What is Data Enrichment: A Comprehensive Guide to Boost Your Business

Data is a valuable asset for any business. That’s why companies often invest significant resources in collecting and storing vast amounts of data. But, they don’t usually bother having strategies in place for proper data segmentation or usage. Additionally, much of this data ends up being forgotten and unregulated once it's stored away. 

Also, companies often create data that is not relevant to their business operations. It's similar to Scrat from the Ice Age, who used to collect acorns – just to store them.

Here’s where data enrichment comes in. It’s a process that helps organizations enhance the quality and value of their data by adding or refining information. In this blog, we'll explore the importance of data enrichment and how it helps businesses make better use of their data assets.

What Is Data Enrichment in B2B Marketing?

Data enrichment refers to the process of augmenting an already-existing data profile. You can do this by sourcing and incorporating additional, relevant information to bolster value.

Data enrichment has a seemingly straightforward definition. However, executing it requires considerable effort and familiarity with the appropriate research tools. 

On the surface, much of the data available is not particularly unique. Therefore, it contributes  minimal value to your marketing efforts. 

You must always aim to uncover truly transformative insights. For this, you need to delve into granular B2B databases. Further, you must devote time to carefully studying and filtering the available information.

What Are the Upsides of Enriching B2B Data? 

Now, why do you need data enrichment? In other words, what are data enrichment benefits? Well, let’s start with some stats.

• Marketo reports that a mere 10% improvement in lead quality can result in a substantial 40% increase in sales revenue.
• Another report indicates that approximately 1-6.5% of customer data becomes invalid every month due to turnover rates.

So, how are the above stats interlinked with data enrichment? For starters, there’s an undeniable correlation between data enrichment and lead generation. 

You already understand that the effectiveness of any marketing campaign hinges on your prospect data quality. Now, subpar data can depress conversion rates for both marketing and sales. And this ultimately results in underwhelming revenue outcomes. 

Next, your challenge lies in identifying the root cause of low-quality data. It can manifest in various forms such as:

• Duplicate data
• Incomplete data
• Fabricated data

So, this is where data enrichment and audit comes into play. By enabling your sales and marketing teams to enhance customers' data quality, data enrichment services can help for the following metrics:

• Drive conversions
• Generate strong ROI

So, you might be wondering, how exactly does this work? 

Let’s move ahead to understand the processes involved in data enrichment to improve data quality: 

1. Segmentation

Despite the proven benefits, only a minority of marketers utilize segmentation to tap into their customers' potential fully. And this is the case even when they’re armed with high-quality data. 

By providing access to a wealth of information, data enrichment enables you to categorize customers based on criteria such effectively:

• Company size
• Industry
• Location
• Job title
• Revenue – and more

Plus, more refined segmentation translates to superior experiences for your B2B leads and customers alike. Why? It's due to personalized content that aligns with their specific needs and objectives.

2. Lead Nurturing

Nurturing leads throughout the sales funnel journey is a critical aspect of a B2B marketer's role. Some leads require additional nurturing to convert into a sale. 

By enriching data with pertinent information, you gain vital insights to nurture leads effectively across various channels. This is particularly true for social media – which has a large user base. After all, 59% of the global populace is active across social media platforms.

3. Lead Scoring

To make the most of your lead generation campaigns, your sales and marketing teams must set clear goals. It’s done by evaluating and ranking their leads AKA lead scoring. However, lead scoring done improperly can hurt your campaign's effectiveness. 

Plus, this task becomes even harder when you have limited information to go on. Without reliable metrics, you're left guessing when scoring partial client profiles.

But there's good news. Data enrichment can help turn those partial profiles into complete ones. As such, it allows for more precise lead scoring. 

Additionally, your sales and marketing teams can discover the most important information needed for lead scoring. By using these data enrichment tools together, you can reliably score leads and generate better results for your campaign.

