Marketing Data Enrichment

Marketing teams today sit on more customer data than ever, but raw data rarely tells the whole story. A phone number without a location, a purchase without a device type, a lead without a company size – each is a fragment that limits how precisely you can target, segment, or personalize. Customer data enrichment closes these gaps by pulling in external information to build out what you already have, turning scattered records into complete, actionable customer profiles.

This article breaks down what data enrichment involves, the data sources and methods behind it, and how server-side tools – including Stape's own HTTP Lookup Variable, Store Lookup, and Enricher power-up – fit into the process alongside established platforms like HubSpot CRM, Clearbit, and LeadGenius.

Data enrichment is a key process in information management that increases its accuracy, value, and content. Combining existing data with external sources allows businesses to refine and improve the original array of information. The importance of this process lies in ensuring the quality of data for companies and organizations that use it to make decisions, create strategies, and maintain competitive advantages.

Enriching data is not just adding new information but improving and expanding their usefulness. The main tasks of this process include standardizing data formats for consistency and adding context that makes data arrays more valuable.

For example, enriching customer data in marketing can involve supplementing profiles with information about demographic characteristics, purchasing behavior, or social media activity. This is customer profile enrichment in practice – taking a bare-bones record and layering in the details that make it useful for personalized engagement. This helps to improve the personalized approach to customer interaction and increase the effectiveness of marketing strategies.

• Personalization. Rising customer acquisition costs and a crowded digital space make an effective attribution model crucial for tracking touchpoints and optimizing budgets, while data enrichment helps companies tailor products and strategies to audience preferences and behaviors. Personalized offers, recommendations, and notifications are sent to customers based on their demographic characteristics, interests, geographical location, and purchase history, which allows for more accurate segmentation of the customer base.
• Better decision-making. Enriched data provides a complete understanding of the topic, allowing businesses to make more informed decisions. By continuously enriching your customer profiles, you can perform much more accurate digital marketing analysis, leading to highly personalized campaigns and reduced acquisition costs.
• Targeted advertising. Enriched data allows companies to better understand their target audience, leading to more effective and personalized marketing strategies.
• Efficiency. Data enrichment can help improve performance and procedures. For example, it can improve the effectiveness of campaigns targeting specific audiences.
• Increase sales efficiency. Enriched data improves sales performance by providing sales teams with practical information and priority leads.
• Innovation. Data enrichment can open up new opportunities and solutions by providing more diverse and comprehensive data. For example, it can be used to identify new markets, trends, and patterns in data, from which new functions, products, and business plans can be created.

Various sources are utilized to enrich the data by providing additional information about customers and the market. The main ones include:

1. 1.
   Internal company data. These are information about sales, consumer complaints, accounting, pending orders, etc. This internal layer is the starting point for first party data enrichment – strengthening data collected directly from your own customers before supplementing it with external sources.
2. 2.
   External public sources. Data is available through government agencies, non-profit organizations, and non-governmental organizations that publish information about demographics, economics, and other aspects that may be useful to marketers.
3. 3.
   Online resources. Tools such as Google Trends provide data on the popularity of search queries, which helps to understand the interests and needs of the target audience.
4. 4.
   Data from social networks. Information from platforms such as Facebook, LinkedIn, and Twitter allows you to analyze user behavior, interests, and interaction with the brand.
5. 5.
   Data on competitors. Analyzing public information about competitors, their marketing campaigns, and their positioning in the market helps to understand the competitive environment.
6. 6.
   Customer Data Platforms and Data Vendors - CDP collects data from different sources in the organization and creates a single view of the customer. It helps avoid the use of isolated or inaccurate data by structuring real data into individual customer profiles. CDP works in conjunction with CRM to provide enhanced capabilities, such as tracking customer interactions across multiple channels (online and offline).

Existing data can be enriched in the following ways:

• Contact data enrichment, such as phone numbers and email addresses, allows direct customer communication
• Geographic data, such as country, region, or city of residence, helps to run location-based campaigns.
• Demographic data, particularly age, sex, or marital status, is especially useful for personalized B2C data enrichment and consumer data enrichment campaigns.
  
• Technological data, such as customer device type, usage habits, or technology stack, which contributes to more efficient targeting.
• Behavior data, from browsing duration to search history, helps to understand individual customer models and plan accordingly.
  

• Proprietary data such as company industry, size, and revenue are especially important for targeting decision-makers in B2B campaigns – the core of company data enrichment.

Regardless of the size of databases, manually viewing data today is too resource-intensive. Special services have already been developed to simplify and automate this task, greatly facilitating the management and enrichment of data.

Here are the best data enrichment solutions:

- Stape offers powerful data enrichment capabilities through server tracking and integration with Google Tag Manager (GTM):

• HTTP Lookup Variable is a function for server GTM that allows HTTP requests to external APIs or web services, which makes it possible to configure and enrich data directly in the tracking process.

• Stape Store Lookup  is a variable developed in the Google Tag Manager (sGTM) Stape server environment. It allows users to retrieve data from the Stape Store by specifying a document ID or setting query criteria to search for specific documents. This functionality facilitates efficient data retrieval and management for server-side tag implementation.
• Enricher power-up is a built-in feature for server GTM that automatically enriches incoming events with previously collected user data (email, phone, location, and more), helping recognize returning users and maintain a consistent customer profile across sessions – a practical, automated form of first party data enrichment.

