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What is Data Enrichment?
Data fuels modern businesses and enables leaders to make precise, evidence-based decisions rather than guessing or speculating. Many organizations are preparing for the future by acknowledging that data is unstructured and diverse, and that there is a need for data enrichment, discovery, cleaning, structuring, and validating.
Data enrichment is a critical first step in gleaning valuable insights that can benefit a company based on data collected through analytics or machine learning. It involves the merging of authoritative, externally-sourced third-party data with the information in an existing first-party customer database. The goal of this kind of merge is to make more informed decisions by enhancing the existing data.
There are three types of typical data sources. First-party data enrichment includes the information the organization directly collects from its customers. First-party data can be collected from transactions, marketing responses, website and app user behaviors, social media analytics, subscription lists, customer surveys, customer service/support interactions, or customer relationship management (CRM) platform data.
Second-party data is the first-party data of someone else. Businesses use it to target customers similar to theirs, or to break into a new demographic that makes up a notable piece of the customer base of the source organization.
Third-party data (external data) is aggregated, collected, compiled first-party data from a range of sources. With third-party data, an organization can purchase specific, targeted datasets from data aggregators to enhance their existing first-party information for enrichment purposes.
All three varieties of data can help businesses understand and more precisely target an ideal audience. Direct, first-party data is usually the most valuable, but it may lack scale. A panoramic consumer view at scale demands machine learning data enrichment with second- and third-party data. This is also an essential step toward uncovering valuable insights and identifying patterns by applying analytics in more meaningful ways.
No matter the source, all customer data starts in raw form. A central data store often ingests data that flows into it and collects there in discrete datasets. The end result of this pattern is a data swamp of raw information.
After cleansing and preparing this raw data, consumer data enrichment makes it more useful in multiple contexts. Brands acquire much deeper insights into the lives of customers by adding third-party data to the picture. The result is a much more detailed, personalized message based on the enriched data that hits home for customers.
Strong data enrichment processes are part and parcel of developing a functional customer record, because a single dataset can never include enough transactional or behavioral data, no matter how detailed, to build a comprehensive view of the customer. This is why data enrichment techniques are critical to delivering personalized user experiences and better customer value.
What are Some Use Cases?
Several types of data fuel the enrichment process, and they vary by source and data volume. From there, organizations can select which categories can best enrich the data in their existing databases to help achieve mission critical goals.
Enriching behavioral data means enhancing user profiles of customers with their behavioral patterns. Some examples using behavioral data might include social media data enrichment and business data enrichment using internet search behavior data that reveal how customers act online, or consumer lifestyle and spending data that reveals how customers behave in other settings.
This allows businesses to identify customer interest areas and clarify what the purchasing decision and customer journey look like. Enriched behavioral data also assists in justifying marketing budgets and determining the ROI for advertising campaigns.
Demographic Data and Contact Data Enrichment
To accurately target messaging to specific demographic groups and ensure messaging and advertisements are relatable, it’s critical to enrich demographic data. This also also empowers organizations to customize messaging toward both individuals and other organizations.
Examples of company-level demographic data include firmographic data with key contacts; data on payroll, sales, and economic indicators about stability and growth; technological data such as global and regional website rank and site trends and traffic; and data on business health and reviews. Examples of individual demographic data include information about customers’ financial health and income, and of course the standard demographic details you imagine like age, gender, income, education level, marital status, and other statistics. Financial data enrichment is an important area for most businesses.
Enrich geographic data to target messaging to ensure content is relatable to users based on their time zone, country, and/or city. Address data enrichment is an example of this. Geographic data enables businesses to check user locations by looking up an IP address and personalize content based on trends specific to that area. Details about property value, population density, tourism, and even foot traffic data in specific locations can all shape these kinds of personalization initiatives.
Many data enrichment services are potentially valid, based on the specific goals of the organization. There are many ways that data enrichment companies or platforms might enhance different types of data for better results. Here are some common data enrichment examples to consider:
Advanced Account Scoring with Fewer Form Fields
In the past, fewer form fields for lead capture came at a cost: more conversions but a longer buying cycle as sales or marketing would struggle to collect missing data. Data enrichment AI technology can power sales intelligence tools that can complete missing data for each account in a contacts database so marketers can balance not turning away potential leads with long, daunting forms while still getting necessary lead quality information. Real-time data enrichment solutions can automatically categorize prospect priority so your team can stay out of the scoring process entirely.
For example, without data enrichment in marketing, if a lead enters the contact database with only a personal email address and name, a lead scoring system will probably score it low. To determine product fit and buying intent more accurately, a sales intelligence tool can use machine learning data enrichment to link the lead to more company data-points without any additional effort.
Every Customer Interaction Personalized
Insight into a target consumer increases exponentially with enriched data, enabling your team to create the right customer journey for each user with hyper-targeted customer segments. Marketing and sales both rely on relevant, personalized customer interactions shaped by the technographics, firmographics, and any recent relevant events for the prospect organization.