4. Personalization

• Personalized promotional mailings have 29% higher open rates
• As per the same report, they have 41% higher click rates
• Plus, personalized subject lines boost open rates by 26% overall

Check out the below-given stats on personalized email open rates across different industries:

Enriched business data can help personalize your email outreach as you gain knowledge and insights about your prospects through it. As such, you can tailor your email messaging to their specific needs. 

For example, let’s assume you're selling employee onboarding software to HR teams. Here, data enrichment can help you identify companies that are recruiting and likely facing onboarding challenges. You can use this information to make your email outreach more personalized and relevant to their specific pain points.

5. Cost Savings

Managing one petabyte of data costs an organization around $650,000 annually. However, most of these data are unnecessary or drive minimal real value. Thus, by enriching your data, you can save significant costs. Here’s how:

• By avoiding the storage of irrelevant data
• Augmenting your internal data with external sources that are beneficial to your organization

This frees up funds that would have otherwise been spent on databases. As such, it allows for investment in other activities that positively impact your organization's bottom line.

6. Brand Image

A positive brand reputation hinges on having complete and accurate customer data. Incomplete or duplicate information can lead to missed opportunities and frustrated customers.

Now, this is particularly important for top-of-the-funnel demand generation strategy. Why? Here, the quality of data can make or break a campaign. Data enrichment helps:

• Weed out bad data
• Extract valuable lead insights
• Boost sales efficiency and effectiveness

7. Better Sales

Data enrichment can significantly improve your firm’s sales efficiency and boost ROI. This is because it provides clean, updated, and accurate contact lists. After all, investing significant funds in outdated contact lists can result in substantial losses. Data enrichment mitigates this risk by providing up-to-date contact information. 

Plus, it offers other valuable insights that allow your business to know customers better. Armed with this information, you can identify opportunities for cross-selling and upselling. This further enhances your team’s sales performance.

8. Reduced Lead Form Sizes

Did you know? 58% of buyers don’t want to disclose their phone numbers. As per the same source, another 53% prefer not to release their addresses. So, what does this imply?

Asking for extensive information on a lead generation form can discourage potential customers from filling it out. To increase conversion rates, it is best to keep your forms simple and concise.

By utilizing data enrichment, you can keep forms short by only including the most essential questions about a particular client such as:

• Name
• Email address
• Phone Number

Once a lead is captured, you can enrich your customer profiles with additional details such as:

• Job title
• Phone number
• Team Size
• Total Number of employees
• Industry

9. Business Signal Alerts

Business signals refer to time-sensitive events that indicate a good opportunity to reach out to a lead. You can identify these signals by monitoring a company's social media profiles and other media outlets. Examples of business signals include:

• Fundraising
• Changes in management
• Recruitment drives
• Opening a new office location

By using data enrichment, you can extract this type of information from the internet and add it to your contact database. This helps you prepare an effective pitch beforehand. Plus, it provides an opportunity to make your outreach more personalized.

10. Reviving Lost Leads

Just because a lead wasn't interested or ready in the past doesn't mean they won't be in the future. Don't let an old list go to waste. Use data enrichment to add new information and insights about them. It could reveal new opportunities you never knew existed!

Data Enrichment Techniques

There are several data enrichment techniques used. Here’s a breakdown of the primary types:

• Appending data: By adding data from various sources, you can create a more comprehensive and accurate customer profile – such as combining data from CRM, financial, and marketing systems.
• Data segmentation: Dividing a data object into groups based on common variables like age, gender, or income provides you with a better classification and description of the object.
• Derived attributes: This includes fields created from one or more fields but not included in the original data collection, such as calculating age from the date of birth column.
• Data manipulation: The technique involves substituting missing or inconsistent data values, such as estimating an order's value based on a customer's past purchases.
• Entity extraction: It implies extracting useful structured data from unstructured or semi-structured data, such as identifying persons, locations, organizations, concepts, and numerical or temporal expressions.
• Data categorization: This is the process of classifying unstructured information to make it structured and analyzable. It includes sentiment analysis and topical analysis. Sentiment analysis extracts emotions and feelings from text, while topical analysis identifies the text's subject.

