Table of Contents
What is Data Enrichment?
Data enrichment is the process of adding valuable context to raw data in order to make it more useful for analysis. It involves collecting, organizing, and combining multiple sources of information into a single dataset that businesses use to gain insights and uncover patterns in customer behavior.
On its own, customer data doesn’t say much. It’s a bunch of numbers, words, and symbols. And most of it is siloed in different areas, including:
- Marketing automation
- Customer support databases
- Social media channels
- Third-party sources
Each data point starts out as raw data, meaning it has no context or true meaning. When it’s created (e.g., from a form submission or customer purchase), it sits in a data store. And since most of it is disparate, it’s difficult to get any real meaning from it.
When it’s enriched, it can tell a much more powerful story. By compiling all of these different pieces of information together, businesses get a more complete picture of their customers and prospects — their interests, needs, behaviors, and preferences. This allows them to tailor products and services more effectively and create sustained competitive advantage.
- CRM data enrichment
- customer data enrichment
- Lead data enrichment
- Marketing data enrichment
How Does Data Enrichment Work?
Typically, businesses have a wealth of first-party data, collected directly from customers through interactions, transactions, and product use. At its core, data enrichment is a practice of blending first-party data from different sources, processing it, and supplementing it with additional information (third-party data) to provide a more comprehensive view of those instances.
To understand how it works, let’s take a look at a few data enrichment examples in practice.
Suppose a company’s sales and marketing team want to analyze their lead score fluctuation over time. Immediately, they might have initial data points from the company CRM:
- The lead’s industry
- Their interaction with emails
- A timeline of interaction events
This is first-party data.
But, what if the marketing and sales teams could supplement this with additional data — say, their activity on the company website, organization size, or market trends? Suddenly, they have a much broader view of this lead, and you’re able to track how their behavior changes as their lead score does.
With enriched data, they aren’t just seeing the lead score as a standalone number, but as a dynamic attribute interlinked with a host of other parameters. They can see, in near real time, how changes in these parameters affect the lead score.
Lending and Credit Scoring
Any kind of digital lending process is highly reliant on data enrichment. Banks and lending institutions have access to third-party databases that help them build holistic borrower profiles, vet them for risk, and assess their creditworthiness.
To determine creditworthiness, they compile data like payment history, credit mix, employment, and debt-to-income ratio from multiple sources (including public records and bureau data). This data is enriched with third-party information, such as alternative credit scores (which are based on things like rental history and utility payments), to create an accurate snapshot of the borrower.
Online busiensses can reduce their fraud rates by enriching customer data with external sources. For example, they might look up the IP address of a transaction to verify its location or cross-reference it with a known list of fraudsters. They can also use device fingerprinting techniques, like tracking mouse movement and typing speed, to confirm if someone is actually who they say they are.
Then there’s behavioral biometrics, which is used to assess suspicious activities. For instance, if someone logs in from an unfamiliar IP address or multiple devices at once, it can be flagged as fraudulent activity and blocked.
When ecommerce product pages show a few products underneath the main one with the header, “You might also like,” that’s a prime example of data enrichment. On the website’s backend, algorithms scan customer behavior to determine what other items are most likely to be of interest them.
Looking at aggregate data, a business can determine how their customers interact with products, what they purchase most often in combination with other items, and which products tend to have higher conversion rates. The website automatically combines this information with the individual user’s current shopping activity to create customized product recommendations that are tailored to individual customer segments.
Benefits of Data Enrichment
Data enrichment represents a huge opportunity for companies to turn data points that already exist within their organization into deeper insight that helps them make informed decisions for just about anything.
Improves Data Accuracy
Raw data isn’t technically “accurate” or “inaccurate.” It’s objective. But it’s also sitting untouched in internal sources. For it to mean anything, it must be extracted, transformed, and loaded it into a single data warehouse for further analysis.
Analyzing customer data from multiple sources is like playing an expert-level game of connect the dots. If there are a few pieces missing, the entire picture is ruined.
Data enrichment brings missing pieces into the fold, so teams can get a more accurate view of their customers.
Puts Actionable Insights Into Practice
Enriching data can create powerful opportunities for businesses to act on customer information quickly. For instance, if they spot an uptick in fraudulent activities from certain IP addresses or geographic regions they can block those users. Or if they see that a customer segment is increasingly engaged with their product, they can adjust their marketing campaigns accordingly.
Improves Customer Segmentation
Through an ETL process or within a metadata layer, data enrichment can help companies create a more granular understanding of their customers. This is especially helpful in industries that use segmentation for targeted marketing and sales initiatives.
Demographics, psychographics, and other kinds of data already exist somewhere in company systems. Data enrichment helps connect the dots, so marketers and salespeople can make smarter decisions on who to target and how.
Enhances Customer Experience
90% of customers say personalization is desirable to them, and 80% say they want to do business with organizations that offer them tailored experiences.
When data is up-to-date and accurate, companies find it easier to personalize their marketing and sales initiatives. Data enrichment enables them to segment customer data to improve the relevancy of ads, offers, and content.
An added benefit to data enrichment, in this case, is some of it is on autopilot. Content recommendations, for example, don’t require much human input if the data is already connected to the website’s backend.
Types of CRM Data Enrichment
CRM data includes account profiles, contact information, and sales activities. CRM data enrichment gives teams the opportunity to create buyer personas, zero in on their ideal customer profile (ICP), and refine their sales and marketing efforts.
