Tom B.

If one client slows down, there is no plan B.

• The fix is not more leads or a bigger list.
• Start with the client the owner would take ten more of.
• Document the trigger that made them look, the title of the person who signed, and the problem they could not solve alone.
• Then find companies with that same shape, not the same industry label.
• Three good matches beat three hundred loose ones because they make it possible to write a message that sounds like the sender knows the buyer's business.

In one post, Tom B. contrasts asking an agency owner to name their best client, which gets an instant answer, with asking them to name ten companies exactly like that client, which gets silence. They then describe picking up the phone to ask, 'Which client would you take 10 more of?' and offering three lookalike companies, who to contact, and what to say.

Read original · Original 2

The first question is who is buying: current clients or companies that do not know the firm yet.

• Referrals pass along what people already know the agency for.
• A client who sends work might say the agency runs their SEO, but that client will not explain a service launched in the last 90 days.
• The new offer therefore tends to sell to the existing client list, which is a fine start but also a ceiling.
• Reaching buyers who have never heard of the firm is a different job.
• It does not require volume; it requires a short list of the right companies, a reason each one fits, and a first message worth answering.

The post describes reading agency homepages and seeing a pattern: a new offer launched in the last 90 days, built for the one industry the agency already serves. The clearest illustration is the client who says the agency runs their SEO, not that the agency launched something new last month.

Read original

Instead of using AI for a clever first line, a user could ask it to find companies that fit an ICP and show a buying signal in the past 30 days, identify decision makers, verify contact information, research accounts, segment the list, build campaigns, and show everything before anything sends.

• They see this as a bigger change than AI personalization.
• The outbound expert's value moves away from operating six tools and toward knowing who to target, which signals matter, what offer will resonate, how to structure the infrastructure, what to test, and how to interpret results.
• AI can operate the machinery; someone still has to know what machine to build.

The post lists the old process and then the new prompt: 'Find 400 companies that fit this ICP and have shown one of these buying signals in the last 30 days... Then show me everything before anything sends.'

It extends Tom B.'s earlier priority on fit and human judgment into an AI-operated approach, rather than replacing that judgment.

Read original

Ask an agency owner for one best-client name.

• Return three companies like that client, who to contact, and the first message to send.
• The offer is free and does not require a call.
• It positions the value as a small diagnostic sample rather than a large lead list.
• One post presents it as phone calls; others ask for a direct message.
• The repeated structure shows what they prioritize: a named client, a shortlist with fit reasons, a contact, and a message.

In one post, Tom B. writes, 'DM me the name of your best client.

• I'll send back three companies like them, who I'd contact, and what I'd say.
• Free.
• No call required.' Another post asks readers to DM 'THREE' and promises the next three companies, who to contact, why they fit, and the first message.

Read original · Original 2 · Original 3

A newcomer can start here for the diagnostic question and the working method behind Tom B.’s outbound approach.

Tom B. opens with a test: ask an agency owner who their best client is and they answer in two seconds; ask them to name ten more companies exactly like that one and they go silent.

• They say that silence is the outbound problem.
• Referral-led firms grow because the right people find them, but referrals are out of the owner’s control.
• The usual loud response—a big list, a generic pitch, and a thousand sends—can burn the agency’s name with the very buyers it wants.
• A better starting point is smaller: take the best client, note the trigger that made them look, the title of the person who signed, and the problem they could not solve alone.
• Then find companies with the same shape, not just the same industry label.
• Three good matches beat three hundred loose ones: three is enough to write a message that shows the sender understands the buyer’s business.
• The post ends with an offer to send back three companies like the reader’s best client, who to contact, and what to say.

For brands planning a campaign

For an agency expanding its client base, start with focus, not volume.

• Ask the owner to name the one client they would take ten more of, then build a shortlist around the same trigger, signer title, and unsolved problem.
• The first message should show that specific reasoning, not just name an industry.

It lays out Tom B.’s offer plainly: name the best client, then get a small lookalike list and a first message.

Tom B. asks B2B agency owners where new work comes from.

• The usual answer is two words: repeat clients.
• That is good news—the work is good and clients return—but it also means growth depends on existing clients.
• If one slows down, there is no plan B.
• Most of these owners do not need more leads; they need more clients like their best ones.
• They describe a direct action: pick up the phone, ask each agency owner which client they would take ten more of, and offer to find three companies like that client, with who to contact and what to say, free and with no call required.
• The post shows how Tom B. turns referral dependency into a repeatable list-building question.

For brands planning a campaign

For a B2B outbound sales agency, use the owner’s best client as the seed for expansion.

• Ask for one named client, then produce a three-company shortlist with a contact and first message for each.
• This low-volume, high-relevance approach can be tested without building a broad list.

A newcomer can see how Tom B. connects a new service to a named best client and a targeted list.

Tom B. describes reading agency homepages and spotting a pattern: a new offer launched in the last ninety days, often AI search, ChatGPT ads, or a RevOps add-on, built for the one industry the agency already serves.

