Ashlee Starr is a full-cycle sales professional and content creator who shares practical advice on prospecting, enterprise sales, and using AI in sales work.
Ashlee Starr is most useful for practical B2B sales content on prospecting, enterprise deals, and AI in sales workflows.
Best for
A B2B sales-software brand wanting thought leadership on enterprise sales and AI workflows.
Not for
Consumer DTC skincare or household-goods UGC campaigns needing short-form video and measurable conversion.
Key takeaways
Test outbound messages in controlled batches, changing one variable at a time.
Audit your own job before deciding what AI should take over.
In enterprise deals, find the people who can say yes.
Check first: Confirm recent LinkedIn audience analytics and documented role or seniority data if needed.
Main ideas
Test outbound in controlled batches, not by spraying messages · Audit your own job before deciding what AI should take over · In enterprise deals, find the people who can say yes
Ashlee Starr shares practical advice from the work of selling: how to test outbound messages, navigate enterprise buying groups, and use AI to reduce sales busywork.
The advice is concrete: test a consistent prospect group and change one variable at a time; don’t treat a frontline “no” as the whole buying group’s answer; and equip a champion with a sourced, editable deck they can present without you.
They also write about asking difficult deal questions early and using AI for tasks such as prospecting and CRM updates.
The result is a blend of scripts, sales judgment, and hands-on process examples.
Starr makes sales advice useful by showing the action behind the principle.
In a messaging test, that means holding the audience and sequence steady, tracking not just replies but later outcomes, and changing one variable if results are weak.
In enterprise sales, it means looking beyond an initial rejection to find who can approve a purchase—and preparing a champion to explain the case to people the seller may never meet.
Their examples connect AI to specific jobs, from identifying automatable tasks to finding prospects.
Readers get ready-to-adapt tactics and a practical way to think through the work.
Ashlee Starr writes about selling from inside the job: testing outbound without fooling yourself, where AI helps a full-cycle account executive, and how enterprise deals are won or lost.
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They emphasize discipline and preparation: controlled conditions for tests, an honest task audit for AI, and sellers who arm their champions and ask the questions they are afraid to hear answered.
Test outbound in controlled batches, not by spraying messages
Ashlee Starr argues that most “message testing” amounts to sending random messages to random prospects, teaching nothing and burning leads.
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A real test uses a nearly identical list of 25 to 200 prospects from one industry and one job title, run through a five-to-ten-step sequence over three weeks without changing anything mid-test.
Track where people reply, where they opt out, reply rate, demos booked, demo hold rate and close rate.
Signs the messaging works include reply rates above baseline, demos that actually hold, and prospects engaging with pain-point questions.
Signs it does not work include high opt-outs or silence after a full sequence, demos that book but do not hold, and the same objection recurring without an answer.
When a test fails, change one variable and run it again rather than scrapping the approach.
The post sets working signs—reply rate above baseline, strong demo hold rate and engagement with pain-point questions—against signs of failure: a high opt-out rate or no replies after the full sequence, demos booked but not held, and repeated objections with no answer.
Audit your own job before deciding what AI should take over
Ashlee Starr treats AI adoption as a task-level audit, not a tool choice.
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Write down every task in the role, ask an assistant which ones it can do, and sort the results into fully automatable, AI-does-the-heavy-lifting-with-human-review, and genuinely human.
In a full-cycle AE role, the stated findings were that twelve tasks were fully automatable but had been done by hand daily, fourteen more could be handled with review, and the remaining work that genuinely needed a person was a much shorter list.
They add that those remaining human tasks are also the ones that move the needle.
The same logic appears in day-to-day use, where a chat connection to a prospecting platform replaces switching among four tabs and three tools.
Asking Claude for a list of founders interested in ghostwriting returned an interactive table of 8,600 US founders running small tech startups, with prompts to narrow it by niche or segment.
The process then moved to creating multi-step email and LinkedIn sequences and answering pipeline-health questions, such as which leads had opened a sequence but had not replied.
In another post, an AI assistant connected to a company knowledge base surfaces answers during live calls and drafts CRM updates for review.
In enterprise deals, find the people who can say yes
Ashlee Starr contrasts SMB selling, where deals are quick and transactional, with enterprise selling, where many people can say no and few can say yes.
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A front-line no should not be taken at face value: the same priority can turn out to be a C-suite priority once the conversation reaches the right level. The seller’s job is to find who holds decision-making power rather than accept the first rejection.
