The B2B customers data product includes firmographic enrichment from public sources, that adds corporate hierarchy and other referential data to your customer entities. To help you validate the accuracy of firmographic enrichment, Tamr provides similarity scores between key attribute values in the mastered entity and the corresponding values returned by your selected data provider. For example, Tamr calculates the similarity between the customer's company name in the mastered entity and the company name returned by the public firmographic enrichment provider. See Public Source Enrichment or more information about attributes provided by the public sources. Firmographic enrichment can help solve problems such as:
- Missing corporate hierarchy information.
- Out-of-date information, such as company names that have changed due to acquisition.
- Missing details about an organization, such as number of employees, revenue, SIC codes, national identifier, and so on.
- Misidentifying different companies that reside in the same building as the same company.
This data product includes data quality services for the following values:
These services supplement your source data with standardized, validated values for these essential attributes. They examine values for these attributes, and add any resulting validated, standardized values to each record in new enrichment-specific attributes. The original values mapped from your source datasets remain present and unchanged. They also help identify additional communication channels, including which phone numbers can receive text messages. With reliable, up-to-date information, your organization’s representatives can more efficiently and effectively work on their accounts.
See the topics linked above for processing details and added attributes.
The B2B customers model groups records as follows:
First, by trusted_id. Records with the same
trusted_id are always clustered together. Records with different
trusted_ids are never clustered together.
Records with null/empty
trusted_id are clustered based on either
tamr_enrich_id or similarity, meaning that they may be clustered with records that have a
Then, by tamr_enrich_id, when matched based on company name and full address. Records without
trusted_ids are clustered by
tamr_enrich_id when the tamr_firmographic_match_status is MATCH_NAME_ADDRESS. (This is also referred to as a site-level match.) In this case, records with the same
tamr_enrich_id are clustered together, and records with different
tamr_enrich_id are not clustered together. This step is always overridden by the results of matching/splitting based on
tamr_enrich_ids in which the tamr_firmographic_match_status is MATCH_NAME_COUNTRY, MATCH_PHONE, or MATCH_WEBSITE are treated as having null
tamr_enrich_id for the purposes of clustering. Hence they may be clustered with records that have a different (or null)
tamr_enrich_id based on either similarity as described below, or because of
trusted_id being the same.
Finally, by similarity.
Records with null or empty
trusted_ids and without
tamr_enrich_ids based on company name and full address are clustered based on similarities between these attribute fields:
- Company name
- Alternate company names
- Full address and address components
- Phone number
The model considers similarities in these fields, not exact matches. For example, two addresses on the same street can correspond to the same company site.
These records are clustered together because:
- They have the same
tamr_enrich_id, matched by company name and full address (MATCH_NAME_ADDRESS).
- Record 2 does not have a
|Column||Record 1 Value||Record 2 Value|
|company_name||ESTRELLA SALES PTY LTD||SOLAR BILL BUSTERS|
|address_line_1||25 SAVOY GROVE||25 SAVOY GROVE|
|city||CRANBOURNE NORTH||CRANBOURNE NORTH|
These records are not clustered together because they have dissimilar company names and do not have matching
|Column||Record 1 Value||Record 2 Value|
|company_name||New York State Department||City of New York|
|address_line_1||City Hall||City Hall|
|city||New York City||New York City|
Updated about 1 month ago