• They would first ask who is buying it: current clients or companies that do not know the agency yet.
• Referrals pass along what people already know the agency for; the client who sends work says the agency runs their SEO, not that it launched something new last month.
• So the new service tends to sell to the existing client list.
• That is a fine start, but it also sets a ceiling.
• Reaching buyers who have never heard of the agency takes a different approach from waiting for calls.
• It does not take volume; it takes a short list of the right companies, a reason each one fits, and a first message worth answering.
• Tom B. ends by asking which current client the reader would take ten more of and offers to send the next three companies, who to contact, why they fit, and the first message.

For brands planning a campaign

When expanding with a new outbound offer, do not assume current clients or referral sources will sell it. Identify companies outside the current client list that match the best-client profile, give a specific reason each one fits, and write a first message that does not rely on prior familiarity.

A newcomer can read it to understand their view of targeting, signals, offers, and testing as the durable skills.

Tom B. predicts a significant change in cold email by contrasting the old outbound stack with a new one.

• The old stack included lead data, enrichment, AI, a sending platform, a CRM, and a unified inbox, but someone still had to bounce between tools to make it work.
• They say outbound infrastructure is starting to connect directly to AI assistants.
• Instead of using AI only to write a clever first line, a user could ask it to find 400 companies that fit an ICP and showed a buying signal in the last thirty days, identify the right decision makers, verify contact information, research each account, segment the list, build campaigns, and show everything before anything sends.
• In that setup, AI becomes the control layer—a bigger development, they say, than AI-generated personalization.
• The value of an outbound expert shifts: knowing how to click around six sales tools becomes less valuable, while knowing who to target, which signals matter, what offer will resonate, how to structure the infrastructure, what to test, and how to interpret results becomes more valuable.
• AI can operate the machinery, but someone still has to know what machine to build.

For brands planning a campaign

For a B2B outbound agency, position expertise around targeting, signal selection, offer design, infrastructure, testing, and interpretation. AI can help assemble and pre-check a list, but a human should still decide which signals matter and what offer is worth sending.

The recommended approach is to start smaller: document why the best client bought — the trigger that made them look, the title of the person who signed, and the problem they could not solve alone — then find companies with the same shape rather than the same industry label. Three good matches are presented as more useful than three hundred loose ones because they allow a message that sounds like it understands the buyer's business.

Take one best client, extract the buying trigger, signer title, and unsolved problem, then source companies with the same shape and write from that evidence.

This approach is framed for referral-led firms that already have a strong client to model, not as a universal replacement for inbound, referrals, or broader market coverage.

For brands planning a campaign

A brand evaluating Tom B. for an outbound campaign can test whether the work produces a small, high-fit account list, with a clear reason for each account and a first message tied to the buyer's situation. The approach is less suitable when the campaign is mainly about mass volume or generic list building.

Examples: Source 1 · Source 2 · Source 3

A client who sends work may say the firm runs their SEO, but they will not explain a service launched in the last 90 days.

• As a result, new offers such as AI search, ChatGPT ads, or a RevOps add-on tend to be sold to the existing client list.
• That is a fine start, but also a ceiling.
• Reaching buyers who have never heard of the firm is a different job: it requires a short list of the right companies, a reason each one fits, and a first message worth answering.

Define the current client type, identify prospects that look like them but do not know the firm, and build outreach around a specific fit reason and first message.

For brands planning a campaign

For a service launch, a brand can evaluate Tom B. on whether the plan separates existing-client upsell from new-buyer acquisition, names the right companies, and gives a credible first-contact message. This approach is useful when the goal is to grow beyond referrals.

Examples: Source 1 · Source 2 · Source 3

They expect outbound infrastructure to connect directly to AI assistants, so a user could ask for companies that fit an ICP, show a buying signal, identify decision makers, verify contact information, research accounts, segment the list, build campaigns, and review everything before sending. In that view, AI becomes the control layer, and the outbound expert's value shifts from operating six tools to choosing who to target, which signals matter, what offer will resonate, how to structure the infrastructure, what to test, and how to interpret results.

Frame outbound as a control-layer process and judge value by targeting, signal choice, offer design, infrastructure, testing, and interpretation rather than tool operation.

The post forecasts where outbound may be heading. If current feature availability matters to the campaign, ask for confirmation from the product team.

For brands planning a campaign

An AI or outbound technology brand can evaluate Tom B. on how clearly they explain where AI fits in the process and what human judgment remains.

• The topic suits content or a campaign about targeting, signals, offer design, and testing.
• If the campaign needs to validate a product, ask for evidence that a named tool performs the described process.

Examples: Source 1

One post presents this through phone calls; others ask for a direct message. The offer avoids a required call and positions the value as a small diagnostic sample rather than a large lead list.

Ask the prospect for one best-client name, return a short list with fit reasons and a first-message recommendation, and remove friction by not requiring a call.

