What does data enrichment mean?

Data enrichment is adding practical context to raw data to make it more useful for research. Multiple sources of information are gathered, organized, and put together into a single dataset that businesses use to learn more about their customers and find trends in how they act.

Customer information doesn’t tell you much by itself. It has a lot of letters, numbers, and symbols. Most of it is locked away in different places, such as

• CRM
• Automation in marketing
• Databases for customer service
•  Websites
• Social media sites
• Sources from outside the company

Starting with “raw data” means that each data point has no background or real value. It goes to a data store when it’s made, like when a form is filled out, or a customer buys something. It’s also hard to figure out what it all means because most of it is unrelated.

If you add to it, it can tell a much stronger story. When companies combine all of this information, they get a fuller picture of their customers and potential customers, including their wants, needs, behaviors, and likes. This lets them better tailor their goods and services to meet the needs of their customers and gives them a long-term competitive edge.

Like words

• Adding to CRM info
• Adding to customer info
• Lead the improvement of data
• Adding to marketing information

How Does Adding to Data Work?

Most of the time, businesses have a lot of first-party data they get directly from customers when they connect with, buy from, or use their products. Data enrichment is combining first-party data from different sources, processing it, and adding extra information (third-party data) to those cases to get a fuller picture of them.

Let’s look at some real-life cases of data enrichment to understand better how it works.

Scores for Leads

Let’s say that a company’s sales and marketing team wants to examine how their lead score changes over time. Right away, they might have some basic information from the company CRM:

• The lead’s business
• Emails they sent and received
• A list of all the interactions that happened

This is information from a first party.

But what if the sales and marketing teams could add to this with other information, like what they do on the company website, the size of the business, or market trends? They can now see a lot more about this lead, and you can see how their behavior changes as their lead score changes.

With enriched data, they don’t just see the lead score as a number; they see it as a dynamic attribute tied to many other factors. They can see how changes to these factors affect the lead score almost instantly.

Credit Scores and Lending

Any digital loan process depends a lot on data enrichment. Third-party databases give banks and other loan institutions information about potential borrowers that helps them make complete profiles, check them for risk, and decide if they are creditworthy.

They get information about a person’s payment history, credit mix, job, and debt-to-income ratio from many different places, like public records and credit bureaus, to determine if they are creditworthy. Third-party data, like alternative credit scores (based on rental records and utility payments), is added to this data to make a complete picture of the borrower.

Stopping Fraud

Adding information about their customers from outside sources can help online businesses lower the number of frauds they see. For instance, they might look up a transaction’s IP address to see where it happened or compare it to a list of known scammers. They can also use “device fingerprinting,” which includes watching how the mouse moves and how fast you type to make sure someone is who they say they are.

Next is behavioral biometrics, used to check out actions that seem fishy. If someone logs in from an IP address they aren’t familiar with or from multiple devices simultaneously, this could be seen as fraudulent behavior and blocked.

Online shopping

One great example of data expansion is when e-commerce product pages show a few other items below the main one with the heading “You might also like.” On the website’s back end, algorithms look at how customers use it to determine what other things they are most likely to be interested in.

By looking at the data together, a company can see how their customers use their goods, what they often buy with other things, and which products tend to have higher conversion rates. The website instantly combines this information with what each user is currently shopping for to make product suggestions specific to each customer group.

Advantages of Adding to Data

Companies can turn data points they already have into deeper insights that help them make better decisions about almost anything through data enrichment.

It makes data more accurate

Of course, raw data is neither “accurate” nor “inaccurate.” It is unbiased. But it’s also sitting there in internal sources without being used. It must be removed, changed, and put into a single data warehouse to be analyzed further before it can mean anything.

Looking at customer information from many different sources is like connecting the dots at a very high level. The whole picture is ruined if a few bits are missing.

People who work with data can get a more complete picture of their customers when they add missing data.

Does Something With Actionable Insights

Businesses can take decisive actions quickly based on customer knowledge when they enrich their data. For example, if they see a rise in fraud from specific IP addresses or parts of the world, they can block those people. Or, if they notice that a particular group of customers is becoming more interested in their product, they can change their marketing efforts to reflect that.

Helps more Divide customers into groups

Data enrichment can help businesses learn more about their customers by adding more information during an ETL process or in a metadata layer. This is especially useful for businesses that use segmentation to make their marketing and sales more focused.

The company tools already have some demographic, psychographic, and other data. It helps marketers and salespeople figure out who to target and how to do it better by compiling more complete data sets.

It makes the customer experience better

People want customization 90% of the time and want to do business with companies that give them customized experiences 80% of the time.

Companies can better tailor their marketing and sales efforts with correct and up-to-date information. With data enrichment, they can divide customer data into groups that help ads, deals, and content be more relevant.

One more good thing about data improvement in this case is that some of it is done automatically. For example, content suggestions don’t need much help from a person if the data is already linked to the website’s backend.

Different kinds of CRM Adding to Data

Account profiles, contact information, and sales actions are all types of CRM data. By adding more information to CRM data, teams can create client personas, find their ideal customer profile (ICP), and make their sales and marketing more effective.

