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Contact Data

A person's work history, bio, skills, and more.

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The Contact Data insight provides comprehensive information about an individual, including their professional profile, work history, education, skills, and more. This insight can be retrieved using either the contact's email address or their LinkedIn URL.

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Note: the only current available parameters for generate-insights are contactEmail and contactLinkedinUrl.

  • The system will pass back an array of insights, ranked by relevance.
  • The below documentation still accurately covers our schema, and demonstrates what might exist in that array.
  • Soon, you'll be able to specify certain insight types in your API request. Coming Q3 2024.

Retrieving Contact Data Insight

To retrieve the Contact Data insight using the Autobound Insights API, you need to make a POST request to the /generate-insights endpoint with the appropriate parameters.

import requests

url = 'https://api.autobound.ai/api/external/generate-insights/v1.0.0'
headers = {
    'X-API-KEY': 'YOUR-API-KEY-HERE',
    'Content-Type': 'application/json'
}
data = {
    "contactEmail": "[email protected]",
    "contactLinkedInUrl": "https://www.linkedin.com/in/johndoe",
     # you can either use contactLinkedinURL, contactEmail, or both
    "insightType": "CONTACT_DATA"
}

response = requests.post(url, headers=headers, json=data)
print(response.text)

Response Format

The API will respond with the Contact Data insight in JSON format. The response will contain detailed information about the contact, including their:

  • Basic profile information (name, title, location)
  • Work history and experience
  • Education and qualifications
  • Skills and expertise
  • Contact details (email, phone number, social media profiles)
  • Professional achievements and awards
  • Recommendations and endorsements

You can parse the response and extract the relevant information based on your requirements.

