Peter Micciche. June 12, 2026. By Peter Micciche, CEO, Certain.
Most of the AI conversation in marketing right now lives in the future tense. Someday agents will run the funnel. Someday the data will be clean. Someday the org will be ready. That framing has a cost. Teams postpone the plays they could run today with the data they already collect.
Events are the best place to start. The reason is simple. The data is first-party. The data is behavioral. The data is generated by people who chose to show up. An AI system can act on that input without you having to buy anything new to produce it. You are already running the events. The question is whether systems can use what happens inside events while it still matters.
Here are seven plays you can put in motion before the quarter ends. None of them require a year-long platform project. Each play comes with one metric. A play that cannot be measured cannot be defended in the next budget review.
Play 1: Read registration as a buying committee signal
Registration data is treated as an attendee list. Registration data is closer to a behavioral dataset. When three people from the same account register, they have already talked about it internally. When registrants come from different functions, it usually means the account has decided the event warrants several people’s time.
An AI model can be pointed at the registration file. Registrations can be clustered by account and role instead of reading registrations as rows of names. The clustering looks for accounts with two or more registrants. The clustering also looks for a mix of seniority. The clustering looks for late additions from senior leaders in the final week. Those patterns tend to precede a real evaluation.
The play: cluster registrations by account weekly as they come in. Send a sales team account-level brief before the event instead of a name list. What to measure: number of accounts flagged with two or more registrants. Measure the share of those accounts that the sales team had a planned conversation with on site. Show me Play 1 in the guide →Play 2: Turn check-in into a real-time sensor
The first moment of an event happens when someone walks in and checks in. That moment is the most concentrated buying signal from that person all year. The attendee cleared a calendar. The attendee traveled. The attendee chose the door. In most stacks, that moment becomes a tally. In most stacks, that moment becomes a printed badge. The record reaches sales days later.
A check-in layer can capture who arrived. The check-in layer can capture when arrival happens. The check-in layer can capture with whom arrival happens. The check-in layer can route the captured information to a CRM and to Slack in seconds. Routing turns the front door into a live sensor. The AE covering a named account gets the alert while the buyer is still in the room.
The play: route VIP and target-account arrivals to the account owner’s phone in real time. Detect when multiple colleagues from one account arrive within minutes of each other. What to measure: time from arrival to rep notification, in seconds. Measure the number of same-day or next-day meetings booked from on-site alerts. Harvard Business Review’s analysis of more than two million leads found that reaching a prospect within an hour makes prospects nearly seven times more likely to qualify. The metric that matters is the clock. Show me Play 2 in the guide →Play 3: Ask declared intent questions at badge pick-up
Behavioral data tells you what someone did. Declared data tells you what someone wants. Check-in is the highest-attention moment to ask. Check-in is the highest-consent moment to ask. Badge pick-up can produce a first-party signal. A declared-intent question set can produce a first-party signal no inference model can match.
The question set should be kept to four or five questions an agent can act on. The questions should ask where attendees are in their evaluation. The questions should ask what role attendees play in the decision. The questions should ask which 2026 initiative brought attendees in. The questions should ask which platform attendees run today. The questions should ask whether attendees would like to meet the team before leaving. Each answer should land in the CRM as a structured field. Each answer should land as a structured field rather than as a note someone has to transcribe later.
The play: add declared-intent questions to the check-in flow. Pipe the answers straight into the CRM record. What to measure: percentage of attendees who complete the questions. Measure the share of resulting records that carry a declared evaluation stage. This measurement matters more every quarter as third-party intent softens. This measurement matters more every quarter as cookie-based identity keeps eroding. A declared answer from a verified buyer is the cleanest input. Show me Play 3 in the guide →Play 4: Deliver signals in hours, not days
Perfect data can still lose a deal if it arrives late. Follow-up math can be brutal. MarketingProfs found that 74% of B2B marketers take four or more days to follow up with event leads. MarketingProfs found that only 2% reach the prospect the same day. By day four, urgency that brought the buyer to the event is gone.
