Scale your HR Tech, B2B Sales, and Market Intelligence engines with the world’s most comprehensive database of public professional profiles. This dataset offers a structured, historical view of the global workforce, capturing the career trajectories, educational backgrounds, and skill sets of over 830,042,175 professionals across 190+ countries.

Unlike static contact lists, our Professional Identity Graph provides a dynamic view of an individual's career. We map the relationships between People, Companies, Skills, and Schools, allowing you to answer complex questions about talent migration, skill supply, and organizational hierarchies.

All profiles are matched to a public Linkedin URL

Key Use Cases 1. B2B Data Enrichment & CRM Hygiene Turn a simple email address or name into a full 360-degree prospect profile.

Append: Add currentCompanies, jobTitle, and industry to your existing leads.

Lead Scoring: Use connectionsCount and recommendations as proxies for influence and decision-making power.

Refresh: Identify when a prospect has changed jobs (lastUpdated) to trigger "New Role" outreach campaigns.

1. Talent Intelligence & Recruitment Build the next generation of hiring tools.

Sourcing: Query by complex skill combinations (e.g., "Python" + "TensorFlow" + "5 Years Experience").

Alumni Targeting: Use educations data to find candidates from target universities.

DEI Analytics: Leverage pronoun and volunteerExperiences data for diversity and inclusion benchmarking.

1. Investment & Labor Market Analysis

Headcount Growth: Track currentCompanies vs. previousCompanies to measure company growth or attrition rates in real-time.

Skill Trends: Analyze the rise of specific skills (e.g., "Generative AI") across specific industries or regions.

Data Dictionary & Schema Attributes Our schema is normalized for easy ingestion. We provide over 30 rich attributes per profile, grouped into five core intelligence clusters:

1. Identity & Social (The "Who")

publicId / vanity: The unique handle for the profile (e.g., /in/john-doe).

urn: The immutable, system-unique identifier.

fullName, firstName, lastName: Parsed name fields.

headline & summary: The professional's self-described bio and taglines.

pronoun: Self-identified pronouns (he/him, she/her, etc.).

logoUrl: Profile image link.

openToWork: Indicator of active job-seeking status.

1. Professional Graph (The "What")

currentCompanies: Detailed object containing Company Name, Title, Start Date.

previousCompanies: Historical array of past roles, creating a full resume view.

industry: Standardized industry classification.

1. Capability & Skills (The "How")

skills: Array of endorsed skills (e.g., "Project Management", "SQL").

languages: Spoken languages and proficiency levels.

certifications: Professional licenses and validity dates.

courses & honors: Academic and professional awards.

educations: Full academic history including Degree, School, and Dates.

1. Influence & Content (The "Reach")

connectionsCount: Total network size.

followersCount: Measure of audience reach.

recommendations: Text of received professional endorsements.

organizations: Memberships in professional bodies or non-profits.

patents, projects, publications: Intellectual property and portfolio items.

1. Location & Metadata

locationName: City/Metro area (e.g., "Greater New York City Area").

locationCountry: ISO-2 Country Code.

lastUpdated: Timestamp of the most recent data refresh.

fullName: 830042175 - Fill Rate: 99.99% firstName: 830023323 - Fill Rate: 99.98% lastName: 822995392 - Fill Rate: 99.14% publicId / vanity: 830159658 - Fill Rate: 100% urn: 830159658 - Fill Rate: 100% headline: 829660649 - Fill Rate: 99.94% summary: 154826408 - Fill Rate: 18.65% industry: 569584072 - Fill Rate: 68.61% locationName: 829491476 - Fill Rate: 99.92% locationCountry: 830159658 - Fill Rate: 100% logoUrl: 225683142 - Fill Rate: 27.19% connectionsCount: 563236676 - Fill Rate: 67.85% followersCount: 569950689 - Fill Rate: 68.66% currentCompanies: 544595655 - Fill Rate: 65.6% previousCompanies: 244822218 - Fill Rate: 29.49% educations: 378348844 - Fill Rate: 45.58% volunteerExperiences: 33804455 - Fill Rate: 4.07% skills: 296336188 - Fill Rate: 35.7% pronoun: 38741090 - Fill Rate: 4.67% related: 576329691 - Fill Rate: 69.42% languages: 73444194 - Fill Rate: 8.85% recommendations: 27940603 - Fill Rate: 3.37% certifications: 65446443 - Fill Rate: 7.88% courses: 21095553 - Fill Rate: 2.54% honors: 17348831 - Fill Rate: 2.09% organizations: 14691528 - Fill Rate: 1.77% patents: 1012239 - Fill Rate: 0.12% projects: 16879774 - Fill Rate: 2.03% publications: 9748127 - Fill Rate: 1.17% lastUpdated: 830159658 - Fill Rate: 100% openToWork: 42157137 - Fill Rate: 5.08%

Compliance & Data Governance We understand that compliance is paramount when handling professional data.

Source: All data is aggregated strictly from Public Web Sources. We do not hack, credential-stuff, or access data behind login walls....

Power your US Operations, HR Tech, and Market Intelligence engines with the most comprehensive database of the American workforce. This dataset offers a structured, historical view of 194,408,909 US professionals, capturing career trajectories, educational backgrounds, and skill sets across every state and industry.

With coverage nearing 100% of the active US white-collar workforce, our US Professional Identity Graph provides a dynamic view of talent. We map the relationships between People, Companies, Skills, and Schools, allowing you to answer complex questions about domestic talent migration, skill supply, and organizational hierarchies.

Key Use Cases 1. B2B Data Enrichment & CRM Hygiene Turn a simple email address or name into a full 360-degree US prospect profile.