- HubSpot CRM is an effective solution for managing client data. It offers customizable properties, tasks, web forms for collecting data, built-in chat, tracking, and email sending.

- Vainu is B2B sales and business intelligence software. It allows you to filter the database with millions of companies and find potential customers that match the ideal profile. Integrates with HubSpot, Salesforce, Pipedrive, etc.

- Clearbit is a platform for enriching data about companies and contacts, useful for company data enrichment specifically. It allows you to update, qualify, and evaluate potential B2B clients thanks to access to more than 200 million contacts. 

- Reverse Contact is a reverse email search service that only finds information about a person and company by email. Uses real-time public data and meets GDPR and CCPA requirements.

- LeadSpace is a technology that updates information by combining multiple data sources and CRM systems. It uses artificial intelligence (AI) to collect data based on user behavior on the Internet, which helps to target potential customers more accurately.

- Datanyze is a service that searches for and communicates with potential customers around the world. It integrates with CRM systems and allows you to collect data while browsing social networks, create segmented lists, and target ads.

- LeadGenius allows checking information about B2B leads so that teams refer only to the most accurate and interested potential customers. It creates personalized datasets, improving the quality of communication and customer engagement.

- Trifacta offers an open and interactive cloud data platform that enables data preparation and management for analytics and machine learning. It also helps track quality and data sources in enrichment processes.

- Accurate Append enriches B2C data by providing detailed information on demographics, income, and donation history. This service helps improve the quality of leads and allows you to more accurately target marketing strategies, providing businesses with additional data to attract customers effectively.

Data enrichment is a key step to improving customer information quality and accuracy. Here are some effective data enrichment examples:

1. Define clear enrichment criteria

The first step in any data enrichment process is to set specific goals. For example, a company may aim to achieve 90% accuracy in customer data verified by third-party sources. It will assess the effectiveness of enrichment efforts and identify areas for improvement. Before defining those goals, it's worth reviewing tips for improving data quality in data analytics to make sure your baseline data is reliable enough to enrich effectively.

2. Development of repeatable enrichment processes

Creating standardized procedures for data enrichment ensures efficiency and consistency. For example, the automated process of validating customer profile data through predefined third-party sources can be reused for different datasets, ensuring consistency and reducing task time.

3. Scalability of enrichment efforts.

If data volumes are increasing, it is important that enrichment processes can scale accordingly. Implementing automation, such as through server-side tracking, can help reduce the impact of ad blockers and improve the accuracy of data collection, which positively impacts the scalability of enrichment efforts.

4. Unification of enrichment processes for different datasets.

The unification of approaches to data enrichment for different sources ensures consistency. For example, introducing a single process for verifying customer data obtained through web forms on computers and mobile devices ensures that data from both platforms is processed similarly, ensuring their integrity and accuracy.

5. Using server-side tracking to improve data quality.

Server-based tracking allows for more accurate data collection and conversion tracking as it is less affected by ad blockers and tracking restrictions. It also strengthens first party data enrichment efforts, since server-side setups capture and structure first-party signals more reliably than client-side tags alone. For example, according to a study conducted by Google, Square has seen a 46% increase in registered conversions from Google Ads after the introduction of server-side tracking.

6. Data enrichment through integration with Stape Store.

Integrating the Google Tag Manager (sGTM) server container with Firestore allows you to read and write data, which opens up ample opportunities for data enrichment. It provides data collection and processing flexibility, adapting processes to business needs.

Data cleansing focuses on the accuracy of the available data. It is identifying and correcting errors, inconsistencies, and redundancies in a dataset. The main tasks of cleaning:

• Bug fixes. For example, fixing misspelled names, incorrectly entered values, and errors in addresses or phone numbers.
• Updating outdated information. Checking for the relevance of data, such as a change of address or customer status.
• Remove duplicates. Detect and remove duplicate records that may falsely represent the number of clients.

This process maintains the integrity of the data, ensuring its correctness and relevance. Cleaning usually takes place in the initial stages of working with data to ensure that they do not contain errors that may affect the accuracy of the analysis or decision-making.

When should you invest in data cleanup?

• If the level of involvement drops with an increase in the number of contacts, this can be a signal to clean up the data.
• Data cleansing should occur at least every six months to ensure its relevance and accuracy.

Data enrichment is the process of improving and expanding existing data through external sources. Rather than simply correcting errors, enrichment adds new, relevant information that can add context and depth to existing data. It allows for the creation of a more complete customer or prospect profile through customer profile enrichment.

Examples of enrichment:

• Adding demographic data. Enriching contacts with data about age, gender, family, or interests.
• Geographic enrichment. Adding customer location information can be helpful for targeted advertising or special offers.
• Psychographic enrichment involves obtaining more information about customer preferences, lifestyles, and buying habits, which allows better targeting of marketing campaigns.

Enrichment helps create more accurate and personalized customer profiles, which can improve interactions with customers and offer more relevant products or services.

• When your database is cleared, and you want to add more depth to your customer profiles for personalized marketing campaigns.
• If you are interested in improving customer segmentation accuracy or conducting more detailed analyses.

• Data cleansing focuses on correcting errors and inaccuracies and removing unnecessary or outdated data.
• Data enrichment adds new information to improve and enhance existing data, allowing for more accurate and profound profiles.

Cleansing is usually the first step, after which you can move on to enrichment to create more prosperous and valuable data.