6 Step Data Enrichment Process Guide

Now, your next question may be: what’s the ideal data enrichment process for my organization? Well, the answer is simple. You need to be aware of the different steps in the process. So, here we go!

1. Understand the Types of Data You’ll Use

Identifying the target audience is crucial for any B2B marketer. And data enrichment can help you do just that. It’s true that B2B data enrichment vendors can provide a wealth of data points. However, it's important to use only the data that's relevant to your target audience.

Now, for this, you need to know about the major data types:

1. Socio-demographic Data
Enriching your datasets with socio-demographic data involves adding information about an individual's social and economic status, such:

• Their income
• Their occupation
• The family size

It's crucial to know the purpose of your data enrichment project in advance. Only then, the enriched data will be relevant to your goals. For instance, 

• You might want credit ratings if your business is offering credit cards
  Or
• You could aim for home values for homeowner insurance rates

Adding socio-demographic data can help create personalized messaging and improve your targeted marketing efforts.

2. Geographic Data

Enriching your datasets with geographic data adds information such as:

• Postal codes
• Geographic boundaries between cities and towns

This type of data can be helpful in various scenarios, such as:

• Determining where to open a new office
• Targeting the maximum number of customers in a particular location

3. Psychographic Data

Psychographic data refers to information about a person's:

• Values
• Interests
• Attitudes
• Behaviors

You can use this data in your marketing and advertising efforts. Why? To better understand the target audience and create more effective campaigns. You can collect psychographic data through:

• Surveys
• Social media analysis
• Other methods that capture people's preferences and opinions

4. Firmographic Data

This means information about a company, such as its:

• Size
• Industry
• Location
• Revenue

In the B2B scene, you can utilize this data to identify potential customers. Then, you can tailor sales and marketing strategies to their specific needs. You can gather such data points through:

• Public records
• Industry reports
• Additional sources that provide information about businesses and their characteristics

5. Purchase Intent Data

Purchase Intent Data Enriching datasets with purchase intent data involves gathering real shopping data and product view frequencies.  You can do this to gain a better understanding of a potential customer's willingness to buy. This type of data enables you to deliver highly targeted campaigns. Such campaigns aim to steer consumers closer to making a purchase decision.

6. App Usage Data

Enriching datasets with app usage data provides you with insights into the following:

• Which apps a customer interacts most with
• The operating system they use to access the apps
• The devices they use to access them

By analyzing this data, you can:

• Better identify user preferences
• Develop relevant apps
• Personalize customer experiences

Next, to determine which data to use, follow the following steps:

• Focus on the attributes that define your target profiles
• Make sure everyone in your company who interacts with customers agrees on your Ideal Customer Profile (ICP)
• Once you have this profile defined, you can use data enrichment to gather the information that corresponds to these criteria

By using data enrichment in this way, you can:

• Keep your database structured and readable
• Ensure GDPR compliance by avoiding unnecessary personal data
• Better identify potential customers who fit your target audience

As such, you can tailor your marketing efforts accordingly.

2. Find the Apt Tools and Technology

Next, it's important to identify the right tools and solutions that meet your specific needs. Think of it like creating a wedding guest list. You already have a clear idea of who you want to invite. And you don't want to waste time and money on unnecessary people.

So, to ensure you get the data you need, focus on quality rather than quantity. So how can you go about finding the right data? There are three main methods to consider:

• Web scraping
• Manual search
• Data enrichment service providers

Let's explore each of these options in more detail to help you make an informed decision.

1. Web Scraping

Web scraping is an extremely useful technique for importing information from the web into your file. You can use XPath queries in Google Sheets to retrieve specific data from search engine content or web pages. 

For example, you can retrieve all the H2 titles of an article by writing an XPath query in a Google Sheets cell. 