{
  "data": {
    "firstName": "Daniel",
    "fullName": "Daniel Wiener",
    "about": "Obsessed with solving the #1 problem every sales development org faces: writing personalized, compelling emails with any level of scale is damn hard.\n\nWe're building a platform that systematizes the best practices of top performing sellers. By using AI, Autobound is able to conduct deep research on a prospect, and compose a hyper-personalized email instantly.\n\nIf you'd like to jam on this problem space together, shoot me a connection request.\n\nDenver raised. Runner, weightlifter, snowboarder, swimmer, wakesurf teacher (in that order). Pretty good at Fortnite and nasty at Super Smash 64. Pretty stellar Pokemon collection too. Got a toy chihuahua during my freshman year at USC who is still my best friend.",
    "city": Austin,
    "mobile": "1231231231",
    "company": "Autobound",
    "companyDomain": "autobound.ai",
    "companyEmployeeRange": "10-50",
    "jobTitle": "Co-Founder and CEO",
    "languages": [
      "English",
      "Spanish"
    ],
    "lastName": "Wiener",
    "linkedinUrl": "https://www.linkedin.com/in/daniel-wiener",
    "location": "Austin, Texas Metropolitan Area",
    "profileImageUrl": "https://media.licdn.com/dms/image/D5603AQEJkJ4lWd-fdQ/profile-displayphoto-shrink_800_800/0/1675894803335?e=1714608000&v=beta&t=NxqW1Tm80OE5f8pyaZPZof4jU6BRp6mJmEsatOmCZQU",
    "companyIndustry": "Software Development",
    "companyLinkedinUrl": "https://www.linkedin.com/company/autobound",
    "companyLogoUrl": "https://media.licdn.com/dms/image/C560BAQEq5k3IvExN5w/company-logo_400_400/0/1630564297108/autobound_logo?e=1708560000&v=beta&t=pIQgon26ME-6mv1L79WVehBzFbDFMVSG9lpWjdDVbvs",
    "companyWebsite": "https://www.autobound.ai/",
    "companyYearFounded": 2018,
    "connectionsCount": 500,
    "country": "USA",
    "currentCompanyJoinMonth": 3,
    "currentCompanyJoinYear": 2019,
    "educations": [
      {
        "degree": "Bachelor of Arts",
        "fieldOfStudy": "(Major) Human Biology - (Minors) Business & Music Industry",
        "school": "University of Southern California",
        "schoolLinkedinUrl": "https://www.linkedin.com/company/3084/"
      },
      {
        "degree": "Continuing Studies",
        "fieldOfStudy": "Entrepreneurship/Entrepreneurial Studies",
        "school": "Stanford University",
        "schoolLinkedinUrl": "https://www.linkedin.com/company/1792/"
      },
      {
        "school": "Denver East High School",
        "schoolLinkedinUrl": "https://www.linkedin.com/company/35430232/"
      }
    ],
    "experiences": [
      {
        "company": "Autobound",
        "companyLinkedinUrl": "https://www.linkedin.com/company/18953516",
        "currentCompanyJoinMonth": 3,
        "currentCompanyJoinYear": 2019,
        "dateRange": "Mar 2019 - present",
        "description": "With 320B emails sent daily, inboxes are flooded. Being personalized and relevant in your messaging is a must-have for anyone in sales. Autobound increases email reply rate by generating 1:1 hyper-personalized emails using AI, based on news, social media, company initiatives, shared experiences and more.",
        "duration": "5 yrs",
        "isCurrent": true,
        "location": "Austin, Texas, United States",
        "startMonth": 3,
        "startYear": 2019,
        "title": "Co-Founder and CEO"
      },
      {
        "company": "Modern Sales Pros",
        "companyLinkedinUrl": "https://www.linkedin.com/company/18762737",
        "dateRange": "Jun 2019 - present",
        "duration": "4 yrs 9 mos",
        "isCurrent": true,
        "location": "San Francisco Bay Area",
        "startMonth": 6,
        "startYear": 2019,
        "title": "Member"
      },
      {
        "company": "Golden Gate University (Business Analytics Program)",
        "companyLinkedinUrl": "https://www.linkedin.com/company/9293",
        "dateRange": "Aug 2021 - Feb 2023",
        "description": "Evangelized GGU's MSBA program, provided guidance on their curriculum, and offered internship opportunities on various ML initiatives at Autobound.",
        "duration": "1 yr 7 mos",
        "endMonth": 2,
        "endYear": 2023,
        "isCurrent": false,
        "location": "San Francisco, California, United States",
        "startMonth": 8,
        "startYear": 2021,
        "title": "Advisor"
      },
      {
        "company": "Intralinks",
        "companyLinkedinUrl": "https://www.linkedin.com/company/7994",
        "dateRange": "2017 - Jan 2019",
        "description": "Sold dataroom software to corp dev, legal and finance M&A teams. Owned all companies HQ'd in the Bay Area & SoCal.",
        "duration": "2 yrs",
        "endMonth": 1,
        "endYear": 2019,
        "isCurrent": false,
        "location": "San Francisco Bay Area",
        "startYear": 2017,
        "title": "Sales, Corp Dev"
      },
      {
        "company": "Oracle",
        "companyLinkedinUrl": "https://www.linkedin.com/company/1028",
        "dateRange": "2016 - 2017",
        "description": "#2 BDR in the US. Hit 8x quota using the same fundamentals we've built into Autobound's AI. Sold database, middleware and cloud.",
        "duration": "1 yr",
        "endYear": 2017,
        "isCurrent": false,
        "location": "Redwood Shores, California",
        "startYear": 2016,
        "title": "Sales"
      },
      {
        "company": "Northwestern Mutual",
        "companyLinkedinUrl": "https://www.linkedin.com/company/2445",
        "dateRange": "Nov 2014 - May 2016",
        "description": "80+ cold calls / day selling life insurance. #1 intern in LA office. Thus began the 5 year journey of using excel as CRM...",
        "duration": "1 yr 7 mos",
        "endMonth": 5,
        "endYear": 2016,
        "isCurrent": false,
        "location": "Greater Los Angeles Area",
        "startMonth": 11,
        "startYear": 2014,
        "title": "Insurance Sales"
      },
      {
        "company": "USC Interfraternity Council",
        "companyLinkedinUrl": "https://www.linkedin.com/company/3084",
        "dateRange": "2014 - 2015",
        "duration": "1 yr",
        "endYear": 2015,
        "isCurrent": false,
        "location": "University of Southern California, Los Angeles",
        "startYear": 2014,
        "title": "New Member Education, Exec Board"
      },
      {
        "company": "Crestmoor Community Assn",
        "companyLinkedinUrl": "https://www.linkedin.com/company/8612713",
        "dateRange": "2010 - 2014",
        "duration": "4 yrs",
        "endYear": 2014,
        "isCurrent": false,
        "location": "Denver, Colorado",
        "startYear": 2010,
        "title": "Tennis Coach"
      }
    ],
    "followersCount": 20212,
    "headline": "CEO of Autobound (ChatGPT for Sales)",
    "hqCity": "San Francisco",
    "hqCountry": "US",
    "hqRegion": "CA",
    "school": "University of Southern California",
    "state": "Texas",
    "personalEmails": [
      "[email protected]",
      "[email protected]"
    ],
    "businessEmail": "[email protected]",
    "linkedinDataUpdatedAt": "2023-06-15T10:30:00Z",
    "emailDataUpdatedAt": "2023-06-14T09:15:00Z",
    "businessEmailValidationStatus": "valid"
  }
}

Field Descriptions:

