Today, data is everywhere.
But how can you stand out if everyone in your industry has the same data?
This is where data enrichment joins the conversation.
So, what is data enrichment? In layman’s terms, this is the process of updating your database with new information.
This transforms your existing data into an accurate consumer profile. These profiles help you better map your clients’ demands, enabling you to provide them with offers, relevant content, and other tailored interactions across all platforms.
This article will focus on data enrichment examples like:
- Customer data enrichment
- Firmographic data enrichment
- Behavioral data enrichment
- Contact data enrichment
- Sales data enrichment
- Marketing data enrichment
And much more.
Let’s get started!
What is data enrichment in practice?
Data enrichment involves adding missing or incomplete information to existing data records, resulting in a more comprehensive dataset. For instance, you can add demographic data, like population characteristics. This will help you have a deeper insight into your customer base and possible target market.
A data enrichment tool is software that automates the process of adding information to your dataset. To add relevant information, these tools can access various data management sources, including social media accounts, public records, and third-party databases.
Depending on the kind of data being enriched, the sources of data enrichment might differ, but common sources include:
- Public databases
- Internal data sources
- Social media platforms
- Third-party data providers
- Website and behavioral analytics
Data enrichment and validation are closely related processes. While data validation ensures that the data is correct and consistent, data enrichment adds information to your dataset.
Other terms that have been confused with data enrichment are data cleansing and data augmentation. Unlike data enrichment, data cleansing (or scrubbing) enhances the quality and reliability of current data by cleaning, standardizing, and correcting it, while data augmentation enhances model performance by generating new data instances from existing data.
Examples of Data Enrichment in Action
Data enrichment can help businesses make better decisions and understand their clients or possible threats.
In this section, we have divided the examples of data enrichment into two:
- Types of data enrichment.
- Use cases as examples for data enrichment.
Below are some types of data enrichment examples based on the types
- Demographics
- Geographic Data
- Behavioral Data
- Firmographics
- Sales Data
- Financial Data
- Supply Chain Information
Businesses can optimize their processes and change how they engage with customers through data enrichment.
Below are some applications of data enrichment;
- Lead Scoring: Improve lead scoring models by including relevant website activity, industry, and company size data.
- Customer Segmentation: Use richer data to divide your client base and develop more targeted marketing strategies.
- Fraud Prevention: Find anomalies and inconsistencies in your data to spot fraudulent activities.
- Personalization: Provide customized interactions by adjusting suggestions and content according to detailed client profiles.
- Market Research: Learn more about consumer preferences and market trends.
Data Enrichment Examples Based on Types
With enriched data, businesses can lower risks, focus resources where needed, and gain a deeper understanding of client intent.
Here are a few data enrichment examples.
Contact Data
Contact data enrichment involves updating contact information such as names, phone numbers, email addresses, mailing addresses, company names, job titles, and social media accounts into a dataset. This data type is important for many businesses as it is the foundation for communications with clients, partners, suppliers, and prospects.
Using contact information such as job title and company details, the sales team can identify a client’s position as a decision-maker and give them a higher lead score, making them a valuable lead for follow-up.
Demographic Data
Demographic data enrichment adds demographic information, like income level and marital status, to an existing dataset. Knowing your final goal is crucial when adding demographic data to ensure that the database you obtain is relevant to it.
Example: A travel business might incorporate family-related information, like marital status and number of children, to improve customer information. This allows the agency to design customized vacation packages.
Behavioral Data
Behavioral data enrichment adds details about consumer behavior, such as website interactions, content engagement, past purchases, or other first party data, to a current dataset. This helps companies understand their clients’ goals and interests.
Example: An e-commerce platform can add browsing history to consumer accounts, such as pages visited or products viewed. After that, it can send email follow-up offers or personalized product recommendations to customers who frequently visit particular products.
Geographic Data
Geographic data enrichment involves adding location-specific information to customer or corporate data. Examples include ZIP codes, city names, regions, or even closeness to landmarks.
