A roadmap for your B2B data enrichment strategy

Congratulations. You've decided that this is the year you tackle your data enrichment strategy.

The good news is that you couldn't have picked a better time to take this project on.

The ecosystem of data providers has increased substantially, with many new companies entering the space. Unlike some of the vendors you've worked with in the past, this new crop of providers is hungrier than ever to win your business. And, thanks to advances in harvesting technology for B2B data, more companies provide signals that just several years ago only a few providers could claim.

Now for the bad news. As the provider landscape has swelled, so have the complexities around picking the best data sources for your data enrichment strategy. The terms used and the promises made are more nuanced than ever before. How does one compare claims or degrees of human-verified across vendors? How do you judge and rate compliance among the various sources?

For those fantasy sports enthusiasts among us, it's about as daunting as trying to draft your team while on the clock. So many players to choose from, so many stats to assess, and so little time to do so.

But while it's okay to do a little guessing when it comes to picking your fantasy teams, the stakes are higher when it comes to placing bets on your data GTM stack.

However, whatever your stage in the data enrichment cycle—whether you're a company that currently uses multiple data streams and feeds or a company that still relies on a single provider, there's room to improve.

In this post, we'll provide an initial RevOps roadmap for navigating the data provider space, the Do's and Don'ts in charting your strategy, and some of the data enrichment best practices we're seeing.

Why you need to optimize your data enrichment strategy

It's no exaggeration to say that your company's GTM effectiveness—everything from account segmentation, lead sourcing and routing, and territory management—depends on how good and actionable your data is.

One of the biggest casualties of bad data is often a company's ideal customer profile (ICP). Surprisingly, too many companies, even large and well-financed ones, still struggle with getting this critical metric right. How can this be you ask? Truthfully, few companies perform the proper analysis of their best-fit customers as well as their customers who are less than ideal. And if they do this analysis, it's usually hampered due to the lack of data fields that are captured.

Most companies don't know enough about their prospects to create a firm ICP.

Countless studies support this.

In a survey of sales operations professionals by Modern Sales Pros, only 6% of RevOps leaders said they had a high level of confidence in their ability to personalize their sales and marketing efforts.

Yes, you read that right. Fewer than 10% of marketing and sales leaders at some of the most data-centric companies believe enough in their data to do the type of personal outreach and marketing essential to engaging, nurturing, and making a sale.

While most everyone can agree to the need to upgrade the state of your data records, the problem has been how to assess the tangible value or ROI of better data.

The science of ROI calculations and formulas is a topic that deserves its own blog post. The fundamental challenge to performing a true ROI is that you need to be able to isolate and measure the value of the new data that you've added.

For example, if you've enriched your records with more granular industry and job function data, how do you track the impact? Creating a custom field around job function and level is certainly a data enrichment best practice. But to do this properly, you need a true end-to-end process or solution. It's not enough to simply acquire new data. You need the ability to use it in your segmentation, lead scoring, and routing—across the entire process. Some refer to this as data orchestration. Everyone can agree that it's all about optimizing your revenue operations systems and processes.

Here's something that should sharpen your sense of urgency: the match rate gap between a typical single-vendor enrichment approach and a well-executed multi-vendor strategy is much larger than most teams expect.

Based on Openprise's analysis across the B2B data enrichment landscape, the average single enrichment vendor achieves a company match rate of around 49% and a contact match rate of around 56%. That means if you're relying on one vendor today, roughly half your database isn't getting enriched at all — no industry, no job function, no company size, no revenue band. Those gaps cascade directly into your scoring models, routing logic, and campaign segmentation.

When organizations move to a multi-vendor enrichment waterfall, routing each unmatched record through secondary and tertiary vendors in sequence, company match rates reach as high as 94% and contact match rates as high as 83%. That's not a marginal improvement — it's the difference between a scoring model running on half your database and one running on nearly all of it.

The ICP and TAM work described below only produces reliable results if your underlying match rates are high enough to make the analysis meaningful. If you're starting from 49% coverage, your ICP is built on an incomplete picture. Starting from 94% coverage changes what's possible strategically.

Things to do and things to avoid when putting together your data enrichment strategy

Ask yourself: how did you end up with your current data provider(s)?

Did you and your team do a lot of research or maybe even create a full RFP process, where you created specific use cases, sent out files to multiple data providers, and performed an analysis and comparison of match rates, signals offered, and the accuracy of these attributes?

Let's be honest, such analysis takes time and resources.

If you're like most companies we talk to, you've been bombarded with calls from various providers and from time to time have decided to take the leap of faith and add these providers to your enrichment process. Often, your choice of data vendors has been a result of how persistent their salespeople have been and perhaps, by how much "manual" enrichment work that provider did to impress you during any test you might have asked them to perform.

At Openprise, as part of our data enrichment strategy, we recently underwent a several month-long testing analysis where we evaluated some 13 data providers on enrichment at both the account or company level, as well as with contacts. Our findings were illuminating.

For the Account-level test, we asked providers to submit companies in their databases across three industry sets: software companies, financial services, and healthcare services. Our criteria varied by industry. For one vertical set, we asked them to deliver companies with revenues between $100m to $250m. For another category, we asked for companies with 5,000 or more employees.

The disparity between the providers was vast. A few providers sent us 1,000+ records for an industry segment, while others sent us just a few hundred.

For the contacts test, the results also varied. For this test, we delivered each provider a list of 600 records (including name of contact and company) for Enterprise-size companies. We asked them to provide us what they had for each contact (across eight fields).