However, web scraping is not feasible on a large scale. Plus, it requires infrastructure beyond Google Sheets. So, if you're not able to write your own scripts, use an apt tool. It’s handy for web scraping and exporting structured data. Also, the data can be automatically synchronized with your tools using an API. 

You can also use the following resources to collect data:

• The company website: Here you can find key contact details, links to social media profiles, and other relevant information about the company.
• Google Maps: Particularly useful for small businesses such as shops and tradespeople. Local searches can yield valuable information like addresses, phone numbers, and websites.
• Yellow Pages and industry directories: These can be helpful when targeting companies in a specific industry or niche, but it's important to ensure that the information is up-to-date before using it.

But keep in mind that the web is a messy place. Hence, you may need to clean up and transform the data before it's usable.

2. Manual Search

If you prefer a more hands-on approach, a manual search is another option to obtain information about your prospects. You can visit their company website, LinkedIn, or other relevant sources. 

However, if you're dealing with a large number of prospects, outsourcing the research work to offshore platforms could be a more efficient solution.

3. Data Enrichment Service Providers

This is the simplest and easiest solution. These tools are designed to:

• Retrieve all available public data from the internet
• Clean and format them
• Aggregate data from different sources around a matching key such as email, domain name, or IP address

Revnew is a great platform to outsource your data enrichment requirements. Why?

• Our primary sources for obtaining data are:

• Email marketing
• Surveys
• Blogs
• Online directories
• Events
• Online registrations

• We prioritize validation to ensure the completeness and accuracy of contact records. By creating a targeted list of leads, we help minimize lost revenue and increase your team's productivity.
• Our team focuses on results and can help boost your profitability by analyzing, cleaning, and organizing business records. By ensuring the accuracy and completeness of your CRM, you can confidently use your data to achieve your business goals.

You can also check out this case study on how Revnew helped the deep learning-powered Surface Scan™ generate awareness and 120+ quality appointments.

3. Pinpoint Your Data Sources

Understanding data sources involves more than just finding and sourcing your data. It's crucial to comprehend how your various sources correspond to each other across your tools. For example, capturing data about a certain company role, for instance, branding lead or content marketer, can be done in different ways:

• 1st-party data: Directly from the lead or customer, via a form, chat, or phone call.
• 2nd-party data: By analyzing the behavior of leads or customers on the website, product, etc., using partner tools.
• 3rd-party data: By using a B2B data enrichment service.

Effective teams understand how these different sources interact with each other. Plus, they know how to prioritize them in data flow organization. This involves mapping:

• Data
• Flows
• Databases
• Sources

For instance, the "Name" attribute may appear as "First Name Last Name" on Salesforce. But it shows as simply "Name" on Intercom. Hence, it's crucial to map data from all tools that store prospect or customer data. You should also consider all the data points that define your target audience.

The next step is to prioritize data sources by understanding their hierarchy. For example, you may find that data from lead forms is more accurate than 3rd-party data. Additionally, your data must be consistent. It should also be written in the same way for all sources. 

As such, always select the most precise and complete writing format and apply it to all tools. You can utilize CDP (Customer Data Platform) tools to accomplish this. By the end of several iterations, you will clearly understand:

• Where your data comes from 
  And
• How it's distributed across various tools

4. Distribute Your Data Across All Available Platforms

To distribute enriched data properly, synchronize all your company's tools. Only then, each tool will display the same view of each lead and customer. However, this is often not the case. Teams tend to start by enriching individual tools through plug-and-play integrations. And it leads to a siloed data organization.

A better approach is to choose a service that can enrich all databases across your company. It’ll enhance your core lead management system, such as:

• A CRM
• Marketing Automation
• A CDP

This way, lead data can be synchronized between this system and other marketing channels such as:

• Email
• Advertising
• Live chat
• Website personalization
• Outbound calls
• Postal mail
• Analytics

Moreover, you must prioritize data sources. Plus, you should also understand how different data sources interact with each other. Thus, mapping data, flows, databases, and sources is a necessary step to achieve this. 

