Plays / Two Outbound Tools, No One Running Them Properly
cold email · Seed / Series A / Series B
Two Outbound Tools, No One Running Them Properly
Target
VP Marketing / Head of Marketing / Head of Revenue / Founder
Company size
20–80 employees
Stage
Seed / Series A / Series B
Test batch
75 contacts
Expected reply
5-9%
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 LinkedIn for VP Marketing at companies that seem like they might have outbound
- 02
Send a generic email about helping with demand generation
- 03
Hope the subject line gets the open
- 04
Try to sound like you are not using a template
- 05
Check replies over days, update nothing
- 06
Have no idea which companies to prioritize
- 07
Wonder why reply rates are under 1%
How this play runs
- 01
BuiltWith detects companies running two or more outbound tools — that is the trigger, not a job title search
- 02
LinkedIn confirms no Marketing Ops or RevOps hire owns the stack — the gap is confirmed before contact
- 03
Opener references both specific tools by name and makes one specific claim about why the data spend is underperforming
- 04
Sent from a warmed domain — inbox placement is managed
- 05
Every reply routes to Slack with full enrichment context: tools in stack, headcount, ICP score
- 06
CRM deal auto-created with signal data attached — zero manual logging
- 07
Signal feedback loop tightens targeting after every run
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.
Tech stack detection for dual-tool signal
BuiltWith / ClearbitThis play starts from the stack signal, not the person. The trigger is any company showing two or more outbound-category tools in their stack simultaneously, such as Apollo plus ZoomInfo, ZoomInfo plus Salesloft, or Apollo plus Outreach. BuiltWith is the most reliable source. Filter out enterprise companies where multiple tools are expected and managed — the signal is strongest at 20-80 headcount.
LinkedIn ops hire check
LinkedIn Sales Navigator / LinkedIn company pageConfirm the company does not have a Marketing Operations, Revenue Operations, Sales Operations, or GTM Engineer on the team. If someone owns the stack already, this play is not the right fit. Check current employee titles on LinkedIn.
Contact identification
Apollo.io / LinkedIn Sales NavigatorThe target is whoever owns marketing or revenue at the company. VP Marketing or Head of Marketing is primary. If none exists, reach the founder or CEO directly. Avoid pitching a sales leader on an enrichment-layer problem — they tend to externalize the problem to their reps rather than see it as infrastructure.
Email finding waterfall
Apollo.io / Hunter.io / Dropcontact / FindymailVP Marketing contacts at this size company are generally findable. Apollo hits first for US companies. European contacts lean toward Hunter.io and Dropcontact.
Email verification
NeverBounce / ZeroBounceVerify before enrolling. This batch size is large enough that a deliverability hit matters.
Warmed sending domain
Instantly / Smartlead / MailreachA secondary domain warmed for 30+ days. Run this play on a domain that is not your primary sending domain — the volume and the audience type make it worth protecting your primary.
Reply routing to Slack
n8n / Zapier / MakeVP Marketing contacts who engage usually do so quickly in the first hour. 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.
Two or more outbound tools in stack
Source: BuiltWith
No Marketing Ops or RevOps hire
Source: LinkedIn company page
Company headcount under 80
Source: LinkedIn / Apollo.io
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.
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.
Seed List Pull
Apollo.io→ 150–200 raw accountsFilter Apollo's 270M+ contact database by funding stage, headcount, industry, and target job title. Apollo is the starting point for US-focused prospecting — high volume, filtered down by the steps that follow.
Define the ICP filters. Spot-check 10–15 random results before the full pull to catch anything the filters missed.
LinkedIn Profile Verification
LinkedIn Sales Navigator→ Verified titles, tenure, headcountPull current role, start date, and company headcount from LinkedIn. Cross-check against the seed list data — Apollo and LinkedIn often disagree on headcount and title. LinkedIn wins.
Set the tenure rules for this play. Review contacts flagged at the edge of the range — these are judgment calls.
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.
Headcount Confirmation
LinkedIn / Apollo.io→ Headcount range confirmedConfirm the company is in the 20–80 employee range where the overtooled signal is meaningful. Above 80, multi-tool stacks are expected and managed. Apollo and LinkedIn sometimes disagree — LinkedIn is more current.
Set the band based on this play's ICP. If results are skewing too small or too large, adjust the filter.