The post draws on a quote from Susan Zuzic on Scott Ingram’s Sales Success Stories Podcast: at big companies, lots of people can say no, but not many can say yes, and finding those people is the job. Ashlee Starr describes moving up the ladder after a front-line no, only to find that the account was a top priority for executives who simply had not heard of the product yet.
The enterprise lesson draws on Ashlee Starr’s earlier SMB background, making the shift in approach concrete. Ask how much of their current work focuses on enterprise selling.
Arm the champion: they sell the deal in the room you’re not in
Enterprise deals are decided by an executive buying team the seller often never meets, and the champion is usually the only person in the room who has heard the pitch.
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The work, then, is not just to convince the champion but to equip them to sell on the seller’s behalf. Build materials for the champion to present, not for the seller’s own delivery; cite every statistic on the slide so a CFO’s question about the source is already answered; and export the deck in a format the champion can edit without breaking the layout.
The AI deck tool connects to where the deals live, including Slack, HubSpot and a call note taker, then asks the agent for a champion-ready deck. Every stat carries its source on the slide, and the deck exports to PowerPoint so the champion can drop it into their own template.
Ask the questions you’re afraid to hear answered, while the deal can still change
Ashlee Starr argues that solid deals hold up under hard questions, so sellers should raise risks early rather than discover them in a pipeline review.
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Each question is meant to get a real answer while there is still time to influence the outcome, whether the risk is budget approval, a cheaper competitor, silence or a champion who will not make an introduction.
The scripts are direct: “What happens if the CFO says no to this?” for sign-off risk; “What happens if you get a price quote that's half ours but doesn't have XYZ features?” for competitor risk; “How long until you know next steps? If I don't hear anything by [X date], should I assume you're no longer interested?” for ghosting; and “What will it take to make you confident enough in me to put me in front of [decision-maker's name]?” for access.
Starting with the statistic that 67% of B2B buyers prefer a rep-free experience, Ashlee Starr does not argue that buyers are wrong.
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Instead, they identify specific, fixable behaviors that drive buyers away: repeated “just circling back” messages, insisting the buyer has a problem the rep can fix, overpromising and underdelivering, and lying.
Acting like a human, telling people why they should not buy, telling the truth, nurturing with value and updating the CRM do the opposite.
The conclusion: sales does not have a demand problem; it has a bad-rep problem.
Using a schoolroom framing, the post sorts behavior into red and green choices. It closes with the line that we do not have a sales problem but a bad rep problem, then asks readers which choices are missing from the lists.
Engineer a warm introduction by going peer to peer
Warm introductions work better than cold ones, so Ashlee Starr’s approach is to manufacture one.
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Rather than approach the ideal customer profile directly, the message goes from one account executive to another and asks a peer to forward a short blurb internally. It carries no pitch, states the ask plainly and explicitly gives the peer permission to decline.
The verbatim message reads: “Hey [NAME]!
AE to AE here.
[COMPANY] is in my territory.
Us sales folks have to stick together, if I send you a quick blurb, would you be open to forwarding it to someone on your [ICP] team?
No sweat if not, just figured I'd ask!”
The post reports a 75% reply rate for the campaign and says the sequencing platform finds the contacts and runs the sequence, keeping replies from getting scattered across tools.
Ashlee Starr says plainly that they are a content creator who does not want to leave their job, pushing back on the idea that going independent is the only good outcome.
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Their priority is doing both: keeping a full-time role with a team and clients they like while publishing sales content. That shapes the material: the advice comes from someone still doing the job, and some posts also serve as product announcements for the employer.
The post names brands the team says it works with, including OUAI, Nutrafol and Gruns, and announces the employer’s launch in the Klaviyo App Marketplace.
The product collects video customer feedback by email on what customers expected, what they got and whether the two matched.
Related posts address DTC growth marketers dealing with falling lifetime value, weak product-detail-page conversion and slipping ROAS, and describe the product’s value as uncovering the why behind the metrics.
Later posts mix sales advice with employer product announcements, a useful distinction when judging how commercial a collaboration would be. Ask what share of the proposed content would promote the employer’s product.
Answers the question "how does this person think about prospecting?" and gives a newcomer the clearest single statement of Ashlee Starr's working method for outbound.
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Ashlee Starr calls out spray-and-pray testing, then lays out a procedure: choose 25 to 200 prospects from one industry and one job title; run a five-to-ten-step sequence over three weeks without changing anything mid-test; then track replies, opt-outs, response rate, demos booked, hold rate and close rate.
