Data – the building block of customer intelligence – always begins unprocessed, no matter the origin (e.g., email lists, social media platforms, or website traffic).

Any customer data gathered is stored in a centralized database, awaiting the next phase. After the cleansing and structuring of this raw material, as it were, it’s supplemented with external data to add further insights.

Data enrichment increases the usability of the data by adding a layer of value, purpose, and meaning. It assists brands in acquiring a more profound comprehension of their customers and their driving forces and goals. For marketers, data enrichment is essential to intelligent decisions and more effective campaigns – without which achieving ROI would be difficult.

Fortunately, modern data enrichment techniques make it possible to obtain value from information in several easily accessible ways.

Understanding Data Enrichment

Data enrichment is a process by which the standard and value of digital consumer profiles are enhanced by using various techniques and tools.

The genesis of your customer data is diverse and includes email lists or lead forms, among others. However, problems arise when the data source is faulty or fails to offer sufficient information to designate the dataset as valuable.

A lot of businesses, for instance, depend on lead capture as a way of obtaining consumer data. However, relying on customers as the sole data source can lead to issues such as getting duplicate phone numbers or even fraudulent identities and email addresses.

The main objective of data enrichment tools is to rectify the presence of inaccurate or inadequate data. It’s possible to match existing first-party data with information collected by second and third parties. As a result, a data enrichment system augments operational efficiencies by appending new details to the previously gathered and retained archives.

After a company decides to enhance its data, this is what happens:

Data is assimilated from third-party and second-party data providers, like ComScore or Nielsen, and augmented onto pre-existing individual customer data tables using taxonomies, ontologies, and third-party libraries built into the data processing architecture.

Next, duplicate records are deleted. The data is then verified and validated and is continuously updated after data cleansing and storage in a centralized location (e.g., a customer data platform). It’s ready for analysis by marketers and data analysts – to derive insights and refine campaign strategies.

Types of Data Enrichment

Data enrichment techniques can be applied to several types of information sets:

1. Geographic data

Geographic data enrichment refers to the augmentation of pre-existing datasets with geographical information, spanning a wide range of insights like postal codes and the geographical divisions between municipalities. Geographic information can be used in various contexts, like determining the perfect spot for a new store or maximizing the number of customers served at a given location.

2. Social demographic data

Enhancing a current dataset with demographic details, such as income levels and marital status, is termed demographic data enrichment. A plethora of demographic data can be gathered, such as the type of vehicle driven or the number of children.

Before augmentation with demographic data, it’s critical to ascertain the intended purpose of finding a database germane to that cause. Consider your customers’ credit ratings, for example, if your company wants to offer financing options.

3. Intent data

By enriching purchase intent and interest data, brands can garner a more accurate assessment of a prospective customer’s purchasing tendencies. This requires collecting actual purchasing data and the frequency of product views. It enables the launch of targeted, performance-driven campaigns that precisely identify and guide potential buyers toward completing a purchase.

4. App usage data

App usage data provides enterprises valuable insights into the apps that customers interact with the most. Also, the data will comprise the operating system and device types used when accessing the app in question.

Including app usage data in datasets allows businesses to accurately discern user preferences and usage patterns. It will enable organizations to optimize the overall client experience with customized programs and tailored applications.

5. Contact data enrichment

Contact data enrichment appends contact information (including genuine business email addresses, work titles, and phone numbers) to an existing database to create an extensive record of potential and existing customers. It is among the most important and widely adopted techniques – helping organizations sift through changing contact details and incomplete forms to acquire complete information for email campaigns.

Data Enrichment Techniques and Tools

Generally, three methodologies encompass data enrichment:

• Enriching data manually: You can manually input data into a database or spreadsheet. While this approach may be laborious and prone to errors, it can be advantageous when dealing with moderate data volumes.

• Scraping data through manually built tools: Using specialized software or programs can extract data from websites or other online resources. Large quantities of data can be gathered in this manner with efficiency; nevertheless, it may be tedious, demanding technical proficiency.

• Using a commercial data enrichment tool: A data enrichment tool is a software app or service that aggregates and organizes data from various sources, such as third-party entities, prioritizing data hygiene and structure. Data enrichment tools include Clearbit, Cognism, LeadGenius, and ZoomInfo. Although these tools possess considerable power, they may be limited by the data sources available – unlike manually built tools.

No matter which approach you use – manual, commercial tool, or home-grown scripts – the enrichment process will incorporate one or more of the following techniques:

1. Segmentation

A data object (like a client, merchandise, or site) is divided into distinct categories using data segmentation. This is determined by an established set of predetermined variables (for consumers, these may include age, gender, or income). After this, the object is defined more precisely through segmentation.

2. Structuring

This is the classification of unstructured data, transforming it into a structured format that may be analyzed. This can be accomplished using themes or sentiments. For instance, the feedback from the consumer may have been favorable, unfavorable, or neutral. You could also identify the “subject” of the text. Did the text pertain to real estate, politics, or sports? Structuring identifies the text’s sentiment and/or topic and assigns a tag for analysis.

3. Imputation

Data imputation is the process of replacing absent or inconsistent data within fields with corresponding values. Using the estimated value enables a more precise data analysis than treating the absent value as zero, which might introduce bias into aggregations.

4. Derived attributes

While potentially derived from one or more other fields, these fields were not initially incorporated into the data collection. For example, the “date of birth” column can be used to determine age. Derivative elements are beneficial since they contain logic commonly used during analysis.

5. Appending

You consolidate data from multiple sources to create a more thorough, precise, and uniform set compared to the sum of its individual components – by enhancing an existing dataset with additional data. You can create a 360-degree customer profile, for example, by including the information from your customer relationship management (CRM), financial records, and marketing & sales archives.

The Importance of Data Enrichment in Modern Marketing

Today, you have access to a wealth of customer data. However, a clear customer representation is critical for marketing leaders to extract valuable and reliable insights from the data – built on the broadest possible and most exhaustive dataset.

One potential drawback is that focusing entirely on first-party data might make you miss out on other recently acquired material that emerges over time. This is where data enrichment is crucial. It enables the finding and collecting of new candidates, including contacts and accounts – along with broader attributes like the “total addressable market.”

Quality data is also a prerequisite for sales enablement. Data enrichment can optimize sales processes by supplementing inadequate prospect profiles with critical details like job titles or direct phone numbers. You can identify consumer sub-groups most susceptible to churn using enriched data and then develop and assess relevant case studies and hypotheses to prevent that attrition.