Plays / New VP Marketing, Inherited Stack, No Results Yet
cold email · Seed / Series A / Series B
New VP Marketing, Inherited Stack, No Results Yet
Target
VP Marketing / Head of Marketing
Company size
20–100 employees
Stage
Seed / Series A / Series B
Test batch
75 contacts
Expected reply
5-10%
Expected meetings
1-3%
Setup time
1w
Time to meetings
5w
Context
How this is usually done. And how this play runs instead.
The manual approach
- 01
Search Apollo or LinkedIn manually, export what looks right
- 02
Paste into a spreadsheet, deduplicate by hand
- 03
Look up emails one at a time or guess the format
- 04
Write one template with [first name] swapped in
- 05
Send from your main work inbox
- 06
Check email twice a day to catch replies
- 07
Update the spreadsheet if you remember to
- 08
Try to figure out why CPL is still climbing
How this play runs
- 01
Accounts surface when the right signals appear — Apollo in stack, new VP Marketing hire, open demand gen JD
- 02
Each contact enriched and scored across multiple sources automatically
- 03
Emails verified before a single message goes out
- 04
Sequence sent from a warmed secondary domain — main domain stays clean
- 05
Replies route to Slack in under 60 seconds with full context
- 06
CRM deal auto-created with all enrichment data attached
- 07
Outcomes feed back into targeting — next month's list is sharper
Before this play runs
Prerequisites
Everything below needs to be confirmed before the first contact is enrolled. Each gap creates a broken loop somewhere downstream.
Warmed sending domain
Instantly / Smartlead / MailreachA secondary domain warmed for at least 30 days. Never use your primary domain for cold outreach. Any cold email platform handles warmup.
Contact database with hire date data
LinkedIn Sales Navigator / Apollo.ioLinkedIn Sales Navigator is the most reliable source for recent hire date signals — which is the core filter for this play. Apollo can supplement but tends to lag on tenure data.
Tech stack detection
BuiltWith / ClearbitConfirming Apollo, ZoomInfo, or Lusha is in the company's stack is the secondary qualifying signal. BuiltWith is the primary source.
Email finding waterfall
Apollo.io / Hunter.io / Dropcontact / FindymailVP Marketing contacts are findable via standard email finders. Apollo usually hits first. European contacts lean toward Dropcontact.
Email verification
NeverBounce / ZeroBounceVerify before enrolling. VP-level contacts at this company size usually have valid emails but always check.
CRM with outbound pipeline stage
Attio / HubSpotA Cold Outbound stage where replies auto-land. VP Marketing buyers move fast when they engage — you need the context there when they respond.
Reply routing to Slack
n8n / Zapier / MakeReplies within the first 4 hours of sending typically have 3x higher meeting conversion than replies caught later. Route to Slack immediately.
Setup time: approximately 1 week before sequences go live.
What triggers this play
Signal Detection
A contact needs at least one of these signals confirmed before entering the waterfall. Two signals puts them in the top tier and gets them enrolled first.
VP Marketing hire < 90 days
Source: LinkedIn Sales Navigator
Apollo in tech stack
Source: BuiltWith
ZoomInfo in tech stack
Source: BuiltWith
No Marketing Ops or RevOps hire visible
Source: LinkedIn company page
The enrichment waterfall
From raw list to sequence-ready contacts
Each step filters or scores before the next runs. The final output is a list of contacts above the ICP threshold, verified, and ready to enroll. Human review points are marked.
Seed List Pull
LinkedIn Sales Navigator→ 150–200 raw accountsBuild a saved search in Sales Navigator filtered by title, seniority, headcount, and hire date. LinkedIn Sales Nav is the most reliable source for recent job changes — which is the core signal for this play.
Build and review the saved search before exporting. Narrow or widen the title filter based on how the results look.
Funding Stage Confirmation
Crunchbase→ Stage and raise date confirmedPull funding stage and last raise date from Crunchbase. Confirms the company is in the right stage window and the raise is recent enough to still be in the buying cycle for new infrastructure.
Flag unusual funding histories — bridge rounds, down rounds, or stale data — for manual review before enrolling.
