GTM Plays

This is a companion to the published Always-On Outbound Engine.

The system that scores and ranks every account and every contact in our ICP into one list for our SDRs, rebuilt daily. This playbook covers those who don't meet the threshold for a 'warm' contact: roughly 90% of the universe, who never clear the bar.

Cold here is a state rather than a list. Contacts fall into it when their signals age out, and they arrive in it having never engaged at all. What follows is what the system does with them, and how around 5,000 buyers have crossed back the other way.

The full addressable universe. Every contact a seller would plausibly want to speak to, which comes to roughly 100,000 people.

This isn’t a target list. It’s the population the engine works across continuously, and the problem it creates is the reason the whole system exists: 100,000 contacts, and no way for a rep to know which ones matter this morning.

It’s built from ZoomInfo data and filtered against Salesforce, which strips out existing customers, legal blocks, and any status flagged do-not-contact.

Scoring runs continuously at both account and contact level. Below the threshold routes to cold. Every contact carries a score between 0 and 1, made up of:

• Each recorded signal, individually weighted. Company growth, person and account web visits, person-level intent, social media engagement, marketing activities they have taken part in, past sales engagements and champion moves all feed the same number.

• An initial score, before any behavior is recorded.

• Half-life and decay, applied on top.

• A likely-to-engage modifier, which boosts the total.

Decay is the mechanic worth spending a paragraph on, because it’s what makes cold a state rather than a list. A content syndication download from four months ago scores zero. Nobody should be picking up the phone today on the strength of a blog somebody downloaded a third of a year ago, and the score reflects that.

Two populations end up in cold:

• Decayed. They engaged once, and it aged out.

• Genuinely unknown. Zero contact signals, zero account signals, and they may never have heard of ZoomInfo.

Every cold contact gets copy built specifically for them before anything sends. This is the step teams would often skip on cold, and it’s the one that makes the play interesting. It’s per-contact generation rather than segment-level templating.

The inputs:

• Persona and function

• Account summary, pulled from ZoomInfo

• Contact brief

• Company scoops

• Any prior company-level engagement

A Head of Brand Marketing receives copy about marketing use cases and content. A sales director at the same company receives sales specific content. Company-level context is fed into the copy, or relevant historical information from the contact brief, to build relevance into the messaging.

What’s deliberately not used: Contact-level signals, because there are none, and that’s the definition of cold. Account-level signals can be fed in, so a relevant company scoop could inform the copy.

Five emails, sent on behalf of sales VPs. No SDR involvement at any point.

Sending from a VP alias rather than an SDR does two things. It keeps rep capacity entirely on warm contacts, and it sets up the later handoff. When somebody does convert, the SDR can open on or leverage "I work on his team," which is a warmer entry than a cold dial.

Every CTA points at content, never at a meeting. Nobody sends five emails to a zero-signal contact and asks for nothing, which is exactly what makes it work.

The destinations used are blogs, case studies, e-books and GTM.AI. The reasoning behind that:

• A demo ask against zero signal converts close to nothing, and burns the contact.

• A content click is a signal, and the engine only needs a signal.

• Familiarity is the secondary objective. For many of these contacts, the goal is simply that the name ZoomInfo registers at all.

The sequence isn’t trying to book a meeting. It’s trying to manufacture a data point.

Any signal, from any source, not just this campaign. Contacts don’t have to engage with the sequence to progress, and that detail changes how the whole play should be understood.

There are multiple routes to then evaluate and re-score the contact:

• Campaign-driven. They click, engage or they visit GTM.AI within the hour, and it registers.

• Independent. They start a new job. They visit a competitor's G2 page, which registers as category intent.

Somebody who never opens a single email but shows an unrelated signal still feeds into the system. The sequence runs underneath as support, and the scoring engine is what’s actually doing the work.

Read it accordingly. The cold sequence is there to support the overarching scoring and prioritization of the full ICP, not a conversion mechanism.

The new signal enters the same scoring model described in step 2. Weighted, decayed, added to the total.

There’s no separate cold graduation logic. One scoring model, one threshold, and contacts moving across it in both directions continuously.

The contact crosses the threshold, joins the daily prioritized list, and goes to an SDR. They land in the top 150 to 400 sent to sellers each morning, alongside contacts who were never cold. From this point the Always-On Outbound Engine takes over.