Ask for client names, results, paid sponsorship terms, and partnership details before assessing the offer for a partnership.

For brands planning a campaign

A brand considering Tom B. for a partnership can evaluate whether this offer format translates into a sponsored or co-branded campaign with tangible outputs: a shortlist, contact roles, and a first-message recommendation. It is a direct-response format built around a specific business question, not a broad lead magnet.

Examples: Source 1 · Source 2 · Source 3

Vacca's five-layer model and Tom B.'s best-client exercise both teach readers to work backwards from who fits before writing anything.

Vacca lays out a five-part system — AI search visibility, signal-based outbound, founder and employee content, paid ads, RevOps — names the tools used at each layer, and cites campaign and revenue figures. Tom B. sticks to a narrative single-client diagnostic and a shortlist offer, with no tool stacks or performance data.

Read original · Original 2

Both treat familiarity with the right accounts as the compounding asset.

Malviya writes about founder and employee content as distribution and reports client revenue across two dozen industries and seven countries. Tom B. writes about selecting lookalikes of one best client and drafting the first message, but does not publish content-system case studies.

Read original · Original 2

Hammed works in ecommerce and retention — AI shopping referrals, product pages, and email automations for Shopify and Klaviyo brands. Tom B. works in B2B agency outbound, not retail, product pages, or email flows.

Read original · Original 2

If the agency's campaigns are bouncing, its lists are half-empty, replies come back as "not us" or "not now," and email, LinkedIn, and calls are not running as one sequence, the repair starts with the stack, not a shortlist.

• The posts set out a five-part GTM built in order — sending foundation, market mapping, signals, synced channels, and the replies and revenue layer — with outbound as one of five parts alongside AI search visibility, founder and employee content, paid ads, and RevOps.
• They name the tools used at each layer and report outcomes: 830 qualified contacts at a 5.5% reply rate and under 2% bounces; a Salesforce database of 3,655 accounts built and refreshed in six months; $7.83M in qualified pipeline and $1.52M closed-won over ten months without new SDRs.
• Vacca also offers a measurement frame for evaluating campaigns: positive reply rate and revenue closed, on the argument that meetings booked is a number any tool can inflate.

This is a systems engagement, not a single deliverable.

• It commits the agency to tooling and to building the layers before a first message is written: the posts assume Clay, Prospeo, Instantly, lemlist, Apify, and a clean CRM.
• The Tom B. offer returns three named companies, who to contact, and a sendable message, with no tooling change or sequence to run.
• The one-client question and lookalike shortlist do not appear here; the entry point is diagnosing which layer broke.
• The client outcome figures are specific and useful, but self-reported.
• Ask for the basis behind them before relying on the claims.

Examples: Source 1 · Source 2

The argument: consistent posting from several people inside a company looks like vanity activity in month one, brings inbound DMs from quiet watchers by month three, and leaves a familiarity gap by month six that a competitor cannot close by outspending it on ads.

• A cold email then lands with someone who already recognises the sender, and sales cycles shorten.
• That maps onto a referral-led firm whose growth is capped by its existing clients, provided the founder and a few employees are willing to publish on a schedule.
• The offer spans personal brand, outbound, employee-generated content, and packaging expertise into a course, workshop, or framework.

The timeline is months, and the work depends on the agency's own people showing up consistently — slower and more labor-intensive than one shortlist and one first message.

• The results span a cybersecurity firm in San Francisco, a solar founder in India, an SEO agency in the UAE, a web-development agency in the US, and a B2B SaaS founder in California, across two dozen industries and seven countries.
• That breadth suggests the system repeats, but does not prove it has been run for a mid-market outbound agency.
• There is no lookalike-client exercise or ready-to-send message in what is shown.
• The $2.1M revenue figure and per-client numbers are self-reported; ask for the basis behind them.

Examples: Source 1 · Source 2

The material covers how the funnel shifts before customers reach a brand's site: AI-assisted discovery is changing what buyers consider, with GenAI referrals to retail ecommerce up 203% year over year and 51% of U.S. consumers saying they used an AI shopping tool in the past month.

• It also covers the economics of email automation, citing a 2026 report across 150,000 brands that found automations account for 2% of email sends but 30% of email-driven revenue.
• Use this when the deliverable is a channel and retention recommendation for a Shopify brand: better product information so AI tools can compare the offer, and flows built around behaviour rather than more campaigns.

This is a different buyer task from the agency's own expansion goal.

• Neither post addresses B2B new-business outbound, the referral ceiling, lookalike shortlists, or a first message to a prospect; instead, the pieces analyse industry data and argue how ecommerce brands should prepare product pages, flows, and the journey after discovery.
• Choosing Aminat Hammed to solve the agency's pipeline problem would be a subject and audience mismatch.
• What transfers is the method — grounding a recommendation in third-party data and naming what still has to work after the customer arrives — not the campaign itself.

Examples: Source 1 · Source 2