Append: Add currentCompanies, jobTitle, and industry to your existing US leads.

Lead Scoring: Use connectionsCount and recommendations as proxies for influence within US markets.

Refresh: Identify when a prospect has changed jobs (lastUpdated) to trigger "New Role" outreach campaigns.

1. Talent Intelligence & Recruitment Build the next generation of US hiring tools.

Sourcing: Query by complex skill combinations (e.g., "Python" + "TensorFlow" + "5 Years Experience" in "San Francisco").

Alumni Targeting: Use educations data to find candidates from specific US Universities (Ivy League, State Colleges, etc.).

DEI Analytics: Leverage pronoun and volunteerExperiences data for diversity and inclusion benchmarking.

1. US Labor Market Analysis

Migration Trends: Track talent movement between states (e.g., "Tech talent moving from CA to TX").

Skill Trends: Analyze the rise of specific skills across US industries.

Data Dictionary & Schema Attributes Our schema is normalized for easy ingestion. We provide over 30 rich attributes per profile, grouped into five core intelligence clusters:

1. Identity & Social (The "Who")

publicId / vanity: The unique handle for the profile (e.g., /in/john-doe).

urn: The immutable, system-unique identifier.

fullName, firstName, lastName: Parsed name fields.

headline & summary: The professional's self-described bio and taglines.

pronoun: Self-identified pronouns.

logoUrl: Profile image link.

openToWork: Indicator of active job-seeking status.

1. Professional Graph (The "What")

currentCompanies: Detailed object containing Company Name, Title, Start Date.

previousCompanies: Historical array of past roles, creating a full resume view.

industry: Standardized industry classification.

1. Capability & Skills (The "How")

skills: Array of endorsed skills (e.g., "Project Management", "SQL").

languages: Spoken languages and proficiency levels.

certifications: Professional licenses and validity dates.

courses & honors: Academic and professional awards.

educations: Full academic history including Degree, School, and Dates.

1. Influence & Content (The "Reach")

connectionsCount: Total network size.

followersCount: Measure of audience reach.

recommendations: Text of received professional endorsements.

organizations: Memberships in professional bodies or non-profits.

patents, projects, publications: Intellectual property and portfolio items.

1. Location & Metadata

locationName: City/Metro area (e.g., "Greater New York City Area", "Austin, Texas").

locationCountry: Fixed to "US".

lastUpdated: Timestamp of the most recent data refresh.

id: 194408909 - Fill Rate: 100% fullName: 194392269 - Fill Rate: 99.99% firstName: 194391083 - Fill Rate: 99.99% lastName: 193031965 - Fill Rate: 99.29% publicId: 194408909 - Fill Rate: 100% urn: 194408909 - Fill Rate: 100% headline: 194260405 - Fill Rate: 99.92% summary: 41525593 - Fill Rate: 21.36% industry: 143067057 - Fill Rate: 73.59% locationName: 194408824 - Fill Rate: 100% locationCountry: 194408909 - Fill Rate: 100% logoUrl: 62644925 - Fill Rate: 32.22% connectionsCount: 139069652 - Fill Rate: 71.53% followersCount: 140881048 - Fill Rate: 72.47% currentCompanies: 133983286 - Fill Rate: 68.92% previousCompanies: 67758867 - Fill Rate: 34.85% educations: 88604497 - Fill Rate: 45.58% volunteerExperiences: 12375279 - Fill Rate: 6.37% skills: 75429843 - Fill Rate: 38.8% pronoun: 14806274 - Fill Rate: 7.62% related: 141341109 - Fill Rate: 72.7% languages: 14267971 - Fill Rate: 7.34% recommendations: 10304568 - Fill Rate: 5.3% certifications: 19279558 - Fill Rate: 9.92% courses: 5153692 - Fill Rate: 2.65% honors: 7139463 - Fill Rate: 3.67% organizations: 6840143 - Fill Rate: 3.52% patents: 411407 - Fill Rate: 0.21% projects: 4099324 - Fill Rate: 2.11% publications: 2927800 - Fill Rate: 1.51% lastUpdated: 194408909 - Fill Rate: 100% member_id: 193803832 - Fill Rate: 99.69% company_id: 85095974 - Fill Rate: 43.77% num_recommenders: 10304568 - Fill Rate: 5.3% experiences_count: 146291011 - Fill Rate: 75.25% educations_count: 88604834 - Fill Rate: 45.58% linkedin_name: 194408909 - Fill Rate: 100% endorsers: 6508123 - Fill Rate: 3.35% open_to_work: 6433122 - Fill Rate: 3.3...

The web map contains layers for top ten code violation in Dallas city in 30 days window. The source of the data is from the CRM layer which is getting update daily through python automatic update.

Contacts Dataset The Contacts dataset simulates a list of individual contacts with detailed personal and contact information, useful for representing a customer database:

ID: A unique identifier for each contact, generated as a UUID. Email: A randomly generated realistic email address. Country: Randomly chosen from a list of six countries (France, US, UK, Germany, Portugal, China), represented by their respective codes. City: A random city, ideally from the chosen country, although this script uses a random city generator without specific country linkage for simplicity. Phone: A randomly generated phone number, which should ideally include the country prefix (not implemented in this simple version). Firstname: A randomly generated first name. Birthdate: A randomly generated birth date for each contact, ranging from 1930 to 2008, making the age of contacts between 15 and 93. Postal Code: A postal code corresponding to the randomly chosen city. Acquisition Source: The source through which the contact was acquired, chosen randomly from options like Facebook ad, Google ad, promotional email, or a birthday mail campaign. Created At: The timestamp when the contact record was created, ranging from 2019 to the present. Updated At: The timestamp of the last update to the contact record, which is any time between the creation date and the current date.