The post distinguishes signs the messaging is working (reply rate above baseline, demos that actually hold, prospects engaging with pain-point questions) from signs it is not (high opt-outs, demos booked but not held, the same objections repeating with no way around them).
The advice: change one variable and test again rather than abandoning the whole approach.
For brands planning a campaign
A sales software company could build a co-created playbook or webinar segment around this framework. It gives audiences a concrete testing process and the vendor a natural way to demonstrate sequencing and analytics features in a real testing loop, not a demo scenario.
Shows how this creator frames enterprise selling against their own SMB background, and answers "what does Ashlee Starr actually believe about enterprise deals?"
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The post opens with a quote from Susan Zuzic on Scott Ingram's Sales Success Stories Podcast: at big companies, many people can say no but few can say yes, and finding the yes-sayers is the job.
Ashlee Starr contrasts their prior SMB experience, where deals were quick and transactional, with the slower enterprise dynamic.
Their argument: don't take a no from the front lines at face value, because the same priority may turn out to be a C-suite priority once the conversation reaches the right level.
The post closes by asking who turned a no into a yes.
For brands planning a campaign
An enterprise sales software company could partner on content about navigating buying committees. Ashlee Starr uses enterprise language comfortably and cites third-party sales practitioners by name, making a thought leadership collaboration feel like an extension of an existing habit rather than a new format.
Answers "how does Ashlee Starr approach the later stages of an enterprise deal?" and shows a concrete AI tool process instead of a general productivity claim.
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Ashlee Starr argues that the champion is the only person in the executive buying room who has heard the pitch, so the seller's job is to arm them to sell on the seller's behalf.
The process connects the new Gamma to where deals live, including Slack, HubSpot and a note taker, then asks the agent for a deck the champion can present.
Each statistic is cited on the slide, so a CFO's question about the source is already answered.
The deck can be edited in PowerPoint without breaking formatting.
For brands planning a campaign
A sales enablement or deck-building vendor has a ready-made use case: Ashlee Starr describes a specific champion-facing artifact and explains each design choice. A partner could build a jointly branded template or workshop around it.
Answers "how does Ashlee Starr treat AI in real-time deal execution?" and illustrates their habit of wrapping product mentions in a story rather than a feature list.
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The post describes an executive asking an obscure question on a call and a rep deferring to a follow-up email.
Ashlee Starr's point: that email rarely lands, and the deal goes to a competitor who answered on the call.
The product is Fireflies for Sales, which connects to a company knowledge base, surfaces answers during the call, then drafts CRM updates for approval afterward.
A joke about the Owl City song keeps the tone light; the post ends by asking about panic messages sent to a manager during a demo.
For brands planning a campaign
A sales AI vendor can see how Ashlee Starr carries a product narrative through a story about a specific deal-stage failure—the shape most thought leadership collaborations need. The post also shows which category of tool they naturally reach for when discussing multi-stakeholder enterprise deals.
Answers "what is Ashlee Starr's stated position on AI in a sales role?" and gives a newcomer a repeatable exercise to judge the argument by.
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Ashlee Starr listed every task they perform as a full-cycle AE, pasted the list into Claude and asked which items AI could do.
The result, as described, was twelve fully automatable tasks, fourteen where AI could do the heavy lifting with human review, and a shorter list of tasks that genuinely need a person.
They note that the remaining human tasks are the ones that move the needle.
The post turns the exercise into a four-step process for readers and credits Lindsay Rios and Brianna Chapman for first recommending Gamma.
For brands planning a campaign
An AI sales tool vendor could co-create a worksheet or self-assessment around this structure. Ashlee Starr turns a personal experiment into a reader-facing exercise, making it usable rather than promotional.
Answers "will this creator share real, usable sales copy?" and is the strongest single piece for judging how concretely Ashlee Starr teaches outreach.
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Ashlee Starr shares a campaign reported at a 75 percent reply rate.
Rather than contacting the ICP directly, the message goes from account executive to account executive and asks a peer to forward a short blurb internally, with no pitch and explicit permission to decline.
The post includes the verbatim message and credits lemlist with finding the contacts and running the sequence in one place.
The call to action invites readers to comment for the full setup.
For brands planning a campaign
Outreach, sequencing or prospecting vendors could use this as a case study. Ashlee Starr publishes the actual copy, not just a description; a partner can add measurement, deliverability or testing depth around that concrete artifact.
Answers “what does Ashlee Starr say about trust and buyer behavior?” and shows how they turn a statistic into a memorable teaching device.
