PredictLeads provides company intelligence datasets that are indicative of company performance.
We track globally 70+ million companies' information such as:
Our data can be delivered to you in Flat Files, through API Endpoints or via API Webhooks and it is structured and organized using Data Models.
Companies dataset includes additional information about the company e.g. normalized company location, ticker, meta title, meta description...
Job Openings are found on companies' career sites and job boards. Job Openings and other descriptions can be used to identify company investment areas, current priorities and future developments.
Provided are fields like Job Category (marketing, sales, HR, support...), job description, salary, location and many more.
News Events are categorized events found in News Articles from various sources such as press releases, industry news, blogs and PR sites.
All events are provided in a structured format for all categories and always include company domain name to which the News Event belongs.
Because of the structured nature the News Events are better deduplicated and there is high signal to noise ratio.
News Event signals also include News Article title, body, author and more.
Lead Scoring
Personalization
The News Events data is very granular so it can be used to create automated personalized messaging to prospects.
E.g. personalized emails on the go that are optionally reviewed by a human being before sending out.
Property in schema: category
Category | Description |
---|---|
acquires |
Group name: acquisition Company acquired another company. |
merges_with |
Group name: acquisition Company merges with another company. |
sells_assets_to |
Group name: acquisition Company sells assets (like properties or warehouses) to other company. |
signs_new_client |
Group name: contract Company signs new client. |
files_suit_against |
Group name: corporate_challenges Company files suit against other company. |
has_issues_with |
Group name: corporate_challenges Company has vulnerability problems. |
closes_offices_in |
Group name: cost_cutting Company closes existing offices. |
decreases_headcount_by |
Group name: cost_cutting Company lays off employees. |
expands_facilities |
Group name: expansion Company opens new or expands existing facilities like warehouses, data centers, manufacturing plants etc. |
expands_offices_in |
Group name: expansion Company expands existing offices. |
expands_offices_to |
Group name: expansion Company opens new offices in another town, state, country or continent. |
increases_headcount_by |
Group name: expansion Company offers new job vacancies. |
opens_new_location |
Group name: expansion Company opens new service location like hotels, restaurants, bars, hospitals etc. |
goes_public |
Group name: investment Company issues shares to the public for the first time. |
invests_into |
Group name: investment Company invests into other company. |
invests_into_assets |
Group name: investment Company buys assets (like properties or warehouses) from other company. |
receives_financing |
Group name: investment Company receives financing like venture funding, loan, grant etc. |
hires |
Group name: leadership Company hired new executive or senior personnel. |
leaves |
Group name: leadership Executive or senior personnel left the company. |
promotes |
Group name: leadership Company promoted existing executive or senior personnel. |
retires_from |
Group name: leadership Executive or senior personnel retire from the company. |
integrates_with |
Group name: new_offering Company integrates with other company. |
is_developing |
Group name: new_offering Company is developing a new offering. |
launches |
Group name: new_offering Company launches new offering. |
partners_with |
Group name: partnership Company partners with other company. |
receives_award |
Group name: recognition Company or person at the company receives an award. |
recognized_as |
Group name: recognition Company or person at the company receives recognition. |
identified_as_competitor_of |
Group name: relational New or existing competitor was identified. |
Financing Events are events extracted from News Events data.
These include e.g. Venture capital rounds, Private Equity investments etc.
Financing events are grouped by date of investment and include investment amount, names of investors and more.
Company Connections describe relationships between companies.
It is sourced from Case Study pages, Testimonials pages, Logo images and other resources.
The following use cases highlight some of the most common applications of our dataset in driving business decisions, investment strategies, and competitive analysis. These examples illustrate how key customer data can be leveraged to enhance connections, validate market position, and support strategic growth. For additional, more specialized use cases, please feel free to reach out for further details.
Leveraging Shared Customers
Identifying mutual customers or partners between a company and a potential lead of theirs can facilitate warmer introductions and build trust. Mentioning common partners or customers in outreach can make communications more relevant and increase engagement.
Outreach Personalization
This dataset also helps in identifying companies that are working with your competitors, presenting an opportunity to highlight your advantages.
Assessing Market Validation
Companies with well-known key customers or partnerships have demonstrated market acceptance and validation, making them more attractive prospects for investment due to their proven credibility and success.
Identifying Strategic Relationships
Information about a company's partners, vendors, and customers provides insights into its business ecosystem, helping asset managers evaluate its market position and growth potential.
Supporting Investment Decisions
Key customer data can strengthen investment decisions by providing evidence of a company's strong client base and revenue stability.
Partnerships and Collaborations
Identifying new partnerships or significant clients can signal a company's growth potential and market trust. For instance, a small tech company signing a partnership with a large enterprise can be a strong positive signal.
Company Network Health and Reputation
Analyzing the network health through PageRank algorithms or similar can provide insights into the strength and potential of a company's business relationships. Companies with strong, positive relationships are often more resilient and have better growth prospects. Companies with prestigious key customers or strategic partnerships demonstrate a higher degree of market validation and stability.
Market Position Analysis
Information about a company's clients and partners provides insights into its market position and competitive landscape, which can inform investment strategies.
Property in schema: category
We make sure to keep the direction of the category consistent, so the results can be read like a sentence:
Company1 is a {category} to Company2.
The special case is the other
category due to its non-specificity.