Products Dataset The Products dataset simulates an inventory of items that might be sold by a company:

ID: A unique identifier for each product, generated as a UUID. SKU: A unique Stock Keeping Unit code for each product. Categories: A list of categories assigned to each product, chosen from predefined options like Electronics, Clothing, Home, Toys, Books. Each product can belong to multiple categories. Price: A randomly generated price for each product, ranging from $10 to $500. Name: A randomly generated name for the product. Description: A short description for the product, generated randomly. Parent ID: The ID of a parent product if the current product is a variant; this is left blank in the script for simplicity. URL: A fake URL simulating a product page on an e-commerce site. Image URL: A fake URL for the product’s image. Brand: The brand of the product, either randomly selected from a list or generated. Created At: The timestamp when the product record was created, within the last two years. Modified At: The timestamp of the last update to the product record.

Stores Dataset The Stores dataset represents physical or conceptual locations where the company operates:

ID: A unique identifier for each store, generated as a UUID. Name: The name of the store, generated by appending "Store" to a randomly generated company name. City: The city where the store is located, chosen randomly. Country: The country where the store is located, chosen randomly. Postal Code: A postal code for the store, generated randomly. Created At: The timestamp when the store record was created, within the last two years. Modified At: The timestamp of the last update to the store record.

These datasets can be used individually or combined to simulate real-world business applications like customer relationship management, inventory tracking, and retail operations. If you need these datasets linked (e.g., linking products to stores, or contacts to purchases), additional scripting would be needed to establish these relationships within the data.

Success.ai’s B2B Email Data API provides direct access to over 170 million verified business emails worldwide, empowering your sales and marketing teams to streamline communications and establish meaningful connections with prospects.

Designed to enhance outreach, prospecting, and relationship-building efforts, this API ensures every message reaches the right inbox, minimizing bounce rates and maximizing engagement. With AI-validated accuracy, continuously updated datasets, and our Best Price Guarantee, this solution enables you to scale confidently and efficiently in a dynamic global market.

Why Choose Success.ai’s B2B Email Data API?

1. Direct and On-Demand Access to Business Emails

   • Instantly query and retrieve verified business emails, ensuring your contact lists remain current and actionable.
   • AI-driven validation ensures 99% accuracy, reducing wasted outreach and improving conversion rates.

2. Comprehensive Global Coverage

   • Includes professionals from diverse industries, regions, and roles, enabling you to scale campaigns and explore new markets.
   • Access real-time data updates to stay ahead of organizational changes and industry shifts.

3. Continuous Data Refresh and Real-Time Updates

   • Receive ongoing updates that keep your CRM or marketing platforms enriched with the latest, most relevant business emails.
   • Adapt swiftly to new opportunities, product launches, and market conditions by ensuring your contact data never goes stale.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful use of all email data.

Data Highlights:

• 170M+ Verified Business Emails: Engage with global prospects, customers, and partners confidently.
• AI-Validated Accuracy: Reduce errors, improve deliverability, and optimize outreach performance.
• Real-Time Data: Access updated email lists at any time, ensuring your messages always reach current inboxes.
• Global Reach: Explore new markets, industries, and opportunities without data constraints.

Key Features of the B2B Email Data API:

1. Instant Email Enrichment

   • Enhance existing CRM or prospect lists with verified business emails, improving targeting and personalization.
   • Save time and resources by automating contact data refreshes and expansions.

2. Flexible and Scalable Integration

   • Seamlessly integrate the API into your CRM, marketing automation tools, or analytics platforms.
   • Customizable and scalable solutions allow you to adapt the API’s capabilities as your business needs evolve.

3. Granular Filters and Query Parameters

   • Filter contacts by region, industry, company size, or job role to focus on high-potential leads.
   • Zero in on the most relevant prospects, driving higher conversion rates and long-term value.

4. Real-Time Validation and Updates

   • Rely on real-time data to ensure your outreach targets active, accurate emails.
   • Respond quickly to new market opportunities, product launches, and strategic initiatives.

Strategic Use Cases:

1. Sales and Lead Generation

   • Empower sales teams with fresh, accurate contact data to shorten sales cycles and increase deal closures.
   • Improve lead quality and segmentation to focus on high-value prospects who align with your ICP.

2. Marketing Campaigns and ABM Strategies

   • Refine account-based marketing efforts by targeting decision-makers with verified emails.
   • Personalize campaigns, improve engagement, and enhance overall campaign ROI.

3. Partnership Development and Supplier Outreach

   • Identify and connect with potential partners, suppliers, or distributors who match your strategic goals.
   • Strengthen your network and forge meaningful relationships that drive mutual growth.

4. Recruitment and Talent Acquisition

   • Access verified emails for HR managers, talent acquisition specialists, and department heads.
   • Fill critical roles faster by reaching out to potential candidates or HR contacts at desired companies.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality email data at unbeatable prices, ensuring excellent ROI for your outreach efforts.

2. Seamless Integration

   • Easily incorporate the API into existing workflows, eliminating manual updates and data discrepancies.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions, streamline targeting, and enhance conversion rates.

4. Customizable and Scalable Solutions

   • Tailor the dataset and filters to your specific roles, industries, or regions, adapting as your business requirements change.

Additional APIs for Enhanced Functionality:

1. Data Enrichment API
   • Combine the email data API with enrichment solutions for comprehensive contact profiles, refining targeting and improving personalization.

...