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
Any B2B company with two or more outbound data or sequencing tools in their stack, a headcount of 20–80, and no Marketing Operations, Revenue Operations, or GTM Engineering hire visible on LinkedIn.
The contact is whoever owns marketing or revenue at the company — VP Marketing, Head of Marketing, or if neither exists, the founder directly.
This play starts from the stack, not the person. It is structured as a signal-first pull: find the companies first, then find the right contact within them.
Why This Audience
A company running Apollo plus ZoomInfo, or ZoomInfo plus Salesloft, at 50 people is spending real money on outbound data without a dedicated person to run it properly. The pattern is consistent: the first tool gets bought, the results are disappointing, a second tool gets added to fix it. The second tool does not fix it either, because the issue was never the data.
The issue is the layer between the data and the sequences: enrichment logic, ICP scoring, list refresh cadence, signal weighting. Without it, both tools produce the same broad unscored list that every SDR at every competitor is also working.
The message of this play is blunt: you are already paying for the infrastructure. We build the part that makes it perform.
This is the core SortedGTM pitch made as an outbound play. It is the most broadly applicable targeting signal and should be the highest-volume play in the rotation.
ICP Scoring Breakdown
| Signal | Points | |--------|--------| | Three or more outbound tools in stack | 40 | | Two outbound tools in stack | 25 | | No Marketing Ops or RevOps hire visible | 30 | | Company headcount 25–60 (sweet spot) | 20 | | VP Marketing or Head of Marketing identified | 10 | | Sequencing tool (Salesloft, Outreach) plus data tool | 15 |
Threshold: enroll at 55+. The sweet spot is 25–60 employees with two tools and no ops hire — that combination rarely leaves the threshold.
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], [Company] is running [Tool A] and [Tool B]. At your headcount, that is a meaningful data spend — probably $2,000–$5,000 a month combined. Most teams at that size get about half the value they should. The gap is almost never the data itself. It is the enrichment and scoring layer that is missing: the logic that decides which accounts to work, in what order, and why. Without it, both tools are pointing at the same broad list everyone else is working. We build that layer. Usually 4–6 weeks to first meetings. Worth 20 minutes? CJ SortedGTM
[First name], A quick test to run at [Company]: Pull your last 100 sequence enrollments. Score them by ICP fit — tech stack match, headcount, funding stage, hiring signals. What percentage would you call high-confidence accounts? Most teams running dual data sources without a scoring layer land at 20–30%. The sources are good. The layer between them and the sequences is what is missing. We build the scoring model. 4–6 weeks. Happy to show you what it looks like for [Company]. CJ
[First name], Last note. Either the timing is off or it is not the problem this quarter. If the stack utilization question comes up: sortedgtm.com. We do a free assessment that usually shows exactly where the data spend is not converting into pipeline. 30 minutes. 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
5-9%
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 does having two outbound tools signal a problem rather than a strong stack?
At 20–80 employees, having two data or outbound tools usually means the company added a second tool because the first one was not working — and then found the second one was not working either. The cause is almost never the tools themselves. It is the enrichment logic and ICP scoring that sits between the data and the sequences. Two tools without that layer just create two expensive sources of broadly targeted leads.
Why target the marketing owner rather than the sales leader?
The marketing owner feels the stack utilization problem more directly. CPL climbs, list quality degrades, the tool renewal comes up and they cannot explain the ROI. Sales leaders tend to externalize the problem to the reps or the data source, both of which are usually not the root cause. The marketing owner is more likely to own the infrastructure fix as a solution.
What if the company has a RevOps or Marketing Ops person?
Then the ops hire check filters them out. This play targets companies where nobody owns the stack — the tool is there but no one is running enrichment logic or scoring. If a RevOps hire exists, they are either already fixing it (not the right target) or they are the person to pitch directly (different conversation, different frame).
How do you personalize at scale when the signal is a tech stack combination?
The opener references both specific tools by name and the headcount range. That level of specificity is enough to distinguish this from generic outreach. The key is that the email does not read like a cold pitch — it reads like someone who noticed something specific about the company and formed a view about why the data spend is underperforming.
Is there a version of this play for larger companies?
Not as a cold outreach play. At 150+ employees, a dual outbound tool stack is expected and managed — it is not a signal of a problem. The signal only works in the 20–80 employee range where you would not expect a full-stack outbound motion and multiple tool redundancy is an indicator of something not working.
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.