Pipeline, revenue, and the systems behind them

Go-to-market: the work behind the outcomes.

I find who the buyer is, build the motion that reaches them, and measure whether it drove pipeline.

Five cases show the problem, the play I ran, what I owned, and the result. Every number says what it does and does not claim.

$400Kqualified pipeline
134tracked accounts
36senior event attendees
27design partners
24%less early abandonment
The loop I run

Positioning becomes reach, and reach has to become pipeline

01PositionBuyer research, ICP definition, and the narrative that holds up in a skeptical room
02ReachField and events, content, and outbound into named accounts
03ConvertFollow-up, nurture, and the path from first touch to a booked demo
04ProveEvery touch tagged and tracked through to pipeline by stage
Five cases

What changed, why it changed, and what I can prove

01

Category launch

Ran a launch event end to end for 36 senior marketers

Owned start to finish

The problem

A pre-seed company with no brand needed to reach senior product marketing leaders in San Francisco. That audience ignores vendor outreach and there was no budget to buy their attention, so the room itself had to be worth their evening.

What I owned

  1. ProgrammedBuilt the event around a debate practitioners were already having about their own role, rather than a product pitch.
  2. RecruitedBooked four panelists, from Atlassian, Meta Reality Labs, Google, and a fourth whose background spans Celona, Google, and Zscaler.
  3. ProducedRan venue, logistics, run of show, and promotion for the November 11, 2025 date in San Francisco.
  4. ConvertedOwned follow-up after the event, moving attendees into product conversations and the design partner program.
Event I hosted San Francisco · Nov 11, 2025
PMM 2.0: Redefining the role for maximum impact
  • Lara McCaskill · Atlassian
  • Laura Thompson · Meta Reality Labs
  • Liz Flores · Google
  • Sajag Chikarsal · ex-Celona, Google, Zscaler
See the event page on aisepedia.com ›

36 senior attendees filled the room, drawn without paid promotion.

4 panelists came from Atlassian, Meta Reality Labs, Google, and Celona.

No events team behind it. I owned venue, speakers, run of show, promotion, and follow-up.

A live event page still runs on aisepedia.com as a lasting brand asset.

Attendance and the panel are verified. Pipeline generated by the event is not separately attributed here. It flows into the tracked accounts in case 02.

Read the full first-person write-up of this event ›

02

Pipeline generation

Built $400K in qualified pipeline from a standing start

From no list to tracked pipeline

The problem

No brand, no list, no inbound, and no sales development support. The buyers were senior practitioners at large technology companies who receive dozens of vendor messages a week. Every account had to be found, researched, reached, and tracked by one person.

What I owned

  1. TargetedDefined the ICP and built the named-account list, then tracked 134 accounts from first touch through qualification.
  2. ReachedRan 1,000 targeted LinkedIn and outbound reach-outs through a six-week sequence.
  3. QualifiedMoved responses into discovery calls, demos, and the design partner program.
  4. TrackedHeld every account in HubSpot so pipeline could be read by stage instead of by anecdote.
$400K
in qualified pipeline

Across 134 tracked accounts, built with no paid media, no sales development rep, and no inherited list.

$400K in qualified pipeline across the tracked account base.

134 accounts tracked from first touch through qualification.

1,000 reach-outs ran through a six-week outbound sequence.

20 signups converted out of the first cold outbound cohort.

Qualified pipeline, not booked revenue. These are accounts that reached a defined qualification bar.

03

Enterprise commercialization

Turned 27 design partners into proof, panelists, and the first enterprise deal

From cold list to public advocates

The problem

A pre-seed AI product had no customers, no references, and no credibility with enterprise buyers. Senior practitioners had to be recruited cold, given a reason to keep showing up, and then be willing to put their name to it in public.

