Boost quality with data enrichment

Data enrichment, data appending, and data cleansing

There is a shared common ground between those three terms and it might get ambiguous at times. Let's explore what are the main differences between data enrichment, data appending, and data cleansing.

Data enrichment

Data enrichment generally defines the act of upgrading, refining, and altogether improving raw data by adding information from external sources.

Data appending

Data appending is a synonym of data enrichment, and those two terms can be used interchangeably. It also refers to adding new elements to raw data and enhancing it for further use.

Data cleansing

Data cleansing, on the other hand, is quite the opposite of data appending or enrichment. Data cleansing refers to deleting inaccurate or breached data from your dataset. After cleansing the data, you can enrich it via data appending or data enrichment processes.

Preparation for data enrichment

As mentioned before, data enrichment is used to complement and enhance raw data with additional information for more accurate and applicable use. However, before enriching data, you must have a clean existing database free of corrupt or inaccurate data. Appending data, no matter how up-to-date or relevant, to a corrupt and unmanaged database is detrimental for your business.

That's where data cleansing comes in handy. Cleaning your current data of errors and inconsistencies is crucial before adding new data. Sophisticated data management is extremely important in seeking successful data-driven decisions.

Data enrichment for lead scoring

Lead scoring is a well-used method by marketing and sales teams to establish a score of how likely a certain lead is to make a purchase decision. The score range is 1-100.

Enriched data allows you to see up-to-date information about a certain lead and its behavioral patterns. By implementing lead scoring you can get a better idea of how your qualified prospect looks like. Furthermore, it allows you to sketch a standard of a qualified lead. With this information, you can segment your audience into possible leads and qualified leads. Then you are able to shift your focus more towards one group or another, depending on circumstances and predefined goals.

As a result, with data enrichment, you can make better-tailored marketing campaigns and approach your target audience more effectively.

Data enrichment for lead generation

Lead generation is closely connected to quality data. External data about your prospects gives you the space to come up with strategic marketing campaigns tailored specifically for your target audience. Data in its raw form is useful, but enriching data is particularly important to gain deeper insights.

Data enrichment allows for a better understanding of your prospective audience. The more knowledge you have, the more personalized the marketing campaigns can be. Consequently, sales teams also benefit from those personalized campaigns, because they are now better equipped to deliver a sales pitch that results in a profit.

Data enrichment improves the quality of your data; as a result, you can make better data-driven business decisions and boost your lead generation efforts.

Demographic data enrichment

Demographic data enrichment is one of the many types of data enrichment. Mainly, demographic data consists of population, race, income, educational information, and employment status.

Why is it important?

You may have a certain amount of information about your customers already, but that might only be a drop in the ocean. The data you have gathered on your clients internally is only what they bothered to give you. There is a number of other data points that you may not have access to, and that data could make a significant contribution to your overall knowledge of a client. Imagine having access to all the relevant information there is about your customers. It substantially improves your chances of success to satisfy their needs and turn them into repeat clients.

To dig a little deeper into how demographic data enrichment makes your business operations more successful, let's see an example of how to effectively use the data.

Data use in practice

Employment information allows you to see the job position of a person. Let's call her Amanda. By knowing what Amanda does, you can look up the job description for the position in the company Amanda works at. You see that Amanda is a senior software engineer. She might be having difficulties acquiring large datasets of quality public web data for product development. Or perhaps she struggles with maintaining a pipeline of up-to-date and relevant data. There you have Amanda's pain points. And you have exactly the product to alleviate her struggles. At this point, you have enough information to approach her as a prospective client and show the value that you can bring.

All that from just one data point. Imagine working with a combination of datasets.

Coresignal's employee and job posting datasets allow for such insights. Implement the use of data-driven decisions and gather valuable insights to tackle your target audience better.

Data enrichment process

The data enrichment process is not a one-and-done sort of operation. It is a process that takes time and effort to keep the data accurate, fresh, and updated. Customer data constantly changes. It is rarely set in stone. Companies come up with ideas that make the business skyrocket exponentially or collapse miserably. People get promotions or get fired; hence, the change in income and headcount data. Technology gets upgraded, changed, or tossed. There are numerous things that could change in a blink of an eye; therefore, keeping the data fresh is key to making well-informed data-driven decisions.

If you choose not to

What happens if you refuse to embrace the enrichment process? Well, for starters, you will no longer have the ability to present your customers with relevant offers. The problems they faced and the products they needed three months ago might be solved and unnecessary now. What they search for today could be something entirely different and not in your area of expertise. At this point, you would be living in the past while the world keeps moving forward. To avoid that, proper data management and data enrichment processes must be implemented in your everyday work-life.

Automation of data enrichment processes

Keeping the data accurate manually can prove to be a menial work. However, we live in an age of technology where artificial intelligence and machine learning are better than ever. Automation of the data enrichment process helps keep the records applicable and up-to-date. AI machine learning algorithms run continuously on a daily basis. Furthermore, they can match the records more efficiently than a human being. As a result, with the use of automation, you are constantly provided with fresh and accurate data for data-driven business affairs.

Utilizing data enrichment tools

Data enrichment tools allow companies to integrate their CRM with high-quality online people and company records in real-time. Data enrichment improves your existing data and turns it into a more valuable asset. Data enrichment tools and services enhance existing databases by providing missing or outdated firmographic, technographic, and employee data points.

How to enrich data

The most convenient method to enrich data is by buying public web datasets from third-party providers. Third-party data is external data collected from various public web sources and stored in relevant datasets.

Consider your data needs

Buying public web datasets fills the missing data points and provides you with more complete data for further and more accurate decision-making. Different datasets consist of different data points. You can select one dataset to supplement your existing first-party data, or you can opt for two datasets or more, depending on your data needs.

Analyze existing information

Remember to keep your priorities straight; more data is not the goal. Right data is what you need to aim for. After all, you need to improve raw data, not cluster it with irrelevant data points. Check your existing information, analyze it, and decide what else would you need to satisfy your customer needs better.

Choose the right data

If you need marital status to improve your buyer persona visualization, don't opt for technographic data of a company. If you're researching income levels, you do not need excessively big data of the same company. If you're looking for contact data, tech review data is irrelevant.

You should seek to complete data, not cluster it.

Make it a valuable asset

One dataset that provides you with deeper insights is better than two datasets that are missing data. Enrich your data with caution, embrace the enrichment processes, utilize sophisticated management of existing data, and maintain data quality throughout the whole operation.

Benefits of enriched data

After all the processes of data cleansing and data appending, you are now provided with a handful of enriched data.

Customer engagement and customer experience are better than ever, personalized marketing campaigns have seen the light of day, informed decisions are being made left and right, and quality data issues are left behind in the past.

It goes without saying that enriched data has numerous benefits for you and your business. Here are some more:

• Increase business intelligence and cost-efficiency
• Improve acquisition and sales
• Better understand your customers
• Eliminate data redundancy and improve data quality
• Promote customer retention and engagement
• Enhance your data-driven decision making