Selecting a data provider is a strategic decision - one that directly impacts pipeline quality, sales productivity, and revenue performance.
Yet many teams have experienced the gap between promise and performance. Impressive coverage claims can quickly give way to high bounce rates, inaccurate job titles, incomplete buying committee data, and limited accountability once implementation begins.
This playbook is designed for marketing and RevOps leaders who are taking a more rigorous approach to vendor evaluation. It provides a structured framework for assessing whether a data partner can deliver what truly matters: accuracy, recency, relevance, and commercial impact.
Because in modern go-to-market execution, data quality is not a “nice to have.” It is a foundational driver of predictable growth.
Data quality is the foundation of revenue precision. Even the most beautifully built GTM strategy will underdeliver if your contact data is stale, misaligned, or incomplete.
You should be evaluating data quality first if:
Jeff Ignacio, Head of GTM Ops, Keystone AI said:
"If we start with poor data, we set up the whole funnel to fail. High bounce rates don’t just hurt us today - they hurt us for weeks after with spam filters and SDR burnout."
Yes - and it should be.
Known data testing (where vendors enrich records from your CRM) is a great way to compare real accuracy.
Unknown data testing can still show recency and field completeness.
If you’re targeting niche segments, senior personas, or running outbound motions, quality matters more than scale.
Better to have 500 quality, trusted leads than 10,000 duds.
Here’s how a quality-first strategy compares to other approaches:
In a quality-driven evaluation, your goal is to reduce wasted effort and boost conversion.
Here’s what you should be scoring:
When evaluating a data provider for quality, your focus shifts from quantity and scale to accuracy, freshness, and completeness.
This means looking beyond flashy volume stats and digging into the real usability and trustworthiness of the data.
Here’s how to structure your evaluation for a quality-first decision.
Known data testing is your reality check. By asking each vendor to enrich records that you already trust, you can assess their true ability to deliver accurate, up-to-date, and relevant data.
Viktoria Ruuble, Chief Product, Data & Technology Officer at Cognism said:
"Known data tests help you separate signal from sales spin. If a vendor can’t get your known good data right, how will they handle unknowns?"
Bonus tip:
Manually spot-check 20–30 records on LinkedIn to validate job titles, seniority, and current employer.
This helps catch subtle gaps or outdated data that automated checks may miss.
It’s not just about whether a contact can be found - it’s about how complete and useful the data is once you have it.
True quality shows up in the depth of enrichment, not just surface-level contact info.
Data goes stale fast, especially in fast-moving industries like tech, finance, or marketing.
Even if the initial record is accurate, outdated info becomes a liability over time.
Sandy Tsang, VP of Revenue Operations at Cognism said:
"The more senior the buyer, the faster they change roles. Stale data means lost deals and wasted effort."
Pro tip:
Evaluate vendors not just on how often they refresh, but how visible and accessible that freshness is to your team.
By adapting your evaluation to focus on quality-specific metrics and methods, you ensure that the provider you choose will improve, not pollute, your CRM and GTM motion.
Let’s walk through a hands-on, realistic testing workflow to help you evaluate a data provider based on accuracy, recency, and field completeness, not just database size.
Let’s say you’re a Marketing Ops leader at a B2B SaaS company:
You’ve been tasked with finding a new provider that can actually deliver clean, accurate, and usable contact data - the kind that drives meetings and pipeline, not frustration.
Your ICP includes:
The goal is not just to find more contacts but to find better ones that your team can reach and convert.
Start by pulling a clean, validated list from your CRM. This should act as your benchmark dataset.
Why this matters:
This sets a fair and consistent baseline across vendors. You’re not testing hypothetical data - you’re testing against reality.
Send the same contact list to 2-3 vendors and ask them to enrich it using their own data. Be specific in your request:
What to return: Enriched fields such as email, mobile number, direct dial, job title, company size, industry, and LinkedIn profile.
What to flag: Any changes to the original data, such as:
Metadata to include: Last verified date, confidence scores, or source if available.
This is a chance for vendors to prove their quality with real, high-context data, not cherry-picked net-new contacts.
Once each vendor returns the enriched list, conduct a structured comparison using a simple scorecard.
