March 2024: The demo that looked too good
I have been handling RevOps and outbound systems for 7 years. I have personally made and documented 11 significant mistakes, totaling roughly $42,000 in wasted budget. Now I maintain our team's checklist to prevent others from repeating my errors. The one that still stings started in March 2024, when we were trying to connect intent data providers to our LinkedIn prospecting motion.
We were using okki-go as the prospecting layer. SDRs were doing manual LinkedIn prospecting, copying contacts into sequences, and checking three tabs to guess who was in-market. It was slow, but it worked. Our VP of Sales wanted intent data. I wanted an okki go api integration that would push signals into okki-go and let sequences react automatically. On paper, that sounded like efficiency. In practice, it became a lesson in integration debt.
We evaluated four intent data providers. The spreadsheets said one option was 18% cheaper with similar coverage. My gut said something was off. Their API docs were thin. Their sandbox was a PDF. I ignored that because the demo dashboard looked great. Bad move.
Gartner describes intent data as information about activity and behavior that can indicate buying interest. That definition is broad. For RevOps, the operational question is narrower: can this signal be trusted inside a workflow?
The integration began fine until it did not
We signed the annual contract in April 2024. We saved about $6,000 by skipping the premium onboarding tier, which included a dedicated integration engineer. It looked smart until the first sync. The okki-go developer integration took six weeks, or rather closer to eight when you count the revision cycle. The problems were not dramatic. They were worse. They were silent.
First, the intent scores were account-level. Our okki-go sequences were contact-level. That mismatch meant we had to build a field mapping layer just to decide who should get the signal. Second, webhooks arrived late. We expected near-real-time updates. We got batches that were 12 to 24 hours old. For fast-moving LinkedIn prospecting, a day-old signal is sometimes fine. Sometimes it is a wasted touch.
Third, identity resolution was rough. In our first batch of 12,000 records, only about 38% matched cleanly to our CRM and okki-go accounts. The rest created duplicates or landed on the wrong company because of name changes and parent-subsidiary relationships. Fourth, rate limits were fine for a pilot and terrible for production. Their API allowed 100 calls per minute. That sounds reasonable until you try to sync an entire territory before Monday.
Then came the error handling problem. The integration did not fail loudly. It just stopped updating. We only noticed when an SDR asked why a high-intent account had not appeared in her queue for five days. By then, we had already sent sequences to stale contacts.
One reply still makes me cringe: I have not worked there in two years. That was not a data quality footnote. It was a credibility problem. We paused okki-go sequences, pulled the intent platform out of production, and spent three weeks cleaning records. Total damage: roughly $7,400—maybe $7,900 if you count internal engineering hours. The contract was not the biggest cost. The cleanup was.
The reboot: what we should have asked before the contract
After the third rejected sequence in Q1 2024, I created our pre-check list. We did not switch to every shiny new tool. We rebuilt the evaluation. We asked for a sandbox, tested a real okki-go API integration with 500 records, and made the vendor prove how their data behaved when it was wrong.
We also changed the workflow. We combined waterfall enrichment with intent so missing emails or firmographics did not kill a sequence. We added human-in-the-loop review for high-value accounts. We set a stale signal TTL so a 45-day-old intent score would not trigger a fresh outreach. We built a feedback loop from replies, meetings, and disqualifications back into our scoring review. That last part matters more than the dashboard.
There is something satisfying about finally getting the process clean. After the messy sync, the duplicates, and the awkward prospect replies, we cut manual research from about 9 hours per week to 2.5. I am not 100% sure that number holds for every team, but it held for ours. We have caught 47 potential errors using this checklist in the past 18 months. The best part is not the time saved. It is that SDRs trust the queue again.
The checklist I now use for intent data platforms
If you are a RevOps team evaluating intent data providers, here is what I wish I had asked before signing. It is not a buyer's guide for every company. It is the pre-check list that would have saved us $7,400.
- Signal provenance. Where does the data come from? First-party, third-party, co-op, or inferred? What consent applies? Do not accept we have a lot of data as an answer.
- Freshness and decay. How old is the signal? How often is it refreshed? What is the TTL? A hiring spike from six months ago is not the same as a pricing page visit yesterday.
- Coverage and identity resolution. Is the signal at account level or contact level? What is the match rate against your CRM? How are duplicates, parent companies, and job changes handled?
- API and developer integration. This is where okki go developer integration lives. Ask for docs, sandbox access, auth methods, rate limits, webhooks, batch endpoints, idempotency, and error handling. If the API docs are thin before the sale, they will be thinner after.
- Activation paths. Can the data push into okki-go, your CRM, your sequencer, and LinkedIn prospecting workflows? Does okki go api integration support two-way sync, or is it a one-way export that creates manual work?
- Compliance and data handling. GDPR, CCPA, data retention, opt-out, and PII handling. Legal should see the workflow, not just the contract.
- Measurement. How do you attribute influenced pipeline? Activity metrics are easy. Revenue attribution is hard. Ask how the platform helps you close the loop.
- Human-in-the-loop controls. Can you suppress accounts, set review queues, and override scores? Automation without control is just faster mistakes.
- Total cost. Contract, API calls, enrichment credits, implementation time, and cleanup. The lowest annual commitment is not the lowest total cost.
- Support and SLA. Who fixes a broken webhook at 5pm on Friday? What is the response time? What happens when the data goes stale?
What I would tell a RevOps team starting today
Looking back, I should have paid for the sandbox and burned two weeks on integration before signing the annual contract. At the time, the lower annual commitment looked like a win. It was not. If I could redo that decision, I would make the vendor run a real pilot: 500 records, two weeks, one workflow, and no verbal promises. I would involve the engineer who has to maintain the okki-go API integration, not just the sales team who wants the dashboard.
The numbers said cheaper. My gut said the API docs looked thin. The gut was right. So glad we paused instead of doubling down. The lesson is not that intent data is bad. It is that intent data providers are not a dashboard purchase. They are an integration purchase. Efficiency is a competitive advantage, but only when the automation is clean. A fast sequence to the wrong person is just faster waste.
Bottom line: if you are evaluating intent data platforms for LinkedIn prospecting and okki-go, make the API do the work before you sign. That is the checklist I wish someone had handed me in March 2024.