Two details worth keeping in mind:

• A cool-down period runs before an SDR calls. Deliberate, not incidental.

• The talk track references the VP's emails, not the click. "I work on his team, wanted to see if you had any questions" rather than "we saw you visited our site."

Watch Florin utilize this play on GTM Studio

FAQs for the cold path

What makes a contact cold in this system?

A score, not a label. Every contact carries a score between 0 and 1 built from weighted signals, and decay pulls that score down as signals age. Two populations end up below the threshold: contacts who engaged once and aged out, and contacts with zero signals who may never have heard of ZoomInfo.

Why send five emails to someone who has never engaged?

The sequence isn’t trying to convert. It’s trying to produce a data point the scoring engine can act on. A content click is a signal it can use to factor into re-scoring. Familiarity is the secondary objective, since for many of these contacts the goal is simply that the name registers at all.

Why does the sequence never ask for a meeting?

A demo ask against a zero-signal contact converts close to nothing and burns the contact for later. Every CTA points at content instead: blogs, case studies, e-books and GTM.AI. The click is what the system is actually after.

Do contacts have to engage with the emails to move to warm?

No. The engine tracks any signal from any source. A contact who never opens a single email but starts a new job, or visits a competitor's G2 page, still crosses the threshold. The sequence runs underneath as support rather than as the conversion mechanism.

Why send from a sales VP rather than an SDR?

It keeps rep capacity entirely on warm contacts, and it sets up the later handoff. When a contact does convert, the SDR opens with a reference to the VP's emails, which is a warmer entry than a cold dial.

This is a companion piece to the Always-On Outbound Engine.

The system that scores and ranks every account and every contact in our ICP into one list for our SDRs, rebuilt daily.

This playbook is what happens at the top of that list. The warmest of the warm: the contacts we want SDRs hitting first, what a seller actually receives when one lands, and what they do with it.

Last year that team averaged a 2.5% demo booked rate. Since we started sending a full context package with every warm contact, it's 7%.

This playbook walks through the full warm routing path and gives away the context package we deliver.

Scoring runs continuously at both account and contact level. Above the threshold routes to warm - direct to an SDR. Every contact carries a score between 0 and 1, made up of:

• Each recorded signal, individually weighted. Company growth, person and account web visits, person-level intent, social media engagement, marketing activities they have taken part in, past sales engagements and champion moves all feed the same number.

• An initial score, before any behavior is recorded.

• Half-life and decay, applied on top.

• A likely-to-engage modifier, which boosts the total.

Decay is the mechanic worth spending a paragraph on, because it's what makes warm a state rather than a fixed status. A content syndication download from four months ago scores zero. Nobody should be picking up the phone today on the strength of a blog somebody downloaded a third of a year ago, and the score reflects that. 

Warm has to be earned recently, and it has to be re-earned; the list rebuilds every day, and a contact who sat at the top of it last week can be back below the threshold very quickly if the signal/signals reach a point of decay.

Every morning, each segment gets the top 150 to 400 contacts.

We throw the traditional signal based play out the window. A signal-based play works one way: a contact does one thing, that one thing puts them in a sequence. This doesn't do that. We capture everything we know across a contact and use all of it to trigger the outreach, not the single signal that happened to fire last.

So the outreach is customized for the context: the signals hit, who the person is, the company details. And the SDR is given the context too, why this contact ended up in their book of business in the first place.

The ranking decides the order. Rep capacity decides how many names come off the top. Some mornings that's 150, some mornings it's 400, however many the team can actually work that day.

Preparing for a single call used to mean LinkedIn, the CRM, the ZoomInfo record, a web search, then pasting all of it into an LLM to get a talk track. Multiply that by every rep and every call and it becomes the biggest cost in the day.

That’s why it now arrives in one view, built once, automatically, for every contact routed that morning

Why this matters. No tab-hopping, and no rebuilding the account story before every dial. But the prep time is the smaller half of it.

Most reps open a call with a name, a title, and a reason to ring. Ours open with a play the data picked, a competitor the prospect knows who's already a customer, a peer in their segment, and the signal that says this person is in-market right now.

This is what moves you into a commercial conversation, instead of trying to find out if there's a fit.

