Get Verified DATA which increases conversions and the quality of leads, while also helping to cut ad spend by an average of 48%
We remove data listed on the Do Not Call Lists, every 6 hours!
Target Your Ideal Client on a level like no other.
- Customers search for keywords on Google, Yahoo & Bing
- We get their data and filter out what we don’t want based on 79 data points
- We run the data through the Do Not Call Lists to ensure the data is legal and reliable
- Our API feeds it into Ad Platforms for Custom Audiences
- Verified Data Refreshes every 6 hours
- Custom SuperPixel
- Closed feedback loop making ads cheaper while converting both more and better quality leads
- Optional database exports of emails and phone numbers for cold-calling
Welcome to a Data Revolution with DATA!
Imagine there was 1 central place you could target absolutely anyone and advertise on whatever platform you wanted…
That means instead of sifting through basic interests on Facebook, searching over broad targeting on Google, or looking through the limited options available on TikTok, you could just type in exactly who you want to target, download the file, and plug into any platform you want.
After months of rigorous testing, leveraging the expertise of top data scientists, and compiling over 270 million digital profiles tracking trillions of behaviors across the web, we have made that possible with our Clever DATA Builder.
Join us in this data revolution and unlock the potential of targeted advertising like never before. Say goodbye to the limitations of traditional targeting and embrace the freedom to reach exactly who you want, wherever you want. Clever DATA Builder puts the power of precision targeting in your hands.
Don’t miss out on this game-changing opportunity to elevate your advertising strategies. Embrace the future of data-driven advertising with Clever DATA Builder.
How It Works
All website right rubbish on how things actually work things like this “Our working process is designed to simplify complex tasks, optimize operations, and maximize productivity. From initial planning and ideation to execution”. When in reality what actually happens is Imagine someone doing a search engine, search for a key phrase or word that you want to be found for them all the information about the person their name, telephone number, email address, etc was gathered and hand it to you in an excel spreadsheet.
All the data has been gathered through data mining and search engine cookies across the United States. When someone enters the search engine in which they accept all cookies. Then they go looking for a key phrase / keyword that device that which thereon is freely available. However please be aware that this information needs to be added to the DNC first before approaching.
The National Do Not Call Registry is a database maintained by the United States federal government, listing the telephone numbers of individuals and families who have requested that telemarketers not contact them. Certain callers are required by federal law to respect this request. Therefore please note that any information that we provide to you needs to be entered into the DNC to ensure that you cannot approach them. We do not take any responsibility of any information that we provide to you, as we have not entered this into the DNC.
The best way to make sure you don’t call a number on the Do Not Call (DNC) list is to purchase a SAN, download the list, and use it to scrub numbers from your calling lists. The procedures should be similar for state DNC lists. The Federal Trade Commission (FTC) does offer a way to check up to three numbers to see if they are on the federal DNC list, but this tool is intended to be use for consumers to check if their own numbers are registered.
Bulk validation (also known as “batch validation” or “phone number list cleaning”) enables you to upload the contact phone database and clean up your list.
