4 Questions RevOps Must Ask About Data Enrichment

Every vendor claims 95% accuracy. Every sales deck shows impressive match rates. And every demo somehow makes enrichment look effortless.

Then you sign the contract, and reality hits. The records that matter most to your business aren't covered. The data decays faster than you expected. The integration takes months instead of weeks. Sound familiar?

Data enrichment decisions shouldn't be made based on demos and slide decks. They should be made by asking hard questions that expose what actually happens once you're a paying customer. Here are five questions every RevOpsprofessional needs to ask before committing to an enrichment provider, along with what the answers reveal about whether a vendor will actually work for your GTM motion.

Question 1: What's Your Real Match Rate for Our Specific ICP?

This seems obvious, but most teams skip the critical part: testing against their own data.

Vendors quote aggregate match rates, typically somewhere between 70% and 90%. Those numbers come from blending performance across all their customers, all industries, all geographies. They tell you almost nothing about whether the provider will work for your specific use case.

If you sell to mid-market financial services companies in DACH, the aggregate match rate is irrelevant. You need to know what happens when you send over 500 real contacts from your target segment. Can they find accurate emails? Do they have mobile numbers? What about job titles and seniority levels?

Here's how to actually test this:

Pull a benchmark list from your CRM. Start with 20-50 contacts you know are accurate because sales has validated them, they've converted, or they've engaged in campaigns. This becomes your ground truth for measuring accuracy.

Send the same list to multiple vendors. Request enrichment of identical fields: email, direct dial, job title, company size, industry, LinkedIn URL. Give each vendor the same inputs and compare outputs.

Manually validate a sample. Have someone on your team check 50 to 75 records against LinkedIn. Are the job titles current? Do the emails look legitimate (not generic role-based addresses)? This 10% sample tells you more than any stat the vendor provides.

Test coverage by segment. If you have multiple ICPs or target different regions, test each separately. A vendor might be excellent in North America but weak in Europe, or strong with enterprise but terrible at mid-market.

The vendors that perform well on these tests might not be the ones with the best reputation or the biggest logos on their website. That's precisely why you test.

Question 2: How Fresh Is the Data, and What Happens When It Decays?

Match rates only tell part of the story. The other part is how long that data stays accurate.

B2B contact data decays rapidly. People change jobs, get promoted, leave companies entirely. Phone numbers go stale. Email addresses bounce. Research suggests that anywhere from 22% to 70% of B2B contact data becomes outdated within a single year, depending on the industry and the specific data type.

So when a vendor tells you they have a contact's information, you need to know: when did they last verify it?

Ask about verification methodology. Some vendors verify records continuously through various signals (email engagement, LinkedIn activity, web scraping). Others verify only when records are requested. Still others use "confidence scores" that are essentially educated guesses. The methodology matters because it determines how much you can trust the freshness claims.

Request last-verification timestamps. Good providers can tell you when each data point was last confirmed accurate. If they can't provide this at the field level, they probably don't have robust verification processes.

Understand the refresh policy. If you enrich a contact today and their data changes next month, what happens? Do you get automatic updates? Do you need to pay to re-enrich? Is there even a mechanism to know something changed?

Build decay into your planning. If the vendor says data is typically verified every 90 days, and you're enriching records for a campaign that runs in 60 days, you're probably fine. But if you're building a database you'll use over the next year, you need ongoing refresh, not one-time enrichment.

The best enrichment setups treat data freshness as an ongoing concern rather than a point-in-time event. That might mean scheduling regular re-enrichment cycles, setting up change detection workflows, or using platforms that handle refresh automatically.

Question 3: How Does This Actually Integrate With Our Systems?

This question trips up more RevOps teams than any other. The enrichment data itself might be excellent, but if getting it into your CRM requires three days of manual work per month, the solution isn't actually working.

Native integrations versus API calls. Does the provider have a direct integration with your CRM (Salesforce, HubSpot, etc.), or will you need to build custom connections through APIs or middleware? Native integrations are faster to set up and usually more reliable long-term. API-based approaches give more flexibility but require engineering resources to maintain.

Real-time versus batch processing. Can you enrich records as they enter your system (form fills, imports, manual creation), or only in scheduled batches? For inbound lead routing, real-time enrichment makes a huge difference. A lead that gets enriched and routed in seconds converts better than one that sits in a queue for hours.

Field mapping complexity. Your CRM has specific fields with specific formats. The enrichment provider returns data in their format. Who handles the translation? Can you map their "employee_count_range" to your "Company Size" picklist values automatically, or does someone need to build logic for that?

Overwrite rules and conflict handling. What happens when enriched data conflicts with existing data? If your sales rep manually entered a phone number last week, and the enrichment provider returns a different number, which one wins? Good systems let you define rules: trust manual entries, prefer certain sources, only update if field is empty, etc.