Socio-demographics are data points that offer insight into consumer behavior. They include:
- Income level
- Marital status
Demographic data enrichment involves fusing these data points with customer profiles inside the CRM. Businesses often draw on it to supplement their existing customer data, forming a more comprehensive understanding of their audience.
Take, for instance, a SaaS company with first-party data about a user’s behavior on its platform — the features they use, their interaction frequency, and their subscription level. While it offers meaningful insights, it only offers a partial view of the user.
By integrating socio-demographic data, the company can see beyond the user’s interactions with the platform. Sellers gain insights into who the user is as an individual and incorporates this data into their sales engagement strategy.
For B2B companies, firmographic data is a great way to get better insight into their customers. It includes information about the business, such as:
- Company size
- Annual revenue
Firmographics are essentially demographics, but for businesses. They function about the same, but for B2B organizations (which can’t create buyer personas based on individual attributes).
Geographic data allows businesses to understand the regional and global distribution of their customers. It also helps them create digital heat maps showing where they sell most successfully.
Perhaps the most important use case for geographic data enrichment is localizing content. Businesses can use it to tailor their messaging and campaigns according to location, creating a more personalized customer experience.
Geographic data also goes hand-in-hand with demographic data — between the two, businesses can create a better picture of their potential customers and how to manage them.
Purchase intent data
Purchase intent can come from first-party and third-party data sources. Website visits, product views, and click-throughs are all signs of purchase intent.
B2B and B2C companies benefit from purchase intent data equally. For B2B companies, it can help them identify buying signals in the early stages of the sales cycle and help them focus on higher-quality leads first. B2C companies gain deeper insight into their customers’ preferences and habits, which they can account for in their marketing efforts.
Steps in the Data Enrichment Process
Data enrichment is a multi-step process that involves various facets of data management, including collection, validation, augmentation, and analysis. Here’s a more technical breakdown of the entire process:
1. Appending Data
To “append” data is to add additional data sources to a profile. It’s the foundation of the process.
Most data enrichment strategies start with appending data from internal sources, such as CRM, marketing automation, and billing software. Doing so creates a logical relationship between customers, their demographics/firmographics, what types of messaging they respond to, and their spending habits.
Appending data also means sourcing it from third parties and merging it with internal data. Examples include exchange rates for foreign orders, weather data from a particular region, or customer sentiment gathered from social media.
Data segmentation involves placing the object in question (i.e., the customer) into different subgroups based on its predefined variables. This helps the data model attribute different values and characteristics to each segment.
Segmentation examples include:
- Customer behaviors
In data enrichment, the data team would create calculated fields to assign segments to customers based on their attributes using an ETL process.
3. Derived Attributes
A derived attribute is a quality or characteristic of an object that’s calculated from existing data but not stored within it. They are commonly used to create visualizations for easy comparison and analysis.
- Weighted average of customer purchase values
- Average basket size per region
- Number of products bought by time period
- Date/time conversions
- Time-between calculations
- Dimensional tables
- Higher orders (e.g., parent/child product configurations)
In data enrichment, derived attributes are usually added to increase the accuracy of predictions and forecasts. Data science models can generate advanced derived attributes based on your data. This can include modeling and running assessments for customer churn risk or spending likelihood.
To manipulate data is to perform a series of operations on it from a “manipulation layer” in the data warehouse. Common manipulation functions include:
These operations enable businesses to join different data sources, add/modify variables, and replace missing or inconsistent data. It also helps them draw correlations between existing data points and uncover meaningful patterns.
When the team is ready to take unstructured or semi-structured data from a source and make it structured, they use an extraction layer. This is commonly used to further manipulate data, as well as assign values to missing or invalid fields.
Data scientists use this technique to clean up the data before feeding it through a machine-learning platform for analytics. It’s also used in natural language processing (NLP) applications such as voice recognition, sentiment analysis, and text classification.
The point of extraction is it brings forth the entities the data represents, such as people, locations, products, and companies.
Categorization is the step where unstructured data finally becomes structured for analysis. This could be for either a sentiment analysis or categorizing topics, products, and services.
For instance, if you’re running an NLP application to detect customer sentiment from a review page, you’ll need to sort out the reviews into positive/negative categories. Data science models can help with this by assigning numerical values to each individual review. Then, the team can measure how customers are responding to different products or services.
Purpose of Data Enrichment Tools
Data enrichment is impossible for a human to carry out at scale. Cleansing hundreds of thousands or millions of data points is impossible without the help of software.
Data enrichment tools fill in the gaps in the process by reading and transforming raw data into structured data. They also automate the process of combining multiple data sources, enriching records, identifying outliers, detecting errors, and eliminating duplicates.
These tools are particularly helpful when dealing with large datasets or complex formats like XML or JSON, which require specific processing for data cleansing and enrichment.
People Also Ask
Why is data enrichment important?
Data enrichment is important because it helps businesses get a better understanding of their customers. By enriching data with additional sources, companies can gain insights into customer preferences and buying habits. This kind of information is invaluable for crafting more effective marketing campaigns and strategies.
What is the difference between data enrichment and data cleansing?
Data cleansing is a part of data enrichment, but the two terms are not interchangeable. Data cleansing is primarily focused on improving data quality by identifying and eliminating errors in the dataset, such as missing or incorrect values. Data enrichment is a larger process that involves adding additional attributes to existing records to create a more complete picture of customers, prospects, and products.