Tech Stack Detection
BuiltWith→ Tool signals scoredDetect which outbound and marketing tools are live on the company's domain. BuiltWith crawls publicly visible tech signals — tracking scripts, analytics tags, email platform headers. A company running Apollo or ZoomInfo has already decided outbound is worth paying for.
Decide which tools score as high signals for this specific ICP. Update the scoring weights after each run based on what converts.
Ops Function Check
LinkedIn company page→ Ops hire status confirmedScan the company's current employee list for Marketing Operations, Revenue Operations, GTM Engineer, or Sales Operations titles. Presence means someone already owns the stack — adjust the message or skip the account.
Spot-check edge cases: contractors, advisors, and part-time consultants sometimes appear as employees. Get this right — it affects the core premise of the play.
Email Finding Waterfall
Apollo.io → Hunter.io → Dropcontact → Findymail→ 70–85% email coverageTry each provider in order. Apollo first (largest database, 1 credit). If not found, Hunter.io (pattern-based, reliable for common formats). If not found, Dropcontact (strong GDPR coverage, European contacts). If still not found, Findymail (highest accuracy, 2 credits). Stop at the first hit — only pay for what is found.
Set the waterfall order based on your audience geography. US-heavy: Apollo first. European mix: Dropcontact earlier in the chain.
Email Verification
NeverBounce / ZeroBounce→ Clean, sendable listRun all found emails through a verifier before enrolling. Separate into valid, risky (catch-all), and invalid. Send to valid. Decide on risky — catch-all addresses often work but carry slightly more bounce risk. Discard invalid entirely.
Review the risky/catch-all bucket before discarding — many valid contacts land there. For high-value accounts, a manual check on the catch-all domain is worth it.
ICP Scoring
Combined signal formula→ Score 0–100 per contactA weighted score across all signals present for this contact. Contacts above the threshold enter the sequence. Below-threshold contacts go to a watchlist — they re-enter when new signals appear.
Review the top 10 and bottom 10 scores before launching. Adjust weights if the distribution looks wrong. This model improves with every run as outcome data feeds back in.
Enroll in Sequence
Sending platform→ 75 contacts enrolledContacts above the ICP threshold enroll in the email sequence. Contacts below go to a watchlist — they re-enter when new signals appear.
Final review before launch. Check the list size, the score distribution, and the first few emails the system would send. Then launch.
Targeting logic
Who, why, and how to score them
Who This Play Is For
VP Marketing or Head of Marketing who has been in the role for less than 90 days at a B2B company with an existing outbound data investment. They inherited Apollo, ZoomInfo, or a similar tool from whoever ran marketing before. The sequences are probably running. The CPL is probably climbing. Nobody has audited the enrichment logic or the ICP scoring since the tool was first set up.
This is the most common version of the problem SortedGTM solves: the tools are there, the spend is there, the results are not there, and the person responsible for fixing it just arrived.
Why This Audience
A new VP Marketing inheriting an outbound stack faces a specific challenge: they know the output is wrong, but they did not build the system and do not fully understand what is broken. Their instinct is usually to blame the data source or the sequences. The actual problem is almost always the layer between them — the enrichment logic, the ICP scoring, the list refresh cadence.
They have budget authority. They have board pressure to show results within 90 days. They have not yet committed to a plan. That window is the reason this play exists.
The secondary signal — Apollo or ZoomInfo in stack — confirms two things: the company has already decided outbound is worth paying for, and the previous team built something that the new person inherited. That combination is the exact situation where our engagement makes the most sense.
ICP Scoring Breakdown
| Signal | Points | |--------|--------| | VP Marketing hire less than 30 days ago | 40 | | VP Marketing hire 30–90 days ago | 25 | | Apollo or ZoomInfo confirmed in stack | 30 | | No Marketing Ops or RevOps hire visible on LinkedIn | 20 | | Headcount 25–75 (sweet spot) | 15 | | Second outbound tool in stack (Salesloft, Outreach) | 10 |
Threshold: enroll at 65+. Contacts scoring 80+ are in the ideal batch — reach these first.
Lessons From Running This Play
No runs logged yet. This section updates after each run with reply rate, disqualification reasons, and what changed in the sequence or targeting.
The sequence
3 touches. Sent from a dedicated outreach domain.
