What is data enrichment?
Data enrichment is the process of improving the accuracy and reliability of your raw customer data by filling in missing or incomplete information. Teams enhance data by adding new and supplemental information and verifying it against third-party sources.
Data enrichment (also known as data appending) ensures that your data accurately and thoroughly represents your audience. As salespeople, accurate data is crucial to understanding our customers' wants and needs, improving their experience, and personalizing our sales efforts to meet them where they are.
Enriched data helps brands gain deeper insights into their customers' lives. There are many ways to enrich data. For example, enriching internal sales data with third-party advertisement data can improve understanding of advertising effectiveness.
What does data enrichment do?
Data enrichment begins by using your internal data. You can combine first-party data from internal sources with disparate data from other internal systems or third-party data from external sources.
With Bardeen.ai, you can easily enrich your data using our integration with data enrichment services like Clearbit. We also utilize the power of web data scrapers to improve data accuracy. Additionally, you can quickly connect your internal data from CRMs such as Salesforce, HubSpot, Pipedrive, or even Google Sheets, Airtable, and Notion with our data enrichment service.
What are the Methods for Data Enrichment?
There are several ways to enrich data, including:
- Data Scraping: This involves using specialized software or scripts to extract data from websites or other online sources. It can be an efficient method for collecting large amounts of data, but may require some technical expertise and can be time-consuming. Explore 100+ website data scrapers.
- Manual Research: This involves manually entering data into a database or spreadsheet. While this method is relatively slow and prone to errors, it can be useful for small amounts of data.
- Data Enrichment Tools: These are software or services that gather, organize, clean, and format data from third-party sources and aggregate it from different sources. Examples of data enrichment tools include Clearbit, Snov.io, and Zoominfo. These tools can be effective, but they may have limitations in terms of the data points and sources available. Data scraping may offer more flexibility in terms of selecting data points and sources, but it may require more technical expertise and may be more time-consuming.
What are the Data Enrichment Techniques?
- Data combining: By adding data to your data set, you combine data from several sources to form a more comprehensive, accurate, and consistent set. For example, combining customer data from your CRM, financial system, marketing system, and 3rd party data providers can provide a better overall image of your customer than any one system alone.
- Data categorization: The process of classifying unstructured information to make it structured and analyzable is called data categorization. This can be divided into two methods:
- Sentiment analysis is the process of extracting emotions and feelings from text. For example, was the consumer feedback negative, positive, or neutral?
- Topic modeling is the process of identifying the "subject" of the text. Was the text about sports, politics, or real estate prices?
- Data imputation: The process of substituting values for missing or inconsistent data within fields is known as data imputation. This approach allows for more accurate analysis of your data compared to treating missing values as zeros, which would skew aggregations. For instance, if the order value is unknown, it can be estimated based on the customer's past purchases or the specific bundle of goods.
- Data extraction: The process of extracting useful structured data from unstructured or semi-structured data is known as entity extraction. Entities that can be identified include persons, locations, organizations, concepts, numerical expressions (dates, hours, quantities in money, phone numbers, etc.) and temporal expressions (dates, time, duration, frequency).
Types Of Data Enrichment
Data enrichment comes in many types, but the most common types of data enrichment are as follows:
Demographic Enrichment
Demographic data enrichment involves adding demographic information, such as gender, age, marital status, social media profiles, geographic and income level, to an existing dataset.
When enriching a dataset with demographic data, it is important to consider the end purpose so that you can obtain a relevant database.
Enriching data with demographic information can significantly improve targeted marketing efforts by enabling personalized messaging.
Firmographic Enrichment
Compared to demographic enrichment, the process of adding firmographic information such as valid business emails, job titles, and work experience is known as firmographic enrichment. This process allows you to extract more value from your customer data.
To begin, validate the contact data you already have, such as names, email addresses, and LinkedIn profiles. Then, append additional data like:
- Company
- Job role
- Work experience
- Education experience
Alternatively, you can start with the data you already have about your accounts, such as:
- Company name
- Company domain
For company data enrichment, data enrichment services can add data such as:
- Company industry
- Revenue estimation and range
- Employee count
- Tech stack
Purchase Intent Enrichment
Enriching data based on purchase interest and intent provides brands with a more accurate understanding of a potential customer's likelihood to make a purchase. By gathering actual shopping data and tracking product view frequencies, marketers can develop targeted campaigns that emphasize performance and appeal to the right consumers, ultimately guiding them towards a purchase decision.
Behavioral Enrichment
App usage data provides businesses with insights into which apps a customer interacts with, as well as which operating system they use to access the app and what devices the app is accessed through.
