How applied data science helps accurately map relationships between locations, at scale
For modern sales, marketing, and operations teams, having a clear 360-degree view of their target accounts and customers can have powerful implications. These corporate relationships or ‘links’ include parent-child, owner-subsidiary, domestic parent-global parent, and others. Accurately understanding these relationships can impact critical business functions like strategic decision making, credit risk evaluations, territory planning, lead routing, resource allocation, and more.
Defining Corporate Linkage
For sales and marketing teams, a common question is often: “How are these accounts related?”. The answer is found in corporate linkage data. Corporate linkage is a system of defining the relationship between one business and another; data that categorizes the type of connection between business locations.
Such business relationships and company ownership data include parent-child, subsidiaries, headquarters, and individual/branch locations. For more complex businesses, we also have to consider Global Parent vs. Domestic Parent(s).
FOR EXAMPLE: Microsoft Corporation (MSFT) is HQ in Seattle, WA, and owns LinkedIn (LNKD) which is HQ in Sunnyvale, CA with locations in San Francisco, CA and New York City, NY. Lynda.com, is also located in San Francisco, CA, and is a subsidiary of LinkedIn. Corporate linkage data provides a structured way of defining the various relationships between these corporations and locations. (Table 1 below illustrates this example in grid form).
These connections are not mutually exclusive, increasing the complexity of accurately mapping them at scale. In other words, one business location may hold multiple relationship roles. Consider that an office location may be the Company’s Headquarters as well as the Domestic Parent Company in their home country. More information about this can be found below.
Understanding Corporate Linkage Data
Corporate linkage data is when the relationships between companies are structured into classification codes which can be ingested into a database. As mentioned above, since locations may hold multiple linkage classifications, hierarchy codes are used to define in a structured way, the relationships between business locations.
FOR EXAMPLE: If Ariba is one of your target accounts, how would you determine that its parent company is SAP? Well, you might start by visiting the company’s website (www.ariba.com), where you’d spot SAP in their homepage header. However, doing that takes time and traditional data vendors are still doing all this by hand. Applied data science, machine learning, and artificial intelligence combine to accurately map these corporate linkages at scale unmatched by any legacy data vendor.
Commonly Used Corporate Linkage Terms & Classifications
- Corporate Linkage – A system of relationships between companies, business entities, and locations.
- Corporate Hierarchy – A group of companies, ranked one above the other according to status or authority.
- Hierarchy Classification Code – The corporate linkage or relationship definition, stated in abbreviated form.
- Unstructured data – Information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy but may contain data such as dates, numbers, and facts as well.
- Structured data – Information that resides in a fixed field within a record, entity, or file, including information contained in relational databases and spreadsheets.
- Headquarters (HQ) – The company headquarters. This entity could be a Subsidiary (SU), a Domestic Parent (DP), or a Global Parent (GP).
- Individual Location / Branch (IL) – A standalone location under a business entity with multiple locations; could be a Subsidiary (SU) of a Domestic Parent (DP) or a Global Parent (GP) company.
- Immediate Parent (IP) – A parent company that acquired the child company; listed immediately next in hierarchy.
- Domestic Parent (DP) – The top decision making entity in a corporation’s hierarchy, that is local to the company’s own country.
- Global Parent (GP) – The top decision making entity in a corporation’s hierarchy, regardless of country.
- Subsidiary (SU) – A company that has been acquired by another company.
Mapping Corporate Relationships Together
Modern data providers leverage AI and machine learning technology to build a corporate structure database, identifying and categorizing the business connections and assigning a string of corporate linkage codes to each location.
You may be wondering: ‘How do these relationships get organized?’ Well, the answer is: Corporate Hierarchy Classification Codes.
Corporate hierarchy classification codes tell a story of how a particular company location is related to another location. These relationship codes provide a clear, easy-to-ingest, structured dataset, enabling organizations to deliver this data directly into their master database.