Sociodemographic information

Sociodemographics are sets of facts that show how people act as consumers. Some of them are age, income, marital status, gender, and amount of education.

Demographic data enrichment is all about adding customer profiles to demographic data in the CRM. Businesses often use it to add to the information they already have about their customers and get a fuller picture of their audience.

Think about a SaaS company with first-party data about how users interact with its platform, such as the features they use, how often they interact, and their subscription amount. It gives you helpful information but only shows a small part of the person.

By adding sociodemographic information, the business can see more about the user than just how they use the app. Sellers learn more about the user as a person and use this information to improve how they connect with customers.

Data about the company

When businesses sell to other businesses, firmographic data can help them learn more about their buyers. It has details about the company, like ·Industry ·Size ·Human Resources ·Location and amount of money made each year.

Firmographics are like demographics, but they are used for companies. For B2B companies, which can’t make buyer characters based on individual traits, they work pretty much the same.

Data about places

Geographic data helps businesses figure out where in the world and in what area their customers are. It also helps them make visual “heat maps” that show where their best sales are happening.

Putting information in the right place may be the most essential use case for adding geographic data to other data. It lets businesses customize their ads and messages based on where the customer is, making the experience more personal.

Demographic and geographic data go hand in hand. Together, they help businesses better understand their possible customers and how to handle them.

Data on purchase plan

Both first-party and third-party data sources can show a desire to buy. Viewing products on a website and clicking through to them are all signs that someone wants to buy something.

Purchase-purpose data is helpful for both B2B and B2C businesses. For B2B companies, it can help them find buying signs early in the sales cycle and put their attention on better leads first. Businesses that sell to consumers learn more about their likes and dislikes and what they usually do. This helps them make better marketing decisions.

The steps that are taken to enrich the data

The multistep data enrichment process includes many aspects of data management, such as collecting, validating, adding to, and analyzing data. Here is a more detailed explanation of the whole process:

1. Adding Information

Adding more data sources to a profile is what it means to “append” data. It’s what the process is built on.

Adding data from internal sources like CRM, marketing automation, and billing software is the first step in most data enrichment plans. In this way, there is a clear link between customers, their firmographics, the messages they react to, and how much money they spend.

Adding data also means getting it from outside sources and combining it with data from inside the company. Some examples are exchange rates for sales from other countries, weather information from a particular area, or social media data that shows how customers feel about a product.

2. Splitting up

Putting an item (like a customer) into different subgroups based on its known variables is what data segmentation does. This makes the data model give each segment a unique set of values and traits.

Some examples of segmentation are demographics and firmographics

• Geography
• Technological
• Psychological
• Behavioural
• Customer

In data enrichment, the data team would use an ETL process to make calculated fields that would be used to give segments to customers based on their attributes.

Part 3: Derived Attributes

A derived attribute is a property or trait of an object calculated from data already available but not saved in that data. They are often used to make images that make it easy to compare and analyze things.

One example is the weighted average of the amounts that customers have bought.

• Average basket size by region
• Number of items bought by period
• Conversions of dates and times
• Calculations in between
• Tables with dimensions

More complex orders, like parent-child product combinations

Derived traits are often added to data enrichment to make predictions and forecasts more accurate. Based on your data, data science models can come up with advanced-derived characteristics. This can include making models and tests to see how likely a customer will leave or spend money.

4. Changing things

Changing data means doing many things to it from a “manipulation layer” in the data warehouse. Some everyday editing tasks are:

• Filtering
• Aggregation
• Joining
• Sorting
• Summarizing

Businesses can use these processes to combine data from different sources, add or change variables, and replace missing or irrelevant data. It also helps them find patterns and make connections between pieces of information that they already have.

5. Getting out

The team uses this to take random or semi-structured data from a source and put it in a way that makes sense. This often changes data and fills in blank or incorrect fields with values.

Data scientists use this method to clean up data before putting it through a machine-learning tool for analysis. Voice detection, sentiment analysis, and text classification are some natural language processing (NLP) tasks used.

The point of extraction is to bring out what the data stands for, like people, places, goods, and businesses.

6. Categorization: This is the last step in turning unstructured data into ordered data that can be analyzed. This could be used to either figure out how people feel about something or put topics, goods, and services into groups.

If you’re using NLP to figure out how a customer feels about a product review page, for example, you’ll need to sort the reviews into positive and negative groups. This can be made more accessible with the help of data science models that give each review a numerical value. The group can then check out how customers feel about various goods or services.

What Data Enrichment Tools Are Used For

A person can’t improve a lot of data at once. Without software, cleaning up hundreds of thousands or millions of data bits is impossible.

These tools read raw data and turn it into organized data, filling in the process’s blanks. They also simplify combining data from different sources, adding to records, finding outliers, finding mistakes, and eliminating duplicates.

These tools are convenient when working with big datasets or complicated forms like XML or JSON, which need special processing to clean up and add to the data.