A routing layer can move event signals into systems in real time. The routing layer can move event signals into Salesforce. The routing layer can move event signals into Marketo. The routing layer can move event signals into Eloqua. The routing layer can move event signals into HubSpot. The routing layer can move event signals into Slack in real time. The routing layer should include enough context so a rep knows why the signal matters now. Intelligence that sits in an export is intelligence you didn’t capture.
The play: wire event signals to flow into the systems your revenue team already works in. Do the flow automatically instead of through a weekly export. What to measure: median time-to-first-touch for event-sourced leads, in hours. Measure the percentage of high-intent signals actioned within 72 hours. Pick a number and defend the number. Start with a 72-hour ceiling. Tighten from there. Show me Play 4 in the guide →Play 5: Use AI recommendations to deepen engagement
The richer someone’s experience at your event, the more signal someone generates. The richer someone’s experience at your event, the more reason someone has to come back. AI recommendation tools can suggest sessions. AI recommendation tools can suggest exhibitors. AI recommendation tools can suggest connections. The suggestions can be based on each attendee’s profile and behavior. The recommendations can be delivered through the app. The recommendations can also be delivered through the event site. The recommendations can also be delivered through email.
This play helps the attendee and the revenue team at the same time. The attendee gets a more useful day. The revenue team gets a clearer read on what each person cares about. Sessions the attendee accepts are themselves a signal.
The play: turn on AI session and networking recommendations for the next event. Let acceptance behavior feed the intent picture. What to measure: recommendation acceptance rate. Measure sessions attended per attendee compared with a prior event. Rising engagement depth is a leading indicator that the experience is working. Show me Play 5 in the guide →Play 6: Let AI prioritize and draft the follow-up
Follow-up slips under volume. A rep back from an event faces hundreds of contacts. The rep faces no obvious order to work the contacts in. The safe move becomes a generic sequence. The generic sequence treats the hand-raiser and the badge-scanner the same way.
AI can fix the triage problem. AI can score every signal by intent. AI can surface the highest-priority accounts first. AI can draft outreach that references the specific session someone attended. AI can also draft outreach that references the question someone declared at check-in. The rep edits and sends the outreach instead of starting from a blank screen. The follow-up reads like someone paid attention because the follow-up was produced from attention signals.
The play: use AI to rank post-event signals by intent. Use AI to pre-draft context-specific outreach for the top tier. What to measure: percentage of high-intent signals contacted within 24 to 72 hours. Measure reply rate on signal-based outreach versus the standard post-event sequence. The gap between those two reply rates is the size of the prize. Show me Play 6 in the guide →Play 7: Run portfolio analysis to justify the budget
The single hardest question a CMO answers is which events are worth the money. Most teams answer with anecdotes. Teams use anecdotes because the data lives in a different system for every event. AI changes the approach by aggregating signals across the whole portfolio. AI shows which formats, regions, and sessions produce qualified pipeline.
This play protects the rest. Budgets are tight. The Spring 2026 CMO Survey from Deloitte, Duke, and the AMA found that marketing gets cut 45% of the time profits fall short. A channel that can show cost-per-qualified-opportunity by event type can survive the review. A channel defended with vibes cannot survive the review.
The play: consolidate event data into one analytics layer. Compare cost-per-qualified-opportunity across event types each quarter. What to measure: cost-per-qualified-opportunity by format. Measure the budget reallocated as a result. The goal is not a prettier dashboard. The goal is a defensible decision about where the next dollar goes. Show me Play 7 in the guide →Start with one
You do not need all seven plays running within a week. Pick the one play that fixes the most expensive gap. If follow-up is slow, start with routing. If ROI cannot be proven, start with portfolio analysis. If the sales team walks into events blind, start with registration clustering.
Events are the right place to begin with AI. The data is already yours. Signals are unusually clean. Outcomes are measurable enough to build a case on. Run one play. Measure it honestly. Use the result to fund the next one.
All seven plays are pulled into a single playbook. The playbook includes the metric for each play. The playbook includes a maturity model to find the starting rung. The playbook includes a starter artifact for every play. The playbook is free and ungated. Download the Event Signal Playbook and run the first play this quarter.
This is Part 1 of 2. Part 1 shows the plays. Part 2, Part 2: Orchestrating Event Signals at Scale, shows how to run the plays without stitching together five tools.
Read the guide