The play, the opener, the voicemail, the questions and the value prop are all automatically generated FROM the context package. Our reps never start from zero.

• One suggested play, built from contact and account history and context

• Two scripts, one for a pickup and one for no answer.

• Three discovery questions for the call itself, pulled from the context package

• And the value prop framed three ways: tied to the suggested play, tied to the role they're in, tied to the outcome the buyers own

This means reps are never starting from zero, and can move faster to work the warmest leads in our ICP.

The rep works the list. Read the opener, or the voicemail. Run discovery, pitch and book.

The same pack is often used differently across segments. Mid-market often runs closer to point and shoot, higher volume, work down the list, read the opener, leave the scripted voicemail if there's no answer, ask the discovery questions if they stay on the line, pitch and book the meeting. Enterprise treats it as the starting layer: prep time collapses, and the rep spends what's left on tailoring rather than assembling. Neither is the wrong use of it.

What changes is how repeatable the calls are. Before, a good conversation depended on whether that particular rep went and found the right information that morning. Now the base layer is there every time, for every contact, whether or not anyone goes looking. The floor comes up. That's where the productivity and the demo rate come from, not from any single script being read word for word.

Watch Florin utilize this play on GTM Studio

FAQs for the warm path

How is this different from a signal-triggered play?

Most teams build plays as triggers, where one signal fires and one sequence runs. This ranks the entire universe daily and takes the top slice. Everything known about a contact feeds one score, and the outreach is built from that whole picture rather than from the single signal that happened to fire.

How does a contact get onto the daily list?

By scoring above the threshold. The score runs from 0 to 1 and is built from weighted signals, an initial score, half-life and decay, plus a likely-to-engage boost. Out of everything trackable that morning, the top 150 to 400 per segment go to sellers.

What’s actually in the context package?

Everything we want the rep to know in one view: the contact, the account, why them and why today, why they should care, the fit, peers and competitors, and the signal history. The AI then builds on top of it, a suggested play, the opener and voicemail, and the discovery questions and value prop.

Do reps follow the scripts word for word?

We don't enforce that reps follow the scripts verbatim or the exact recommendations. Mid-market is closest to working straight down the list, while enterprise reps self-direct considerably more. What's driven the results is that the base layer is there every time, for every contact, whether or not anyone goes looking. The floor comes up. That's where the productivity and the demo rate increase comes from.

Why 150 to 400 rather than a fixed number?

The list is per segment, and the number moves with what’s genuinely warm that morning. Ranking decides the order, and rep capacity decides how far down the list the day reaches.

Run job change detection across every contact attached to those renewal accounts, and flag any account where someone from the original buying group has left the business or moved into a different role internally.

Data this step needs

• Job change alerts

• Internal role change

• Departure date

• Original buying group
  

Any member counts. Seniority is a poor guide to impact here. The admin who ran the day to day implementation can be a bigger loss than the VP who signed the contract and never logged in, so flag the departure whatever the title.

Internal moves. A champion who transfers to another team is as absent from your renewal as one who left the company.

Time since departure. The gap between someone leaving and the renewal landing tells you how much runway you have to rebuild the relationship.

Map the account as it looks today. Pull the current org chart and identify everyone with a say in the renewal: the budget holder, the operational owner, the team using the product day to day, and whoever picked up the departed member's remit.Data this step needs

• Org charts

• Contact data

• Reporting lines

• Role seniority

The backfill. If the departed champion has been replaced, that new hire is the most important contact on the account, and almost certainly has no relationship with you.

Growth since signature. Accounts that expanded often have stakeholders who were never part of the original evaluation and have no idea why the product was chosen.

Cross-reference the rebuilt committee against your own engagement records and produce a named list of the people on it with no meaningful contact history. That list is what the rest of the play acts on.

Data this step needs

• CRM activity history

• Email history

• Call and meeting history

• Signed contract
  

Two kinds of unengaged. Some of these contacts sit in your CRM with no logged activity. Others were never captured at all, because they arrived after the deal closed. Step 3 surfaces both, so the flag rests on engagement history and not on whether a record already exists.

The signatory. If the person who signed has gone and nobody else on the committee has engaged, the account has no internal advocate left.

Run automated research across the flagged accounts so the outreach has something specific to say.

