How to operationalize intent data: From in-market to meetings booked
Most revenue teams have intent data. Few know what to do with it.
You've got a platform telling you that Company X is researching your category. Great. Now what? Who do you call? What do you say? How do you coordinate sales outbound with marketing ads so you're not running two separate programs that happen to target the same logo?
This is where most ABM programs stall. The data exists, but the system to turn signals into coordinated action doesn't. Sales gets a hot account list they can't operationalize. Marketing runs ads to personas, not people. Nobody can confidently answer "who should we reach out to right now, and why?"
We built UserGems to close that gap. Our AI Command Center unifies every buyer and account signal, scores contacts based on your actual conversion patterns, and then executes through Gem-E, our AI agent that writes emails, enrolls contacts in sequences, and syncs LinkedIn audiences automatically.
This guide walks through the practical system for turning intent signals into pipeline: how to choose which signals matter, route them to the right owner, build workflows that execute automatically, and measure what's actually working.
Why intent data rarely produces pipeline on its own
Intent data tells you a company is researching. It doesn't tell you who at that company to talk to, or why this week is the right time to reach out.
Most intent platforms operate at the account level. They score Company X as "in-market" based on aggregated activity across their IP range or domain. But in enterprise ABM, "same account" doesn't mean "same people." Your AE might be emailing a director they found on LinkedIn while marketing serves ads to VPs filtered by job title. You're targeting the same company with zero contact overlap.
Here's what breaks down:
No contact-level intelligence. Account-level intent gives you a company name. It doesn't surface the specific person showing research behavior, or tell you if that person is actually in a buying role.
No reason to reach out right now. Knowing someone visited a competitor comparison page six weeks ago isn't a reason to call today. You need recency, frequency, and a triggering event that creates urgency.
No system to act on the signal. Even when you know the account and the contact, someone still has to write the email, add them to a sequence, update the CRM, and sync the ad audience. Most teams do this manually, which means it doesn't happen consistently.
Sales and marketing activate different contacts. Marketing targets personas. Sales targets whoever they can find. Without contact-level coordination, your overlap approaches zero and you're running two separate motions under the same ABM label.
UserGems operates at the contact level. We surface the specific person, the specific signal, and the specific reason to reach out. Then Gem-E executes on it automatically: writes the email, adds the contact to sequences, queues tasks in Outreach or Salesloft, and syncs LinkedIn ad audiences in real time.
How UserGems compares to 6sense
6sense gives you account-level intent. UserGems gives you contact-level intelligence and automated execution.
Here's where the approaches differ:
Account scoring vs. contact scoring. 6sense scores accounts using industry-wide models. UserGems scores contacts based on your CRM and historical conversion data, which personas appear in your winning deals, which signals actually precede pipeline for you.
Opacity vs. transparency. 6sense's AI scoring is a black box. UserGems shows reps exactly why an account or contact was surfaced, with 600+ signals and adjustable weights. Reps trust what they can see.
Insight vs. action. 6sense surfaces intent. UserGems closes the gap between signal and execution. Gem-E writes the email, adds contacts to sequences, and syncs LinkedIn ad audiences automatically.
Sales and marketing coordination. 6sense gives both teams the same account list. UserGems syncs both motions to the same contacts for the same reasons at the same time. When a prospect gets an outbound email referencing their new role and then sees a LinkedIn ad about that same move, the experience feels intentional.
We maintain a 95% match rate and less than 5% bounce rate on email addresses, with contact and account data refreshed biweekly and monthly.
What's the workflow to go from signal to sequence enrollment?
Here's the step-by-step system we run internally and our customers use to turn signals into pipeline.
Step 1: Identify real buying signals
Start by talking to your best reps. Ask them: who do you reach out to first on a Monday morning, and why?
You'll hear things like "I always go after companies where I know someone" or "I watch for new leadership because they actually have budget." That's signal intelligence. It just lives in people's heads instead of your CRM.
Run a short discovery session with three or four top reps and document their instincts. What patterns come up consistently? What account or contact triggers reliably lead to conversations?
Once you know what your best reps are already doing intuitively, you can build a scoring model that reflects it and scale it across the whole team.
Step 2: Configure signal orchestration
UserGems brings every signal into one system:
- Contact-level intent. Know exactly who is researching your topics, not just which company
- 42,000+ intent topics. Precision monitoring across the keywords that matter for your market, including competitor research activity
- Contact-level website de-anonymization. See who is visiting your site, how often, and how recently
- Native UserGems signals. Job changes, past champions, new hires and promotions into buying roles
- Your 1st, 2nd, and 3rd-party data. Event registrations, webinar attendees, G2 intent, CSVs
All unified, deduplicated, and verified in one place.
Step 3: Set up AI scoring
Gem-E analyzes 600+ signals weekly to generate account scores, contact scores, and a clear explanation behind each one.
Admins can adjust signal weights and preview changes before saving. No data science team required. When reps can see exactly why an account was surfaced, they trust it.