Example: A retail store chain can add geographic data, such as climate or regional information, to its Customer information. Clients in colder climates, for instance, would see ads for winter clothing, while clients in warmer climates might see sales for summer clothing.
Sales Data
Sales data enrichment entails adding data to an already-existing dataset, such as the product or service purchased, to improve lead conversion rates, provide deeper insights, and boost sales performance.
Example: Using sales data enrichment, a B2B company can find high-value clients and adjust its sales strategy accordingly. For instance, the sales team may offer customers a discount or package offer if they have a history of buying large amounts of a specific product.
Firmographic Data
Firmographic enrichment entails updating current records with information such as firm size, revenue, industry, and growth data. Firmographic enrichment improves outreach strategies and lead prioritization by assisting sales and marketing teams in identifying high-value prospects.
Example: A recruitment firm can leverage customer data enrichment by adding information on the number of employees. This can help the agency use the data to identify which clients are growing and may want further hiring support.
Financial Data
Financial data enrichment adds more context or external data, such as payment history and credit scores, to raw financial information to increase insights for financial planning, analysis, or decision-making.
Example: Financial data enrichment is a tool financial services firms can use to evaluate prospective customers’ creditworthiness and determine appropriate lending limits.
Supply Chain Information
Supply chain data enrichment incorporates external data, such as order status and supplier details, to increase visibility, streamline processes, and control risks throughout the supply chain,
Example: Adding information about a supplier’s past delays to the supplier profile enables procurement teams to assess and compare providers efficiently.
Data Enrichment Examples by Business Function
Data enrichment is essential for various business operations, especially since almost four out of five marketers believe that data quality is crucial for marketing-led growth and improving customer experiences.
Let’s look at several instances of how data Integration can help various business operations.
Sales Department
The sales department can use data enrichment to score leads by including firmographic information such as revenue, industry, and company size. For example, a B2B software firm can add details like the number of workers and industry classification to its list of leads.
The sales team can rank the most promising prospects by assigning a score to these leads according to how closely they match the company’s ideal client profile.
Marketing Department
According to 87% of marketers, data is the most underutilized resource in their organization.
This is a waste of highly resourceful item, especially noting that marketing teams can use enriched data to categorize audiences better.
By including demographic information such as age, gender, and income in customer records, marketing can develop customized ads that appeal to particular categories. For example, a retail brand can provide discounts for budget-conscious customers to target different categories with relevant promotions.
Fraud Prevention
Risk management teams can add insights like transaction locations and purchase amounts to customer activity data. This can help identify suspicious activity by enhancing the client profile with anomalies, such as recent transactions from an unexpected region or a sudden increase in value.
Customer Support
Customer support teams can leverage data enrichment to offer personalized support by adding historical data, like previous issues, support tickets, and product usage, to client records. For instance, a client’s complete interaction history, including past support requests and product preferences, is visible to the representative when a customer calls support.
This updated information allows the support representative to customize their answer better and provide individualized solutions.
Finance Department
Finance teams can use information like industry, vendor size, and pricing history to enhance spending records. For instance, adding more vendor information to spending data might help find trends in procurement, including which vendors are offering the best prices for repeat business.
Lead Scoring
Data enrichment can improve lead scoring algorithms by offering more comprehensive and detailed information about possible clients. Combining behavioral, social, firmographic, and demographic data lets you prioritize your sales efforts and generate more lead scores.
Unlocking Data Enrichment’s Potential
Data enrichment changes the rules of the game for companies trying data enhancement strategies. It involves carefully selecting and validating information against reliable sources rather than flooding databases with data.
Furthermore, it plays an important role in developing a deeper understanding of your clients, tailoring experiences to suit their requirements, and making well-informed decisions. By automating the repetitive tasks, you can concentrate on exploring the data to spur innovation and expansion.
AI Ark provides data enrichment solutions to help businesses realize the full potential of their data. We help businesses to make data-driven decisions and spur growth by utilizing AI-driven analysis.
Ready to see how AI Ark can transform your data into a competitive advantage?
Try our AI-powered B2B data platform today and get a demo.