What the research pulls

• Product and usage data

• Hiring signals

• Competitor research

Product and usage context. How the account is actually using what they bought, and which teams have adopted it. This is what makes a first message credible to someone who has never spoken to you.

Hiring signals. Open roles show where the account is investing, and whether the departed member's role is being backfilled or absorbed.

Competitor research. Activity on review sites like G2 changes a renewal conversation into a defense, and it's better to know going in.

Generate one message per contact. The unengaged people on a committee each have a different reason to care: the budget holder is thinking about cost and outcome, while the person who inherited the departed champion's work is mostly wondering whether this is about to become their problem.

The renewal date. State it plainly, so the reason for the message is obvious.

Their specific role. What their remit has to do with the account, drawn from the org mapping in step 3.

Something the account actually did. A usage detail or a change in the business, so the message could not have been sent to anyone else.

Route to whoever owns the renewal, then refresh the play monthly. Job changes happen continuously, and an account that looked well covered in January can lose its champion in March.

How to run this play in GTM Studio

FAQs for renewal risk

Why use job changes to predict churn instead of product usage?

Usage is a lagging indicator. It only moves once the account has started to disengage, which is late in a process that began earlier. A departure inside the buying group happens before any of it reaches the product.

What counts as the original buying group?

Everyone who shaped the original purchase: the signatory, the budget holder, the person who ran the evaluation, and the operational owner who implemented it. If any of them has left or moved internally, the account has lost part of the reason it bought.

What if an account has no unengaged contacts?

Then it's in better shape than most and drops down the priority order. Still worth re-running as the committee changes, since a single resignation can undo good coverage.

How far ahead of the renewal should this run?

A full quarter ahead. Building a relationship with a committee member who has never spoken to you takes several touches, and accounts inside the last 30 days need a more direct save motion instead.

Does it matter how senior the departed contact was?

Not for triggering the play. Flag the account whenever anyone from the original buying group leaves, then judge the impact when you rebuild the committee in step 3. Operational owners often leave a bigger gap than the executive who signed, because they were the ones actually using the product.

Do I need product usage data to run this play?

No. The core build runs on your CRM records plus job change and contact data. Usage data makes the research step richer, though the play works without it.

Every company that matches the profile of your best customers. Store this as a definition that re-runs rather than a list you export, so accounts growing into the ICP get picked up on their own.

Filters

• Industry

• Location

• Company size

• B2B

• Always-on refresh
  

Why always-on - A static list decays from the day it's exported. Storing the ICP as a filter that re-runs keeps the top of the engine full without a quarterly rebuild.

Strip out everything where cold outreach would be wrong or embarrassing, so no customer, live deal, or rep-owned account ever receives it. Each of these is a check you run against an individual account, which is why they sit here instead of inside the ICP definition in step 1.

Exclusions

• Existing customers

• Open opportunities

• Rep-owned accounts

• No sales team
  

Order of operations. Scrubbing at the end means you've already spent scoring and research effort on accounts that were never eligible.

No sales team. A company can match on industry, location and size and still have no sales function, which leaves nobody to use what you are selling. Sales headcount is not one of the firmographic fields you filter on in step 1, so it has to be caught here.

Score the surviving accounts against criteria that estimate whether they will open an opportunity in the next six months.

Scoring criteria

• Firmographics

• Leadership changes

• Funding

• Tech stack

• Hiring growth
  

What belongs in the score. Each criterion should move the likelihood of an opportunity in the next two quarters up or down. Criteria that only describe the company add weight to the score without improving it.

Factor recent activity into the score. This is what separates accounts that are warm this week from accounts that simply match the profile.

Live signals

• Review site visits

• New CRO hire

• Recent tech install
  

Recency. A G2 visit from last week is worth acting on, where the same visit from last year is mostly noise. Let signals age out instead of accumulating.

Find every decision maker on the deal. These are not won through one person, so a single contact per account is a gamble.

Functions to cover

• Sales

• RevOps

• Marketing
  

Function before names. Start from which teams have a stake in the decision, then find the named contacts inside each one. Working the other way around gets you whoever happened to be in the database.

