The panelhop method
How we design account-based GTM at high ACV.
This is the playbook we use on every engagement: the rules, the scoring logic, the thresholds and the standards. Use it with us or without us. Most teams find the hard part is running it every week.
Deal size sets the motion, the depth and the budget.
A €30k deal and a €400k deal need different tiering, different touch depth and different spend per account. Most clients run two tiers at once. The design decision is how many accounts go in each tier, and how much to spend per account relative to ACV and win rate.
| ACV | Motion | Accounts | In practice | Role of automation |
|---|---|---|---|---|
| €25k–75k | 1:many Programmatic | Hundreds to low thousands | Signal-triggered engagement, shared content by segment, tight SDR routing | Does the heavy lifting: speed-to-lead, programmatic plays, scoring |
| €75k–250k | 1:few Clusters | Dozens to a few hundred | Accounts in clusters, content per cluster, multi-threaded outreach to the buying group | Prepares; people engage. Cluster plays, coverage, semi-automated research |
| €250k+ | 1:1 Strategic | A handful | Account plans, executive engagement, custom content, sales and marketing working each account together | Supports people only: briefs, alerts, account plans, risk flags |
What automation does at each deal size. Full circle: automation does the work. Empty: people do.
| Activity | 1:many €25–75k | 1:few €75–250k | 1:1 €250k+ |
|---|---|---|---|
| Fit scoring and tiering | |||
| Signal alerts | |||
| Account research | |||
| Routing and SLAs | |||
| Ad audiences | |||
| Outreach sequences | |||
| Content | |||
| Executive engagement |
Why we leave broad media buying out
When pipeline efficiency accounts for win rate, deal size and cycle length together, paid media comes last and referrals and inbound come first. At high ACV, budget does more when it follows named accounts and live signals than when it buys reach.
We still run matched audiences on LinkedIn and Google. They are pointed at the buying groups of target accounts and paused for open deals and customers.
Pipeline efficiency by source channel.
Your best customers already tell you who to target.
We build the account-fit model from your closed-won and closed-lost deals, not from a persona workshop. Revenue in high-ACV B2B is usually concentrated, so the model decides where most of your effort goes.
How the target list is built
1. Closed-won analysis
Firmographic and technographic patterns behind won and lost deals over 24 months: size, region, stack, trigger events, cycle length, deal size.
2. Addressable list
Every company in the niche from data providers, deduplicated against your CRM, with a fit score on each.
3. Tiering
Tier = fit score × ACV potential × team capacity. An alert fires when a tier holds more accounts than the team can work.
4. Buying-group template
For each tier, the roles that must be engaged before a deal is realistic. Coverage is tracked against it.
A small share of accounts usually carries most of the revenue.
One account score. Every signal weighted, and every signal fading.
Most of any market is not buying right now; the B2B Institute’s 95-5 rule puts it at 95% of potential buyers. Signals find the few that are. A single intent spike means little. Several signals from the same account inside a few weeks usually mean a buying process has started. We combine them into one score, and each signal decays at its own rate so old news stops counting.
How an account earns a sales conversation: six signals over twelve weeks.
Signal library (extract)
| Signal | What it usually means | Play it triggers | Decay |
|---|---|---|---|
| New sales or revenue leader | A 90-day plan is being written. Tools and processes get re-evaluated | Executive intro from your leader, relevant peer story | Slow |
| Job post for RevOps, SDRs or a platform role | Budget exists and the team is building capacity | Buying-group mapping, role-specific content | Medium |
| Funding, acquisition or new site | Growth targets rising faster than the team | Tier upgrade, cluster play | Slow |
| Competitor tool removed from stack | A replacement decision is open | Displacement play with migration proof | Fast |
| Category intent surge | Someone is researching the problem | Matched ads to the buying group, SDR watch | Fast |
| Repeat visits to pricing or comparison pages | Evaluation stage; the shortlist is forming | Owner alert with a brief, same-day touch | Very fast |
| Champion changes job | Risk at the old account, opportunity at the new one | Risk alert to CSM; new-logo lead at the new company | Medium |
Each niche we work in gets its own library, with signals only insiders know: regulatory filings, tenders, fleet or site expansions, certification deadlines.
We qualify accounts, not form fills.
A qualified account needs fit and engagement at the same time. Lead quality is defined, in writing, before lead volume is counted. Then speed does the rest.
Fit and engagement together decide what happens next.
