You’re probably employing data enrichment on a regular basis without even realizing it. Google’s autocomplete tool, for example, is compatible with it: It takes raw data (the characters you type in) and enhances it with a massive database of (nearly) all potential words. The end result? A smarter tool that enhances the user experience. But did you know that data enrichment (also known as data augmentation or appending) is now at the heart of many online businesses? Did you know that it’s now easier than ever to get started?
In this piece, we’ll go over the fundamentals of data enrichment in a broader context and why it’s such an important element for online fraud protection.
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What is Data Enrichment?
The process of increasing the correctness and reliability of your raw consumer data is known as data enrichment. Teams enrich data by adding new and extra information and cross-referencing it with third-party sources.
Data enrichment (also known as data appending) guarantees that your data accurately and completely represents your target demographic.
We can all agree on the need for reliable data as salespeople. This data enables us to better understand our customers’ wants and needs, improve our customer experience, and tailor our sales efforts to meet our leads wherever they are.
Advantages of Data Enrichment
Data enrichment has the potential to greatly improve the consumer experience. Here are some studies that demonstrate the value of tailored customer experiences:
- Sixty-six percent of customers want brands to understand their specific wants and expectations.
- 52% want all offerings from brands to be tailored.
- Customers are 54% more likely to look at things in-store and buy them online (or vice versa), and brands are 53% more inclined to invest in omnichannel strategies to match.
- The key to generating targeted, tailored customer experiences is accurate, enhanced data – and a lack of it can turn customers away.
The key to generating targeted, tailored customer experiences is accurate, enhanced data – and a lack of it can turn customers away.
Data enrichment can also aid in cost reduction. This is why: With a good data enrichment plan in place, you can focus on preserving data that is important to your business, such as client contact information or transaction history.
Other, less important materials can then be destroyed or moved to less expensive long-term storage facilities. Furthermore, enrichment allows for the detection and elimination of redundant data, which reduces overall spending.
The Best Data Enrichment Practices For Your Company
While each company’s enrichment process will be unique based on the sort of data collected and their strategic business goals, there are several best practices that can benefit brands regardless of their strategy.
Establish explicit criteria
The first step is to establish specific criteria. This entails determining the purpose of your data enrichment activities and then developing criteria to help you achieve that goal.
For example, if you want to increase the completeness and correctness of your customer data, you could establish a goal of 90% or higher data accuracy in customer profiles when evaluated against a third-party verification source.
Make processes as repeatable as possible.
The next step is to create repeatable processes.
It is a waste of time and money to design and implement new processes over and over again. You can apply consistent and accurate data analysis frameworks to several enrichment efforts by developing frameworks for data analysis.
Imagine the process of validating customer profile data using a standard set of third-party sources. You may easily reapply the function as needed by building a process that automatically scans these sites for certain data kinds.
Ensure that your efforts are scalable.
You want enrichment activities that can scale with your data volumes. In practice, this entails using automation wherever possible in order to remove manual touchpoints that may create additional complexity or unanticipated failures.
Put general applications first.
Finally, consider how processes will generalize to different datasets.
For example, if you develop a process to validate customer data given via desktop website forms, it’s a good idea to work with partners or services that can ensure this process is equally applicable to mobile customers.
Remember that Data Enrichment is A Process.
Data enrichment is not a one-time event. On the other hand, effective enrichment necessitates constant effort to ensure that the obtained data is relevant, accurate, and current.
That makes sense: data is continuously streaming into and out of your organization, and your data environment is ever-changing. Continuous enrichment is essential for getting the most out of data sources.
Data Cleansing vs. Data Enrichment
While data enrichment is primarily concerned with adding extra data to your CRM, data cleansing removes erroneous, irrelevant, or outdated data.
Both are necessary for maintaining a healthy, dynamic database, although data cleansing is usually done first to allow room for the updated, extra information supplied by data enrichment.
The same is true for your CRM data, which includes demographic, regional, and psychographic data. Your CRM’s purpose is not to collect as much information as possible but to acquire the highest-quality data that best represents your prospects and customers. When should you spend money on data cleansing? If your email list is growing, but your engagement rate is decreasing, you know it’s time to clean up your data. The same is true for any additional data you use to connect with your prospects and customers.
Keep a close check on your engagement rates (opens, click-throughs, and so on) in comparison to your total subscribers. They’ll inform you how well your database is doing.
Aside from evaluating your data’s performance, data cleansing should occur every six months or so. About half of the businesses spend more time cleaning data than using it.
Conclusion
Best practices for data enrichment, when combined with the correct tools and services, can assist in increasing the accuracy and dependability of lead and customer data. The end result? The improved value enables teams to capitalize on data possibilities.