Rate how likely each individual is to engage a rep, based on their own signals rather than their account's.Warmth values

• Pricing page - 100

• Other pages - 50

• Past user - 50

• New hire - 0
  

Why contact scoring exists. A high-fit account full of cold contacts still produces cold calls, and contact scoring is what stops reps spending their best accounts on the wrong person.

Past users. Somebody who used the product at a previous employer needs no explanation of what it does, which is why they score level with a warm page visit.

Why a new hire scores zero. These values measure warmth, meaning what the individual has already done that suggests they will reply, and someone three weeks into a job has no history with you yet. The hire itself is already counted at the account level, where leadership changes feed the fit score in step 3 and a new CRO hire is a live signal in step 4.

Zero is not a disqualifier. A contact with no warmth signals still moves through the system. They land on the cold path in step 9, get warmed by email, and graduate to a rep the moment they engage.

The same discipline as step 2, applied to people. Nobody gets double-touched, re-sequenced, or emailed after opting out.

Exclusions

• Already in a sequence

• Inactive 2 months

• Opted out
  

Deliverability. Mailing someone after they have opted out costs more than the extra volume is worth.

Combine the two scores into a single number, and let it produce one ranked list.

Account fit × Contact score = Overall score

Why the scores multiply. Adding them lets a strong account carry a hopeless contact through to a rep. Multiplying requires both to be true.

Volume follows capacity. The ranked order decides who gets worked first, and rep capacity decides how far down the list that reaches.

The ranked list splits in two. The dividing question is whether a human picks up the phone.

Engagement graduates cold contacts to the SDR path

The two paths need very different first messages. The SDR path leads with the signal that surfaced the account, while the cold path makes no ask at all.

Every outcome feeds back into the scoring, so replies and closed deals keep sharpening the model. ZoomInfo's own build of this engine produced $2M in pipeline over a four month runtime.

FAQs for always-on outbound

Why score accounts and contacts separately?

Account fit tells you whether the company is worth pursuing. Contact score tells you whether this particular person is likely to reply. Multiplying them requires both to be true before a rep spends time on the account, where adding them would let one strong half cover for a weak one.

What does always-on actually mean here?

The ICP is stored as a definition that re-runs, so it is never exported and frozen. Companies that grow into the profile get picked up automatically and companies that fall out of it drop away, which means the top of the funnel maintains itself.

How do you decide who gets a human and who gets email?

The overall score sets the order and rep capacity sets the cut-off. The volume reaching the SDR path is whatever your team can work with a full brief behind each account, and everything below that line goes to the cold path.

What happens to cold path contacts who never engage?

They pause after five ignored emails. Contacts who do engage graduate to the SDR path, so a human only ever picks up the conversation once the person has shown real intent.

Why do new hires score zero when new hire signals drive so many other plays?

Because this score measures individual warmth rather than account opportunity. A new hire has no engagement history with you on the day they start, so they add nothing on that axis. The opportunity a new hire represents is captured at the account level instead, through leadership changes in the fit score and a new CRO hire as a live signal. Contacts scoring zero are routed to the cold path and graduate to a rep as soon as they engage.

Do I need my own CRM data to run this?

For the two scrub steps, yes. Removing customers, open opportunities and contacts already in a sequence all depend on your own records. Fit scoring and live signals run on third-party data.

Pull companies from your CRM, a B2B data platform, or a CSV and apply your ICP filters: industry, employee band, geography. A broad "has raised any funding" filter is a useful pre-qualifier before velocity scoring.

• Keep it manageable. Around 7,000 accounts or fewer if you plan to layer AI enrichment later.

• Stay at company level for now. Once the hypergrowth accounts surface, you layer on contacts in step 5.

• Funding rounds in the last 18 months. The core velocity metric. A count of 3+ separates aggressive scalers from steady, single-round companies.

• Most recent funding date. Feeds the recency calculation in the next step.

• Full funding history. Dates, amounts, round types, and investors. The raw narrative the AI layer summarizes later.

• Total funding to date and founded year. A young company with heavy cumulative funding is a stronger hypergrowth signal than an older company doing the same.

• Verified funding news. Round details and investor names often surface in the news before structured data catches up.