Cloud CRM Market Outlook

According to our latest research, the global Cloud CRM market size reached USD 51.3 billion in 2024, reflecting robust adoption across various industries. The market is projected to grow at a CAGR of 13.8% from 2025 to 2033, reaching an impressive USD 162.1 billion by 2033. This significant expansion is primarily driven by the increasing demand for scalable customer relationship management platforms, digital transformation initiatives, and the growing importance of customer-centric business strategies worldwide.

One of the primary growth factors fueling the Cloud CRM market is the rapid digitalization of businesses and the need for real-time customer insights. Organizations are increasingly recognizing the importance of leveraging cloud-based CRM solutions to enhance customer engagement, streamline sales and marketing processes, and improve overall operational efficiency. The shift from traditional on-premises CRM systems to cloud-based alternatives allows businesses to access advanced features, integrate with other enterprise applications, and scale their operations according to dynamic market demands. The proliferation of mobile devices and the growing use of social media for business interactions have further compelled companies to adopt cloud CRM solutions, ensuring seamless customer experiences across multiple touchpoints.

Another critical driver is the cost-effectiveness and flexibility offered by cloud CRM platforms. Unlike conventional CRM systems that require substantial upfront investments in hardware and infrastructure, cloud-based CRM solutions operate on a subscription-based model, reducing capital expenditure and providing predictable operational costs. This model is particularly attractive to small and medium enterprises (SMEs), enabling them to access enterprise-grade CRM functionalities without the financial burden of maintaining complex IT infrastructure. Furthermore, cloud CRM vendors continuously update their offerings with the latest features, security enhancements, and compliance capabilities, ensuring that organizations remain agile and competitive in the fast-evolving business landscape.

The integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), and analytics into cloud CRM systems is also accelerating market growth. These technologies empower businesses to derive actionable insights from vast volumes of customer data, automate routine tasks, and personalize customer interactions at scale. AI-powered features such as predictive analytics, chatbots, and intelligent workflow automation are becoming standard components of modern cloud CRM platforms, enabling organizations to anticipate customer needs, improve response times, and drive higher levels of customer satisfaction and loyalty. As companies increasingly prioritize data-driven decision-making, the demand for intelligent cloud CRM solutions is expected to surge in the coming years.

From a regional perspective, North America continues to dominate the Cloud CRM market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region's leadership is attributed to the presence of major cloud CRM vendors, high technology adoption rates, and a strong focus on customer experience management. However, Asia Pacific is anticipated to witness the fastest growth during the forecast period, driven by the rapid expansion of digital infrastructure, rising adoption of cloud technologies by SMEs, and increasing investments in customer engagement solutions. Latin America and the Middle East & Africa are also experiencing steady growth, supported by ongoing digital transformation initiatives and the growing awareness of the benefits of cloud-based CRM systems.

Component Analysis

The Component segment of the Cloud CRM market is primarily divided into Software and Services. Cloud CRM software forms the backbone of the market, encompassing a wide array of solutions designed to manage customer interactions, automate sales processes, and provide real-time analytics. These software platforms are increasingly feature-rich, offering modules for marketing automation, sales force automation, customer service, and analytics, among others. The growing demand for integrated solutions that can seamlessly connect with other enterprise applications, such as ERP and marketing platforms, is driving continuous innovation in the software segment. Leading vend

The layer contains Dallas 311 data for 30 days time window. The data get update daily via python scripting.Usage: it is available for the public and being used to track 311 request changes in time.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Reference Data Management Market Outlook

According to our latest research, the global Reference Data Management market size reached USD 4.1 billion in 2024, reflecting robust demand across industries. The market is expected to grow at a CAGR of 13.2% from 2025 to 2033, reaching a projected value of USD 12.1 billion by 2033. This growth is primarily driven by the increasing need for data consistency, regulatory compliance, and operational efficiency in organizations worldwide. As per our analysis, the surge in digital transformation initiatives and the proliferation of data across enterprises significantly contribute to the escalating adoption of reference data management solutions.

A key growth factor for the Reference Data Management market is the intensifying regulatory landscape, especially within the BFSI and healthcare sectors. Organizations are under mounting pressure to comply with stringent data governance and reporting requirements imposed by global regulatory bodies. The need to efficiently manage and maintain high-quality reference data has become more critical than ever. This has led to a surge in demand for robust reference data management solutions capable of ensuring data accuracy, consistency, and traceability. Furthermore, the rise in data breaches and cyber threats has underscored the importance of maintaining clean and reliable reference data, further propelling market growth.

Another significant driver is the rapid digital transformation across industries, leading to exponential growth in data volumes and complexity. Enterprises are increasingly leveraging advanced analytics, artificial intelligence, and machine learning for strategic decision-making, all of which require high-quality, standardized reference data. The integration of reference data management tools with enterprise resource planning (ERP), customer relationship management (CRM), and other business applications is becoming standard practice, enabling organizations to derive actionable insights and enhance business agility. Additionally, the growing adoption of cloud-based solutions has made reference data management more accessible and scalable for enterprises of all sizes.

The proliferation of cloud computing and the shift towards hybrid IT environments have also played a pivotal role in market expansion. Cloud-based reference data management solutions offer unmatched scalability, flexibility, and cost-effectiveness, making them particularly attractive to small and medium enterprises (SMEs) with limited IT resources. These solutions facilitate seamless integration with existing systems and support remote access, which has become increasingly important in the post-pandemic era. The ability to quickly deploy and update reference data management platforms in the cloud has accelerated adoption rates, contributing to sustained market growth.

