6 Ways to Enrich Your Business Data

Keyword: Data Enrichment

Every organization recognizes the value of its data. It is essential that end users have confidence and trust in their organization’s data to make business decisions. A fundamental way to add value to your data is through data enrichment.

Data enrichment polishes improves and augments the dataset by introducing new attributes. For instance, using postal/ZIP codes, you can enhance primary address data by incorporating demographic information regarding socioeconomic status (household size, average income, and population characteristics).

Here are some significant research findings that emphasize the importance of personalized customer experiences:

• 66% of customers expect brands to comprehend their unique needs and preferences.
• 52% of customers want all brand offers tailored to their specific requirements.
• 54% of customers report being likely to browse items in-store and purchase them online (or vice-versa), while 53% of brands invest in omnichannel strategies to accommodate this trend.

What is Data Enrichment?

Customer information can originate from various sources.  It can be obtained directly from prospects by having them fill out a form to acquire a white paper, request a product presentation, or schedule a meeting with a salesperson  or from platform for journalists as well. Furthermore, this information can be obtained from data monitoring software that monitors user engagement on your own properties (such as Google Analytics, Happierleads, Mixpanel, etc.). Additionally, there is a concept known as third-party data. 

Third-party data is information about website visitors, customers, and prospects derived from their interactions with external digital and offline properties. For instance, certain third-party data enrichment services would compare LinkedIn data with web tracking data to “enrich” user data and better understand our website traffic.

 

Ways to Enrich Your Business Data

There are several ways to enrich your business data, which can help you gain deeper insights into your customers and improve decision-making processes. In this article, we will discuss six common ways in which data can be enriched:

• Incorporating data
• Data Segmentation approach
• Derived Features
• Data Imputation
• Entity Extraction
• Data Classification

Incorporating Data

By enhancing your dataset through data integration, you can merge information from multiple sources to create a more comprehensive, accurate, and uniform dataset than any individual source could provide. For instance, collecting customer details from your CRM, Financial System, and marketing platforms and then combining them will offer a superior understanding of your customers compared to a single system.

Incorporating data as a method of enrichment also involves acquiring third-party information, such as demographic or spatial data based on postal codes, and incorporating it into your existing dataset. Other valuable examples include weather data, traffic data, and exchange rates.

Data Segmentation Approach

Data Segmentation is a technique that categorizes data entities (e.g., customers, products, or locations) into groups based on shared characteristics defined by specific variables (e.g., age, gender, and customer income). This segmentation enables better organization and understanding of the subject. Some examples of customer segmentation include:

• Demographic Segmentation – It involves age, gender, profession, marital status, earnings, and more.
• Geographic Segmentation – It is categorized by country, state, or city.
• Technographic Segmentation – It is based on devices, software, and technologies of preference.
• Psychographic Segmentation – It depends on a person’s beliefs, attitudes, and values.
• Behavioral Segmentation – It focuses on behavior or lack thereof, spending habits, feature usage, browsing regularity, search history, order value, and more.

These classifications can result in customer groups like Trend Setters and Tree Changers. By using calculated fields in either an Extract, transform, and load (ETL) process or a metadata layer, you can develop your own segmentation based on your available data attributes.

Derived Features

Derived features are data fields that are not present in the original dataset but can be obtained from one or more existing fields. For example, ‘Age’ is hardly stored directly, but it can be calculated from a field containing a date of birth. Derived features are advantageous because they frequently contain analysis-relevant logic. 

By generating them within an ETL process or at the metadata layer, you can speed up the creation of new assessments while preserving the accuracy and consistency of the employed measures.

Common examples of derived features involve:

• Counter Field – uses a unique ID for each dataset, enabling easy aggregations.
• Date Time Conversions – extracting date fields such as weekdays, months, quarters, etc.
• Time – calculating an elapsed time, such as ticket response times.
• Dimensional Counts – creating new counter fields for specific areas by counting values within a field, like the count of narcotics offenses, weapons offenses, or other crimes. This facilitates easier comparative analysis at the report level.
• Higher-level Classifications – deriving product categories from products or groups of ages.
  

