# Ashlee S.

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.

- Profile: https://b2binfluencer.marketing/creators/ashlee-s/
- LinkedIn: https://www.linkedin.com/in/ashleestarr
- Topics: artificial-intelligence, ecommerce, sales, ai-in-sales, enterprise-sales, outbound-sales
- Based on 17 dated posts, latest 2026-10-08

## Summary

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.
- Check first: Confirm recent LinkedIn audience analytics and documented role or seniority data if needed.

### 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.

## Main ideas

- **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. 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.
- **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. 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.
- **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. 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.
- **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. 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.
- **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. 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 problem is bad reps, not sales**: Starting with the statistic that 67% of B2B buyers prefer a rep-free experience, Ashlee Starr does not argue that buyers are wrong. 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.
- **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. 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.
- **A content creator can keep the day job on purpose**: 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. 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.

## Work to read

- [A four-step framework for testing outbound messaging](https://www.linkedin.com/posts/ashleestarr_are-you-testing-messaging-or-just-gaslighting-activity-7434949533056516096-qDwO)
- [Finding the people in an enterprise account who can say yes](https://www.linkedin.com/posts/ashleestarr_at-big-companies-there-are-lots-of-people-activity-7430729218063499264-QInq)
- [Champion enablement: build the deck the champion will present](https://www.linkedin.com/posts/ashleestarr_let-your-champion-close-the-deal-for-you-activity-7513260981418995712-Cv1S)
- [When a live question has no answer: AI in the middle of a call](https://www.linkedin.com/posts/ashleestarr_moving-from-transactional-sales-to-multi-stakeholder-activity-7488592420083781632-PX6G)
- [Auditing your own job to decide what AI should take over](https://www.linkedin.com/posts/ashleestarr_full-cycle-ae-ai-help-activity-7440007862132658177-TjOC)
- [The AE-to-AE campaign and the exact message behind it](https://www.linkedin.com/posts/ashleestarr_steal-my-ae-to-ae-campaign-thats-sitting-activity-7446899553590583296-jSME)
- [Red and green choices: why buyers avoid sales reps](https://www.linkedin.com/posts/ashleestarr_67-of-b2b-buyers-prefer-a-rep-free-experience-activity-7457764860018442240-bVgu)

## Brand relationships

- **Hark** (employment)
- **lemlist** (disclosed_partnership)
- **Fireflies.ai** (disclosed_partnership)
- **Gamma** (disclosed_partnership)
- **Klaviyo** (mention)

## Partnerships

Ashlee Starr's posts show one repeatable partnership pattern: a product appears as a tool for real sales tasks, not as a standalone ad. 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.

## Alternatives

- Alex Vacca: 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. 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.
- Omprakash Karuppanan: Choose Omprakash Karuppanan when the campaign centers on the middle of the funnel: why enterprise deals stall rather than why they never started. 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.
- Chris Tveter: 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. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/