Skip to main content

CRM Data Enrichment: How and Why to Fill the Gaps

Published Date: Thursday, Oct 26, 2023
Last Updated on: Wednesday, Nov 01, 2023
Marketing team strategizing with clean, enriched data

Data influences everything in the customer lifecycle: From initial outreach to customer retention and everything in between.

But it is no longer enough to apply a blanket approach. Businesses need to leverage behavioral science to determine what consumers want, and when they want it. 

That is where data enrichment plays its part. Sales and marketing leaders regularly invest in enrichment services to bolster the quality of their targeting and provide the context to personalize at scale.

And, given the fact that 60% of B2B sales organizations are expected to shift to data-centric selling by 2025 – it’s a process that’s expected to grow exponentially in the coming years.

In this article, we’ll explore the purpose of data enrichment and how it can add value to your marketing and sales initiatives.

What is data enrichment?

CRM data enrichment is the process of enhancing and improving existing datasets by adding additional information, context or attributes to existing customer or prospect records. It involves combining first-party data from internal sources with supplementary insights provided by a third party or external source.

When customer data is initially collected, it is in its most primitive state – usually revealing little more than a name or contact details. We refer to this as “raw data.” The goal of enrichment is to improve the quality and depth of this raw contact data, so it can be better used in targeted marketing and sales initiatives. Businesses typically look to enrich datasets to supplement audience targeting by making these contact profiles more useful and insightful – building a deeper understanding of the customer, their habits and their preferences. 

Enrichment can be particularly practical for businesses:

  • Moving into new markets
  • Initiating new outreach campaigns
  • Segmenting audiences based on qualitative traits
  • Looking to conduct more granular targeting
  • Reducing customer friction without asking for additional information 
  • Improving personalization efforts
Man using Macbook to implement enriched CRM data

Why is data enrichment valuable?

Data enrichment helps businesses to actively meet the expectations of their customers with hyper-targeted, timely and relevant communications. In today’s competitive, consumer-intensive landscape, spray-and-pray outreach rarely delivers the desired outcome. In fact, 52% of customers now expect all offers to be personalized – a figure that is growing year over year according to global research.

Enrichment essentially helps businesses become more agile and tactile. By putting additional consumer context at the center of their marketing strategy, businesses can understand and target customers more granularly – and deliver personalized messaging on a 1:1, 1:few or 1:many approach. 

That extended insight brings with it an abundance of benefits beyond just targeting and personalization:

Informed business decision-making: Data enrichment strengthens customer attributes, enabling businesses to make more informed, strategic marketing moves. By gaining greater customer context, leaders can reduce guesswork and spend budget more confidently. Enrichment won’t guarantee success for every customer – but it will increase the likelihood of resonating.
Improved marketing ROI: Personalized, targeted marketing campaigns driven by enriched customer data are more likely to yield a higher return on investment (ROI) by reducing wasted marketing spend and improving conversion rates. Companies that grow faster drive 40% more of their revenue from personalization than their slower-growing counterparts, according to McKinsey.
Improved accuracy and reliability: As organizations continue to scale their data collection capabilities, effective management and control processes become pivotal. The reality is, business data is notoriously inaccurate. In fact, 75% of executives say they can’t trust the quality of their data. Enhancing data doesn’t just broaden customer context, it also ensures that businesses can rely on their insights to actively contribute to marketing and sales performance.
Reduce data redundancy: Data enrichment enables businesses to shift focus toward insights that actively contribute to their growth. Other less relevant attributes can be deleted and removed at source – eliminating unnecessary data capture and lowering the risk of breaching stringent data protection regulations by housing outdated or redundant personal data. By removing non-essential attributes, businesses can also drive down the cost and effort tied to database-wide cleansing.
Customer retention and upsell opportunities: Enriched data creates visibility into existing buyers: their habits, preferences and personality traits. This information can help businesses to anticipate their needs early, identify potential signs of attrition and provide proactive customer support to retain their business. It can equally play a role in improving customer lifetime value (CLV) by providing the insights to justify inclusion in upselling and cross-selling campaigns for new product launches.
Propensity modeling: Enriched data serves as the foundation for predictive modeling. Executives can create predictive models to identify prospects who resemble your best customers or have a high likelihood of conversion. This helps you focus your efforts on those who are most likely to convert – and craft specific strategies for segmented audiences or individual prospects.
Why is Data Enrichment Valuable?

Appending vs enriching: What's the difference?

Data enrichment is also typically referred to as “data appending.” While appending and enriching serve similar purposes, we believe there’s a difference. 