What I owned

  1. RecruitedBuilt a 27-member design partner cohort, 18 at Director, VP, or SVP level, from Atlassian, Databricks, Reddit, Splunk, and Taboola.
  2. RanHeld 32 structured sessions on workflow, pain, trust, and buying criteria across 13 industries and 9 countries.
  3. ActivatedMoved partners into public quotes, panel seats, and reference conversations with prospective buyers.
  4. ConvertedHelped move one design partner relationship into the first signed enterprise deal, Splunk (a Cisco company).
“Aisepedia brings structure to that ambiguity in a way I’ve never seen before. It lets you get hyper-specific about your ICP and seamlessly thread that precision across every artifact you create.”
Rinita Datta, Director of Product Marketing, Splunk (a Cisco company)

27 design partners recruited, 18 at Director, VP, or SVP level.

32 sessions ran across 13 industries and 9 countries.

4 practitioners carried the story onto the PMM 2.0 stage.

First enterprise deal, Splunk (a Cisco company), converted from a design partner.

04

GTM systems

Built the marketing operating system that runs the motion

Direct measured result

The problem

One marketer cannot chase every lead, book every meeting, and still know what is working. Follow-up decayed whenever I was in a discovery call, and nobody could say where people dropped out between first touch and real product use.

What I owned

  1. AutomatedBuilt lead follow-up and lifecycle flows across n8n, HubSpot, and Calendly triggers so no inbound lead sat waiting on me.
  2. InstrumentedDefined an eight-stage funnel and 11 acquisition and activation events across Mixpanel and PostHog.
  3. DiagnosedQueried drop-off in SQL and mapped 12 abandonment stages to observed behavior, likely cause, and a recovery action.
  4. MeasuredSet 22 lifecycle KPIs so acquisition, activation, and retention each had a named signal and a warning threshold.
24%
reduction in early-stage abandonment

Result of automated follow-up, instrumentation, onboarding changes, and recovery flows working together.

24% less early abandonment after the instrumentation and recovery work landed.

22 lifecycle KPIs gave acquisition and activation named signals.

11 tracked events mapped across 8 defined funnel stages.

3 systems connected: n8n, HubSpot, and Calendly running follow-up without me.

05

Revenue intelligence

Built the reporting a $125M subscription book ran on

From revenue data to retention decisions

The problem

Leadership needed consistent signals across renewals, expansion, wallet retention, product lines, and vertical performance to make timely calls. Marketing and sales were arguing about which accounts were actually at risk, with no shared number to point at.

What I owned

  1. BuiltCreated the daily wallet-retention, weekly renewal, monthly MRR, and vertical-profitability reporting, read by the CEO, COO, and VP of Finance.
  2. AnalyzedConnected renewal, expansion, product-line, and wallet-retention signals across the subscription book.
  3. SurfacedFlagged recoverable ACV, monthly expansion openings, and verticals slipping month over month.
  4. DirectedLed a Salesforce and OMS migration for 21 account executives with automated validation across more than 26,000 deal records.

Decision systems

Daily wallet-retention reporting.
Weekly renewal risk reporting.
Monthly MRR and vertical-profitability analysis.

$125M in active ACV across five product lines was covered by reporting I built and ran.

110% net wallet retention was tracked for the flagship product.

$2.6M in recoverable ACV was surfaced through renewal and retention analysis.

$250K per month in expansion was identified through MRR and product-line reporting.

POLITICO figures were reported on and analyzed. They were not revenue I owned or closed.

Metric attribution

What the numbers do and do not claim

Directly measured

24% less early-stage abandonment

Tied to tracked behavior and a defined system of automated follow-up, instrumentation, onboarding changes, and recovery actions.

Qualified, not booked

$400K pipeline across 134 accounts

Qualified pipeline means accounts that reached a defined qualification bar, not signed revenue. The first enterprise deal, Splunk (a Cisco company), converted from a design partner relationship.

Reported and analyzed

$125M portfolio reporting and retention signals

Figures were covered by reporting and analysis I built at POLITICO. They represent the decisions supported, not revenue ownership.

Want the longer version?

I can walk through the problem, evidence, decisions, trade-offs, and results.