PeakOps — Always-on coverage of 500 named accounts
An intent-driven programmatic ABM layer that kept 500 named accounts warm, lifted meeting-acceptance 62%, and fed the field team a daily, signal-ranked play list that turned long-tail accounts into a $62M influenced-pipeline machine.
PeakOps had 500 named accounts but only enough SDR capacity to actively cover 80 of them in any given month. The remaining 420 went cold between touches — and every time a buying signal fired in one of them, the team found out two quarters too late. Marketing was already producing strong content but had no way to systematically deploy it across the long tail. The SDR team spent its mornings deciding who to call instead of calling. Inside the platform, six dashboards offered six contradictory opinions about which account was 'hot'. The result was a team that was busy but not productive.
How modern DevOps buying actually works
DevOps buying has moved decisively self-serve in the discovery phase. 67% of B2B buyers now complete most of their research before the vendor ever knows they exist — and in DevOps that number is even higher, because the buying group lives inside Slack, GitHub, Stack Overflow and Reddit, not on the vendor's website. By the time a buying signal becomes visible to the vendor, the buyer has already shortlisted.
Compounding the problem, the DevOps buying committee is unusually distributed. A typical platform decision involves 6–10 stakeholders across engineering leadership, platform engineering, security, FinOps and finance. Each has their own buying signal pattern, their own preferred content surface, and their own veto power. No single intent source captures the whole committee.
The teams that are winning are the teams that have collapsed their signal stack to a single, daily, account-level priority score — and have wired that score into an SDR workflow that produces one decision per morning: who do we call today? Latency is the new conversion rate. The team with the shortest signal-to-action loop wins the meeting.
Of B2B buyers complete most of the journey before sales engagement
Forrester B2B Buying Pulse 2025
Stakeholders in a typical DevOps platform decision
Gartner Software Buying Report
Average intent-to-action latency in B2B orgs without unified signal
Demandbase
"AI-era buyer signals fire faster than humans can respond. The team with the shortest signal-to-action loop wins the meeting — every other team is selling to a buyer who has already chosen."
How we actually ran it.
Signal stack unification
Collapse six intent sources into one account-level priority score, updated daily.
Ingested Bombora, G2, 6sense, web behaviour, product trial signals and a second-party partner feed into a single warehouse. Built a weighted scoring model: recency, depth, role-fit and stage-fit. Made every score explainable in one sentence ('This account just spiked on 3 of your top categories and a senior platform-engineer visited the pricing page twice in 48h').
One daily account-priority score per named account. Six dashboards retired. One source of truth installed.
Programmatic content layer
Keep all 500 accounts warm between human touches, without burning the brand.
Sequenced ungated thought leadership, LinkedIn dark posts, ABM display retargeting and partner-co-branded content per buying-stage signal. Refused gated PDFs and refused interruptive retargeting on accounts already in active sales conversations.
100% of named accounts touched by at least one programmatic asset per week. Average engaged-account-rate per week climbed from 18% to 54% inside the first two months.
Daily Slack play list
Give the SDR team one decision per morning: who to call today.
Built a Slack workflow that posted the top-15 priority accounts at 8:30am each weekday, with: the most-engaged contact, the active signal, the recommended next play from the library, and a one-click handoff to the SDR's preferred outreach tool.
SDR 'who do I call?' decision time dropped from ~45 minutes/day to <5 minutes/day.
SDR cadence retooling
Replace generic outbound sequences with signal-specific plays.
Built a 9-play library mapped to the most common signal patterns: 'security veto active', 'FinOps cost-control surge', 'platform-engineer pricing-page revisit', etc. Each play had a specific opener, asset, and follow-up sequence. SDRs ran the play matched to the signal — not the cadence matched to the calendar.
Meeting-acceptance rate moved from 18% → 30% by week 12, on its way to 41% by month 6.
Model retraining loop
Make the priority score get smarter every week.
Closed the loop: every meeting accepted, declined or no-showed was logged back into the model. Weekly retrain. Quarterly review of which signal sources were earning their place. Cut two signal sources in month 4 because they were degrading model precision.
Model precision (accepted-meetings / recommended-meetings) improved from 0.31 to 0.58 over 6 months.
Field handoff protocol
Make sure the AE inherits the full story when an SDR books the meeting.
Auto-generated a one-page 'meeting brief' for every booked meeting: the signal that triggered the call, the play that was run, every asset the buying group had engaged with in the prior 90 days, and the recommended opening question for the AE.
AE-side meeting → opportunity conversion lifted 3.4x. AE adoption of the brief: 92%.
The chronology of the work.
- Day 0
Signal stack unified — single daily priority score live
- Day 14
First daily Slack play list posted to SDR channel
- Day 21
First SDR meeting booked off a model-recommended play
- Day 45
Model retrain v1 — precision moves from 0.31 to 0.42
- Day 75
Meeting-acceptance crosses 30%
- Day 120
Meeting-acceptance hits 41% — programme exits pilot
- Day 150
$30M influenced pipeline crossed
- Day 180
$62M influenced pipeline crossed. Programme institutionalised.
The three places most ABM programmes die.
Six dashboards, no decision.
Collapsed to one priority score, posted in one Slack message, every morning at 8:30am. Latency was the bottleneck — not data.
Programmatic ABM read as spammy retargeting.
Led with ungated thought leadership and partner-co-branded content. Refused interruptive retargeting on accounts already in active sales conversations.
Black-box AI that SDRs would not trust.
Every score was explainable in one sentence on the Slack post. SDRs adopted the model in week 3, not week 13, because they could see why it was right.
Within 6 months, all 500 accounts had measurable engagement. Meeting-acceptance jumped from 18% to 41%. Influenced pipeline lifted $62M and the field team reported a 3.4x increase in the proportion of meetings that converted to opportunities. The programme has since been institutionalised inside PeakOps and the same model is being extended to a second named-account tier of 1,200 logos.
Lift in meeting-acceptance rate
Influenced pipeline in 6 months
Meeting → opportunity conversion uplift
We stopped guessing which 80 of the 500 to work this week. The model told us — and the model was right four times out of five.
Five lessons we'll carry into the next programme.
Latency is the new conversion rate. The team with the shortest signal-to-action loop wins the meeting.
One score beats six dashboards. Make the decision easier, not the data prettier.
Programmatic ABM is brand work in disguise. Lead with thought leadership; refuse interruptive retargeting on active deals.
Explainable AI gets adopted in week 3. Black-box AI gets adopted in week 30, if ever.
Programmatic doesn't replace 1-1 — it earns the right to graduate accounts into it. The long tail is where the next quarter's 1-1 list lives.
Programmatic ABM isn't a downgrade of 1-1 ABM. It's the only way to keep the long tail of named accounts warm — and to know, in real time, which ones just earned a human touch.
More from the ABM playbook.
Want a programme like this one?
Tell us your top 20 accounts — or your 500. We'll show you how the next 90 days could look.