By enriching datasets with app usage information, businesses can more effectively identify user preferences to enhance the overall customer experience, identify the need for developing new apps, and assist in personalization efforts.
Examples Of Data Enrichment
Using enriched data to advance customer segments
Segmentation is a vital marketing practice that helps target marketing efforts to specific groups of people. By leveraging segmentation, marketers are able to create targeted messages that will resonate with consumers who might not have been interested before.
Data enrichment allows marketers the opportunity to segment based on third-party data, often including dimensions such as purchase intent or app usage. This better equips marketers to find new customers and create content tailored specifically towards those different groups’ interests.
Improving conversion rates with more accurate lead scores
Lead scoring plays a critical role in improving conversion rates while also developing an effective working relationship between sales and marketing teams, but manual lead scoring can be tedious work.
Data enrichment can help automate how businesses assign lead scores by using information from outside sources.
For example, say a lead had interacted with a business previously but had never subscribed to a mailing list. On this occasion, the lead opts to subscribe to the mailing list and enters the business database using the correct first and last name that they had used previously, but neglects to give their address, which usually lead to increased conversion rate.
On this occasion, a data enrichment tool using socio-demographic data would be able to compare the entered data against accurate postal records and automatically append address data, potentially boosting their lead score.
Lending
Credit scoring is built on data enrichment. Banks or loaning providers access third-party / alternative databases which help them create a complete profile of the customers they’re dealing with (and hopefully reject potential defaulting customers).
The Benefits of Data Enrichment and Why is data enrichment important
Data enrichment can significantly improve the customer experience. Here’s some notable research that shows the importance of personalized customer experiences:
- 66% of customers want brands to understand their unique needs and expectations.
- 52% want all offers that come from a brand to be personalized.
- 54% of customers say they’re likely to look at items in-store and buy them online (or vice-versa), and 53% of brands are investing in omnichannel strategies to match.
Accurate, enriched data is the key to creating targeted, personalized customer experiences — and the lack thereof can turn customers away. Data enrichment can also help reduce total costs. Here’s why: With a solid data enrichment strategy, your focus shifts to keeping data that matters to your company, such as customer contact information or transaction histories. Other, less relevant data can then be deleted or shifted to lower-cost long-term storage sites. In addition, enrichment makes it possible to detect and eliminate redundant data to reduce overall spending.
Data enrichment is important because it helps you know more about your users without asking them for extra information. For instance, you can verify someone’s identity simply by asking them their email address. It helps to reduce risk without increasing user friction and slowing down the user experience. When it comes to businesses, the more data you have, the smarter your business decisions can be. This is especially true for companies who lack crucial data, for instance when:
- Moving to a new market
- Trying to keep up with trends
- Starting a new business (like moving from brick and mortar to online)
- Trying to reduce customer friction by only collecting the essential info
- Looking to improve targeting
- Reducing fraud rates
Data enrichment has numerous benefits.
- Cost savings of Data EnrichmentA report by Global Databerg contends that an organization with one petabyte of data spends around $650,000 annually to manage the data, yet these companies only use a fraction of their data for any true benefit. Data enrichment saves you money because you don’t store information that is not useful to your business. Instead, you enhance the internal data with external sources of data for the benefit of your organization. The funds that would otherwise be used on databases are used on other activities that have a positive effect on the bottom line.
- Data enrichment fosters meaningful customer relationshipsEnriched data promotes personalized communications and increases the likelihood of meaningful customer relationships and business opportunities. With relevant customer data, your business can develop communication strategies that meet customer preferences and needs. A customer is more likely to make a purchase when they feel that your company understands their needs.
- Data enrichment maximizes customer nurturingData enrichment maximizes customer nurturing by identifying segments of customers to be nurtured. A segment offers value-driven information that has the potential to evoke a purchase.
- Data enrichment boost successful targeted marketingTargeted marketing is the future of marketing, and many businesses are realizing that a one-size-fits-all marketing approach does not work. They are turning to targeted marketing. For targeted marketing to be successful, an organization requires data enrichment to segment data effectively.
- Get greater sales with data enrichmentImagine investing a huge sum of money on your contact list hoping to get customers and prospects only to discover that your contact list is outdated. Organizations cannot afford such losses. Data enrichment ensures you have a clean and accurate contact list to increase sales efficiency and boost ROI. Also, it offers opportunities for cross-sells and upsells because a business has the right data and knows its customers well.
- Eradicate redundant data with data enrichmentRedundant data costs a company significantly. It results in revenue loss, customer loss, and damaged reputation. Redundant data is common in organizations because they are uncertain of the data to let go and data to keep. You can get rid of redundant data using data enrichment tools like Trifacta. Data duplication is common in raw data and affects the quality of data. Data enrichment eliminates it and hence enhances data quality.
