Heather Schuck writes about sales process, pipeline quality, and revenue decisions. Their posts help revenue leaders spot where deals stall, assess whether pipeline numbers are reliable, and strengthen sales processes before adding AI tools.
Heather Schuck is most useful for explaining how to inspect pipeline quality and sales process before adding AI tools.
Best for
A revenue-intelligence or sales-process vendor targeting GTM and revenue leaders with diagnostic content.
Not for
A consumer or lifestyle brand wanting broad awareness in short-form video with guaranteed reach.
Key takeaways
30% stale deals can turn apparent 4x pipeline coverage into 2.8x real coverage.
Stage-by-stage deal velocity can show whether friction points to qualification, pricing, or stakeholder alignment.
A 20% win rate usually points to product-market fit, not sales execution.
Check first: Confirm partnership route, audience data, and video or long-form delivery before planning.
Main ideas
Recalculate pipeline coverage after removing stale, blocked, and repriced deals · Revenue leaders need stage velocity as an early-warning system, not backward-looking finance metrics · Low win rates usually point to product-market fit, not sales execution
Heather Schuck writes about sales process, pipeline quality, and revenue decisions. Their LinkedIn posts help revenue leaders look beyond headline metrics to understand where deals stall, whether pipeline is reliable, and what to fix before adopting AI tools.
Heather Schuck treats sales metrics as clues to investigate, not explanations on their own.
In a post about pipeline coverage, they show how 30% stale deals can turn apparent 4x coverage into 2.8x real coverage (pipeline-quality quiz).
They argue that stage-by-stage deal velocity can reveal whether friction points to qualification, pricing, or stakeholder alignment (revenue signals and sales-stage velocity).
Their analysis of a 20% win rate shifts attention from rep performance to whether the product and positioning fit the buyer’s needs (win rates as a product-market-fit signal).
The same diagnostic approach shapes their AI advice: standardize qualification, clean CRM data, and clarify sales stages before adding tools that may amplify existing process problems (AI and sales-process readiness).
Readers making revenue decisions will find practical questions for investigating what lies behind the numbers.
Heather Schuck's posts return to one problem: revenue teams steer by numbers that do not describe the deals they are supposed to describe.
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The recurring arguments are that pipeline coverage must be recalculated for stale, blocked, or repriced deals; revenue leaders need leading stage signals rather than lagging finance reporting; weak win rates usually point to product-market fit rather than rep execution; and AI should come after sales-process hygiene. Across the posts, the method is diagnostic and numerical: run the calculation on your own pipeline, establish a baseline, and find the specific stage where the pattern breaks.
Recalculate pipeline coverage after removing stale, blocked, and repriced deals
Heather Schuck argues that a 4x coverage ratio can look healthy while counting deals that have not moved in 60 or more days, cannot close on time because of supply problems, or need price updates.
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Subtract those deals before treating the ratio as a forecast.
Revenue leaders get into trouble when they make decisions based on numbers that look healthy but are not.
The fix is a working diagnostic, not a theory: run the calculation on your own pipeline.
A worked example shows how 4x coverage with 30% stale deals becomes closer to 2.8x real coverage. The post outlines a six-part playbook for revenue leaders under budget pressure, with a calculator in each section covering activity versus efficiency, pipeline reality, where deals are dying, and which investments to cut or protect.
Revenue leaders need stage velocity as an early-warning system, not backward-looking finance metrics
Heather Schuck contrasts finance teams, which can report, reconcile, and plan from lagging indicators, with GTM leaders, who cannot wait for a missed quarter to reveal a problem.
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Revenue leaders need real-time signals: qualitative context on why deals stall, calibrated to a specific company, industry, and sales motion. Establish baseline velocity by stage, then treat departures from that baseline as diagnostics.
Deals stalling at discovery may point to a qualification issue; slowing at proposal may point to pricing or stakeholder alignment. Negotiations that last longer than usual may mean a competing internal priority has taken the decision-maker's attention or budget.
Low win rates usually point to product-market fit, not sales execution
Heather Schuck argues that a 20% win rate feels like a sales execution problem but almost never is.
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Aggregate loss reasons by deal size and segment to see where the product stops solving the buyer's actual problem.
That gap appears at a consistent stage.
The stage where deals die is a product-market fit signal dressed in a sales costume, and CRM loss data already contains it.
The failing stage varies: sometimes the demo, sometimes procurement, sometimes the moment a second stakeholder enters the room and the positioning falls apart. The post asks which stage deals are dying at right now and points to a full read.
Heather Schuck argues that AI amplifies whatever system it touches: a solid process gets faster, while a broken one breaks faster and at greater scale.
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AI will not create process, fix data integrity, or align a team. Prioritise foundational hygiene before adding another tool.