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Ashlee Starr opens with the claim that 67 percent of B2B buyers prefer a rep-free experience, then uses a schoolroom framing of red and green choices.
Red choices include repeated follow-up messages, insisting the buyer has a problem the rep can fix, overpromising, and withholding the whole truth.
Green choices include acting like a human, explaining why someone should not buy, telling the truth, nurturing with value, and updating the CRM.
The post closes with the line that there is not a sales problem but a bad rep problem.
For brands planning a campaign
A CRM or sales engagement vendor could use this framing for buyer-trust content. Ashlee Starr translates buyer research into behaviors a rep can change, supporting a partnered piece on data hygiene, follow-up quality or buyer-experience metrics.
Enterprise deals: find the people who can say yes1
Asking the uncomfortable deal questions1
Arming a champion to sell internally1
In-call answers and CRM follow-through1
Peer-to-peer warm introductions1
Rep-free buying and sales credibility1
A creator who keeps a full-time sales job1
Controlled outbound-messaging tests
Ashlee Starr outlines a messaging-testing process that starts with a uniform list: 25 to 200 prospects from one industry and one title.
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The sequence runs five to 10 steps over three weeks, with no mid-test changes. They track where people respond and opt out, reply rate, demos booked, demo hold rate, and close rate, then change one variable if the test fails.
Segment tightly, run the sequence consistently, use baseline reply rate and demo hold rate as signals of what is or isn’t working, and isolate one variable after a failed test.
For brands planning a campaign
For a sales-software brand, this is ready-made thought leadership: a practical teardown or worksheet on running a clean outbound test for enterprise teams, not another post about better cold email.
Ashlee Starr writes about auditing every task in a full-cycle AE role and asking an AI assistant which tasks it can do.
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They say the audit found 12 fully automatable tasks and 14 where AI could do the heavy lifting with human review. In another post, they use a lemlist MCP connection to ask Claude for an 8,600-founder prospect list, narrow it by niche, create multistep email and LinkedIn sequences, and check pipeline health without switching among four tabs and three tools.
Inventory tasks; ask AI to classify them as automatable or human; use chat-based list building and pipeline queries; keep human review for tasks that need judgment.
For brands planning a campaign
A thought-leadership collaboration on AI tools can focus on task triage: which sales tasks to hand off, what to review, and what still needs a human. That’s more concrete than a general position on AI in sales.
Ashlee Starr contrasts quick SMB yes-or-no sales with enterprise sales, where many people can say no but few can say yes.
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They quote Susan Zuzic on finding the people with decision-making power and describe moving up the ladder after a front-line no, only to find the priority already existed in the C-suite. They argue against taking no at face value.
For brands planning a campaign
This supports thought leadership on stakeholder mapping and executive engagement in enterprise sales, especially content that helps sellers distinguish a real no from an early no.
Ashlee Starr offers scripts for testing deal risk: ask what happens if the CFO says no, what happens if a cheaper vendor lacks certain features, how long until next steps are known, and what it would take to get introduced to the decision-maker.
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The aim is to surface fears while the deal can still be influenced.
Use direct risk-testing questions with the buyer, including questions about dates for next steps and conditions for access to the decision-maker.
For brands planning a campaign
A collaboration can package these as objection and deal-risk scripts for enterprise sales teams, with examples of how answers change the forecast or the next action.
Ashlee Starr argues that executive buying teams decide enterprise deals, and the champion is often the only person in the room who has heard the pitch.
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Their response is to build a deck for the champion, not themselves: connect Gamma to Slack, HubSpot, and a note taker, ask the agent for a champion-ready deck with sourced stats on each slide, and save it as a PowerPoint so the champion can edit it without breaking formatting.
Create champion-facing materials with cited stats, make them easy to adapt to the champion’s own template, and treat the champion as the seller in the room you can’t enter.
For brands planning a campaign
This is a distinct enterprise-sales angle for a sales-software or productivity brand: internal enablement assets and champion kits, rather than seller-facing pitch decks.
Ashlee Starr describes an enterprise failure mode: saying “good question, I’ll follow up” when an executive asks something obscure.
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They pitch Fireflies for Sales as a knowledge-base tool that surfaces answers during the call and drafts CRM updates for approval, so the seller doesn’t have to chase the answer afterward.
For brands planning a campaign
A campaign can use the meeting-assist and CRM-update angle to show how complex sales teams can reduce follow-up gaps, with a clear before-and-after of a live call.
