When everyone in the market has access to the same information, the playing field may become relatively even. Gaining an advantage over them may be accomplished using first-party data provided voluntarily by the target audience. Surprisingly, even if you have first-party data, it may not be adequate if it is flawed or incomplete.
A solution: address data enrichment. When combined with other methods, data enrichment may help you create a complete picture of your target audience. More detailed profiles of your customers will allow you to understand their wants and needs across channels better and provide them with more tailored services.
A Definition of “Data Enrichment”
The term “data enrichment” refers to merging first-party data gathered from internal sources (such as subscription forms) with data gathered from other internal sources or third-party external sources.
It’s easy to get the two terms mixed up, but data enrichment differs from data cleansing. Data cleaning eliminates incorrect or out-of-date items from datasets, whereas data enrichment adds value to preexisting first-party datasets by drawing from other data sources. In the end, you can understand data enrichment as enhancing the quality of datasets by adding new information.
Data Enrichment Methods
There are several varieties of data enrichment, the most frequent of which are as follows:
Enriching a dataset with additional demographic details, such as marital status and income, is known as demographic data enrichment. A wide variety of demographic information can be gathered, such as the number of children and the type of vehicle owned, among many others.
To ensure that the demographic data you enrich with serves your ultimate goals, you should be clear on those goals before beginning the process. For instance, consumer credit scores for credit card offerings and property valuations for insurance premiums. Your company may significantly boost the effectiveness of targeted marketing by adding demographic information to previously collected data, allowing for the delivery of highly personalized messages to specific audiences.
Indicators of geography
Postal codes and city limits are only two examples of geographical data that may be added to an existing dataset via “geographic data enrichment.” The system may use geographic information in various settings, such as when determining where to locate a new business or when trying to reach the most significant potential audience in a particular area.
Statistics on future purchases
Brands may get a clearer picture of a consumer’s propensity to purchase, enriching their data with information about their interest in and desire to purchase. To provide precise, performance-focused campaigns that target the correct customers and lead them closer to making a purchase decision, marketers need access to actual shopping data and product view frequency.
App-driven data enhancement
Businesses may learn about their customer’s interests and preferences by analyzing data about the applications their customers use, the platforms those apps are accessible on, and the devices they use.
Businesses may learn more about their customers’ tastes, which applications they should be producing, and how they can improve personalization efforts when they supplement their datasets with data about app use.
Improving customer subgroups with the use of augmented data
One of the most critical steps in effective marketing is segmentation, which identifies groups of consumers to focus on. Marketers may reach those who previously weren’t interested with tailored communications thanks to segmentation.
Marketers can segment based on third-party data, such as purchase intent or app activity, through data enrichment marketers. With this information, marketers will be better able to target specific demographics with their messaging.
Higher quality lead scoring increases conversion rates.
Manual lead scoring may be time-consuming, despite its importance in fostering a productive partnership between the sales and marketing departments. Data enrichment allows firms to automate the scoring of leads by including data from other sources.
Let’s imagine a potential customer who has visited a company’s website before but has never signed up for the company’s email newsletter. The potential customer chooses to sign up for the newsletter and puts their name and surname into the company database but leaves out their address.
You might improve the lead quality by employing a data enrichment tool that uses socio-demographic information to match the input data with reliable postal records and automatically append address information.