Ramp

How Ramp Built a Forecastable Demand Engine Across 30+ Webinars (New Draft)
Growth Today built the sending infrastructure, list layer, and per-event launch process behind Ramp's webinar programme, then added a signal layer that detects which ERP each prospect actually runs. The whole thing runs as a self-learning loop: every cycle sharpens which persona, topic, and signal to target next, with an expert reviewing the reads before they drive a decision.
Impact TL;DR
- 30+ events promoted on a rolling calendar, including an eight-event August masterclass series
- 150 domains under managed warm-up, placement testing, and daily limit monitoring
- 1:66 best conversion ratio on the strongest topic and persona pairing
- A repeatable persona map built from results, showing which titles convert on which topic types
- Warm re-engagement of prior positive responders converting at roughly 1 in 6
- An ERP detection layer that identifies a prospect's actual finance stack from hiring signals, refreshed weekly
“We are a bedrock now of the field and events marketing team, and this is pretty much an essential part of our program now.”
Christopher Wingo, Field & Events Marketing, Ramp
An events calendar moving faster than a build-per-campaign process could
Ramp's demand team ships events continuously. A single month included a nonprofit session, a higher education session with a customer spotlight, a Canada market entry event, a monthly product deep dive, and the start of an eight-part AI masterclass. Each needed its own audience.
That cadence breaks a per-campaign approach. Domains need 14 days of warm-up before they can carry volume, which means infrastructure has to be provisioned ahead of a calendar nobody has finalized yet. Audiences overlap across events, so the same person can receive three invitations in a fortnight and stop reading all of them. And Ramp is large enough that a meaningful share of any list is already a customer, an open opportunity, or a partner.
Early in the engagement the sending domains also had to change. The originals did not align closely enough with Ramp's marketing and security conventions, so the programme paused for two weeks while 150 replacement domains were sourced, approved by Ramp's IT team, and warmed.
Key challenges were:
- Event cadence outpaced list building, with several events added per week
- 14 day warm-up meant infrastructure had to lead the calendar
- Audience overlap across events risked message fatigue on the same contacts
- A large existing customer and pipeline base, requiring live suppression rather than static lists
- Domain naming had to satisfy marketing conventions and IT security review
Growth Today built the programme as standing infrastructure plus a per-event configuration step
Instead of treating each event as its own campaign build, we built a system where:
- Infrastructure capacity always leads the calendar rather than following it
- Suppression runs against live customer, opportunity, and do-not-contact data
- Persona and topic pairings are chosen from measured results rather than assumption
- Overlapping audiences receive materially different messaging per event
- Prior positive responders are re-contacted as a standing motion
- Reporting attributes results to persona, topic, and segment so the next event starts better informed
None of this was going to work without infrastructure the client's own security team had signed off. So we sourced and rebuilt the domain estate, including:
- Sourcing 150 candidate domains matching naming conventions Ramp's marketing team approved
- Running 14 day warm-up with scheduled inbox placement tests on every inbox
- Validating daily limits, tags, and script health on dashboards
- Routing do-not-contact and unsubscribe records to a separate endpoint so they never re-enter
Self-learning, with a human in the loop
The programme gets sharper every cycle. Each event feeds persona, topic, and segment results back into the next one, so audience selection becomes an evidence question rather than a guess. We do not leave that read to the model alone. An expert reviews and pressure-tests what the data says before it drives a decision, because automation without judgment draws the wrong conclusion often enough to matter. AI leverage, human judgment.
Learning which persona converts on which topic
What we did: Every event was reported with sends, contacted, replies, and registrations broken down by persona and segment. Across eight campaigns we accumulated enough data to make persona selection an evidence question.
The outcome: SMB CFOs at companies under 150 employees converted on every topic type, producing positives in six of eight campaigns, with a best ratio of 1:66. The pattern that worked was a named practitioner running a live build in a tool the audience uses daily: one such session hit 1:83 against 2,726 contacted, while a concept-led session on a similar audience produced 2 positives from 3,511 contacted.
The negative findings mattered as much. Controllers replied readily to strategic topics and registered for almost none of them. Enterprise FP&A titles produced no registrations across two campaigns and 361 sends. Operations and catch-all titles generated high reply rates and no conversions, and were retired.
“When I look at the conversion data and the leads that you all provide, when you have good timelines, they're all always really good.”
Christopher Wingo, Field & Events Marketing, Ramp
Re-contacting people who already said yes once
What we did: We built a standing motion that contacts prior positive responders for each new event, rather than treating each event's audience as net new.
The outcome: 12 of 71 prior responders registered, roughly 1 in 6, which is an order of magnitude above cold performance. On one event, 15 of 16 total registrations came from this warm re-engagement branch and from out-of-office follow-ups. This now runs on every campaign.
If you are running recurring events, the highest-converting audience you have is the people who registered for the last one. Build that as a standing branch rather than remembering it each time.