  • firstName (string): The first name of the contact.
  • fullName (string): The full name of the contact.
  • about (string): A brief description or summary provided by the contact.
  • city (string, optional): The city where the contact is located.
  • mobile (string, optional): The mobile phone number of the contact.
  • company (string): The name of the company where the contact currently works.
  • companyDomain (string): The domain of the company where the contact currently works.
  • companyEmployeeRange (string): The range of the number of employees at the contact's current company.
  • jobTitle (string): The current job title of the contact.
  • languages (array of strings): The languages spoken by the contact.
  • lastName (string): The last name of the contact.
  • linkedinUrl (string): The LinkedIn URL of the contact.
  • location (string): The location of the contact.contact.
  • profileImageUrl (string): The URL of the contact's profile image.
  • companyIndustry (string): The industry of the contact's current company.
  • companyLinkedinUrl (string): The LinkedIn URL of the contact's current company.
  • companyLogoUrl (string): The URL of the logo of the contact's current company.
  • companyWebsite (string): The website URL of the contact's current company.
  • companyYearFounded (integer, optional): The year the contact's current company was founded.
  • connectionsCount (integer): The number of connections the contact has on LinkedIn.
  • country (string): The country where the contact is located.
  • currentCompanyJoinMonth (integer): The month the contact joined their current company.
  • currentCompanyJoinYear (integer): The year the contact joined their current company.
  • educations (array of objects): An array of the contact's educational background.
    • degree (string, optional): The degree obtained by the contact.
    • fieldOfStudy (string, optional): The field of study of the contact's education.
    • school (string): The name of the school attended by the contact.
    • schoolLinkedinUrl (string): The LinkedIn URL of the school attended by the contact.
  • experiences (array of objects): An array of the contact's work experiences.
    • company (string): The name of the company where the contact worked.
    • companyLinkedinUrl (string): The LinkedIn URL of the company where the contact worked.
    • currentCompanyJoinMonth (integer, optional): The month the contact joined the company (for current experience).
    • currentCompanyJoinYear (integer, optional): The year the contact joined the company (for current experience).
    • dateRange (string): The date range of the contact's experience at the company.
    • description (string, optional): A description of the contact's role or responsibilities at the company.
    • duration (string): The duration of the contact's experience at the company.
    • isCurrent (boolean): Indicates whether the experience is the contact's current job.
    • location (string): The location of the company where the contact worked.
    • startMonth (integer, optional): The month the contact started working at the company.
    • startYear (integer): The year the contact started working at the company.
    • title (string): The job title of the contact at the company.
    • endMonth (integer, optional): The month the contact ended working at the company (for past experiences).
    • endYear (integer, optional): The year the contact ended working at the company (for past experiences).
  • followersCount (integer): The number of followers the contact has on LinkedIn.
  • headline (string): The headline or tagline of the contact's LinkedIn profile.
  • hqCity (string): The city where the contact's current company is headquartered.
  • hqCountry (string): The country where the contact's current company is headquartered.
  • hqRegion (string): The region or state where the contact's current company is headquartered.
  • school (string): The name of the school attended by the contact.
  • state (string): The state where the contact is located.
  • personalEmails (array of strings, optional): An array of the contact's personal email addresses.
  • businessEmail (string, optional): The contact's business email address.
  • linkedinDataUpdatedAt (string, optional): The timestamp indicating when the LinkedIn data was last updated, in UTC.
  • emailDataUpdatedAt (string, optional): The timestamp indicating when the email data was last updated, in UTC.
  • businessEmailValidationStatus (string, optional): The validation status of the contact's business email address.

Use Cases

The Contact Data insight is valuable for various use cases, such as:

  • Lead Enrichment: Enrich your lead database with comprehensive contact information, including job title, company, location, education, and work experience. This information can help you qualify leads, prioritize outreach, and tailor your messaging to the specific needs and background of each contact.
  • Personalized Outreach: Leverage contact data insights to personalize your outreach messages and build rapport with prospects. Mentioning shared experiences, common connections, or relevant educational background can help establish a connection and increase the likelihood of a response.
  • Account-Based Marketing (ABM): Use contact data insights to identify key decision-makers and influencers within your target accounts. Understanding their roles, responsibilities, and background can help you tailor your ABM campaigns and create personalized content that resonates with each contact.
  • Sales Intelligence: Provide your sales team with detailed contact information to help them prepare for meetings, build relationships, and navigate complex organizational structures. Insights into a contact's work history, education, and current role can help sales reps ask relevant questions, address specific pain points, and demonstrate industry expertise.
  • Talent Acquisition: Leverage contact data insights for recruiting and talent acquisition purposes. Identifying potential candidates with relevant skills, experience, and educational background can help streamline the hiring process and find the best fit for open positions.
  • Networking and Referrals: Use contact data insights to identify common connections, shared experiences, or mutual interests. This information can help facilitate introductions, generate referrals, and expand your professional network

Contact Level Insights

The various contact level insights Autobound offers can be found in the lefthand menu.

Updated about 2 months ago