First calculate months since the last raise, then build the Funding Velocity Score out of 8:

• 3 or more rounds in 18 months: +5 (2 rounds +3, 1 round +1)

• Raised within the last 12 months: +2

• More than $50M raised to date: +1

• No raise in more than 36 months: subtract 3

Tier the results: 8 or more is Hypergrowth: Aggressive Scaler, 4-7 Steady Growth, below 4 with 36+ quiet months Stagnant / Exit Watch, and everything else Emerging / Early Signal.

• Funding pattern narrative. AI condenses each company’s history and score into 1-2 sentences on budget availability and trajectory. A call opener like "you’ve raised three rounds in the past 18 months" writes itself.

• Recent news check. AI web research confirms any round in the past 6 months, with amount and lead investor, to validate the structured data.

• First-touch email. Drafted last, referencing the narrative.

• Hypergrowth: Aggressive Scaler. Priority outreach. Fresh budget, fast headcount growth, likely re-evaluating vendors.

• Steady Growth. Nurture and monitor. Solid fit, less urgency.

• Stagnant / Exit Watch. A different angle entirely: M&A and consolidation messaging, or deprioritize if instability is a concern.

Surface Finance, Operations, and Executive stakeholders on the hypergrowth tier (this pairs naturally with the Budget Holder Identification Play), route by territory, export to your CRM, and refresh monthly so every new round re-scores the list.

How to run this play in GTM Studio

FAQs for funding velocity targeting

Why is velocity better than a simple "recently funded" filter?

Because the pattern matters more than the event. Three rounds in 18 months signals fresh budget and aggressive scaling; a single round three years ago can signal stagnation or an approaching exit. One filter treats them the same. This play scores the difference.

What should I do with stagnant accounts?

Don’t bin them. More than 36 months without a raise can point to consolidation or exit, which suits a completely different message. Or deprioritize them if instability is a concern.

Do I need my own customer data?

No. The core build runs entirely on third-party firmographic and funding data, so it works for broad market scanning as well as a defined ICP list.

Who should I contact at hypergrowth accounts?

Finance, Operations, and Executive stakeholders. Pairing this play with the Budget Holder Identification Play gives you a combined account and contact signal.

How often should the play refresh?

Monthly, so every new funding round automatically re-scores the account the moment it happens.

Most competitive displacement plays wait for the account to say it out loud: a bad review, an RFP, a public complaint. By then, the account has raised its hand to everyone. This play finds the quiet quitters first: competitor customers who are dissatisfied but have not said so yet. It stacks a confirmed competitor install with four early warning signals (competitive research intent, turnover on the team running the tool, negative third-party reviews, and public social complaints) to surface accounts that are emotionally ready to switch.

Filter your target universe to confirmed installs of the competitor’s product using technographics. This is the qualifying filter, not just context: every account on the list is a verified competitor customer, which is what makes this a displacement play rather than a prospecting list.

• Layer your ICP on top: industry, employee count, geography.

• Keep the list tight. This play leans on AI web research, which runs per account, so around 7,000 accounts or fewer keeps it efficient.

• Install detail. When the competitor’s product was adopted, plus any recent add or drop activity around it. A long-tenured install is a different conversation than a recent one.

• Competitive intent surge. Researching your category while already owning the competitor’s product is shopping without saying a word.

• Admin hiring spike. Job postings for roles that manage the competitor’s tool often mean the last admin left. Turnover on that team is one of the strongest quiet indicators of frustration.

• Review site visits. Traffic to G2 and TrustRadius pages signals active comparison shopping, not idle browsing.

Two signals live outside structured data, on review sites and social platforms, so use AI web research to find them:

• Negative review detection. Search G2 and Capterra for reviews of the competitor’s product written by this company’s employees, and capture the common complaints. Quotable evidence a rep can reference credibly.

• Social complaint detection. Public posts or forum threads, on Reddit or X for example, from employees venting about the tool. The most candid signal there is.

Keep the research prompts short, natural questions, and test on a handful of accounts before running the full list.

Stack everything into a Quiet Quitter Score out of 12:

• Competitive intent: High +3, Medium +2, Low +1

• Admin job postings: more than one +2, exactly one +1

• Review site visits: +2

• Negative review found: +3

• Social complaint found: +2

Then tier the accounts: 8 or more is Ripe for Displacement, 4-7 a Watch List, and below 4 a Stable Customer to deprioritize for now.