Step 4: Automate execution
Once a contact hits a signal threshold, Gem-E takes over:
- Writes hyper-personalized emails using CRM context, past interactions, and the specific signals that made this person worth reaching out to
- Adds buyers into sequences automatically in Outreach, Salesloft, or wherever your reps work
- Queues tasks directly so reps wake up with outbound ready to ship
- Syncs contact-level LinkedIn ad audiences in real time as signals change
- Updates CRM records and expands buying groups automatically
This doesn't change how reps work. Gem-E meets them inside the tools they already use.
Step 5: Coordinate sales and marketing
The same contacts that get added to sales sequences sync to LinkedIn ad audiences dynamically. Match rates stay above 80% because the contact data is verified and fully enriched.
Creative is grouped by signal type so messaging matches the moment:
- Past champions see role change acknowledgment
- Contacts researching competitors see relevant content
- New hires see onboarding-stage messaging
When a prospect gets an outbound email referencing their new role and then sees a LinkedIn ad about that same move, the experience feels intentional. That coordination is what turns a 1-3% conversion rate into 10-15%.
How do you choose which intent topics to monitor?
Start with the keywords your best customers actually searched before they bought.
Most teams make this harder than it needs to be. They monitor hundreds of generic category terms and end up with noise. Here's a better approach:
Interview recent customers. Ask them what they were researching in the 90 days before they started evaluating your product. You'll get specific phrases, competitor names, and problem statements.
Map topics to buying stages. Early-stage buyers research broad problems. Late-stage buyers research specific solutions and competitors. Monitor both, but weight late-stage topics higher in your scoring model.
Use competitor research as a high-intent signal. If someone is actively comparing you to a competitor, they're in active evaluation. That's a contact you want to reach this week, not next quarter.
Layer intent with other signals. Intent alone isn't enough. Intent plus job change is stronger. Intent plus past champion plus new budget cycle is even better. UserGems lets you stack signals and adjust weights based on what actually converts for you.
We monitor 42,000+ intent topics. You don't need to use all of them. Pick 20-30 that map to your buyer's journey and refine from there based on what produces pipeline.
How do you route intent to the right owner without RevOps busywork?
Gem-E handles routing automatically based on your existing CRM ownership rules.
Ownership is confirmed before any action. When Gem-E surfaces a high-intent contact, it checks your CRM to see who owns the account. If ownership is clear, the contact gets added to that rep's sequence automatically. If ownership is unclear, Gem-E flags it for manual review.
Round-robin for new accounts. For accounts not yet in your CRM, you can configure round-robin assignment rules by territory, segment, or rep capacity. Gem-E assigns ownership and updates the CRM before any outbound goes out.
No manual research required. Gem-E identifies missing personas and buying group members automatically. If you're targeting an account and Gem-E finds a new VP of Sales who just joined, that contact gets surfaced and added to the buying group without anyone having to search LinkedIn.
Tasks appear in the rep's existing workflow. Reps don't log into a separate platform. They see tasks queued in Outreach or Salesloft with full context: why this contact was surfaced, what signal triggered the outreach, and what Gem-E recommends saying.
We doubled capacity per rep last year because research, contact surfacing, and email writing are handled before a rep even logs in. Sendoso generated 47 opportunities and over $1M in pipeline within 30 days of launching Gem-E, with 20% reply rates. Accord now sources 50-60% of outbound meetings through UserGems and Gem-E.
What metrics prove your intent-to-sction workflow is working?
Track these four metrics to know if your system is actually generating pipeline.
Signal-to-action conversion rate
What percentage of high-intent contacts actually get outbound sent within 48 hours?
If you're surfacing 100 high-intent contacts per week but only 30 get outreach, your workflow has a gap. Gem-E should be converting 90%+ of surfaced contacts into actions automatically.
Reply rate by signal type
Which signals produce the highest reply rates?
Break this down by signal category: intent topics, past champions, website visits. You'll quickly see which signals are worth prioritizing and which are just noise.
Our internal ABM program sees 10-15% reply rates on signal-based outbound. Sendoso hit 20% reply rates using Gem-E. If you're below 5%, either your signals aren't strong enough or your messaging isn't connecting the signal to the value prop.
Pipeline generated from signal-based accounts
How much pipeline comes from accounts where you acted on a signal vs. accounts where you didn't?
This is the metric that matters most. Our internal ABM program converts 10-15% of targeted accounts to sales-accepted opportunities. Across our customer base, the median ROI is 47x in pipeline generated and 11x in revenue.
Sales and Marketing contact overlap
What percentage of contacts receiving sales outbound are also in your LinkedIn ad audiences?
If this number is below 50%, you're not running coordinated ABM. UserGems syncs both motions to the same contacts automatically, which is why our customers see match rates above 80%.
ABM Needs a Brain
ABM started with static target account lists and firmographic filters. Then account-level intent tools came along and gave marketing something new to work with. But sales still didn't know who to call or why.
Running ABM from a single command center means your team makes prioritization decisions grounded in real contact-level signals, executes coordinated plays across sales and marketing at the same time, and runs a system that learns from your outcomes and compounds the longer it's active.
We built UserGems to power that shift. If you've spent the last few years feeling the distance between the data your team has and the decisions your team can confidently make with it, this is what closes that gap.
Book a demo with the UserGems team to see the AI Command Center and Gem-E in action.