The data should also be written in the same way for all possible sources to ensure homogeneity. Finally, your marketing team needs to enrich data to:

• Qualify leads
• Segment messages
• Personalize content

Marketing leads are usually managed by a separate Marketing Automation or CDP tool. However, it is essential to enrich leads for all marketing tools. Why? Marketing leads generally exceed those managed by sales. 

5. Extract and Sequence Your Data

To achieve the desired results, it is not always necessary to enrich all leads at once. This is particularly true when multiple layers of enrichment are involved. Controlling which leads to enrichment (Extraction) and the timing of the enrichment (Sequencing) is crucial.

1. Extraction

For extraction, B2B data enrichment services often charge based on a credit system. Therefore, it may be advantageous to remove leads that are of little value or those that you have already qualified for. 

This strategy prevents unnecessary credit expenditure. Enriching your entire database is not advisable. Why? It’s pointless to determine if a contact is a content lead if the company has no interest in your services.

2. Sequence

Sequencing is equally crucial, especially when several layers of enrichment are involved. For instance, firstly, you can obtain data on people and companies. Then, distinguish good from bad leads. Secondly, you can qualify the technology environment of the good leads identified.

You can also choose to enrich the file with the IP address of your contacts. Next, use the company's domain name for the contacts whose IP you've retrieved. Finally, continue to enrich with firmographic data, email addresses, and so on.

In summary, sequencing enables you to avoid buying unwanted data from your supplier. It helps you progressively qualify leads – starting with the least expensive data points up to the most costly contact data. 

6. Convert, Categorize, and Integrate Your Data

Most B2B data enrichment tools excel at transforming cluttered and poorly formatted data into a clear, consistent dataset. However, the best marketing teams use data enrichment intelligently by focusing only on the data that:

• Identifies the best customers
  And
• Successfully engages ideal clients

It is crucial to identify the necessary data and data that isn't worth purchasing. After identifying the data, you need to make it usable. For this, you can use this 3-step process:

1. Conversion

B2B data enrichment services can provide you with access to previously unknown information about your leads, including the specific CRM they use. However, it's important to note that these services typically only identify a limited set of technologies through their API. 

So, if you want to get a complete picture of your leads' tech stack, you may need to use additional tools and customize your emails, notifications, and other outreach efforts accordingly.

2. Categorization

Creating categories allows you to regulate the data flow. As such, the segments are enriched and synchronized with your CRM and other tools. Categorization uses enriched data to create and update:

• Email lists
• Advertising audiences
• Qualified lead files, etc.

3. Integration

The final step involves creating data flows between all your tools. 

For example, requests for demos from leads that arrive via your form or live chat should automatically be enriched. Once leads meet specific criteria, they enter the "Qualified Leads'' segment. Then, they are automatically synchronized on the CRM and on Slack (to notify the sales team).

Data Enrichment Examples and Use Cases

Automating the process of data enrichment is a fundamental aspect of the contemporary digital landscape. It is the backbone of various verticals, a few of which are outlined below:

Finance

1. Lending
Data enrichment is pivotal in credit scoring. Loan providers and banks access alternative and third-party databases to obtain a complete customer profile. It helps them to approve or reject potential customers based on the likelihood of default. 

Data enrichment is indispensable for underwriting risk digitally. How? It equips loan providers with the information they need to protect themselves against fraudulent agents.

2.Fraud Prevention

 Businesses can leverage data enrichment to reduce fraud rates by creating detailed user profiles. A single data point such as an:

• Email address
• Device used
  Or
• IP address 

For instance, there are email lookup tools that automate a search, which aggregates precise data about an email address, including:

• Social media connections
• Domain validity
  Age
• – to name a few

This simple yet effective process can significantly decrease fraud rates in the long run.