Data appending can be considered as gap-filling: The process of acquiring new, simple attributes to uniform data across multiple profiles for consistency and unity. Appending typically involves more rudimentary, quantitative insights such as geographical or contact attributes – ZIP codes, email addresses and phone numbers.

Enrichment, on the other hand, serves a more qualitative purpose, bringing unique, specific attributes into the fold: likes, dislikes, hobbies, habits and more. While most vendors will be able to provide both appending and enrichment services, the latter is typically more expensive as this data is tougher to acquire and verify.

Data enrichment: Commonly enriched attributes

As global businesses clamor for customer insights at ever-increasing volumes, the data acquisition market has grown exponentially. Its size is expected to cross US$3.7 billion by the end of 2035. This growing demand, alongside huge leaps forward in AI capability, has enabled big data firms to compete with more complex and niche insights.

The most commonly enriched attributes include:


Firmographics are considered the “demographics” of companies. They include details such as company size, ARR, geographical presence, SIC code, industry, etc. This information is crucial if executives are to effectively segment, qualify, score, and prioritize B2B leads. Firmographics also help build accurate ICPs and determine whether prospects are the best match for specific products or services. 

Technographic data

Technographics can be another powerful addition to a customer database. These attributes provide a detailed picture of a prospect’s existing technology stack. This type of data enables executives to understand the infrastructure a target account has invested in, what they are likely to purchase next and how they are using their existing software. It also offers key input regarding a prospect’s attitude toward specific software as well as their technology challenges and previous purchasing behaviors.

Psychographic data

Psychographics are essential for businesses striving to create complete, actionable customer profiles. They allow executives to profile their target audience beyond general demographics. Sales and marketing teams can use psychographic data to understand the psychological characteristics of prospects – like their lifestyle choices, interests, attitudes, desires and values – and personalize according to their changing needs and pains. 

How does data enrichment work?

Data enrichment relies on integration of an external data source or third-party vendor. This resource will look to supplement an existing database by sourcing requested insights from their collated records. Each vendor will have its own proprietary database with a variety of records – some providers like Harte Hanks may even have the capabilities to source fresh, brand-new intent- or propensity-based insights for ultra-specific initiatives.

The process typically follows four straightforward steps:

Step one: Cleansing and verification
Most providers will recommend cleansing and verifying datasets or subsets prior to enrichment. By checking existing records for inconsistencies, duplicates and decayed information, businesses can ensure they achieve their best ROI from enriched data – rather than working with poor quality insights from the offset.

Step two: Predictive match rate
Once verified, the vendor will look to run a predictive match rate to determine the accuracy of their input. This match rate can be used as a competitive proof of concept prior to investing fully in the enrichment process. It essentially tells businesses the level of accuracy they can expect, based on the quality of the provider’s data. 

Step three: Enrichment
Once agreed, the dataset will be transferred to the vendor to begin the enrichment process. The vendor will supplement the existing file with new data as per the client’s request. This will be a largely automated process, using proprietary matching algorithms. New data will be standardized and formatted to the client’s chosen specifications, allowing for seamless entry into their customer relationship management software, marketing automation platforms or business intelligence tools. 

Step four: Continuous improvement
Effective data enrichment cannot be a one-and-done process. Businesses need to schedule regular enrichment cycles to ensure their data is current and accurate – particularly for niche behavioral insights, which can change at the drop of a hat.

Buying enrichment data: Key considerations

Cleansing: Ensure that your existing data is clean, accurate and reliable before starting the enrichment process. Low-quality data can lead to incorrect results and decisions that will harm results and ROI.
Affordability: Quality data rarely comes at a low cost – and inexpensive solutions are often ineffective. Look for a partner that can service your enrichment needs with a high predictive match rate. This will ensure you achieve the best possible returns.
Data privacy and compliance: Be aware of data protection regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act). Enrichment activities involving personally identifiable information (PII) must comply with these regulations.
Measuring ROI: Define metrics to measure the return on investment (ROI) of data enrichment efforts. Determine how the enriched data is contributing to improved decision-making, customer engagement and revenue.

Final thoughts

In the era of data-centric selling, data enrichment is not just a trend; it’s a crucial component of success. As businesses strive to meet customer expectations and gain a competitive edge, data enrichment will continue to play a central role in shaping the future of marketing and sales. So embrace the power of data enrichment and watch your customer relationships flourish.

Looking to outsource? Harte Hanks can help. Powered by multi-sourced, multi-validated and relentlessly verified data from over 1,200 customer attributes, Harte Hanks’ data enrichment gives businesses the edge to append datasets confidently – and hit the right notes for their target contacts with every motion.

Contact us today.