Ashlee Starr shares an AE-to-AE campaign asking another account executive to forward a short blurb to someone on their ICP team.
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The message is peer-to-peer, low-pressure, and pitch-free; the post claims a 75% reply rate and says lemlist finds contacts and runs the sequence, keeping replies from being scattered across tools.
Approach the peer AE first, keep the ask small, and use one sequence tool to find contacts and track replies.
For brands planning a campaign
A sales-software brand can feature this concrete outbound play in a collaboration: warm-intro engineering for enterprise accounts, with the actual message and sequence logic.
Ashlee Starr cites a statistic that 67% of B2B buyers prefer a rep-free experience, then sorts sales behavior into red choices—endless 'just circling back' messages, demanding that buyers have a problem, overpromising, and lying—and green choices: acting human, telling people why not to buy, telling the truth, nurturing with value, and updating the CRM.
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They conclude that sales has a bad-rep problem, not just a sales problem.
For brands planning a campaign
A thought-leadership collaboration can focus on buyer trust and modern sales conduct, using the red-choice and green-choice framework as a memorable teaching device.
In Hark-related posts, Ashlee Starr writes about DTC growth marketers and the pain of falling LTV, weak product-detail-page conversion, and slipping ROAS.
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They describe Hark's value as getting the 'why' behind metrics and announce Hark in the Klaviyo App Marketplace, where emails can collect video customer feedback about what customers expected, what they got, and whether the two matched.
For brands planning a campaign
For DTC, ecommerce, and voice-of-customer software brands, this shows a creator who can connect sales experience to retention and customer-feedback topics; the two posts are product-oriented, so campaign claims should be treated as promotional evidence of subject focus.
Ashlee Starr says plainly that they are a content creator who does not want to leave their job, praises their team at Hark, and describes Hark as letting them live in both worlds.
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The post combines their personal position, employer support, and a product announcement about video customer feedback.
For brands planning a campaign
Brands assessing a creator partnership can consider this working-professional angle: sales content comes from someone still doing the job. Ask which posts are employer- or product-linked.
The posts use practical teaching formats: numbered test steps, swipe copy for AE-to-AE outreach, a task-audit walkthrough, red-choice and green-choice lists, deal-risk scripts, and direct closing questions.
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Several ask readers to share their own process or scariest question.
For brands planning a campaign
A collaboration can reuse these formats for repeatable sales plays: the creator gives the script, then asks the audience to compare notes.
Ashlee Starr's posts name a narrow set of businesses.
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The clearest connection is their employer: they write that Hark is their day-to-day job and post about its product launches.
Several other posts offer detailed product walkthroughs for lemlist, Fireflies.ai and Gamma, with links and calls to action.
Ask whether any involved sponsorship, affiliate or partner terms.
Klaviyo appears as the marketplace where Hark's app is listed.
Hark
Employment
Ashlee Starr states in their posts that Hark is their job: “I'm fully focused on Hark day to day,” they write, describing “my team” and the “cool and big names” the company works with, including OUAI, Nutrafol and Grüns.
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They post about Hark's product, which collects video answers from customers about what they expected, what they got and whether the two matched, and announced its launch in the Klaviyo App Marketplace. In a separate post, they say their demo calendar was filling with interest from ecommerce growth marketers and offer to hold spots.
One describes asking Claude to build a prospect list through a lemlist MCP connection.
It returned 8,600 US founders running small tech startups as an interactive lemlist table, cutting list-building from three hours to three minutes.
The other shares an AE-to-AE referral sequence they say is running at a 75% reply rate, credits lemlist with finding the contacts and running the sequence, and invites readers to comment for the full campaign setup.
Both posts promote and link to the product.
Ask whether either post involved sponsorship, affiliate or partnership terms.
A post about moving from transactional sales to multi-stakeholder enterprise deals pitches Fireflies for Sales as a bot that plugs into a company's knowledge base, surfaces answers during a call and drafts CRM updates for approval.
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The post links to the product and argues that promising a follow-up email after a question stumps you loses the deal to a competitor who answered live. Ask whether the post involved sponsorship or partnership terms.
One describes connecting Gamma to Slack, HubSpot and their note taker, then asking the agent for a deck their champion can present, with each statistic citing its source on the slide and an export to PowerPoint so buyers can edit without breaking formatting.
The other says they tried Gamma for the first time on recommendations from Lindsay Rios and Brianna Chapman and found it “amazing.”
Both are product walkthroughs.
Ask whether either involved sponsorship or partnership terms.