3. Insurance

Insurance providers classify customers based on different data points and enrich their dataset to offer relevant insurance products based on risk levels.

Data enrichment can be used as a segmentation and targeting tool. As such, it enables insurance companies to refine their processes to increase efficiency.

Marketing

Data enrichment can assist marketers in creating accurate buyer personas. Hence, it leads to effective targeting and higher conversions. Marketers can enrich customer data with:

• Demographics
• Psychographics
• Firmographics

As a result, marketers can:

• Refine their campaigns
• Optimize their messaging
  And
• Increase the chances of converting leads into customers

Healthcare

Healthcare providers can enrich patient data to improve diagnosis accuracy and enhance patient outcomes. By analyzing patient data, medical businesses can:

• Identify patterns
• Make predictions
  And
• Personalize treatment plans to increase the likelihood of successful treatment

Best Practices for Data Enrichment in B2B Lead Gen

If you want to improve your B2B lead generation through data enrichment, here are some best practices you can follow:

1. Set Data Quality Standards

This means establishing criteria for what qualifies as "good" data. It also implies ensuring that all data you collect meet those norms. This can help you prevent issues like duplicates, inaccuracies, and incomplete information from affecting your lead-generation efforts.

2. Automate Data Enrichment

Automating data enrichment is also critical. This can help you save time and resources by allowing you to collect and process data more quickly and efficiently. Automated processes can also help ensure consistency and accuracy in your data.

3. Use Multiple Sources for Data Enrichment

Using multiple sources for data enrichment is another important best practice. It enables you to gather a more comprehensive and diverse set of data. As such, it can lead to better insights and more effective lead-generation efforts. Consider using sources like:

• Social media
• Industry publications
• 3rd-party data providers

4. Keep Data Privacy in Mind

Finally, it's crucial to keep data privacy in mind. Ensure you're collecting and using data in a way that is compliant with relevant regulations and industry standards. It can help protect your business from legal and reputational risks.

Wrap It Up

Data enrichment is not a one-time task. In fact, it needs to be performed continuously as customer data constantly changes. Failure to enrich data on an ongoing basis can result in irrelevant information and offers being provided to customers. 

A well-executed data enrichment process is crucial for success in today's data-driven business environment.  It ensures you get actionable, easily understandable, and valuable data. Hence, always choose the right data enrichment strategy and platform that meets your business objectives.

Some Commonly Asked Questions About Data Enrichment

• What is data enrichment in ETL?
  In ETL (Extract, Transform, Load), data enrichment refers to the process of enhancing or improving data quality by adding more information or attributes to existing data. It can be achieved by using various techniques such as data segmentation, appending data from different sources, entity extraction, data manipulation, and deriving new attributes.
• What is data enrichment vs data cleansing?
  Data enrichment and data cleansing are both techniques used to improve the quality of data. Data cleansing involves removing or correcting inaccurate or incomplete data, whereas data enrichment involves adding additional data to enhance the value of existing data. Data enrichment can include data cleansing as part of the process.
• Is it data enrichment or data enhancement?
  Both terms are often used interchangeably, and they both refer to the process of improving or enhancing the quality of data by adding more information or attributes to existing data. However, data enrichment is more commonly used in the context of adding external data sources to improve the value of existing data.
• What is data enrichment in data wrangling?
  Data wrangling refers to the process of cleaning and transforming raw data into a usable format. Data enrichment in data wrangling is the process of adding external data sources to improve the value of existing data.
• What is data enrichment in machine learning?
  In machine learning, data enrichment refers to the process of improving the quality of training data by adding more information or attributes to existing data. This can help improve the accuracy and reliability of machine learning models.
• What is CRM data enrichment?
  In CRM (Customer Relationship Management), data enrichment refers to the process of enhancing or enriching customer data with additional information such as demographics, firmographics, behavioral data, and social media data. This can help businesses gain a better understanding of their customers and improve their marketing and sales efforts.