What is Data Enrichment?
Data enrichment is defined as merging third-party data from an external authoritative source with an existing database of first-party customer data. Brands do this to enhance the data they already possess so they can make more informed decisions. All customer data, no matter the source, begins in its raw form. When this collected data flows into a central data store, it often is ingested into the system in discrete datasets. What you often have when this happens is data being dumped into a data lake, or a data swamp, full of raw information that often isn’t useful outside of narrow contexts.
Prior to performing data enrichment, a company should make sure its customer data is in a form and of a quality that meets business needs for raw data, which can include data cleansing services to clean, normalize and standardize data.
Data enrichment makes this raw data more useful. By adding data from a third party, brands gain deeper insight into their customers’ lives. The resulting enriched data is richer and more detailed, which enables brands to more easily personalize their messaging because they know more about their customers. Strong data enrichment processes are a key part of building the golden customer record. One dataset by itself, no matter how detailed, doesn’t include every piece of behavioral or transactional data needed to build a comprehensive single view of the customer. This is why data enrichment practices are vital to marketing’s long-term goal of delivering personalized experiences.
Two Kinds of Data Enrichment Examples
There are as many types of data enrichment as there are sources to acquire data from, but two of the most common are:
- Demographic Data Enrichment: Demographic data enrichment involves acquiring new demographic data, such as marital status and income level, and adding that into an existing customer dataset. The types of demographic data are vast, as are the sources. You could receive a dataset that includes number of kids, type of car driven, median home value, and so on. What matters with demographic enrichment is what your end purpose is. If you want to provide credit card offers, for example, then you might acquire a database that provides the credit rating of a person. Data enriched in this way can be leveraged to improve targeting of marketing offers overall, which is vital in an age where personalized marketing holds sway.
- Geographic Data Enrichment: Geographic data enrichment involves adding postal data or latitude and longitude to an existing dataset that includes customer addresses. There are a number of providers that allow you to purchase this data, which can include ZIP codes, geographic boundaries between cities and towns, mapping insights, and so on. Adding this kind of insight into your data is useful in a few contexts. Retailers could use geographically enriched data to determine their next store location. If the retailer wants to capture the most customers within a specific radius, for example 30 miles, they can leverage their enriched data to make that decision. Marketers could also use geographic enrichment to save on bulk mailings of direct mail.
How can you achieve data enrichment to build an accurate, updated golden customer record? Data cleansing services to cleanse, normalize and standardize customer data is one component of successful data enrichment. Two other important components are data enrichment services and data enrichment tools.
A data enrichment service is a means to call out internal or external components to perform a particular kind of enrichment. An address validation/verification service is one example. Vendors may also provide a nine-digit ZIP code and geographic coding information for a more detailed understanding of a specific address. A more granular understanding of demographic information is another common reason for an external call to a data enrichment service, such as a call to Axciom or Experian with a customer name to receive more information about past buying habits, total income, demographic preferences, etc.
Data enrichment tools are used in data enrichment processes to do things like make internal calculations of customer lifetime value (CLV) by totaling transactions, or calculating customer propensities for churn and other behaviors. A determination of whether a customer is a churn risk or a high-value customer, for example, has obvious value as a data enrichment tool by providing insights from raw data – in many or most cases using machine learning to make calculations at scale.
Every form of data enrichment is valid, depending on your business goals. What’s important is identifying the kind of data you need to seek out and collect or acquire to get a positive solution. A word of caution, however. Whenever you acquire third-party data or attempt to match two first-party datasets, there must be a common factor that links the two datasets together.
For anonymous customers, this can be a device ID signifying a mobile device or desktop computer. For known customers, this could be a first name and last name or a mailing address. Even an email address can be used as an identifier to match and merge two distinct datasets. Otherwise, the original dataset won’t be enriched because there’s nothing to show that the two datasets refer to the same customers.
Keeping the Data Enrichment Process Going
Much like every other aspect of data management, data enrichment isn’t something you can do once and then never do again. Customer data, no matter how detailed, is fundamentally a snapshot in time. Income levels rise and fall, marital status may change, and the type of car and physical address can alter. Even names may change, especially if there is a change in one’s marital status.
Given the possibility of all these changes, data enrichment tools and processes need to run on a continuous basis. The alternative is having outdated information that could lead to customers receiving irrelevant offers because your data is six months out of date. Keeping all this information updated is a titanic undertaking, especially for larger databases, so it’s little surprise that more than 50 percent of businesses spend more time cleaning data than using it.
The time commitment for keeping data up-to-date is a strong argument for automating the process. Machine learning algorithms that run on a continuous basis can substantially streamline the data enrichment process because they can match and merge records much faster than a human data steward. This leads to a data enrichment process that runs 24 hours a day, seven days a week, and results in data that is always the most up-to-date it can be. Ultimately, this allows brands to maintain a high level of enrichment and keep the process moving forward in real time to enhance customer engagement.
A well-functioning data enrichment process is key for a modern brand. Keeping data up to date ensures that the brand can more accurately target consumers, whether it’s geographically based for placing a new store or demographically based for richer targeting of next best offers. More accurate targeting leads to better experiences, which inevitably creates more data and ensures you can continue to work with the most up-to-date customer data possible.
