What Is B2B Data Enrichment?

Data enrichment is the process of updating, augmenting, and creating new data derived from existing data.

The term is used in different contexts like finance for adding metadata to financial transactions and data science for the wider purpose of combining datasets and sources.

In this blog post, we're focusing on data enrichment in the context of sales. More precisely, we'll explore "B2B data enrichment".

Data enrichment in sales

Common paths for data enrichment in sales are finding the email addresses and mobile numbers of prospects.

This process may start with a list of names and employer domains. Sales professionals will then use a tool to enrich the list with additional columns. In our example, this would be a column for the email address and the mobile number of a prospect.

Another common example for data enrichment is a "reverse email lookup." This is especially relevant for companies that try to convert users who signed up on their own to enterprise customers: customers with formal annual contracts.

Here, a user may sign up to an app providing only their name and their email address. This email address may belong to a company or be associated with the user personally (@gmail.com or @outlook.com).

A sales professional may use a tool that can find the person behind the email address: their LinkedIn profile, current employer, titles, etc.

The origins of enriched data

Contact databases

The bulk of enriched data is sourced from large contact databases that are offered by large resellers. Companies like Bright Data, Coresignal, and People Data Labs procure data from smaller datasets or scrape it right from the internet.

Lead capturing services

Many companies generate significant income streams by selling contact data they collect while providing their service. Companies like Check24, insurance brokers, etc. often build tools with the sole purpose of capturing contact data. This data is aggregated into larger datasets, updated, and maintained.

Matching algorithms

Another way to generate enriched data is algorithmically. Let's say you want to find the email address for a person called "Florian Martens" who works at pipe0.com. Learning a company's email structure and then applying a heuristic to guess the correct email address is a common approach to derive B2B contact data.

First-party enrichment data

Not all data enrichment makes use of external services. Arguably the most valuable enrichment data is first-party data: usage from your own product.

Enriching a list with information on how a specific user has used your product in the past is data enrichment as well.

Intent signals

Most enriched data is ephemeral. Addresses, employers, and titles change. Acknowledging this ephemeral nature is what led to the idea of intent signals. Rather than tracking how a user uses your product once, we can define checkpoints and call this a signal.

The first time a user hits a usage limit, a user gets promoted, or changes employers.

Tracking intent signals is another kind of data enrichment.

How companies use enriched data

In sales, almost all enriched data is used to perform some kind of outreach. The purpose of enriched data is to learn more about users and prospects to make informed decisions on how and when to reach out.