- Data enrichment improves customer experienceCustomers have enormous expectations when it comes to their experience with brands. They expect companies to know them, anticipate their needs, and be relevant. Data enrichment enhances customer experiences by providing unique information on customers. Your business can anticipate customer needs and remain relevant through personalized marketing.
Data Cleansing vs. Data Enrichment
While data enrichment is mostly about adding supplemental data that helps strengthen your CRM, data cleansing is the process of removing inaccurate, irrelevant, or outdated data. Both are important components of keeping a healthy, vibrant database, but data cleansing typically happens first to make room for the updated, supplemental information provided through data enrichment. The same goes for your CRM data, including demographic, geographic, and psychographic information.
The goal of your CRM isn’t to collect as much information as possible; it’s to gather the highest-quality data that best represents your leads and customers. When should you invest in data cleansing? Well, for example, if your email list is growing but your engagement rate is dropping, you know it’s time to clean up your data. The same goes for other information you use to connect with your leads and customers. Keep a closer eye on your engagement rates (opens, click-throughs, etc.) versus your total subscribers.
They’ll tell you how healthy your database is. Outside of monitoring your data performance, data cleansing should take place at least every six months or so. Over 50% of organizations spend more time cleaning data than using it. Given the value of accurate data (and the cost of using inaccurate or outdated data), this isn’t too surprising.
Data Enrichment Best Practices
While every company’s enrichment process will look different depending on the type of data they collect and their strategic business goals, there are common best practices that can benefit brands no matter their approach.
Create clear criteria.
First up is creating clear criteria. This means considering the goal of your data enrichment efforts and then defining criteria that help you measure this goal. For example, if you’re looking to improve the completeness and accuracy of customer data, you might set a goal of having 90% or better data accuracy in customer profiles when tested against a 3rd party verification source. If targets aren’t met, you know more work is needed.
Make processes repeatable.
Next is developing repeatable processes. Designing and implementing new processes over and over again is a waste of time and money. By creating frameworks for data analysis that are consistent and reliable, you can apply them to more than one enrichment effort. Consider the process of verifying customer profile data using a set of standard 3rd party sources. By creating a process that automatically checks these sites for specific data types, you can simply reapply the function as necessary.
Ensure efforts can scale.
As your data volumes grow, you want enrichment efforts that can scale in tandem. In practice, this means implementing automation wherever possible to eliminate manual touchpoints that could introduce additional complexity or unexpected errors.
Prioritize general applications.
Finally, it’s worth thinking about how processes will generalize to other datasets. For example, if you create a process to verify customer data submitted via desktop website forms, it’s a good idea to leverage partners or services capable of ensuring this process is also applicable to mobile users.
Data enrichment is an ongoing process.
Data enrichment isn’t a one-and-done process. Instead, effective enrichment requires ongoing effort to ensure that collected data is relevant, accurate, and timely. It makes sense: Data never stops flowing into or out of your organization, and your data environment is constantly changing. To ensure you’re getting the most value from data sources, continual enrichment is critical.
Data Enrichment Tools
We’ve defined data enrichment and data cleansing and discussed when to invest in these processes. Now, let’s talk about how. Depending on the size of your database, you may be turned off by the prospect of manually combing through hundreds or thousands of data points. We don’t blame you. So, we compiled this helpful list of data enrichment tools to help you clean and manage your data. Take a look.
Bardeen
Vainu
Vainu is a B2B business database and sales intelligence software. With Vainu, users can filter through a database of millions of companies and identify prospects that fit their ideal customer personas Vainu allows easy access to data and integrates with multiple platforms. Vainu offers native integrations with HubSpot as well as Salesforce, Pipedrive, and 1000+ more tools.
Clearbit
Clearbit offers updated company and contact information for your sales records. You can focus on B2B lead enrichment, qualification, and scoring. Clearbit provides teams with access to more than 200 million contacts. This platform also continuously updates and enriches contact information automatically.
Datanyze
Quickly find and connect with your next customer using Datanyze. With this tool, you can capture data while you browse social media to connect with potential prospects. You can also tag contacts and companies to create segmented lists. Datanyze integrates with a variety of CRMs and provides data on millions of companies worldwide.
LeadGenius
LeadGenius allows you to verify B2B lead information to ensure your team is reaching out to the most accurate, engaged prospects. You can give your team the personalized datasets they need to make better connections with potential customers.
Apollo
Without accurate, representative data, our sales outreach would fall flat. Maintain high-quality data through data enrichment, and you’ll keep your prospects and customers interested and engaged.