Regionally, North America continues to dominate the Reference Data Management market, accounting for the largest revenue share in 2024, driven by the presence of major technology providers and a mature regulatory environment. Europe follows closely, with strong emphasis on data privacy and compliance, while Asia Pacific is emerging as a high-growth region due to rapid digitalization and increasing investments in IT infrastructure. Latin America and the Middle East & Africa are also witnessing steady growth, supported by ongoing digital transformation initiatives and rising awareness of data management best practices. The regional outlook remains positive, with all major regions expected to contribute to the market’s expansion through 2033.

Component Analysis

The Component segment of the Reference Data Management market is divided into Software and Services. Software solutions account for the largest share of the market, as organizations increasingly rely on advanced platforms to automate and streamline reference data management processes. These software solutions offer a comprehensive set of features, including data modeling, validation, enrichment, and integration with other enterprise systems. The growing complexity of data environments, coupled with the need for real-time data access and analytics, has fueled demand for sophisticated reference data management software. Vendors are continually investing in product innovation, enhancing functionalities with artificial intelligence and machine learning capabilities to deliver superior data quality and operational efficiency.<br /&

Success.ai’s Email Address Data API equips businesses with unparalleled access to over 700 million verified professional email addresses, ensuring every outreach effort lands in the right inbox. Designed to streamline email campaigns and enhance engagement, this API provides continuously updated, AI-validated data that minimizes bounce rates and maximizes response.

Whether you’re targeting new markets, strengthening existing relationships, or launching strategic account-based marketing initiatives, Success.ai’s Email Address Data API offers the reliability and scale you need. Backed by our Best Price Guarantee, this solution empowers your sales and marketing teams to confidently connect with the global professional community.

Why Choose Success.ai’s Email Address Data API?

1. Comprehensive Global Coverage

   • Tap into a vast database of 700M+ verified emails spanning all major industries and geographic regions.
   • Scale your outreach globally, discovering new opportunities and accelerating pipeline growth.

2. AI-Validated Accuracy

   • Rely on 99% data accuracy, minimizing wasted outreach on invalid addresses and ensuring maximum deliverability.
   • Reduce manual data checks and focus on building meaningful connections.

3. Real-Time Data Updates

   • Automatically refresh your contact lists with the latest verified emails, staying aligned with dynamic market and organizational changes.
   • Adapt swiftly to new product launches, seasonal campaigns, or evolving strategic priorities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Professional Emails: Engage with diverse audiences across industries and markets.
• AI-Driven Validation: Leverage automated accuracy checks to boost deliverability and reduce bounce rates.
• Real-Time Updates: Keep your CRM or marketing tools fresh with continuously refreshed email data.
• Best Price Guarantee: Achieve top-tier results at the most competitive prices in the industry.

Key Features of the Email Address Data API:

1. Instant Contact Enrichment

   • Enrich your existing records with verified emails to enhance your communication efforts.
   • Automate data updates and avoid laborious manual checks.

2. Granular Filtering and Segmentation

   • Query the API to target specific roles, industries, or regions for precision marketing.
   • Zero in on your ideal customer profile (ICP) segments, boosting conversion and engagement rates.

3. Scalable and Flexible Integration

   • Easily integrate the API into CRM systems, marketing automation platforms, or analytics tools.
   • Adjust parameters and queries as your business evolves, ensuring the API grows with your needs.

4. Continuous Validation and Reliability

   • Count on AI-driven validation to ensure your email addresses are always up-to-date and actionable.
   • Improve ROI by focusing on verified, relevant contacts who are ready to engage.

Strategic Use Cases:

1. Sales and Lead Generation

   • Empower sales teams with accurate, verified emails to reach decision-makers quickly and efficiently.
   • Personalize outreach efforts to resonate with high-value prospects.

2. Marketing Campaigns and ABM Strategies

   • Integrate verified email data into ABM campaigns for highly targeted, personalized messaging.
   • Increase open and click-through rates by delivering compelling content to the right audience.

3. Market Expansion and Product Launches

   • Identify and reach potential customers in new markets or industries with minimal guesswork.
   • Accelerate time-to-market by connecting with prospects before competitors enter the scene.

4. Recruitment and Talent Acquisition

   • Use verified emails to directly engage qualified candidates, speeding up hiring cycles.
   • Ensure recruiters focus on responsive, relevant talent pools.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-tier email address data at unmatched prices, ensuring excellent ROI on your outreach initiatives.

2. Seamless Integration

   • Effortlessly incorporate the API into your existing workflows, eliminating data silos and manual imports.

3. Data Accuracy with AI Validation

   • Depend on 99% accuracy to guide data-driven decisions, improve targeting, and enhance conversion rates in every campaign.

4. Customizable and Scalable Solutions

   • Tailor your queries, filters, and datasets to align with shifting business objectives or market conditions.

Additional APIs for Enhanced Functionality:

1. Data Enrichment API

   • Combine verified email addresses with other enrichment data for a comprehensive view of your prospects, enabling more informed targeting and personalization.

2. Trend Analysis API

   • Leverage real-time insights into market shifts, ...

These are the data (and related documents) reported in Contadini-Wright et al (2023) J Neurosci. See readme file for more details. The data-set includes the eye tracking (pupil dilation and gaze position) data reported in the paper.