• Dissatisfaction rationale. AI condenses the stacked signals into a 1-2 sentence reason to reach out now, per account.

• Displacement email. Drafted last, referencing the insight without naming the competitor negatively. Curious and professional, never confrontational. The goal is to open a conversation, not to badmouth anyone.

• Ripe for Displacement. Immediate AE outreach. Multiple dissatisfaction signals have stacked.

• Watch List. Monitoring cadence. Re-check as new signals emerge, no outreach yet.

• Stable Customer. Low signal density suggests they are satisfied today. Revisit later.

Surface the buying committee on the ripe tier (IT and Ops decision makers plus the team running the competitor’s tool), route by territory, export with the rationale and draft email attached, and refresh monthly. Job postings, intent, and reviews shift over time, and accounts should move between tiers as they do.

How to run this play in GTM Studio

FAQs for competitive displacement

What makes an account a "quiet quitter"?

A confirmed competitor customer showing dissatisfaction without having publicly churned: shopping your category, losing the person who ran the tool, leaving negative reviews, or venting on social.

Which signal matters most?

The power is in the stacking, but turnover on the team managing the competitor’s tool is one of the strongest single quiet indicators. When the admin leaves, frustration usually follows.

How should reps use the negative reviews they find?

Reference the themes credibly, never quote them back aggressively or name the competitor negatively. Curious and professional opens conversations; confrontational closes them.

Do I need customer data to run this?

No. The whole play is buildable on third-party technographic, intent, and job posting data plus open-web research.

Why keep the audience small?

The web research runs per account, so around 7,000 accounts or fewer keeps costs controlled and the results focused on genuinely displaceable targets.

Win-back outreach usually runs on gut feel. A rep notices a churned account in their territory and reaches out on instinct. This play replaces instinct with a composite signal. An account is in the optimal win-back window only when four things align at once: enough time has passed since churn, a new decision maker has arrived, a competitor tool has been installed, and live intent on your category is present. When all four stack, the timing is right.

This play starts in your CRM. Pull accounts where the status is Churned or Former Customer, or opportunities marked Closed Lost with a close date, and set a sensible lookback window such as the last 3 years.

• The churn date is the anchor. Every other buying signal is evaluated relative to how long the account has been gone.

• Keep it manageable. Around 7,000 accounts or fewer if you plan to layer AI enrichment later.

• Time since churn. Appended from your CRM’s churn or contract end date.

• Leadership change since churn. A new C-Suite or VP hire or promotion. A fresh decision maker has no emotional attachment to why the account originally left. Often the single biggest unlock.

• Competitor install since churn. If they adopted an alternative and are now moving past it or supplementing it, dissatisfaction may be surfacing.

• Live category intent. High, Medium, or Low research activity on your category proves the account is back in-market, not just theoretically ready.

Two supporting moves: pull verified news on executive moves and pain points for context, and refresh your contacts. The original champion may have left with the deal.

Calculate months since churn, then build the Win-Back Timing Score out of 11:

• 6 to 24 months since churn: +3 (under 6 months scores 0, over 24 subtracts 2)

• Leadership change since churn: +3

• Competitor tech installed since churn: +2

• Category intent: High +3, Medium +2, Low +1

Tier the results: 8 or more is the Optimal Win-Back Window, 4-7 Building Signal, under 4 within 6 months of churn Not Yet Ready, and under 4 after more than 24 months Too Stale: Re-qualify.

• Win-back rationale. AI condenses the four signals into 1-2 sentences on why now is, or is not, the moment. Think "since a new VP of Ops joined in March and you’ve been evaluating tools in the category again..."

• Leadership change confirmation. AI web research validates the change: who joined, in what role, announced where.

• Win-back email. Drafted last. Acknowledge the past relationship, keep it warm, and never sound desperate.

• Optimal Win-Back Window. Route straight to AEs or CSMs, ideally the original owner if still active.

• Building Signal. Monitoring cadence. Re-check monthly as signals strengthen.

• Not Yet Ready. Too soon after churn for a credible re-engagement.

• Too Stale. Treat as net-new and run standard prospecting instead.

Export with the rationale and draft email attached, and refresh monthly so accounts move between tiers automatically as new signals land.

How to run this play in GTM Studio

FAQs for win-back timing

When is the right time to re-approach a churned account?