Detecting the finance stack a prospect actually runs
What we did: We built a signal layer that identifies which ERP a company runs by reading its own job postings in the US and Canada. If a company is hiring someone to work in NetSuite, QuickBooks, Sage Intacct, Xero, or Dynamics 365, it runs that system. An AI verification step filters out postings that mention a tool as a nice-to-have rather than a requirement. The detection refreshes weekly.
The outcome: Confirmed stack users are prioritized in send order and their actual system is referenced in the copy. The layer went live against one masterclass audience with a measured baseline to compare against, on both incremental registrations and conversion ratio.
“One of our influencers was doing a Claude in finance thing and we just did it the next month, and we had like 1,800 people there. So those signals are super important.”
Christopher Wingo, Field & Events Marketing, Ramp
Suppression against live pipeline rather than a static list
What we did: Suppression runs against Ramp's customer, former customer, open opportunity, and do-not-contact data rather than a periodic export. When Ramp shared 150 high-intent domains for prospecting, we ran them against suppression before building anything.
The outcome: 140 of the 150 were already on the active suppression list, so the question went back to Ramp as a decision rather than becoming 140 wasted sends into accounts their own team was already working.
What a strong brand does to registration outbound
These campaigns are measured on one thing: how many people register for the event. Nobody is asked to book a demo, and no sales meeting is the call to action.
The programme produces sales conversations anyway. Across the engagement, replies to webinar invitations regularly included direct demo requests, trial requests, and specific competitive questions comparing Ramp against named alternatives. Several arrived from people who had recently implemented a competing platform and wanted a comparison for the next evaluation cycle. Each of those was routed straight to Ramp's sales team while the person was also registered for the upcoming session.
That happens because of who is sending. When the reader already knows the brand, trusts the category position, and has probably seen the product discussed by peers, an event invitation reads as a reason to engage rather than as a cold approach. The invitation lowers the cost of replying, and the brand supplies the reason to.
Just like any demand motion, the strength of the sender changes what the reader does with the message. If your brand is well known in your category, a soft-ask motion like event registration will produce sales conversations as a side effect, and you should build the routing to catch them. If your brand is unknown, the same motion produces registrations and very little else, and the offer has to carry more of the weight.
What the replies look like
Extra pipeline, not just registrations
The outreach reconnects Ramp with buyers, and a meaningful share ask for a demo or pricing. Event promotion that is ignored produces garbage leads, here it produces pipeline.
Existing customers re-engage and expand
The same campaigns surface current customers who want more, a signal the CS and expansion teams would not otherwise see.
Competitor and product intelligence
Replies reveal what buyers use today and what they wish existed, intel the product and competitive teams can act on.
Buyers pull in their colleagues
Positive replies often widen the account, prospects invite teammates, which grows reach at no extra cost.
Why Ramp Chose Growth Today
A team that can absorb an events calendar without a new statement of work per event
Ramp's constraint was throughput. The demand team could produce more events than a campaign-by-campaign process could promote, and the infrastructure work sat outside what their team wanted to own. Growth Today runs the estate, the list layer, the launch process, and weekly reporting, and works directly in Ramp's Slack so events can be added and questions answered without a meeting. When Ramp's team floated expanding into education, government, and nonprofit segments, the response was a request for closed-won rates by segment before adding them, rather than turning the segments on.
- Standing infrastructure. 150 domains warmed and monitored, so capacity leads the calendar
- Measured persona selection. Which titles convert on which topics comes from results, not assumption
- Signal engineering. ERP detection built from public hiring data rather than bought as a field
“I love working with you all. I really appreciate how even just the suggestions you make on topics, I really appreciate that collaboration.”
Christopher Wingo, Field & Events Marketing, Ramp
Results & Impact
RevOps Impact
- 150 domains under managed warm-up, placement testing, and daily limit monitoring
- Suppression against live data, catching 140 of 150 supplied high-intent domains as already worked
- Weekly ERP detection across US and Canada from public hiring signals, with AI verification
- Do-not-contact routing to a separate endpoint so opt-outs cannot re-enter
Sales Impact
- Demo and trial requests arriving from event invitations, routed to sales same day
- Competitive comparison requests surfaced from accounts that recently implemented an alternative
- 1:66 best conversion ratio on the strongest persona and topic pairing
- 1 in 6 conversion on prior positive responders
Leadership Impact
- 30+ events promoted on a rolling calendar without a per-event rebuild
- A persona and topic map that tells the demand team what to programme next
- Segment expansion decisions driven by closed-won data rather than by available volume
By building the estate and the launch process once, Ramp turned an events calendar into a registration motion that absorbs new events instead of restarting for them.
“We had a really strong year, and you all are definitely part of that in the promotions that you did.”
Christopher Wingo, Field & Events Marketing, Ramp