Pro tip:
Use a colour-coded matrix to score each vendor across metrics. This helps your team see the differences clearly without relying on gut feel.
Numbers are great, but don’t forget to sense-check the data.
Ask your SDRs or RevOps analysts to manually review 25–50 records per vendor.
Look for:
This extra step helps you validate the data’s accuracy and relevance to your go-to-market strategy.
Jeff said:
“Don’t just test for correctness - test for usefulness. Will your team actually want to reach out to these people?”
Build a simple comparison table that looks something like this:
This visual approach makes it easier to align with stakeholders and clearly see which vendor is the best fit, not just in theory, but in actual usability and relevance.
When you’re buying for quality, it’s not just about whether a vendor has your target persona or job title in their database - it’s about whether that data is accurate, up-to-date, complete, and reliable enough to drive meaningful results.
Use these questions to separate the vendors who say they prioritise quality from those who can prove it.
Bounce rate is the frontline indicator of email data health.
High bounce rates impact not only your email deliverability but also your domain reputation and SDR morale.
Data decays fast. The average B2B contact has a 30–40% annual turnover rate.
Without regular refreshes, you’re buying stale leads.
Sandy said:
"Stale data means wasted effort. You end up chasing ghosts and irritating prospects."
Senior roles and ICP buyers move jobs frequently.
If your data provider can’t detect changes, your records go out of date fast, and so do your sequences.
Mobile numbers dramatically improve connect rates, but only if they’re real. Job titles determine targeting and segmentation.
Poor data in either area leads to wasted time and pipeline loss.
It’s not just whether a field is populated, but how it was sourced and checked.
The best vendors use multi-layered enrichment and ongoing quality assurance (QA) processes.
Jeff said:
"A good vendor won’t just enrich. They’ll explain where it came from, how fresh it is, and what to expect at field level."
Real customer stories validate whether a vendor’s data performs for your use case.
Speaking to someone in a similar role, region, or vertical provides unmatched insight.
Pro tip:
Standardise these questions across vendors. Document responses in a shared evaluation sheet, scorecard, or Notion doc.
This makes it easier to objectively compare how each vendor stacks up - and creates a paper trail for stakeholder buy-in.
Ensuring you get the data quality you need is crucial for the rest of your GTM motions.
Here are some red flags to watch out for when evaluating your shortlist.
Viktoria said:
"If they can’t explain how they keep data fresh or how they deal with inaccuracies, they’re not a quality-first vendor."
You’ve chosen a data provider based on quality - but how do you know it was the right call?
When you prioritise data accuracy, freshness, and usability, success isn’t just about what gets delivered; it’s about what changes downstream.
From sales adoption to bounce rates to the time your RevOps team spends cleaning up records, the impact of better data should show up quickly and clearly.
Here’s what success looks like - and how to measure it.
Your enriched data should mirror the accuracy of the contacts your team already trusts.
95%+ field accuracy on enriched known data samples
If bounce rates fall after implementation, that’s one of the clearest signs you’ve improved your data quality.
<3% bounce rate (ideally <2%) on net-new or enriched contacts.
If you’re targeting personas that rely on phone outreach, mobile coverage is a strong quality signal.
60%+ mobile coverage in outbound-focused roles (e.g. sales, marketing, RevOps).
Success isn’t just technical - it’s behavioural. If your teams are actively using the enriched data, it means they trust it.
Increased outreach volume, higher reply rates, fewer “bad lead” escalation.
Better data should reduce time-to-connect, increase engagement, and lift early-stage pipeline metrics.
Faster sales cycles and a higher lead-to-opportunity rate.
Bad data clogs workflows.
Good data should free up RevOps time and reduce friction in lead routing and scoring.
Noticeable drop in QA time per campaign and fewer internal data hygiene tasks.
To monitor the impact of quality-driven data improvements, track these KPIs monthly or quarterly:
You can also pull in qualitative feedback:
Bonus tip:
Set a 30-60-90 Day Data Review. Book check-ins with your team and vendor after onboarding:
This review gives you the confidence to continue—or the clarity to course-correct.
If you’re buying for quality:
With the right partner, your data becomes a competitive advantage - not a liability.