Responding inside an hour makes a lead seven times more likely to qualify.
Routing rules we set on day one
- Form fills enriched on submit and matched to their account
- Owner by territory, tier and existing relationship, with capacity caps
- Slack alert and booking link within minutes; SLA clock starts
- SLA breach escalates to the manager
- Disqualification needs a reason, and the reasons retrain the model
The seven stages, in full.
Pick a stage to see what we build, what runs automatically and how we measure it. After the deal, the same account data carries the promise into onboarding, renewal and expansion.
average win-rate lift from multi-threading in deals over $50k.
Gong, 2025 (1.8M deals)buyer contacts, on average, in large strategic deals that reps won.
Gong, 2025higher win rates when a decision-maker is involved early.
Ebsta × Pavilion, 2025of sales leaders and sellers have high confidence in their forecast.
Gartner, 2020- No activity for 14 days
- Single-threaded past stage 2
- Close date pushed twice
Tasks fire at 120, 90 and 60 days before the date, with the health score and the original promises attached.
A champion changing jobs raises a risk alert at the old account and creates a new-logo lead at the new one.
Measured at the account level, against a baseline.
Every number is set against a baseline before work starts, and every after-number is reported against it. We publish results anonymised once a client’s after-numbers are in.
| Metric | Definition | Cadence |
|---|---|---|
| Qualified pipeline from target accounts | Opportunity value created in the period from accounts on the target list | Quarterly |
| Account progression | Share of target accounts that moved at least one stage: unaware → engaged → opportunity → customer → expanded | Monthly |
| Cost per qualified opportunity | Programme and tool cost ÷ qualified opportunities, compared with ACV × win rate × budget | Monthly |
| Speed-to-lead | Median minutes from form fill or threshold crossing to first human touch | Weekly |
| Buying-group coverage | Engaged roles ÷ required roles, per target account and per open deal | Monthly |
| CRM hygiene score | Weighted share of records that are deduplicated, complete and recently verified | Monthly |
| Forecast accuracy | Weekly forecast snapshot vs actual closed-won, per quarter | Quarterly |
Account progression, the headline chart of every monthly report.
Every automation has an owner, a trigger, a log and a metric.
Automations that nobody owns break quietly, and they break on bad data first. So everything we ship is listed in an automation registry in your workspace, and every AI step has a human review gate.
of CRM users say less than half of their CRM data is accurate and complete.
Validity, State of CRM Data Management 2025of sales professionals completely trust the accuracy of their data.
Salesforce, State of Sales 2024average annual cost of poor data quality per organisation.
Gartnerdeals lost per quarter, on average, to poor CRM data.
Validity, 2025An extract from a client’s automation registry.
| Automation | Trigger | Owner | Logged output | Metric | Review gate |
|---|---|---|---|---|---|
| Inbound router | Form submit | RevOps | Account match, owner, SLA start | Speed-to-lead | None needed |
| Signal alert | Score +15 in 7 days | SDR lead | Slack post with brief | Signal → touch time | None needed |
| Research brief | Account enters Tier 1 | AE | AI draft on account record | Meetings per account | Rep approves |
| Call → CRM update | Call transcript ready | AE | Draft next step, stakeholders, timeline | Field completeness | Rep approves |
| Ad audience sync | Daily, 06:00 | Marketing ops | Audience diff, exclusions | Spend per engaged account | None needed |
| Renewal tasks | 120 / 90 / 60 days out | CSM | Task with health score | Gross retention | None needed |
Default stack
Everything is built and documented in your own accounts. We add tools only where they pay for themselves, and we track credit and seat costs against pipeline.
Out of scope, on purpose
- Brand and creative production
- Broad media buying
- Autonomous AI SDRs
- High-volume cold email
| Layer | Default |
|---|---|
| System of record | Your HubSpot or Salesforce |
| Enrichment | Clay, with waterfall providers |
| Orchestration | n8n or native CRM workflows |
| Call transcripts | Gong or similar |
| Alerts | Slack |
| Ad audiences | LinkedIn and Google matched audiences |
| Data warehouse | Postgres or BigQuery, once data outgrows the CRM |
| AI | A language model for drafting and summarising, always behind a review gate |
Every build makes the next one faster
Scoring models, n8n workflows, CRM configurations, dashboards and templates go into a reusable library. Weights, tiers and plays are then tuned to each client’s closed-won data, so you get a proven starting point without a generic result.