The experimental session lasted approximately 1.5 hours and was comprised of three stages: (1) Threshold estimation: A speech-in-noise reception threshold was first obtained from each participant, using the CRM task (see Threshold estimation subsection below). We used an adaptive procedure to determine the 50% correct threshold. (2) Pupil screening procedures:': Prior to the main experimental session, we performed a series of brief basic measures of pupil reactivity (Light reflex, Dark reflex etc.), commonly used to assess pupil function. These included measuring pupil responses to a slow, gradual change in screen brightness; to a sudden flashing white screen; to a sudden flashing black screen; and to a sudden presentation of a brief auditory stimulus (harmonic tone). These measurements were used to confirm normal pupil responsivity (Wang et al., 2018; Bitsios et al., 1996; Loewenfeld, 1999) and to identify outlying participants (none here). (3) Main experiment: In the main experiment participants performed two blocks of the CRM task while their ocular data were being recorded. In one of the blocks (‘High load’) the signal-to-noise ratio (SNR) was set to the threshold obtained in (1), simulating a difficult listening environment. In the second block (‘Low load’) the SNR was set to the threshold obtained in (1) plus 10 dB to create a much easier listening environment (as in McGarrigle et al, 2020). The order of the two blocks was counterbalanced across participants. All experimental tasks were implemented in MATLAB and presented via Psychophysics Toolbox Version 3 (PTB-3). Threshold estimation: Auditory stimuli were sentences introduced by Messaoud-Galusi, Hazan, & Rosen (2011) –which are a modified version of the CRM (“Coordinate Response Measure”) corpus described by Bolia et al., 2000. Sentences in Experiment 1 (including threshold estimation) were in the form “Show the dog where the [color] [number] is”. Sentences in Experiment 2 (including threshold estimation) were in the form “[color] [number] is show the dog where the”. The colors that could appear within a target sentence were black, red, white, blue, green and pink. The numbers could be any digit from 1-9 with the exception of 7, as its bisyllabic phonetic structure makes it easier to identify. Consequently, there were a total of 48 possible combinations of color and number. Sentence duration ranged between 1.9 and 2.4s, with the majority having a duration of 2.1s. Sentences were embedded in Gaussian noise. The overall loudness of the noise+speech mixture was fixed at ~70dB SPL. The SNR between the noise and speech was initially set to 20dB, and was adjusted using a one-up-one-down adaptive procedure, tracking the 50% correct threshold. Initial steps were of 12dB SNR, and decreased steadily following each reversal (8dB, then 5dB) up to a minimum step size of 2dB. The test ended after 7 reversals or after a total of 25 trials and took about 2 minutes to complete. The speech reception threshold was calculated as the mean SNR of the final four reversals. Participants completed 3 runs in total (the first was used as a practice). The threshold obtained from the final run was used for the ‘High load’ condition in the main experiment. The threshold plus 10dB was used for the ‘Low load’ condition in the main experiment. Main task: In the main experiment (‘High load’ (HL) and ‘Low load’ (LL) blocks; 15 min total), the same stimuli were used as for threshold estimation, but the SNR was fixed as described above. Each block contained 30 trials. Participants fixated on a black cross presented at the center of the screen (grey background). The structure of each trial is schematized in Figure 1. Trials began with 0.5s of noise, followed by the onset of the sentence in noise (~2s long) and then a silent period (3s). A response display then appeared on the screen and participants logged their responses to the task by selecting the correct color first, then the number, using a mouse. Visual feedback was provided. At the end of each trial, participants were instructed to re-fixate on the cross in anticipation of the next stimulus. Procedure: Participants sat with their head fixed on a chinrest in front of a monitor (24-inch BENQ XL2420T with a resolution of 1920x1080 pixels and a refresh rate of 60 Hz) in a dimly lit and acoustically shielded room (IAC triple walled sound-attenuating booth). They were instructed to continuously fixate on a black cross presented at the center of the screen against a grey background. An infrared eye-tracking camera (Eyelink 1000 Desktop Mount, SR Research Ltd.) placed below the monitor at a horizontal distance of 62cm from the participant was used to record pupil data. Auditory stimuli were delivered diotically through a Roland Tri-capture 24-bit 96 kHz soundcard connected to a pair of loudspeakers (Inspire T10 Multimedia Speakers, Creative Labs Inc, California) positioned to the left and right of the eye tracking camera. The loudness of the auditory stimuli was adjusted to a comfortable listening level for each participant. The standard five-point calibration procedure for the Eyelink system was conducted prior to each experimental block and participants were instructed to avoid any head movement after calibration. During the experiment, the eye-tracker continuously tracked gaze position and recorded pupil diameter, focusing binocularly at a sampling rate of 1000 Hz. Participants were instructed to blink naturally during the experiment and encouraged to rest their eyes briefly during inter-trial intervals. Prior to each trial, the eye-tracker automatically checked that the participants’ eyes were open and fixated appropriately; trials would not start unless this was confirmed.

Sales Acceleration Software Market size was valued at USD 70.1 Billion in 2023 and is projected to reach USD 109.2 Billion by 2031, growing at a CAGR of 7.1% during the forecast period 2024-2031.

Global Sales Acceleration Software Market Drivers

The market drivers for the Sales Acceleration Software Market can be influenced by various factors. These may include:

