When it comes to data enrichment, there is a lot to learn. It can seem complex at first, but when you break it down it’s not all that difficult to understand!

Read on to discover what you need to know about the three types of data enrichment.

Data Enrichment: What Is It?

Every department within a business uses software to conduct its business, whether it is finance, marketing, sales, or customer support. All of these teams rely heavily on data to make informed decisions. However, to get a complete view of their customer data, they typically have to bounce back and forth between tools.

Data enrichment eliminates this problem by supplying data sets and software with data from external sources. This can have game-changing effects on any business.

How is Data Enrichment Beneficial?

All companies in the modern world are pushing to become more data-driven. This allows them to effectively serve the consumer, improve production, and efficiently make informed decisions. Without data enrichment, it can be difficult to accumulate a 360-degree view of customers. Thus, making it difficult to make decisions.

Data enrichment allows each department to almost instantaneously gather detailed data that can help them make better decisions for the company.

The 3 Types of Data Enrichment

For the most part, there are three main categories of data enrichment to consider. They are geographic, demographic, and behavioral.

Geographic Data Enrichment

Geographic data enrichment specifically pertains to data around the customer. This can significantly help with marketing and product creation.

Demographic Data Enrichment

Demographic data enrichment focuses on specific information about the customer. This can include race, gender, income, age, marital status, zip code, and other personal information that may be relevant to the company.

Behavioral Data Enrichment

Behavioral data enrichment is an important one for the marketing team. This type of data is added to users’ profiles to help determine their behavior on the web. Ultimately, this informs the marketing team on how to best market to specific users and demographics.

Subsets of Data That Benefit From Data Enrichment

Data enrichment is beneficial because it helps all companies do what was virtually impossible, create a 360-degree view of their customers. In addition to the three main categories of data enrichment, there are four data subsets that companies can dramatically improve with data enrichment.

Sales Data

Sales data includes all of the different forms of data that can be obtained throughout the sales process. Here are a few specific examples.

  • Date the product was Demoed
  • Deals that are still active
  • Companies in trial
  • Deal stage
  • First meeting

Finance Data

The finance data pertains to all data acquired throughout the customer’s payment process. Here are a few specific examples.

  • Due dates for payments
  • The latest payment date
  • The size of their contract
  • The type of subscription

Product Data

Product data refers to all customer data that can be ascertained through the product. Here are a few examples.

  • Sign up dates
  • Date of most recent login
  • Sent messages
  • How many users there are

Marketing Data

Marketing data refers to any data drawn from a customer’s journey with the company. Here are a few examples.

  • Resources the customer may have downloaded
  • Links the customer may have clicked on
  • Pages of your website the customer viewed
  • Length of time the customer spends on your website

Implementing Data Enrichment

Learning about data enrichment is one thing, implementing it into your business is another. When it comes to the modern data stack, you generally have three options for the implementation of third-party data enrichment. Here they are.

Reverse ETL

Reverse ETLs can integrate directly into your data warehouse system, allowing for seamless integration of data across your entire company. This is perhaps the simplest and most efficient option of the three and is something that all data-driven companies should consider implementing.

Centralized Data Platforms (CDP)

Centralized data platforms, or CDPs, are great because they give companies the ability to take all of their customer data and place it in one primary location. From there, it can be dispersed into different programs for assessments. The advantage of this platform is that it can integrate with third-party APIs.

IPaaS Platforms

IPaaS platforms act like point-to-point platforms allowing data to get from point A to point B with barely any transformation. The only issue with this form of data transferring is because it is only moving data, it is difficult to ascertain a 360-degree view of your customers with it.

All three of these options have their strengths. But when it comes down to it, reverse ETL is a great way to integrate with software tools across your company and get the most out of data enrichment.

The Bottom Line

Data enrichment allows companies to take data from different sources and create a 360-degree view of their customers. This new, more complete view of customers can help companies make more accurate data-driven decisions, ultimately improving every aspect of their business. The three types of data enrichment are geographic, demographic, and behavioral, and they all play critical roles in the data decision-making process.