Ashlee S. writes practical sales playbooks from inside the job: how to test outbound messaging without fooling yourself, where AI genuinely helps a full-cycle account executive, and how enterprise deals are won or lost late.
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Start with the message-testing post for a repeatable process, the AI task-audit post for a way to decide what to hand off, and the deal-risk questions and champion-enablement posts if you sell to large accounts. Most posts offer a numbered process or copyable script, draw a clear line between what works and what does not, and close with a question for other sellers.
Writes prescriptive B2B outbound playbooks from the operator’s side, breaking campaigns into numbered layers with named tools, sample reply-rate numbers and a read-it-bottom-up diagnosis for campaigns that stall.
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Ashlee S. takes the same teach-through-a-process approach to message testing and the AI task audit.
Alex Vacca treats go-to-market as a five-part system for client companies, backed by agency case-study figures and a broad channel stack spanning AI search visibility, paid ads, content and RevOps. Ashlee S. writes from inside an in-house account executive role, focusing on one seller’s tests, prospecting and deal conversations.
Both apply AI to the middle and late stages of the sales cycle rather than top-of-funnel copy, walk through named tool stacks in the first person, and argue that AI can flag a problem while a person still has to decide.
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Ashlee S. applies that approach to champion enablement and deal-risk questions; Omprakash applies it to stalled deals and buying-committee gaps.
Omprakash runs account-based marketing programs for enterprise SaaS clients and writes at the program and account level, with HubSpot-heavy reporting and a diagnostic for why a deal stopped moving. Ashlee S. focuses on one seller’s working day, covering outbound tests, prospect-list building and peer-to-peer outreach.
Both publish practical walkthroughs of sales AI inside a CRM workflow, going feature by feature and highlighting the human confirmation step before anything is written to a customer record.
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Chris Tveter reviews HubSpot and Claude features product by product and flags rollout details for the people who have to switch them on, such as renamed tabs and admin-only settings. Ashlee S. writes about applying similar tools to their own task list and deal strategy, so the product itself is less often the subject.
Ashlee Starr’s posts focus on enterprise sales, prospecting, and AI tools in sales workflows, making them relevant to a B2B sales-software thought leadership campaign.
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Before booking, ask for recent LinkedIn audience analytics and, if the campaign requires specific job roles or seniority, documented audience survey results or another source covering those professional attributes.
Ashlee Starr's posts show one repeatable partnership pattern: a product appears as a tool for real sales tasks, not as a standalone ad.
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Across the posts, lemlist, Fireflies.ai, and Gamma each feature because Starr used them to complete a concrete task—building a prospect list, answering a buyer's question live on a call, or producing a deck a champion could present without the seller in the room.
The posts are written in first person, name the tool, describe what it returned, and keep the sales lesson usable whether or not the reader adopts the product.
No post states sponsorship terms, a rate, or a formal partnership agreement, so these are examples of a working format, not confirmed paid placements.
Two posts about Hark describe Starr's own employer, not an open sponsorship.
Before pursuing a placement, ask directly about partnership scope, access, deliverables, and terms; the posts publish no media kit, sponsor policy, or partnership contact route.
First-person software walkthrough
Buyer requirement
Give full product access plus one concrete sales task the tool solves—for example, building a qualified prospect list, surfacing an answer mid-call, or generating a deck.
Ashlee Starr frames the post as a numbered, step-by-step account of doing real sales work with the tool, names the product, and describes the output.
Posts usually center on a multi-tool setup rather than a single feature, so the product must fit an existing chain of sales tasks.
Sales-advice post with a linked product recommendation
Buyer requirement
Expect the product to appear in a passage within a broader sales lesson, described in plain language with a link.
The Fireflies.ai post on multi-stakeholder deals and the Gamma post on champion enablement follow this shape: the advice stands on its own, and the product illustrates the fix.
Unlike the walkthrough format, the product does not carry the post, so the lesson must be strong enough to hold a reader who ignores the link.
Strong fit, provided the brand accepts an in-workflow product role rather than a standalone product advertisement.
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Brief
A B2B sales-software company wants a thought leadership collaboration on enterprise sales strategy and AI tools.
Reasons
Starr writes directly about enterprise deal mechanics: finding the people who can say yes inside a large account, moving from SMB transactional selling to multi-stakeholder deals, arming a champion to sell internally, and asking uncomfortable deal-risk questions before forecast review. The posts offer exact scripts and frameworks, not general opinions.
