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The Types And Examples Of Data Enrichment

In the data-driven age that we live in, it’s more important than ever to ensure the accuracy and completeness of your data. This is where data enrichment comes in – a process of adding more information to your data to make it more valuable. 

There are many different types of data enrichment, each with its own benefits. In this post, we’ll take a look at the most common types, and give some examples of how they can be used. So read on to learn more about data enrichment, and see how you can put it to use in your own projects!

Types of Data Enrichment 

1. Geographic data enrichment

Geographic data enrichment is the act of integrating geographical data into an existing dataset, which may reveal a wealth of information ranging from geographic borders between cities to postal codes. 

2. Demographic data enrichment 

Demographic data enrichment refers to the act of adding demographic data to an existing dataset, such as a person’s level of income or marital status. Along with marital status and the level of income, demographic data enrichment also consists of information such as a person’s credit score, family size, gender, etc.

Data enriched with such information may significantly enhance targeted marketing activities by providing personalized communications. 

3. Behavioral data enrichment 

Behavioral data enrichment is a process of enhancing your customer data with information about how they behave. This can include things like purchase history, web browsing activity, social media interactions and even how often they interact with your company. This provides a brand with an accurate picture of what a potential customer desires to buy. 

Marketers can execute targeted, performance-focused campaigns that focus on the relevant customers and guide them closer to making a decision by acquiring actual shopping information and product view frequency.

Examples of Data Enrichment

1. Segmenting customers based on enhanced data 

Adding customer data enrichment to your segmentation strategy can improve your results by helping you to better target and understand your customers. By adding demographic and behavioral data to your customer profile, you can create more accurate segments and identify new growth opportunities. 

Data enrichment is a great approach to segmenting your marketing data based on third-party factors, including purchase intent and interest. This helps you to more accurately find new clients and provide them with material tailored to their individual needs.

2. Increasing conversion rates with lead scoring 

The process of awarding points for leads and evaluation has been time-consuming for marketers, but data enrichment can help them automate this task.

Let’s say, the lead has chosen to join the mailing list and provide their full name, but they failed in providing an address. A data enrichment tool with socio-demographic data could compare the entered data with postal records and automatically add address data, potentially boosting the lead score.

By adding enriched data to your leads, you can get a better idea of who they are and what they’re interested in, which can help you prioritize them appropriately.

Data enrichment is not really a one-time job. Client data evolves all the time, even if it has been meticulously collected from the beginning. Hence, it needs to be continuously monitored and updated to meet the current customer demands. Businesses that ignore continuous data enrichment miss out on possibilities to provide value through relevant services and experiences. 

FunnL is a company that specializes in generating leads. The FunnL Inside Sales Platform can provide you with high-quality potential customers based on your ideal customer persona! We can assist you by supplying B2B data about possible customers to target them specifically!

Contact us if you are seeking a dependable partner to give you high-quality leads.

An inside sales platform built by inside sales professionals.

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