Category | Description |
---|---|
partner |
Read as: Company1 is a partner of Company2. A partner relationship signals a collaboration between companies that goes both in two directions. You will find such connections using titles as "We work with", "Our partners", "The company we keep", and other titles similar in meaning. The implications of this category can be quite wide in their meaning, but in general all partnerships are a positive signal. NOTE: Until February 2024 the partner category did not always signal a true partnership between two companies, but it also included a tighter knit relationship compared to the other category, which are not always as strong as the partner keyword suggests. The meaning is now correctly followed for all new cases or cases that have been last seen since and still have the partner category. |
vendor |
Read as: Company1 is a vendor to Company2. The vendor category is much simpler in its meaning. It simply means that company1 is a supplier for company2 in some way. All vendor relationships are positive, although some are more positive than others. On a webpage, such connections can be seen in lists such as "Our customers", "Trusted by", "Enabling businesses to", etc. For example, to be a vendor to Microsoft, the company has to pass certain requirements as to company size, product reliability, safety, etc. This can be seen as a vetting process of some sort and increases the company's trustworthiness. The vendor category can also be used to determine the supply chain of a certain company and evaluate supply chain risks. |
integration |
Read as: Company1 has an integration with Company2. Most of the time an integration happens between a platform and a service. Case examples:
|
investor |
Read as: Company1 is an investor in Company2. The investor category identifies relationships between companies making investments (Company1) and companies receiving investments (Company2). The more investors the company has, the better. One could also track what companies/sectors competitors are investing in. |
parent |
Read as: Company1 is a parent company of Company2. Parent category defines a parent-subsidiary relationship between websites. In some cases such websites are only localized versions for specific markets. Case examples:
NOTE: Often it makes sense to not only check current hierarchy level e.g. "blizzard.de" but also check their parent connections e.g. "blizzard.com" and "activisionblizzard.com". Since sometimes company connections (customers, investors…) are also available at higher company hierarchical levels. |
rebranding |
Read as: Company1 is a rebranding of Company2. Company1 being a rebranding of Company2 suggests that Company2 has undergone a process to change its brand identity. We detect rebranding based on domain change and similarities between the old and the new website. Case examples:
|
published_in |
Read as: Company1 is published in Company2. The published_in category is usually found on early stage startup websites with fewer accomplishments on the market, but positive press coverage. You can see such connections under titles such as "As seen in", "Featured in", "Talking about us", etc. A published_in connection is a positive signal, but after some months or years, we would expect to see them removed and replaced with other content proving the legitimacy of the company. |
other |
Read as: Company1 is connected to Company2. This is the most general category. If we detected a relationship between companies, but we were unsure which category it belonged to, or it didn't belong to any of our categories, then we would categorize it as other . Outgoing other category connections are a slightly positive signal, while incoming ones are quite more so. Thus a connection Company1 -> other -> Company2 is more positive for Company2, than for Company1. As Company2 was featured on Company1 website. Sometimes the other category can have additional information via the source_category attribute depending on where it was found. An example of such a source_category would be cookie_section , which is explained further below. Such connections could be evaluated differently. |
Property in schema: source_category
The source_category
property provides additional context on where and how the connection was found.
Technologies are detected on websites and jobs related posts.
Technology detections provide insight into tech stacks of companies.
Detections are sourced from company websites and from job opening descriptions.
Each detection is also enriched with company and technology information which includes technology description, technology category, pricing and more.
Provides information on how a given website has been evolving.
This dataset tracks when certain subpages like "about us", "blog", "careers", "api docs", "customer support"... pages have been added. The more subpages a given company adds in a shorter time frame, normally the faster its progress. One could also segment companies based on the categories of subpages they have.
GitHub repositories gives insight into how frequently a website is contributing to its public GitHub repository.
Companies dataset includes additional information about the company e.g. normalized company location, ticker, meta title, meta description...
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parent_company
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redirects_to
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Job Openings are found on companies' career sites and job boards. Job Openings and other descriptions can
be used to identify company investment areas, current priorities and future developments.
Provided are fields like Job Category (marketing, sales, HR, support...), job description, salary, location
and many more.
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News Events are categorized events found in News Articles from various sources such as press releases, industry news, blogs and PR sites.
All events are provided in a structured format for all categories and always include company domain name to which the News Event belongs.
Because of the structured nature the News Events are better deduplicated and there is high signal to noise ratio.
News Event signals also include News Article title, body, author and more.
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One of
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"acquires"
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string
"merges_with"
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string
"sells_assets_to"
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string
"signs_new_client"
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string
"files_suit_against"
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string
"has_issues_with"
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string
"closes_offices_in"
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string
"decreases_headcount_by"
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string
"expands_facilities"
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string
"expands_offices_in"
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string
"expands_offices_to"
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string
"increases_headcount_by"
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string
"opens_new_location"
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string
"goes_public"
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string
"invests_into"
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string
"invests_into_assets"
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string
"receives_financing"
|
string
"hires"
|
string
"leaves"
|
string
"promotes"
|
string
"retires_from"
|
string
"integrates_with"
|
string
"is_developing"
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string
"launches"
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string
"partners_with"
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string
"receives_award"
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string
"recognized_as"
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string
"identified_as_competitor_of"
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boolean
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company1
[optional]
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company2
[optional]
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"type"
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meta
[optional]
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Deleted News Events.
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meta
[optional]
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Financing Events are events extracted from News Events data.
These include e.g. Venture capital rounds, Private Equity investments etc.
Financing events are grouped by date of investment and include investment amount, names of investors and
more.
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