AI tools appear in real sales workflows: auditing every task in a full-cycle AE role, building a prospect list inside a chat interface, surfacing an answer during a live call, and producing a champion-ready deck. That gives a sales-software brand concrete workflows to sponsor instead of a vague AI-in-sales theme.
The teaching format is practical and repeatable. Ashlee Starr publishes numbered test steps, swipe copy, a red-choice/green-choice framework, deal-risk scripts, and closing questions for readers. A thought leadership collaboration can reuse those formats instead of asking for a new one.
The observed partnership format matches the brief: a product enters a first-person account of completing a sales task or appears in a sales lesson as the tool that solves the problem. The product need not carry the whole post, so the collaboration can still read as sales guidance.
The posts provide no audience composition data or sponsor terms, and they do not independently verify the reported 75 percent reply rate or tool outcomes. Request recent audience analytics and agree on deliverables directly.
When to choose a specialist
Poor fit based on the posts.
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Brief
A consumer DTC skincare or household-goods brand wants short-form video UGC with measurable conversion and consumer audience reach.
Reasons
Ashlee Starr's DTC and ecommerce posts are tied to Hark, described in the posts as their employer. They discuss DTC growth marketers, falling LTV, product-detail-page conversion, ROAS, and customer feedback. That shows familiarity with DTC marketing software, not a track record of consumer-brand product campaigns.
The posts are LinkedIn text posts, with no video or audio transcripts, short-form video examples, or production credits showing that Starr creates or appears in video UGC. A consumer brand would be buying a format the posts do not demonstrate.
The posts provide no measured audience composition or engagement data: no follower demographics, role or seniority breakdown, or campaign performance. The account metadata gives a follower count, but not evidence of consumer reach. Request recent audience analytics before booking.
The commercial formats are software walkthroughs and sales-advice posts with linked products, not retail product demos, unboxings, or lifestyle UGC. A consumer brand would need a new creative format, audience proof, and performance proof before booking.
Some of the strongest product mentions are employer-linked Hark posts. They show subject focus, but not an open consumer sponsorship offer or a published rate card; ask about sponsorship availability and rates.
A worked campaign example
Brief
B2B sales-software company focused on enterprise sales strategy and AI tools.
Campaign angle
The Enterprise Deal-Risk Audit: a three-post thought leadership series plus a reusable worksheet showing how an enterprise seller tests deal risk earlier and uses AI in a real deal workflow.
Measurement
Measure replies and demo-hold conversations from the worksheet call to action, not impressions alone. Starr's reported 75 percent reply-rate figure in the AE-to-AE post is not independently verified; do not reuse it as a campaign benchmark without the brand's own test.
Open gaps
Before release, confirm the brand can provide product access and a specific workflow problem, request recent audience analytics, agree on disclosure and deliverables in writing, and test the worksheet with a sales team that has not seen the posts.
Post1
The five questions that surface enterprise deal risk before forecast review. Ashlee Starr shares exact questions about CFO sign-off, a cheaper competing vendor, ghosting, and access to the decision-maker, then explains how each answer changes the deal plan.
Post2
What I ask AI to do before a multi-stakeholder call. Ashlee Starr audits tasks on a multi-stakeholder deal, separates automatable admin from human judgment, and shows where an in-call knowledge assistant or CRM-drafting tool fits.
Post3
Build the deck your champion presents when you are not in the room. Starr walks through a champion-ready deck with sourced stats and an editable export, explaining why the artifact is for the champion, not the seller.
Product role
The sponsor's product should appear inside a workflow Starr actually uses: surfacing answers during a call, drafting CRM updates, building a champion deck, or running sequence and contact lookup. The post must still teach the sales lesson without the link.
Why it works
It draws on Ashlee Starr's existing habits: concrete scripts, numbered processes, first-person tool walkthroughs, enterprise deal framing, and a direct prompt to the reader. It gives the brand a thought leadership role rather than an ad slot, and the worksheet gives readers something to use after the post.
Worksheet
A deal-risk audit worksheet with one column for the question asked, one for the answer, and one for the next action or disqualification signal. Include a completed example from a multi-stakeholder deal and a blank version a sales team can copy.
Choose Alex Vacca when the campaign's real job is to argue that outbound is one layer of a modern go-to-market system, not a list or a script.
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The two posts lay out five parts — AI search visibility, signal-based outbound, founder and employee content, paid ads, and RevOps — and explain the order the layers get built in and what each produces.
If the campaign calls for a full-funnel pipeline narrative with named tooling and published client outcomes, this is closer to it than sales coaching at the level of a single rep.