Enrich CRM Data for Improved Customer Experiences
Data enrichment software for CRM information improves both engagement and customer experiences by enabling businesses to glean more insights and draw more accurate conclusions from information in existing databases. Real-time enrichment allows for reduced churn thanks to tailored scripts for calls, with more relevant opportunities for cross-sales and upselling. By identifying and tracking business signals among existing customers you can ensure a much higher-quality user experience.
Enabling Machine Learning Technology
Artificial intelligence (AI) and machine learning (ML) now allow marketing and sales teams to offer potential and existing customers increasingly personalized touchpoints in ways that would have demanded massive budgets and time in the past.
Chatbots offer a practical example of this use case. Data enrichment solutions can empower your team to collect customer data through chatbot conversations that can then be enriched with a robust sales intelligence tool or existing information.
The GDPR and other laws impose limits on what kinds of customer data businesses can collect, store, process, and for how long. This places the burden of bringing databases into compliance and scrubbing them on the business, setting many up to be penalized or to discard potentially valuable data to avoid that issue.
Although it may be less exciting in some ways, it is at least as important: enrichment techniques can help ensure your organization remains compliant with data privacy laws and regulations. Some techniques can optimize for compliance with do-not-call lists, GDPR, and other regulatory requirements, and ongoing enrichment can preserve the data’s ongoing utility. This ensures that even if regulations change in the future, your secure data strategy is complete, accurate, and legal.
What are the Benefits of Data Enrichment?
Enriching data has many important benefits for businesses.
It is costly to manage the data, yet most businesses only use a fraction of their data for any real benefit. E-commerce data enrichment prevents storing information that is not useful and saves money by enhancing internal data for the organization’s benefit. Use those funds more productively.
User Experience, More Meaningful Customer Relationships
Personalized communication is possible thanks to enriched data, and this increases the likelihood of better business opportunities based on more meaningful customer relationships. Meet customer needs and preferences more adeptly with communication strategies based on relevant customer data
Customers know their data is out there and they have serious expectations when it comes to their brand experience. They may have mixed feelings about their data being used for marketing, but at the same time, they expect brands they like to anticipate their needs, know what they want, and be relevant. Enrichment records provide this detailed information and enhance customer experiences.
Modern consumers expect that any brand that really wants their business will do enough homework to be able to do more than meet expectations. Instead, they want brands to understand their priorities and pain points in a deeper way, and to make personalized moves that are thoughtful. Enriched customer data empowers businesses to deliver bespoke experiences and messaging throughout the sales process.
Customer Nurturing and Targeted Marketing
Your business can use lead data enrichment tools and personalized marketing to stay relevant and encourage customers to buy from your brand. A one-size-fits-all approach is already the way of the past. To succeed with targeted marketing, an organization must effectively segment their data using data enrichment tools and techniques.
Enriching marketing data identifies customer segments to be nurtured. A segment is just a piece of your audience broken down based on interests and other value-driven information.
For example, your organization might want to target marketing content to members of a particular trade organization that is not represented by a field in your landing pages or lead capturing forms. You may use enrichment methods to extract this information from social media and apply that information to your data, allowing those leads to be included without adding extra fields to forms.
Data enrichment software and other solutions can boost ROI and increase sales efficiency by ensuring the organization’s contact list is accurate and clean. It also offers a business more opportunities for upsells and cross-sells because it empowers the sales team with the right customer knowledge and data.
Advanced Lead Scoring
Scoring and evaluating leads helps a sales team prioritize goals effectively, but it is only worth the resources and time with enough information. Your team can reliably score in-depth customer profiles with enriched data, making an accurate, meaningful score possible. This allows the marketing and sales teams to collaborate to define the most relevant data points for lead scoring moving forward, to better focus on how to enrich lead data.
Elimination of Redundant Data
Redundant data causes significant costs for businesses. It results in customer loss, revenue loss, and reputation damage. However, because businesses are uncertain of which data to retain and which to discard, redundant data is common. Data enrichment tools can eliminate redundant data and the data duplication that hurts raw data quality.
What is Automated Data Enrichment?
Like other parts of data management, enriching data is an ongoing process. Even when it is very detailed, customer data provides a glimpse in time. Even names can change, as people get married or divorced, and things like income levels, physical addresses, type of car, and occupations are likely to change over time.
Because customer data is always in flux, data enrichment processes must be continuous. Otherwise, businesses end up with outdated customer information. Yet maintaining all of this information is a massive undertaking, leaving over half of businesses spending more time cleaning data than using it.
Mitigating the time problem caused by maintaining data up-to-date is where automated data enrichment comes in. Data enrichment machine learning algorithms can run constantly to match and merge records and streamline the process substantially. This automation results in data that is always up-to-date thanks to a pattern that is active 24 hours a day. Ultimately, this empowers brands to enhance customer engagement in real-time.
This is another excellent reason to outsource data enrichment. The need for more advanced machine learning data enrichment techniques makes data enrichment outsourcing services an appealing option, especially for SMBs.
