The Benefits of Data Enrichment

A well-functioning data enrichment process is key to crafting meaningful customer communications that drive recurring business. It contextualizes a brand’s existing first-party data with third party authoritative data.

Various processes that can be used to perform this data transformation include discretization (which creates interval labels for continuous data), generalization (utilizing concept hierarchies) and attribute construction.

Segmentation

Data segmentation is a type of data enrichment that divides or organizes a dataset along specific field values. Examples of this include grouping by location, technology, behavior and more. A common use case for this is for marketing purposes such as identifying and targeting segments of customers for specific products or promotions.

Another example is credit bureau data enrichment for lending organizations. The lender can append to their first-party customer profiles additional data from the credit bureau such as credit account balances, inquiries and derogatory marks to better evaluate a borrower’s creditworthiness.

A more specialized use case for data enrichment is identity resolution. This is a prerequisite process for best-practice customer database management that creates a unique customer identifier across internal systems using data from multiple sources. This ensures that data is consistently analyzed and processed to improve business processes. It also enables a unified view of the customer and enables cross-selling and upselling opportunities.

Personalization

Data enrichment can help sales teams provide a personalized value proposition. For example, if you know that a prospect’s company uses certain tools or technologies, you can highlight how your product will integrate seamlessly with their existing systems.

You can also use enriched data to build targeted campaigns for key accounts. This will allow you to craft messages that are relevant and effective, and reduce the friction of your outreach.

A common challenge faced by sales and marketing teams is finding the right data to collect on their leads. For example, you might need to fill in information like industry, revenue, and location. One solution is to use a tool such as Clearbit, which can automatically fill in these details for each contact or lead. This can be especially useful when collecting data from online forms or account signups.

Targeted Marketing

When a marketing team has access to the right information, it can craft campaigns that are much more likely to achieve conversions. Data enrichment turns names on a list into rich, actionable profiles that include demographic data such as income levels, marital status, and geographic information.

The unified profile generated by enrichment software also provides insights into when a prospect is ready to make a purchase. This can help a sales team focus their efforts on converting leads into customers and improving overall marketing ROI over the long term.

A repeatable, automated data enrichment process saves costs by enabling companies to manage existing customer records more efficiently. It also improves efficiencies by helping marketers and sales teams find the best possible targets for their campaigns and outreach. This in turn boosts conversion rates and increases revenue, even without increasing budgets or ad spend. This makes a robust data enrichment protocol crucial for every business. Learn more about how Treasure Data’s real-time customer data enrichment solutions can help your organization.

Data Transformation

Data transformation helps businesses get the most value out of their data. Businesses are constantly producing data from a variety of human and machine sources, and inconsistencies in metadata can make it challenging to organize and understand what’s in your dataset. Data transformation tools allow you to refine your metadata and make it easier for teams to access information.

You can use data transformation to perform a wide range of tasks, including appending data, segmentation, deriving attributes, imputation, and entity extraction. For example, you might use a no-code data blending tool to easily combine and apply data transformations to your datasets, such as changing currency values, converting time zone conversion rates, or creating a new calculated field that represents a weight in kilograms.

This allows you to filter and aggregate data more efficiently and improve your analytics by providing additional context to your models and reports. You can then analyze this curated, enriched data for insights that are actionable and valuable to your business.