When the four signals stack. Inside the score, 6 to 24 months after churn is the sweet spot: under 6 months is usually too raw, and past 24 months the account behaves more like net-new.

Why does a leadership change matter so much?

A new decision maker carries no baggage from the original churn. That is often the single biggest unlock for a win-back conversation, which is why it scores as heavily as live intent.

What data does this play need?

Your CRM’s churn or contract end date is the one required first-party field. Leadership changes, competitor installs, and intent all come from third-party data.

Isn’t a competitor install a bad sign?

Not here. If they adopted an alternative after leaving and are now researching the category again, dissatisfaction with that alternative may be surfacing. That is a classic win-back trigger.

Who should own the outreach?

The original account owner if they are still active, since the history helps. Otherwise re-route by territory.

This play goes deeper. It combines org hierarchy, live buying intent, hiring patterns, and recent technology investment into one composite score, so every contact in your target accounts is ranked by proximity to budget authority, with a data-backed reason to reach out.

Start at the company level. Pull target accounts from your CRM, a B2B data platform, or a CSV, then filter to your ICP: industry, employee band, geography. Optionally exclude existing customers or require a competitor install.

• Keep the list focused. Around 7,000 accounts or fewer works best if you plan to layer AI enrichment later, since AI research runs per record.

• Then surface the people. Generate a contact list across Finance, Procurement, IT, and Operations at C-Suite, VP, and Director level. Start broad. The scoring in step 3 does the filtering for you.

Enrich every contact with the data points the score is built on:

• Management level and department. C-Suite and VP contacts in Finance, Procurement, or IT sit structurally closest to budget.

• Budget and procurement intent. Live research into topics like budget planning and enterprise software purchasing is a timing signal, not a static title.

• Finance and procurement hiring. Open roles across FP&A, budgeting, and sourcing often precede new vendor evaluations.

• Recent technology investment. A tool added in the last 6-12 months proves budget existed and was spent. One of the strongest "they have money and they spend it" signals available.

• Promotions and new hires into budget roles. New budget holders are most receptive to vendor conversations early in their tenure.

Verified company news (pain points, projects, hiring plans) adds qualitative color that structured data misses.

Combine the signals into a single Budget Proximity Score out of 13:

• Management level: C-Suite +4, VP +3, Director +2, Manager +1

• Finance, Procurement, or IT department: +2

• Budget intent: High +3, Medium +2, Low +1

• Active finance or procurement hiring: +1

• Recent technology investment: +2

• Recent promotion or new hire into a budget role: +1

Then tier every contact: 9 or more is an Economic Buyer, 5-8 a Budget Influencer, below 5 a Technical or User Contact. One note: intent data is always High, Medium, or Low, never a number, so score it conditionally rather than multiplying it.

With the score in place, use an AI layer to make it usable at speed:

• Budget authority rationale. A 1-2 sentence explanation per contact of why they are likely close to budget, synthesized from the signals above.

• First-touch email. A short draft referencing that rationale, so outreach shows its research. Build this last, since it depends on the rationale.

Push the output to where reps work:

• Create views per tier so Economic Buyers and Budget Influencers are one click away.

• Route by territory and export to your CRM, with the rationale and draft email attached to each contact.

• Refresh weekly or monthly. Intent, hiring, and technology signals shift constantly, and contacts should move between tiers as they do.

How to run this play in GTM Studio

FAQs for budget holder targeting

Why isn’t targeting by job title enough?

Titles show who might own budget; they say nothing about who is exercising it right now. Live intent, hiring activity, and recent technology spend separate an active economic buyer from a name on an org chart.

What data does this play need?

A target account list plus third-party B2B data covering org structure, buying intent, job postings, and technographics. No customer or first-party data is required.

How should intent data be handled in the score?

Intent is always High, Medium, or Low, never a number. Score it conditionally (High +3, Medium +2, Low +1) rather than trying to multiply it.

How many accounts should I run this on?

Keep to around 7,000 or fewer if you are layering AI enrichment, since AI research runs per record and costs scale with list size. You can always run further batches.

How often should the play refresh?

Weekly or monthly. Budget cycles, hiring, and intent move constantly, and contacts should shift between tiers as the signals do.