Data Enhancement vs Data Enrichment
In the B2B data space, several phrases and terms get used—sometimes interchangeably—which can be confusing. Here we explain how data appending, data cleansing, and data enhancing relate to the data enrichment definition.
What is Data Cleansing?
Data cleansing is the process of identifying and removing inaccurate, incomplete, corrupt, or irrelevant records from a dataset so you can then update and ensure complete data.
What is Data Appending and Data Enhancement?
Data appending and data enhancement both refer to the process of improving first-party data with other data sources. In other words, they are both alternative ways to define data enrichment. These terms are often used interchangeably.
There is no real difference between data enhancement vs data enrichment. These phrases all describe the same basic process of enhancing or improving internal or first-party data with external or third-party data.
Why Data Enrichment is Important
Customer data enrichment enables organizations and businesses to add value to data by making it reliable and useful for end users. Businesses can also better tailor services and products to customer needs, and develop a better understanding of their customers more generally, when they enrich data.
In fact, there are many reasons why enriching data is important, but here are some of the most common.
Improves Data Accuracy
One lone dataset lacks sufficient power to generate a meaningful view of a customer. Through data enrichment providers and platforms, raw data becomes useful as businesses collect data that is valuable to them and add missing information to their original customer dataset. This trustworthy data is something the stakeholders can use moving forward.
Creates a Living Customer Record
By enriching your data, and ensuring it is accurate and up to date, businesses can create a personalized customer record they can capitalize on for the life of the company. Enriched data can increase the ROI on every customer with enhanced outreach and improved conversions on marketing campaigns.
Improves Customer Experiences
There are many ways to signal to customers that you care about their experience with the brand. Enrichment allows the business to engage in many of them while staying relevant to users. Personalized messaging proves to users that you understand them and their needs, and improves customer relations.
Improves the Final ML Model
Big data enrichment enables you to fill in missing information in cases where fields lack data. This is important, especially where the lack of information itself is informative—such as when fraud is present. Enrichment also allows businesses to add new fields while maintaining the same number of records, and connect even heterogeneous data in new ways.
Steps to Enrich Data at Your Organization
The data enrichment process is complex, but there are several steps to follow when implementing the practice.
Establish a Data Enrichment Goal
Establish an overall data enrichment goal first. The general goal of improving the accuracy and quality of the data is too broad in most situations. To establish this goal, consider what data is relevant to the organization and would offer the most insight into its users.
Consider data enrichment logic when deciding which data to enrich. For example, enrichment is usually most accurate in real-time, although in some cases it makes sense to enrich data in a warehouse.
Evaluate Your Data Set
Assess the completeness, quality, and accuracy of the existing data, identifying any gaps in records, before starting the data enrichment process.
Segment the Audiences to Enrich
Target and restrict your data sets based on specific target segments. Enrichment and segmentation should always serve each other so you can realize more ROI from B2C data enrichment.
Use Data Enrichment Tools
The best data enrichment tools allow you to effectively collect, organize, cleanse, format, and enrich data effectively. There are many B2B data enrichment tools available, and businesses often use more than one tool in the process.
Keep Data Updated
Enriching data is in no sense a “one and done” task. Rather, it’s an ongoing process designed to ensure everyone in your company has access to valuable data and doesn’t waste time. It’s critical to be sure that data is continuously enriched and up to date, ideally in real-time. Otherwise, data decays naturally over time, losing its value until it is essentially worthless to collect and store.
Invest in tools for automated data enrichment to keep ahead of this problem and ensure data is accurate. It is also a best practice to implement a data cleansing schedule, and validate your data regularly to ensure it is up to date.
Does Explorium Have Data Enrichment Tools?
Yes. Explorium allows data analysts and data science teams to quickly find and integrate the most relevant external data signals for their advanced analytics pipelines. With the Explorium you can:
- Gain a competitive edge with machine learning data augmentation for better business context.
- Accelerate analysis and results with automated data discovery, matching, and integration.
- Cut your costs of acquiring and onboarding external data at scale by using a single data enrichment platform.
- Extend unlimited big data enrichments across the volume and variety of data sources from a single gateway.
Explorium provides access to thousands of proprietary , premium and public external signals that have been validated and normalized. Explorium’s external data gallery covers multiple categories including company data, people data, geospatial data, time-based data and more.
Next, enrich customer data and more with the most relevant external data signals. Explorium’s machine learning data enrichment tools help you identify the most relevant data signals quickly. It recommends signals with the best coverage and provides a rich description for each signal. Add signals with a single click and our automated data enrichment technology instantly matches and integrates them with your internal data. As you start adding enrichments, the system uncovers more data signals for you to explore.
Prepare your data with inbuilt transformations. Explorium provides various enrichment techniques and ways to prepare your data with many inbuilt transformation functions like adding dates, enriched data such as contact details and other third-party data points, splitting (exploding) values, defining filters based on specific benchmarks, and editing classifications.
Finally, integrate enriched data into your advanced analytics. The platform automatically integrates the data into your production pipelines using recipes for real-time data enrichment. You can define the recipe in your output format and schedule it to meet your business needs.