Short, direct, signal-led. No case studies, no 6-paragraph intros. Each touch has one job and one ask.
[First name], Month two in a new marketing role is usually when the audit ends and the fixing starts. The pattern we see at most companies your stage: the data source is there — Apollo, ZoomInfo, whatever the previous team bought — but there's no enrichment logic running on top of it. So the sequences go out, the CPL climbs, and it's hard to pinpoint exactly why. We build the layer between the data and the sequences. Usually 4–6 weeks to first meetings. Worth 20 minutes this week? CJ SortedGTM
[First name], The thing that's almost always broken in an inherited stack: the ICP score. Not the data source — those are usually fine. The logic on top of it. Without a model weighting by tech stack, hiring activity, and funding recency, your sequences go to anyone who fits a broad filter. Which is the same list everyone else is working. One thing we'd fix first at [Company]: build the scoring layer before running another batch. CJ
[First name], Last note. Either the timing's off or it's not the problem this quarter — both are valid. If pipeline infrastructure becomes urgent: sortedgtm.com. The stack assessment is free and usually surfaces 2–3 things worth fixing immediately. Good luck with Q[quarter]. CJ
After a reply
What happens next. And who does it.
Most outbound playbooks stop at the sequence. This is where the work actually starts. Every reply flows through a closed loop — no manual logging, no inbox hunting, no dropped leads. The steps below are how we run it.
Reply detected
Sending platform
The sending platform fires a webhook the moment a reply comes in — positive, negative, or out-of-office. Every reply is captured regardless of type.
Slack alert fires
Automation layer → Slack
A structured message lands in the pipeline channel with the prospect's name, company, ICP score, reply text, and action buttons. No inbox-checking. No manual logging.
CRM deal auto-created
Automation layer → CRM
A deal is created in the Cold Outbound stage with all enrichment data pre-attached — tech stack signals, ICP score, company details, trigger signals. Zero manual data entry.
Human reviews and responds
humanYour team
A person reads the reply in Slack, uses the play's response template or writes something more specific, and sends. Speed is the variable — the reply window closes fast.
Qualification
humanYour team + CRM
Positive reply: a booking link goes out. Unclear: one clarifying question. Negative: deal closed with reason logged. Nothing sits unactioned at end of day.
Signal feedback loop
humanCRM → enrichment model
After each run, outcome data is reviewed and signal weights are adjusted based on what actually converted. The following month's list is sharper. This is the part that compounds.
The result
Steps marked human require a person. Everything else runs automatically. The motion does not depend on someone checking their inbox or remembering to update the CRM.
Expected outcomes
What the first run should produce.
Test batch
75 contacts
Expected replies
5-10%
Expected meetings
1-3%
Decision rule
One meeting from the first batch → expand to 200+. Zero replies after the full sequence → rewrite the message before expanding. Never scale a sequence that has not proven it can generate replies.
Questions
Common questions about this play.
Why target someone who just started in the role?
A VP Marketing in their first 90 days is running an audit and forming a plan. They have budget authority and a mandate to show results. They have not yet committed to an approach, which means they are open to outside input in a way they will not be in month six. The urgency is highest right now and the openness to a different approach is highest right now.
What if the company doesn't have Apollo or ZoomInfo?
This play is specifically for companies where the data spend already exists. If there is no outbound tool in the stack at all, this is a different buyer with a different problem — they have not yet invested in outbound, which means the conversation starts much earlier and closes slower.
How do you know they're not already getting good results?
You don't for certain. But a VP Marketing hired less than 90 days ago almost always inherited a motion built by someone else. The sequence logic, the ICP definition, the list refresh cadence — none of it was built by them. The probability of it working well enough that they would not be open to a conversation is low.
Why does the ops-hire check matter?
A company with a Marketing Ops or RevOps hire already on the team has someone whose job it is to fix this. That person is the gatekeeper and the competitor. A company without one is running the stack without an owner — which is exactly the gap we fill.
Run this play
Want us to build and run this for you?
We start with a stack assessment to confirm this play fits your situation and what infrastructure is already in place. Free, usually 30 minutes. You leave knowing exactly what to build first — whether you work with us or not.