Error handling and monitoring. When enrichment fails (and it will sometimes), how do you find out? Are there logs? Alerts? A dashboard showing success rates and failures? Without visibility, problems accumulate silently until something breaks downstream.

Platforms that aggregate multiple data sources, like Databar, can simplify some of these challenges by standardizing how data flows into your systems. Instead of managing separate integrations with multiple providers, you manage one connection that handles the orchestration.

Question 4: What's the Real Cost, and How Do I Measure ROI?

Enrichment pricing models are notoriously confusing. Per-record fees, credit systems, subscription tiers, overages, minimum commitments. Understanding true cost requires doing actual math, not just comparing sticker prices.

Calculate cost per usable record. Say a vendor charges $0.50 per enriched record. But only 60% of records match, and of those, only 80% pass your quality standards. Your real cost isn't $0.50. It's $0.50 divided by 0.48, which is about $1.04 per record you can actually use.

Factor in coverage gaps. If Provider A matches 70% of your list at $0.50 and Provider B matches 85% at $0.75, which is actually cheaper? Depends on what those extra 15% of records are worth. If they're high-value accounts you couldn't reach otherwise, paying more makes sense.

Account for operational costs. Does using this provider require someone on your team to run exports, manage uploads, handle field mapping, and fix errors? That's real time with real cost. Fully automated solutions might cost more per record but less in total when you factor in labor.

Define what success looks like. Before you can measure ROI, you need to know what good looks like. Is it higher email deliverability? More direct dials per rep? Better lead scoring accuracy? Faster routing? Define metrics upfront so you can track whether enrichment actually moves them.

Build a business case with ranges. Don't commit to a single ROI projection. Build scenarios: conservative, expected, and optimistic. If your hypothesis is that better data increases demo bookings by 5% to 15%, model all three. Finance teams trust ranges more than single-point estimates because they reflect real-world uncertainty.

One approach that's gained traction is waterfall enrichment, where you check multiple data sources sequentially rather than relying on a single provider. If Source A doesn't have the email, automatically try Source B, then Source C. This can push match rates from 50-60% to 80-90% while controlling costs by only paying for records that actually match. The waterfall enrichment approach is particularly valuable when no single provider has strong coverage across your entire ICP.

Putting It Together

Choosing an enrichment provider isn't just a procurement decision. It's a foundation decision. Data flows from enrichment into segmentation, routing, scoring, personalization, reporting. Get enrichment wrong, and those downstream systems all suffer.

The five questions above give you a framework for making that decision well:

1. Test match rates against your actual ICP, not generic benchmarks
2. Understand data freshness and build decay into your planning
3. Validate that integration actually works with your systems and workflows
4. Calculate true costs and define measurable success criteria

Notice that none of these questions can be answered by looking at a vendor's website. They require testing, conversations with real customers, and honest assessment of your own needs and constraints. The vendors that welcome this scrutiny are usually the ones worth working with.

Asking these questions about data enrichment before signing anything puts you in a much stronger position than teams that rely on sales presentations and trust their vendor's claims. The extra diligence takes time upfront but saves enormous headaches down the road when you discover that the solution you bought doesn't actually work for your RevOps needs.

FAQ

How many vendors should we evaluate?

Two to three is typically sufficient for most categories. More than that becomes logistically difficult to test properly. Select vendors with different strengths (one known for coverage, one for data quality, one for integration) so you're comparing meaningfully different options.

Should we test with real data or synthetic data?

Real data, always. Synthetic tests tell you nothing about how the vendor performs against your actual target market. Pull records from your CRM that represent your ICP and test against those. Anonymize if needed for security, but keep the underlying patterns intact.

What match rate should we consider acceptable?

It depends entirely on your use case and the data type. For email enrichment, 80%+ is achievable with good providers or waterfall approaches. Direct dials are harder, typically 40-60%. The key is comparing against what you get today and what's realistic for your segment.

How often should we re-evaluate our enrichment vendor?

At minimum, annually. The market changes quickly, with new entrants, acquisitions, and shifting data quality. Run a lightweight comparison each year to confirm you're still getting good value. If match rates or data quality drop noticeably, don't wait for the contract renewal to investigate.

What's the biggest mistake teams make with enrichment?

Treating it as a one-time project rather than an ongoing capability. They enrich their database once, assume it's done, and wonder why data quality degrades over time. The best RevOps teams build continuous enrichment into their workflows so records stay fresh automatically.

Is it better to use one provider or multiple?

Multiple, almost always. No single provider has perfect coverage across all segments, geographies, and data types. Layering providers through waterfall logic gives you better overall coverage while letting you use each vendor for what they do best. Just make sure you have a system for managing the complexity.