Signs the team is not ready include inconsistent or incomplete CRM data, reps qualifying deals differently, pipeline stages that do not reflect how deals move, a sales process that cannot be explained in under two minutes, and a forecast the team does not trust.
The fixes are to standardise qualification criteria (MEDDPICC is cited), clean CRM data, define exit criteria for every pipeline stage, align cross-functional definitions of 'qualified', and build one source of truth for pipeline reviews.
The post closes with the question: 'Am I buying a solution or just automating my dysfunction?'
A newcomer should start here to see the question the creator keeps returning to: do the numbers a revenue team uses actually describe the deals they are supposed to describe?
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The post answers in a few lines and offers a repeatable way to check.
The post opens with a familiar comfort—4x pipeline coverage—then subtracts the parts that are not really in play: deals that have not moved in 60 or more days, deals that will not close in time because of supply problems, and deals that will need repricing to match the market.
The arithmetic is blunt: 4x coverage with 30% stale deals is closer to 2.8x.
The point is that healthy-looking pipeline numbers produce bad decisions when nobody adjusts for staleness, supply, or pricing changes.
The post then points to a six-part playbook for revenue leaders under budget pressure, covering how to distinguish an activity problem from an efficiency problem, test whether pipeline is real, identify where deals are dying, and decide which investments to cut or protect.
Each part includes a calculator readers run on their own numbers.
For brands planning a campaign
For a brief about data-driven revenue management, this is the clearest evidence that the creator teaches quantitative diagnostics rather than general sales encouragement. It also signals a resource-led format—playbooks with calculators—that a partner could support or build alongside.
This explains what the creator means by a sales-process signal, as opposed to a metric.
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Read it to make the phrase "stage velocity" concrete.
The post separates two timelines that usually get blurred.
Finance can report, reconcile, and plan from lagging indicators because that is much of the job; GTM leaders cannot, because by the time a lagging indicator shows a problem, the quarter—or two—is already gone.
Revenue leaders need real-time signals: qualitative context on why deals are stalling, not just that they stalled.
Those signals also need to be calibrated to a specific company, industry, and sales motion.
The recommended starting point is sales-stage velocity: understanding the timing, nature, and baseline of how deals move through a particular cycle.
The post gives three worked examples.
Deals stalling at discovery may point to a qualification issue; slowing at proposal may point to pricing or stakeholder alignment; sitting longer than usual in negotiation may mean a competing internal priority has taken the decision-maker's attention or budget.
For brands planning a campaign
A partner selling pipeline analytics, deal-inspection software, or revenue-operations services can use the stage-by-stage examples directly in a campaign. Each names a stage and a plausible cause rather than simply asserting that visibility matters.
This shows how the creator turns a sales diagnosis into a product question—the clearest example of the analytical move that sets their writing apart from standard sales advice.
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The post takes a low win rate—20%—and argues that the obvious reading, weak sales execution, is usually wrong.
Aggregating loss reasons by deal size and segment can reveal where the product stops solving the buyer's actual problem.
That gap appears at a specific stage, which varies: sometimes the demo, sometimes procurement, sometimes the moment a second stakeholder enters the room and the positioning falls apart.
The post's conclusion is that the stage where deals die is a product-market fit signal dressed as a sales problem, and that CRM loss data already contains the answer, even though most organisations never look.
For brands planning a campaign
This is the strongest evidence for a brief centred on sales-process maturity: it connects loss data to positioning and product fit, making it well suited to campaigns aimed at founders, product marketers, and revenue leaders—not just sales managers.
This is the most current-facing of the four posts and the fastest way to see the creator's stance on a question many buyers are asking: what has to be true before AI tools are worth buying?
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The post describes a pattern the creator says they have seen repeatedly: a revenue leader buys an AI tool to solve pipeline problems and, six months later, has more noise rather than less.
AI amplifies whatever system it touches: a solid process gets faster, while a broken one breaks faster and at greater scale.
The post lists conditions that mean a team is not ready: inconsistent or incomplete CRM data, reps qualifying deals differently, pipeline stages that do not reflect how deals actually move, a sales process that cannot be explained in under two minutes, and a team that does not trust the forecast.
First, standardise qualification criteria, clean the CRM, define exit criteria for every pipeline stage, align cross-functional definitions of "qualified", and build one source of truth for pipeline reviews.
For brands planning a campaign
For a brief proposing expertise in sales-process maturity, this post takes a contrarian but practical position on a crowded topic, offering campaign angles with a credible argument against tool-first adoption.
Schuck argues that headline coverage ratios mislead because they count deals that have gone stale, face supply problems or need repricing before they close.
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The worked example: 4x coverage with 30% stale deals is really 2.8x. The post says that gap gets revenue leaders in trouble — not bad strategy, but decisions based on numbers that look healthy until they aren't.
