfaction score of the online retailer was 83 out of 100 ASCI points. Popularity contest Amazon is one of the most popular marketplaces worldwide. In April 2023, the U.S. domain for Amazon ranked the most visited e-commerce and shopping website by share of online visits, with around 13 percent. Ebay came in second with roughly three percent of the visit share, and the Japanese site amazon.co.jp came in third with 2.66 percent. In the same month, global online shoppers visited amazon.com around 2.2 billion times. Why Amazon? Amazon.com is the most used e-commerce website in the world, and in the U.S., the website is far ahead of its competitors. With a significant difference in website visitors of almost 45 percent, ebay.com is second to amazon.com. Furthermore, the retail giant Walmart trails behind with an online visit share of roughly six percent. Amazon is used for various reasons by its customers. For example, the online marketplace is ranked as the leading platform for product research in the U.S., surpassing even search engines in popularity. Low shipping costs, fast deliveries, and affordable product prices are the main reasons for shopping on Amazon.

Baseline socio-demographic and clinical characteristics of 326 participants at Princess Diana Memorial Health Centre IV, Soroti District, April to June 2018.

These data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. This dataset has two layers and includes both the weighted data and unweighted data. Weighting data is a statistical method in which datasets are adjusted through calculations in order to more accurately represent the population being studied. The weighted data are used in the final published PDF report.These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Community Survey results are used as indicators for several city performance measures. The summary data for each performance measure is provided as an open dataset for that measure (separate from this dataset). The performance measures with indicators from the survey include the following (as of 2023):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethods:The survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. Processing and Limitations:The location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city. The weighted data are used by the ETC Institute, in the final published PDF report.The 2023 Annual Community Survey report is available on data.tempe.gov or by visiting https://www.tempe.gov/government/strategic-management-and-innovation/signature-surveys-research-and-dataThe individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.Additional InformationSource: Community Attitude SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

Envestnet®| Yodlee®'s Retail Sales Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

*Significant difference between phases at p

Demographics of dogs of the study sample.

Dog Owners’ Demographics of the study sample.

Key Features: • Enriches CRM and first-party data with verified demographic attributes • Supports both hashed and unhashed email formats • Privacy-compliant and sourced from permission-based datasets • Coverage available across key APAC markets

Use Cases: • Enhance customer profiles with age, gender, and lifestyle indicators • Build detailed personas for refined audience segmentation • Power personalization engines with enriched user data • Boost acquisition and retention strategies with smarter targeting

Key Attributes Available (varies by region): • Age • Gender • Location (City, State, Country) • Household Composition • Income Bracket • Interests & Lifestyle Indicators

Data Format: Hashed (SHA-256) & Unhashed Emails

Data Delivery: SFTP

Perfect For: • CRM Managers • Data & Analytics Teams • Marketing Automation • Ad Tech / Martech Providers • Media Agencies

Selected social and demographic characteristics of the analysis sample.

Descriptive characteristics of the study participants by socio-demographic and depression and/or anxiety (n = 16,901).

Multilevel analysis of variables associated with depression and/or anxiety among DHS of Kenya, 2022.