6 Data Enrichment Techniques Your Business Needs

Data is a treasure trove for all marketers. Companies that have a better understanding of data are in gain. Unless you have accurate insights into your audience, you can’t reach your target audience at the right time with the right offers. 

Research conducted by Ascend2 and its associates observed that one of the most significant data-driving marketing objectives cited by marketing professionals is enriching data quality and completeness (37%) (source). So, data enrichment is a must for your business needs. 

Here in this article,  we will cover important data enrichment techniques that you need to implement for your business growth.

1. Appending data

Each and every business has raw data, which can be your email address or phone number. So how do you enrich such data? You can do it by merging your raw data with similar data points and this can help you improve your overall data’s accuracy and value. 

For example, gathering your customer’s data from your CRM, financial system, and marketing systems and bringing those together will give you a better overall picture of your customer than any one single system. This will eventually help you build a comprehensive profile of your existing and potential customers. 

Appending data as an enrichment technique also includes ingesting data from second and third-party data providers and merging this into your data set as well.

2. Data segmentation

Data segmentation is a process by which you segregate your data objects into groups based on a common set of pre-defined variables such as age, gender, and income, for customers. You can create your own segmentation based on the data attributes you have and discover your potential clients. 

This segmentation will open an advanced opportunity for you to reach your targeted audience and provide tremendous growth potential.

Some of  the common segmentation for customers include:

  • Demographic segmentation: Based on gender, age, and occupation.
  • Geographic segmentation: Based on specific countries or towns.
  • Technographic segmentation: Based on preferred software and technologies.
  • Psychographic segmentation: Based on personal values and interests.
  • Behavioral segmentation: Based on actions, habits, and browsing history. 

3. Data Categorization

Data categorization is the method of labeling unstructured data so that it turns out as structured data. This will help you further analyze the information related to your business. 

This is branched out into two distinct categories:

  • Sentiment analysis: Determining the feelings and emotions from the text. For example: Was the customer feedback positive or negative?
  • Topication: Determining what does your text say? Is it about sports or politics?

Both of these techniques help you to analyze unstructured text to get a better understanding of that data.

4. Entity extraction

Entity extraction is the process of extracting meaningfully structured data from unstructured data. When you apply entity extraction, you will be able to find entities such as people, places, and their organizations and concepts.  

5. Data Imputation

The process of replacing values for missing or inconsistent data within fields is called data imputation. Rather than treating the misplaced value as a zero, which would skew aggregations, the estimated value facilitates a more accurate analysis of your data. 

For example: If the value for an order is missing, you can estimate the value based on previous orders by that customer, or for that bundle of goods.

6. Derived Attributes

Derived attributes are pieces of information that are not stored in the original data set but can be derived from one or more fields.  For example, age is very rarely stored in the documents but you can derive it based on a ‘date of birth’ field. These attributes are pretty useful because they contain logic that is repeatedly used for analysis. 

Through this, you will be able to discover particular information in a very short time and ensure consistency and accuracy in measuring your data.

Some of the common examples of derived attributes include:

  • Data Time Conversions: You can use a date field to extract the day of the week, the month of the year, or the quarter.
  • Time Between: You can use date-time fields to calculate the period elapsed.
  • Higher-order classifications: You can classify product categories. 
  • Dimensional counts: You can create new counter fields for a particular area by counting values within a field. This allows you for easier comparative analysis at the report level.
  • Counter field- You can use a unique id within the data set. This allows for easy aggregations.

As your business grows, you will need to find new and innovative ways to gather data about your customers. By using these techniques, you can improve your lead generation efforts and get closer to understanding your customers on a deeper level.

If you are looking for a B2B data provider that can help you identify potential prospects for your sales team, FunnL is a perfect place. Having amassed years of experience, we aim at delivering qualified leads to grow your sales pipeline.

Contact us today to see how we can help you secure your targeted audience for your business.


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