Fill or match rates for fields like title of the contact and company work phone number were fairly similar, but there were big differences when it came to mobile and direct dial numbers. Several vendors said they no longer bothered with direct dial numbers. For work emails, the match rates ranged from a fill rate of 63% to 99%, with most providers in the 70-80% range.

Interesting to note here—we also benchmarked these results against several of the largest data providers and found that they ranked in the middle of the pack—when it came to match rates.

When it comes to your data enrichment strategy, one size does not fit all

What's the takeaway from this level of testing and analysis? Aside from re-emphasizing the importance of doing such a test, it tells us that there's not one provider that's best across each data set.

The performance variation across vendors isn't just an academic observation — it's the core argument for building a multi-vendor B2B data enrichment strategy. Different providers are strong in different segments, geographies, and data types. The teams that accept this and build a waterfall approach accordingly see dramatically better results than those that stick with a single vendor and accept whatever coverage gap remains.

Three Openprise customers illustrate world-class data enrichment in practice:

• Adobe — Adobe's marketing team was processing 800 lead lists per month against a single enrichment vendor averaging only a 50% match rate. After implementing Openprise's multi-vendor enrichment waterfall with D&B as the anchor, SMB account data match rates improved from 50% to 88% — and the automation of the list loading process generated $250k in net annual savings. The lesson: picking the right anchor vendor and building around it rather than relying on it alone is what drove the lift.
• JumpCloud — Moving from a product-led growth motion to a sales-led motion required a defined ICP and dramatically better contact coverage. Using Openprise's multi-vendor enrichment waterfall, JumpCloud increased match rates by 48%, built an ICP model and a look-alike model within the first 60 days, and grew their addressable market by 3x in just 90 days. A well-executed enrichment strategy didn't just improve the data — it changed the strategic direction the company could pursue.
• Palo Alto Networks — Before adding a multi-vendor waterfall, Palo Alto Networks' match rates ran at 50–60% with a single vendor. After implementing the Openprise waterfall approach — which also cleanses records before sending them to enrichment vendors — their match rates improved to above 85%. The pre-cleanse step alone produced meaningful lift: cleaner inputs mean higher match rates at each stage of the waterfall, regardless of which vendor you're using.

"Something super unique about Openprise is they can clean your data before sending it to the enrichment vendor to provide better match rates. When we were using a single vendor, our match rate was around 50–60%. When we implemented the waterfall approach with Openprise, we improved our match rate to above 85%."

— Megan Cone, Senior Manager, Martech & Integrations, Palo Alto Networks

A consistent pattern across all three: cleansing your records before they go to any enrichment vendor — not after — is one of the highest-leverage steps you can take in your B2B data enrichment strategy. See more customer stories →

Data enrichment best practices for your B2B strategy

Here are the key principles we recommend when building or revisiting your B2B data enrichment strategy:

1. Cleanse before you enrich. Normalization and standardization of existing records improves match rates at every vendor in your waterfall. Don't skip this step.
2. Test before you commit. Send a sample file to prospective vendors before signing. Evaluate match rates, field coverage, and accuracy against your own data — not their marketing claims.
3. Layer vendors, don't replace them. No single provider wins across all segments, geographies, and data types. Build a waterfall that routes unmatched records through secondary and tertiary sources.
4. Limit fields to what you'll actually use. Many vendors return 300+ fields. The most mature data organizations use 15–20. More fields mean more data management overhead without additional business value.
5. Set enrichment frequency by use case. Contact data decays faster than company data. Enrich continuously for active pipeline records, quarterly for broader database hygiene.
6. Measure everything. Track match rates by vendor, by field, by geography, and over time. You need this data to negotiate renewals, justify budget, and optimize your waterfall sequencing.

The new frontier in B2B data enrichment strategy: mining your own data

Third-party vendors are only part of the enrichment picture. One of the most significant developments in B2B data enrichment strategy over the past few years is the recognition that many organizations are sitting on rich, actionable data they already own — they just can't access it.

This is what Openprise calls data fracking: the extraction and structuring of valuable insights that are locked inside unstructured first-party data sources. Think of the signals buried in:

• Call recordings in Gong — product mentions, competitor references, objections, buying signals
• Meeting notes and calendar data — engagement frequency, attendees, topics discussed
• Slack conversations and Gmail threads — informal signals about account health or timing
• Zoom recordings — tone and content of customer conversations

None of this data comes from a vendor. It's already in your stack. The problem is that it exists as unstructured text, not as structured fields your CRM or MAP can query, score, or route against.

Openprise's built-in generative AI capabilities use customizable templates and bots to extract this data, give it structure, and make it as usable as any third-party enrichment source. The result is a layer of first-party enrichment that no competitor can replicate — because it's uniquely yours.

For B2B teams building or refreshing their enrichment strategy in 2025 and beyond, first-party data fracking should sit alongside your multi-vendor waterfall as a core enrichment source — not an afterthought. In many cases, the signals it surfaces (intent, fit, timing) are more predictive than anything a third-party database can provide, because they're based on actual interactions rather than inferred attributes. Learn more about data fracking →

Building a successful enrichment program

A well-executed B2B data enrichment strategy isn't a one-time project — it's an ongoing program. The vendor landscape will keep evolving, your ICP will sharpen as you learn more about your best customers, and new data sources (including your own first-party signals) will continue to emerge.

The teams that build durable enrichment programs share a few traits: they test before they commit, they layer vendors rather than replace them, they clean data before it ever hits an enrichment API, and they measure the output rigorously enough to improve over time.

Ready to build or revisit your B2B data enrichment strategy?

See how Openprise's multi-vendor enrichment platform handles the full enrichment lifecycle — from pre-cleanse through waterfall orchestration to ROI reporting. Explore multi-vendor enrichment →