Growing Demand for AI-Powered Solutions: Sales acceleration software increasingly integrates artificial intelligence (AI) to personalize customer interactions, predict consumer behavior, and automate routine tasks. The adoption of machine learning algorithms and AI-driven tools has become a significant driver in enhancing sales processes, making software more efficient and effective. Increasing Importance of Data-Driven Insights: Modern businesses emphasize the need for data analytics to drive sales strategies. Sales acceleration software provides deep insights through data integration and analysis, enabling companies to make informed decisions based on customer data, market trends, and sales forecasts, contributing to higher sales productivity and success rates. Roliferation of Cloud-Based Solutions: The shift towards cloud computing has dramatically influenced the sales acceleration software market. Cloud-based solutions offer scalability, reduced costs, and ease of access, making it easier for sales teams to collaborate and share information in real-time, regardless of geographical boundaries, thus facilitating seamless operations. Integration with Customer Relationship Management (CRM) Systems: The seamless integration of sales acceleration tools with existing CRM systems allows for streamlined workflows and improved data management. This interoperability ensures sales teams can access comprehensive customer profiles and maintain consistent communication, enhancing overall sales performance. Enhanced Mobile Capabilities: The growing reliance on mobile technology demands that sales professionals have access to critical tools and data on the go. Sales acceleration software with robust mobile functionalities ensures sales representatives can engage with clients, manage leads, and update sales activities in real-time, thus boosting productivity and responsiveness. Emphasis on Personalized Customer Experience: Consumers now expect personalized experiences, and sales acceleration software helps meet this demand by providing tools that tailor interactions based on individual customer preferences and behaviors. This personalization fosters stronger customer relationships and drives sales growth, creating a competitive advantage for businesses. Increased Adoption of Sales Automation Tools: Automation features within sales acceleration software streamline repetitive tasks such as scheduling, follow-ups, and data entry. This automation increases operational efficiency, allowing sales teams to focus more on strategic activities and customer engagement, which leads to higher conversion rates and revenue. Growing Need for Sales Readiness Solutions: The demand for tools that equip sales teams with the necessary skills, knowledge, and content is rising. Sales acceleration software often includes features like training modules, content management, and performance tracking, ensuring that sales representatives are always prepared and effective, directly impacting sales outcomes. Rising Trend of Social Selling: Social media platforms have become vital channels for sales interactions. Sales acceleration software that integrates social selling tools empowers sales teams to leverage social networks for lead generation, relationship building, and brand advocacy, adapting to the modern sales environment where buyers are more active online. Focus on Real-Time Performance Monitoring: Businesses are increasingly adopting real-time performance monitoring to track sales activities and outcomes. Sales acceleration software provides real-time dashboards and analytics, allowing sales managers to monitor progress, identify issues, and make adjustments on the fly, resulting in more agile and responsive sales strategies.

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Preference Center Personalization Market Outlook

According to our latest research, the global Preference Center Personalization market size reached USD 1.48 billion in 2024, and is projected to grow at a robust CAGR of 14.2% during the forecast period, reaching approximately USD 4.13 billion by 2033. This remarkable growth is primarily driven by the escalating demand for hyper-personalized customer experiences and increasing regulatory scrutiny around data privacy. Organizations across sectors are rapidly adopting preference center personalization solutions to empower users with greater control over their communication and data preferences, thereby enhancing trust and engagement.

One of the key growth factors propelling the Preference Center Personalization market is the intensifying focus on customer-centric strategies by enterprises globally. Businesses are recognizing that tailored communication, driven by real-time customer preferences, significantly boosts engagement rates and brand loyalty. The proliferation of digital channels and the exponential growth in data generation have made it imperative for organizations to deploy advanced preference management tools. These solutions enable companies to seamlessly capture, update, and honor customer preferences across touchpoints, minimizing opt-outs and improving overall marketing ROI. As more enterprises embrace omnichannel engagement, the demand for sophisticated and scalable preference center solutions is expected to surge further.

Another critical driver fueling market expansion is the tightening regulatory environment regarding data privacy and consumer consent. With regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and similar frameworks emerging globally, organizations are compelled to provide transparent and user-friendly mechanisms for preference and consent management. Preference center personalization platforms offer robust compliance capabilities by allowing users to manage their data sharing and communication preferences proactively. This not only mitigates regulatory risks but also fosters a culture of trust and transparency, which are increasingly valued by modern consumers. The intersection of compliance and customer experience is thus catalyzing rapid adoption of these solutions.

Technological advancements in artificial intelligence and machine learning are further amplifying the capabilities of preference center personalization platforms. AI-driven analytics enable organizations to derive actionable insights from customer behavior and preferences, facilitating dynamic and context-aware personalization at scale. Integration with customer data platforms (CDPs), marketing automation tools, and CRM systems ensures seamless data flow and unified customer profiles. These innovations empower organizations to deliver highly relevant and timely content, offers, and recommendations, thereby driving higher conversion rates and customer satisfaction. The continuous evolution of these technologies is expected to unlock new use cases and accelerate market growth in the coming years.

From a regional perspective, North America currently dominates the Preference Center Personalization market, accounting for the largest share in 2024. This leadership is attributed to the high adoption of digital marketing technologies, stringent data privacy regulations, and the presence of leading solution providers. Europe follows closely, driven by robust regulatory frameworks and a mature digital ecosystem. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation, expanding e-commerce, and rising awareness about data privacy. Latin America and the Middle East & Africa are also experiencing steady growth, albeit at a slower pace, as organizations in these regions increasingly recognize the value of personalized customer engagement and compliance.

Component Analysis

The Component segment of the Preference Center Personalization market is bifurcated into Software and Services, each playing a pivotal role in shaping market dynamics. Software solutions form the backbone of preference center personalization, providing the core functionalities for capturing, managing, and executing user preferences across digital channels. These platforms are equipped with intuitive interfaces, integration capabilities, and analytics modules that enable organizations to consolidate

The layer contains 311 call locations within the city.The data is getting update daily via python scripting

Context

In small and medium sized firms that aim to do CRM, employees sometimes use Excel to track Customer Feedback. Excel is widely used due to its popularity and clean interface. However, Excel is not similar to other advanced CRM software and websites such as Slack, HubSpot, Salesforce, or Zoho. In cases where an organization aims to collect lower level feedback that can the be uploaded to a larger CRM software, Excel is a good choice. I did some research on how to make it easier for a CRM officer, salesperson, or company data managers to automate client feedback tracking using Excel's VBA functionality and VLOOKUP.