With corporate linkage classification codes, order matters. What that means is these corporate hierarchy codes are assigned to business locations in sequential order: 1. GP 2. DP 3. SU 4. HQ 5. IL.
Given that, the following hierarchy classification codes are possible:
- GPDPHQ: Global Parent Headquarters, also the Domestic Parent
- GPDPIL: Individual Location under the Global and Domestic Parent
- DPSUHQ: Domestic Parent Headquarters that is a Subsidiary of a Global Parent in another country
- DPSUIL: Individual Location of a Domestic Parent that is owned by a Global Parent in another country
- SUHQ: Headquarters of Subsidiary owned by another entity
- SUIL: Individual Location of Subsidiary owned by another entity
Consider this Corporate Linkage Example:
Microsoft Corporation (MSFT) is HQ in Seattle, WA, and owns LinkedIn (LNKD) which is HQ in Sunnyvale, CA with locations all over including in NYC. Lynda.com, also located in San Francisco, is a subsidiary of LinkedIn. In this scenario, corporate linkage hierarchy codes would get assigned as follows:
Table 1: How Corporate Linkage Hierarchy Codes Are Assigned For Microsoft, LinkedIn, And Lynda.com Locations
|Company||Location||GP||DP||SU||HQ||IL||Hierarchy Code||GP||DP||Immediate Parent|
How Is Corporate Linkage Different From A Corporate Family Tree Database?
A corporate family tree database typically depicts the organizational structure from a company ownership perspective, while only reflecting majority ownership stakes. Corporate family tree databases can often be confused with corporate linkage data because both practices include several of the same terms: Headquarters, Parent-Child, Branch, Subsidiary, etc. However, unlike a company tree, EverString corporate linkage data is based on location-specific matching, meaning corporate linkage data covers a broader and more fine-tuned level of detail regarding the business connections.
Unlike corporate family tree databases, corporate linkage data helps connect the dots between business locations you care about, whether they have a majority ownership stake in each other or not. Corporate linkage data also gives a more detailed level of information since it is based on classifying individual locations, rather than stopping at the corporate ownership level.
Different Ways Organizations Are Using Corporate Linkage Data
Organizations can use corporate linkage data to improve marketing, sales, and operations, which contributes to enhanced customer experiences throughout the entire journey.
“Within specific accounts, business momentum signals — like hiring, raising capital, or opening new offices — can shed light on an increase in the total addressable market (TAM)” – Alyssa Merwin, VP of Sales, LinkedIn
There are 2 broad use cases for corporate hierarchy and parent/child linkage data for an organization:
- Sales & Marketing – Whether for understanding buying power and where decisions are made, or getting to know your customer relationships better, revenue teams leverage corporate linkage data to enhance performance.
- Credit, Collections, & Risk – Before extending credit, organizations use corporate linkage information to better evaluate risk. Once a customer, knowing legal ownership improves the efficacy of accounts receivable and collections.
The EverString Approach To Corporate Linkage Information
Traditional data vendors struggle to catch up using this manual-labor approach, that’s being eclipsed by EverString’s machine learning and artificial intelligence system, where expert humans train the systems on what to look for, and how to evaluate unstructured data, then monitor and correct when anomalies arise in a symbiotic and ever-expanding feedback loop.
By scaling the web with machines trained to think by and like expert business professionals, EverString accurately defines corporate linkages across an unprecedented coverage area. This unrivaled solution is turning heads amongst leading brand and industry experts.
“EverString helped us connect with the companies and people that matter most to us. As a result, our teams are more targeted and more efficient.” — Selom Harry Azuma, Director of Business & Product Strategy, Staples
The EverString Resource Center has what you need to know about B2B data, all in one place. Download ebooks, checklists, whitepapers, and more.
Discover Corporate Linkage Data For Your Business
See what’s possible when you harness the power of modern business data, including accurate corporate linkage mapping. Request a Free Demo to learn more.