The detail is operational: an 830-contact campaign reported at 5.5 percent reply and 0.5 percent bounce, waterfall enrichment taking list coverage from roughly 40 percent to 85–90 percent, and a reported $7.83M in qualified pipeline and $1.52M closed-won for one client.
They make the argument readable for a general buyer by naming the five layers and the failure each one causes, so a sponsor can attach a product to one specific layer instead of the whole funnel.
You trade deal-level specificity for system-level breadth.
Ashlee Starr shows the exact questions a seller asks a CFO or a champion; Alex Vacca shows the conditions that decide whether the first email gets a reply at all.
The writing is agency-framed and tool-stack dense, with vendors like Clay, Instantly.ai, Apify, and Prospeo.io named in the flow, so a sponsor's product sits alongside several others rather than inside one rep's workflow.
The calls to action are agency-shaped: building this stack is the service being sold.
That puts a software brand in a different commercial relationship than a first-person tool walkthrough would.
Choose Omprakash Karuppanan when the campaign centers on the middle of the funnel: why enterprise deals stall rather than why they never started.
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The two posts are a practitioner's account of running ABM programs on HubSpot — a ranked list of five AI skills (deal reporting, audience targeting, account research, field marketing, stall diagnosis) and an argument that AI made outbound worse by making the top of the funnel uniform while the real edge sits in deals that quietly stop moving.
The stall-diagnosis and buying-committee material maps directly to a deal-health or pipeline-inspection product, and the posts carry a concrete audit: 31 stalled accounts reviewed, 17 with an unacted trigger event inside 60 days.
They state plainly which decisions AI should not make, giving a sponsor a defensible split between automated detection and human judgment.
They list the data sources already sitting in a sales stack — call recordings, webinar Q&A, email threads, demo questions, internal pipeline reviews — so a brand can point to the exact input its product would read.
You give up Starr's seller scripts and champion-enablement detail.
Omprakash Karuppanan writes about the programs and reports around deals, at program-owner altitude, rather than the call itself.
The posts close with a direct service pitch — running ABM execution programs for enterprise SaaS and IT services companies, with an invitation to DM — which can crowd a software sponsor's call to action and raises a fair question about whose services the campaign is selling.
Starr keeps the product inside a rep's own workflow; this material is more likely to need the sponsor placed alongside a consultancy-style offer, and the sponsored product has to fit inside a skills list rather than a standalone teaching post.
Choose Chris Tveter when the product lives in the CRM or sits directly next to it, and the campaign needs adoption content rather than strategy content.
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The two posts do one thing well: walk through CRM-embedded AI feature by feature.
One takes the eight pre-built Claude skills in the HubSpot Sales Plugin — call prep, daily brief, log call, follow up, pipeline pulse, contact lookup, import contacts, general HubSpot — and gives a concrete use and a reason for each, including which two a rep should start with.
The other covers HubSpot Deal Progression in public beta, including the admin flags that have to be switched on separately, the conversation-recording requirement, and the renamed Overview tab — exactly the kind of detail that determines whether a team can turn on a feature.
For a sponsor selling inside this ecosystem, the posts give a product-led structure: feature, what it replaces, what to watch for.
The scope is narrow.
This is platform-specific release and adoption writing inside the HubSpot ecosystem, so a sponsor outside that stack has little to attach to.
A brand seeking enterprise deal strategy or multi-stakeholder deal coaching gets less here than from Ashlee Starr or Omprakash Karuppanan.
The posts also include explicit positive recommendations of the products being discussed, so sponsors should ask how those recommendations will be handled as a disclosure matter.
The near-release-note format leaves less room for a sales lesson to stand on its own once the product link is removed, making collaboration dependent on readers already caring about that platform's roadmap.
Ashlee Starr's 11 LinkedIn posts address outbound message testing, a peer-to-peer referral campaign, enterprise buying groups, champion enablement, AI task audits and prospecting, in-call answers and CRM follow-through, buyer trust, and product-focused posts tied to Hark, the employer named in the posts.
No post here carries a publication date, so these ideas are described as current positions rather than a dated progression.
Three comparable creators published six posts between 28 September and 8 October 2026.
The posts here do not provide audience composition or engagement data, sponsor or partnership terms, a media kit, or video or audio transcripts.
Performance figures reported in the posts, including the 75 percent reply rate claimed for the account-executive-to-account-executive campaign, have not been independently verified.
Employer and client names come from the posts themselves.
As of 9 October 2026, account information is also available.
