Let's say you have a little data on your leads, and you need to expand it to include richer information and details. Or, say you would like to combine all your data into a single database. Perhaps you are hosting an event, and instead of asking 15 fields of information as people register, you want to keep that number low and then add to that data after they sign up so that you understand them better.
What Data Enrichment Is
All of these situations call for a process known as data enrichment. Data enrichment is a broad term that means a process that enhances, refines, or improves raw data in some way. Before data enrichment, the data is incomplete, outdated, inaccurate, or simply lacking. After data enrichment, the missing or incomplete data is filled in, data is cleansed and verified, and becomes far richer and more useful.
There are lots of reasons to consider a data enrichment process. You may want to take a short lead form and fill it in with more information on each lead. Or, you might be trying to establish a consolidated data store. Whatever your purposes, there are a few steps you can expect during the data enrichment process.
The Steps of Data Enrichment
The first step in data enrichment is normalization. Normalization is the process of stripping the formatting from the data. Every system endows data with its own formatting. For example, when you create a Word document, it's in Word formatting. When you collect data in your CRM software, it's in the formatting created by the developer of your CRM. Normalization strips that formatting so that the data can be collected together in like format.
After normalization, the data is then extracted. In other words, the useful parts of the data are pulled out, discarding the parts you don't need. For example, if each of your data sets contains name, email, age, and hair color, but you have no use for their hair color, the extraction process would pull out the names, emails, and ages. This is oversimplified to illustrate the point, but you can see the potential applications for the extraction process.
Using Data Enrichment to Improve Lead Form Capture
Lastly, the data enrichment takes place. Data enrichment looks for missing data and finds the relationships that exist among your data. For example, if you create a lead form that only captures a lead's name and email, that keeps the form short and you get many more conversions than you would if you collected five or ten data points on each lead. But that richer information is still quite valuable to you. Data enrichment searches the internet and finds other data points on each of the leads who fill out your form.
The data enrichment process then fills in the gaps in your lead forms and delivers a richer, comprehensive collection of data on each of your leads. This is ideal for keeping lead form conversion rates up and for steering clear of the public's perception of marketers becoming Big Brother. Yet you sacrifice nothing when it comes to a full-bodied collection of valuable marketing data.