How does a business decide what kind of buyers to target? How about what kind of ads to launch? The answer is simple—they rely on concrete data to make such informed decisions.
Think of data as the fuel to every business’s engine, helping them better understand their customers, the market, the competition, the success of their operations, and so much more.
The problem is, while all businesses are capable of producing tons of raw data from their everyday sales, marketing, and customer success efforts, oftentimes it’s not enough to paint the full picture.
This is where data enrichment steps into play, enhancing the available raw data with additional insights that, together, transform data into meaningful decisions.
Data enrichment provides businesses with actionable intelligence that empowers them to create more precise, personalized, and effective customer experiences.
In this article, we’ll cover everything you need to know about data enrichment—how it works, why it’s important for all businesses, common use cases, and software to help you on this mission.
What is data enrichment?
Data enrichment refers to the process of fusing raw data with external data sources with the main goal of creating more accurate customer profiles.
Simply having the name and email of a potential lead is not nearly enough in today’s landscape to create a personalized buyer experience, which has become increasingly crucial in today’s customer-centric environment. By creating a data enrichment process, businesses can supplement that information with relevant details such as demographic, technographic, and behavioral data, to name a few.
After all, the better a business knows its customers, the better it can connect with them via its sales and marketing efforts.
That’s why the main goal of data enrichment is to transform incomplete or unstructured data into valuable information, allowing teams to fine-tune their strategies and engage customers with the right message at the right time.
Data enrichment is one of the 3 key elements of transforming data into decisions, and some people even argue that all 3 fall within the wheelhouse of data enrichment:
- Validation → verifying that the existing data from internal and external sources is correct and updated at all times, avoiding any misleading conclusions or decisions. In the context of email outreach, for instance, validating your customer emails is crucial to ensure successful email deliverability and avoid the spam folder.
- Enrichment → supplementing existing (and validated) data with additional internal and external datasets, as well as filling in the gaps of any missing details. This could be integrating additional prospect information from dedicated contact databases, updating existing customer profiles with product usage data, and the list goes on.
- Contextualization → once you’ve established mechanisms to validate and enrich your data, the final piece of the puzzle is to make logical interpretations based on the complete datasets. In other words, explaining the ‘why’ behind the data, for instance, analyzing why certain prospects end up making a purchase while others don’t.