You can derive advanced features from your data using data science models, for example, churn risk or propensity to spend. By incorporating Happierleads into your derived feature generation strategy, you can enrich your datasets with valuable information about potential leads and their companies, such as revenue, size, and key personnel’s contact details.

Data Imputation

A data imputation process involves substituting values for missing or inconsistent data within a field. Data analysis becomes more precise when the estimated value is used instead of taking the missing value as zero, which could distort aggregations. 

For example, you could estimate an order value based on the customer’s previous orders or the specific product bundle.  Moreover, integrating powerful tools like Happierleads into your data imputation efforts can significantly enhance your lead generation capabilities and help you stay ahead in the competitive landscape.

Entity Extraction

Entity extraction involves converting unstructured or semi-structured data into meaningful structured data. A person, place, organization, concept, and numerical and temporal expression can all be identified through entity extraction (dates, times, currency amounts, phone numbers, durations, and frequencies). For example, through data parsing, you could extract a person’s name from an email address, the organization’s web domain they belong to, or separate names, addresses, and other data elements into distinct components from an envelope-type address.

Additionally, organizations might need to access remote data sources unavailable over the public internet to extract data. These data sources may be behind firewalls or other security measures that prevent access outside the organization’s network. In such cases, a VPN can be used to create a secure, encrypted connection between the organization’s network and the remote data source. This allows the organization to access the data source as if it were on its own network without needing complex and potentially insecure workarounds.

Data Classification

Data categorization involves labeling unstructured data and transforming it into structured data that can be analyzed. This process falls into two primary categories:

• Sentiment Analysis – extracting emotions and feelings from text, determining whether the feedback is positive, negative, or neutral.
• Topication – identifying the subject of the message, such as politics, sports, or house prices.

In both cases, unstructured text can be analyzed, making the data better understood.

Best Practices for Data Enrichment

Data enrichment is seldom a one-time procedure. You must repeat your enrichment steps in an analytics environment with continuous data influx. Implementing several best practices ensures you achieve the desired outcomes and maintain high data quality. These practices include:

Replication and Consistency

Each data enrichment attempt should be repeatable, consistently yielding the desired outcomes. Any process you design must follow rules, enabling you to execute it repeatedly with the assurance of achieving the same result each time.

Clear Evaluation Criteria

Each data enrichment initiative must have clearly defined evaluation criteria. You need to confirm the effective execution of the process. For instance, following the execution of a process, you can compare recent results with those of previous tasks to ensure that the outcomes meet expectations.

Scalability

Data enrichment tasks must be scalable in terms of resources, time, and cost. As your data expands or as you add more processes to your transformation responsibilities, any process you develop must be maintainable. For instance, automating processes with a scalable infrastructure can help you process large amounts of data faster.

Completeness

Each data enrichment task must be thorough in relation to the source data and produce outcomes with recognizable traits. This means that you have considered all potential outcomes, even “unknown” outcomes, for any intended output. By being thorough, ensure new input yields valid enrichment output consistently.

Generality

Data enrichment duties must be relevant to a wide variety of datasets. Therefore, you should be able to transition the procedures you develop to various datasets, allowing you to utilize logic for multiple tasks. You should apply the uniform extraction of the day of the week to all date fields. This method ensures result consistency and preserves data conventions across subjects.

Conclusion

Data enrichment is an essential process that empowers businesses to optimize their datasets and gain valuable insights into their customer base and target market. By adopting various techniques such as incorporating data from multiple sources, segmenting data entities based on shared characteristics, deriving features from existing fields, imputing missing or inconsistent data, extracting relevant entities, and classifying unstructured data, businesses can create targeted and individualized customer experiences that lead to increased satisfaction and loyalty.

As the digital landscape evolves, precise and enriched data becomes increasingly crucial for making well-informed decisions and maintaining a competitive edge. By leveraging data enrichment tools, businesses can improve their understanding of customers and identify trends, patterns, and opportunities to drive growth and success. If you’re looking for a way to improve your B2B sales and marketing strategy, consider using Happierleads for data enrichment today!