Intent Data Platforms: What RevOps Teams Should Actually Evaluate (and Why the Demo Won't Tell You)

Your SDR team missed the quarter, and the post-mortem produced the answer everyone expected: we need better intent data. So you start evaluating vendors. You sit through four demos. By the third one they have blurred together — signal dashboards, buyer intent scores, 140 million contacts, Salesforce integration. Every slide looks fine. Every demo ends with you closing the laptop feeling roughly the way you did before you opened it.

If that sounds familiar, the problem is not the vendors you are looking at. It is the evaluation framework you are using. Nearly everyone in this category gets measured against the same six features, and those six features were chosen by the people selling.

Disclosure first. I do quality and brand compliance work at okkigo (it shows up written as okki-go on the product pages), so if this reads like I have a stake in it, I do. My job is to review every batch of customer-facing sales data and every piece of outbound before it reaches anyone. That was about 4,800 records last year, and in our Q1 2024 quality audit I rejected over 18% of first deliveries for traceability or verification problems. Most of what follows comes from reviewing four intent data providers since 2022, including our own.

Everyone evaluates the same six features. That is the first problem.

Every intent data pitch runs on the same benchmarks: signal coverage, contact count, scoring sophistication, integration count, refresh frequency, and price. Those benchmarks exist because they are comparable. They fit on a slide. They are also almost completely disconnected from the question your SDR faces on Tuesday morning, which is who do I call first, and can I trust this phone number.

Look at what each metric actually measures.

Contact count measures how big the database is, not how many people in it still hold the job. Scoring measures the vendor's confidence about a signal, not how much that signal matters to your business. Integration count measures connectors, not how many days pass between a signal firing and someone acting on it. Refresh frequency measures how often data is pulled, not how old it is by the time it lands in front of you.

Those are four separate gaps. Every one of them shows up after the contract is signed.

The real problem is not the data. It is the handoff.

Here is the thing that took me too long to work out. Intent data is not a purchase. It is a handoff.

Most intent data providers resell the same underlying signals — content syndication networks, hiring data, technographic installs. There are maybe a dozen real inputs in this market and a hundred companies packaging them. You are not buying access. You are buying the automation wrapped around it.

Which means the scarce thing was never the signal. The scarce thing is certainty: that this is the right person, at the right company, in the right role, this week.

Nobody sells that cleanly, because nobody has it cleanly. Every provider I have benchmarked lands somewhere between 60% and 90% identity match rates. That range sounds acceptable until you do the arithmetic. On a 40,000-record list, a 40% drift is 16,000 records a human has to fix by hand.

The scoring layer makes it worse. Vendor scores are tuned to the vendor's own coverage. They push up the buyers where they happen to hold data, which is not the same set as the buyers most likely to actually close with you.

Freshness is the thing nobody puts in the contract

When I implemented our verification protocol in 2022, I tracked decay across six weeks. A signal from a pricing page visit converts at week three. By week six it is dead. Most platforms sell historical intent and market it as real-time. When you ask when the signal fired, the honest answer is often last quarter.

I should add that freshness is not a feature. It is an SLA. Either it is under 24 hours or it is not. There is no middle. Near real-time is a phrase that means we do not know.

What it costs when you get it wrong

Start with the obvious cost — manual cleanup. Roughly 30 to 45 minutes per SDR per day spent checking names, titles, and emails. Across five SDRs that is about 12 hours a week, or 600 hours a year, burned on work no human should be doing with their eyes.

Then there is the cost that is harder to forgive. Take a list running a 2% bounce rate — sounds fine — and put it through your outbound. You are slowly torching your primary domain. Three weeks later deliverability drops, and now you are sending at reduced volume for four to eight weeks while you rebuild. That is the real bill, not the per-contact price.

LinkedIn prospecting punishes the same bad data much faster. Account health compounds in both directions. One burn shows up across the whole team, and a restricted account does not come with an appeals number.

And then there is the third cost, the one that never makes it onto a dashboard: the quarter you missed not because the signal was wrong, but because the people who were supposed to act on it were too busy cleaning up after it.

Here is where I will plant a flag. When your CRO is ten days from quarter close and asks which accounts you are working this week, we are still enriching is the most expensive sentence in revenue operations. A cheaper platform promising enrichment in two to four days is not a cheaper platform. It is a different delivery promise, and the difference is whether you can plan anything at all. That is what the premium buys. Not speed — determinism. After getting burned twice by probably-on-time data commitments, I stopped treating that gap as a rounding error and started budgeting for it.

What RevOps teams should actually evaluate in an intent data platform

Seven things. Ask every vendor, write down the answers, and compare the answers rather than the slides.

1. Signal-to-action latency, in hours. Not refresh interval. Ask how long it takes from first signal to a verified contact sitting in a sequence.
2. Identity resolution you can see. If they cannot walk you through one specific match and show how it was made, they are guessing at scale.
3. Verification method, and the failure rate. Any vendor claiming 100% accuracy is telling you either that they have not measured it or that they are comfortable saying things to a buyer that are not true.
4. The integration path. Read the docs before the demo. A 250-row CSV export and a documented sync are different products. If your rollout runs through engineering instead of spreadsheets, the okki go developer integration page should answer most of your questions before the sales call does — and the okki go api integration reference should tell you exactly when a contact was verified and by what method.
5. Compliance and substantiation. Per FTC business guidance on advertising (ftc.gov), claims in outbound — including claims you make about your own data — need to be truthful, substantiated, and not misleading. If the vendor cannot explain where records came from and how the claim is supportable, your legal exposure just became the real cost line.
6. Add-coverage SLA. New contacts verified within X hours or Y days. Written, not verbal.
7. The reverse test. Hand them 500 records you already know are dead. Watch how they report it back. Do they tell you which ones failed, or round up and move on?

On price, for context: public list pricing for intent data platforms as of Q1 2025 clusters into roughly three buckets. Self-serve tiers sit around $500 to $1,500 per month. Mid-market annual contracts land in the $25,000 to $70,000 range. Enterprise pricing never appears in public. Verify current rates before any of this goes into a business case — this category reprices every few months and any number I give you has a shelf life.

The short version

Intent signals are cheap. Anyone can sell you a list of companies researching something, and everyone's list looks about the same. What you are actually buying is certainty — that the person still holds the role, that the signal is fresh enough to matter, and that the gap between the signal and your outbound is measured in hours instead of weeks.

If you change one thing in your evaluation this year, put latency first and scoring last. Ask for the number in hours. The vendors who can answer will. The rest will start talking about coverage again.

And if you want the fastest possible test: go read the okki go api integration docs. They should state exactly when and how a contact was verified. If that answer is not there, that is your answer.