We can recommend this website to check Bulk validation https://www.realvalidito.com/dnc-lookup/ (please note we do not have any affiliation with this organisation)
Consumer Data Data
Field | Description | Fill Rate |
Consumer ID | How we link everyone together | 100% |
first_name | First name of the record | 100% |
personal_emails | Any personal emails (as defined by domain) attributed to this record, excluding any emails previously reported as invalid. | 71% |
additional_personal_emails | Any additional personal emails (as defined by domain) attributed to this record, excluding any emails previously reported as invalid. Stored as a comma-separated array. | 19% |
personal_address | Personal Address | 97% |
personal_address_2 | Personal Address 2 | 15% |
personal_city | Address city – This is representative of a personal address for the contact | 100% |
personal_state | Address state code (2 letter abbreviation). | 100% |
personal_zip | 5 digit Zip Code | 100% |
personal_zip4 | Plus 4 of contact ZIP | 97% |
mobile_phone | Mobile number uniquely associated with person, regardless of business or personal association and is also confirm wireless against the FCC portability data | 45% |
personal_phone | The non-mobile, non business related telephone number of the contact. A personal phone number | 44% |
gender | Gender of contact (M/F/U) | 98% |
married | Determination if the contact is married (Y). A blank value should be treated as unknown | 30% |
children | Are children associated with record? Y/N/Null | 40% |
income_range | Income range mapping. | 58% |
net_worth | Estimate of a households total financial assets minus liabilities. Assets include financial holdings such as deposit accounts, investments, and home value. Liabilities include loans, mortgages, and credit card debt. | 50% |
homeowner | Probabilistic and deterministic data set to capture home ownership (Yes/Probable). A blank value should be treated as unknown | 58% |
personal_emails_validation_status | The validation status of the associated personal email | 71% |
personal_emails_last_seen | The date (unix) by when an email was last seen as contributed via validation or other verifiable data. null values mean that no data is available | 71% |
Business Data
Field | Description | Fill Rate |
business_email | Any business email attributed to this record, excluding any previous emails reported as being invalid. Any domain not included in the domain list for personal emails should be considered a business email. | 75% |
programmatic_business_emails | This feature is an array of all common business email formats, built from the first_name, last_name, and company_domain fields in the B2B and other files. | 83% |
job_title | The most common current job title attributed to this record | 92% |
seniority_level | Seniority derived from the job title using the Seniority Clustering / Department mapping model | 92% |
department | Department derived from the job title using the Seniority Clustering / Department mapping model | 75% |
direct_number | The unique telephone number that is not mobile that is associated with the b2b email | 37% |
linkedin_url | LinkedIn URL for the individual | 81% |
professional_address | professional address – could be the same as personal, company or different depending on company structure | 38% |
professional_address2 | professional address – could be the same as personal, company or different depending on company structure | 11% |
professional_city | professional city – could be the same as personal, company or different depending on company structure | 38% |
professional_state | Address state code (2 letter abbreviation) | 38% |
professional_zip | 5 digit Zip Code | 37% |
professional_zip4 | Plus 4 of contact ZIP | 20% |
company_name | Company name associated to the current job title | 85% |
company_domain | Domain of the company defined by Company Name. This can be derived from the current business email. If we do not have a company domain, extract it from the root of the b2b email.(may need to evaluate multiples based on personal email exclusion) | 85% |
company_phone | General phone number for the company – to a receptionist or default number. | 74% |
primary_industry | The most occurant industry in the array associated with the firmographic data | 79% |
company_sic | The 4 digit sic code(s) associated with the company | 76% |
company_naics | North American Industry Classification System, denoting the primary industry(s) the company is listed in. This field is a comma-separated array with up to 5 NAICS codes. | 67% |
company_address | Physical address of company headquarters | 61% |
company_address_2 | Physical address of company headquarters | 10% |
company_city | City of company headquarters | 68% |
company_state | State code of company headquarters | 67% |
company_zip | Zip of company headquarters | 61% |
company_zip4 | Plus 4 of company ZIP | 7% |
company_llinkedin_url | Corporate LinkedIn URL | 80% |
company_revenue | Revenue range associated with the company. This is derived from the employee count and mapped in accordance with the mapping linked here. | 84% |
company_employee_count | Employee count range associated with the company. This is defined by counting the records within the data set assocatiated with the company and mapping that count into the denoted range. | 84% |
business_email_validation_status | The validation status of the associated business email | 39% |
busines_email_last_seen | The date (unix) by when an email was last seen as contributed via validation or other verifiable data. null values mean that no data is available | 39% |
company_last_updated | This is the Unix timestamp of the last update for the company_name field in the B2B and Universal Person files. This field records when the contact record was last confirmed at the company. Note: companies may have multiple ‘company_last_updated’ values, reflecting the last updates across different employees. | 57% |
job_title_last_updated | This is the Unix timestamp of the last update for the job title. | 57% |
last_updated | The date (unix) by when a value within the record was last updated. | 91% |
35 W 31st St Frnt 1,
New York, NY10001
87 Winsley Hill, Limpley Stoke, Bath, Wiltshire, BA2 7FA