Content

This dataset has one file- CRM Finance Loan Tracking Excel File.xlsm which has columns related to customers of a medium-sized financial institution such as Client, Bank Branch Name, Phone Number, Client Account No., Loan Account No., Product, Loan Amount, Disbursed Date, Maturity, Repaid, Debt Owing, Current Note, 1st Latest Note, 2nd Latest Note, 3rd Latest Note, 4th Latest Note, and 5th Latest Note.

How to Use the Excel File

First, enable macros in the Excel file. Then, you can proceed as follows: On the first sheet called CLIENT LOANS, try typing in column M (Current Note) for any client. The VBA code will automatically update the 1st to 5th Latest Notes in columns N to R. You can look the note logs in the second sheet called LogSheet. The third sheet called CountSpecific shows the count of specific notes for each client.

Note that you can tweak the functionality of these XLSM files to suit your needs, by removing some unneeded columns and adding new ones. Just remember to modify the VBA code accordingly. .

Acknowledgements

This dataset is a compilation of random client names obtained from https://1000randomnames.com/. Other columns also contain random facts of the clients. For illustrative purposes, I typed the notes for the first five clients.

Inspiration

Can we have a simple excel file that helps in tracks client feedback? Can we use Excel formulas to track recurring customer complaints? Can we make it easier to see previous client feedback?

Use Cases - Portfolio management - Sales pipeline management - Client feedback tracking - Student progress tracking - Organizational records tracking - Budget management

Field Sales Automation Market Outlook

According to our latest research, the global Field Sales Automation market size reached USD 4.87 billion in 2024, reflecting robust adoption across industries. The market is projected to grow at a CAGR of 12.6% during the forecast period, reaching USD 14.19 billion by 2033. This impressive growth is primarily fueled by the increasing demand for real-time sales insights, seamless integration of advanced analytics, and the rising necessity to enhance productivity and efficiency in field sales operations. The adoption of digital transformation strategies and the proliferation of mobile devices are also key contributors to this marketÂ’s acceleration, as organizations strive to optimize sales processes and deliver superior customer experiences.

One of the most significant growth factors for the Field Sales Automation market is the widespread adoption of cloud-based solutions. Organizations are increasingly leveraging cloud technology to enable their sales teams with access to critical information anytime and anywhere. This shift not only reduces IT infrastructure costs but also enhances scalability and flexibility, allowing businesses to adapt quickly to changing market conditions. The integration of artificial intelligence (AI) and machine learning (ML) within field sales automation platforms is further amplifying the value proposition, enabling predictive analytics, intelligent lead scoring, and automated task management. These technological advancements are empowering sales teams to make data-driven decisions, optimize routes, and improve overall sales effectiveness, thereby driving market growth.

Another pivotal factor propelling the Field Sales Automation market is the growing emphasis on customer-centric strategies. Modern consumers expect personalized interactions and prompt responses, which necessitates robust sales automation tools capable of delivering tailored experiences. Field sales automation solutions facilitate seamless communication, real-time order processing, and efficient inventory management, ensuring that sales representatives can provide high-quality service at every customer touchpoint. The integration of customer relationship management (CRM) functionalities within these platforms is enabling organizations to build stronger relationships, enhance customer loyalty, and drive repeat business. As a result, companies across sectors such as retail, healthcare, pharmaceuticals, and FMCG are increasingly investing in field sales automation to stay competitive in a dynamic marketplace.

The expansion of mobile device usage among field sales teams has also significantly contributed to the market's upward trajectory. Mobile-enabled field sales automation applications allow representatives to update data, capture leads, and process orders in real-time, regardless of their location. This mobility enhances productivity, reduces administrative overhead, and minimizes errors, leading to improved sales performance. Furthermore, the adoption of advanced analytics and reporting tools is providing organizations with actionable insights into sales activities, enabling continuous optimization of sales strategies. The convergence of these technological trends is expected to sustain strong growth in the Field Sales Automation market over the forecast period.

From a regional perspective, North America continues to dominate the Field Sales Automation market, accounting for the largest revenue share in 2024. The regionÂ’s leadership is attributed to the early adoption of digital technologies, a mature IT infrastructure, and the presence of leading market players. However, Asia Pacific is anticipated to exhibit the highest growth rate during the forecast period, driven by rapid digitalization, expanding retail and FMCG sectors, and increasing investments in sales automation solutions by enterprises in emerging economies such as China and India. Europe also presents significant growth opportunities, supported by stringent regulatory requirements for data management and a rising focus on enhancing customer engagement. Collectively, these regional dynamics are shaping the global landscape of the Field Sales Automation market.

Field Service Automation is increasingly becoming a pivotal component within the broader field sales automation landscape. As organiz

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Enterprise-Ready Delivery ATS/CRM Integrations: Greenhouse, Bullhorn, Salesforce

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Доля предприятий, использующих любое программное обеспечение для ведения бизнеса (ERP, CRM или BI). Период сбора данных: 2023-2023 гг. Количество наблюдений: 252. Код набора данных: TIN00116. Тип: НАБОР ДАННЫХ. Последнее обновление данных: 2023-12-08T11:00:00+0100. Последнее структурное изменение: 2023-12-20T11:00:00+0100. Share of enterprises using any business software (ERP, CRM or BI). Data period: 2023 - 2023. Number of observations: 252. Dataset code: TIN00116. Type: DATASET. Last data update: 2023-12-08T11:00:00+0100. Last structural change: 2023-12-20T11:00:00+0100.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

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