Each diagnostic question comes with a calculation readers can run on their own numbers; the worked example subtracts stale deals from coverage.
For brands planning a campaign
A campaign positioning a product as a way for revenue teams to stress-test pipeline data — through stale-deal detection, recalculated coverage and forecast hygiene — builds on an argument Schuck already makes to this audience. The post offers a working playbook with calculators, not theory, giving a brand a practical diagnostic to support rather than a generic “better insights” claim.
Finance can report on what already happened; revenue leaders cannot.
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Schuck argues they should start with baseline velocity by stage: how deals actually move for a specific product, price and team. Departures from that baseline point to causes, not symptoms: deals stalling at discovery suggest a qualification issue; slowing at proposal suggests pricing or stakeholder alignment; longer negotiations may signal that a competing internal priority has claimed the buyer's attention or budget.
Set a baseline for each stage using the company's own cycle, then treat departures from it as diagnostic signals rather than raw activity metrics.
For brands planning a campaign
For sales-intelligence, CRM or revenue-operations vendors, this offers a ready-made angle: track stage velocity, then connect the signal to a specific coaching or process fix. The stage-by-stage analysis lets a brand demonstrate a process tied to where a deal actually broke down, rather than claim to surface more data.
Win rates and loss reasons as product-market fit signals
Schuck argues that a 20% win rate looks like a sales execution problem but almost never is.
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Aggregating loss reasons by deal size and segment reveals where the product stops solving the buyer's actual problem.
That gap shows up at a consistent stage — sometimes demo, sometimes procurement, sometimes when a second stakeholder enters and the positioning falls apart.
The stage where deals die is a product-market fit signal dressed in a sales costume, and the CRM loss data already contains it.
Aggregate loss reasons by deal size and segment, then identify the stage where the pattern concentrates.
Schuck notes that the failing stage varies by company — demo, procurement or a multi-stakeholder moment — so the conclusion depends on the specific pattern, not a fixed stage.
For brands planning a campaign
Useful for win-loss analysis, product marketing or positioning research offerings. A brand can sponsor the case for reading loss data as product feedback and offer the aggregation step most teams never perform.
Schuck frames AI adoption as a readiness test: AI amplifies whatever system it touches, so a solid process accelerates while a broken one breaks faster and at scale.
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The warning signs are inconsistent CRM data, reps qualifying deals differently, pipeline stages that don't reflect how deals move, a process that can't be explained in under two minutes, and a forecast the team doesn't trust. The fixes are standardizing qualification criteria (MEDDPICC is cited), cleaning CRM data, defining exit criteria for every stage, agreeing cross-functionally on what “qualified” means, and building one source of truth for pipeline reviews.
A gut-check list of failure signals, each paired with a specific foundational fix.
For brands planning a campaign
A strong angle for enablement, CRM and sales-consulting brands: make process hygiene a prerequisite for AI adoption. The checklist gives a campaign a concrete diagnostic asset, while the closing contrast — buying a solution versus automating dysfunction — offers campaign language.
None of the four LinkedIn posts names a company Heather Schuck sponsors, works for, or partners with.
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Two point to commercial assets: a “Closed Won Deal Analysis Tool” the post calls “our” tool, and a six-part “Where to Cut Without Killing Growth” playbook tied to GTM decision-making.
Neither names the business behind the asset; ask Schuck to clarify any brand relationship.
MEDDPICC is referenced as a sales qualification framework, not a vendor.
Heather Schuck makes diagnostic arguments about revenue numbers that look healthier than the deals behind them.
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Start with the pipeline coverage post, which shows how subtracting stale, blocked, or repriced deals turns 4x coverage into roughly 2.8x.
The stage-velocity post offers an early-warning method; the win-rate post reads loss reasons as product-market fit signals; and the AI-readiness post puts sales-process hygiene before new tooling.
The through-line is numerical and stage-specific: run the calculation on your own pipeline, set a baseline, and find the stage where the pattern breaks.
Both focus on the middle of the funnel, where deals are won or lost, rather than top-of-funnel personalization.
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They use CRM and conversation data to diagnose stalled deals.
Karuppanan writes about ABM execution and AI skills for enterprise SaaS and IT services, describing Claude skills and account programs. Schuck takes a revenue-leadership perspective, using coverage math and stage baselines rather than tool-building sequences.
Both argue that CRM pipeline labels can mask wishful thinking, and use stage or milestone discipline to diagnose the problem.
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Hines builds a narrative client case around a mid-market manufacturing CEO and a single milestone question that cuts a pipeline from $14M to $4M. Schuck takes a more analytical approach, showing how to recalculate coverage and read stage velocity by segment.
Both write for revenue operations and sales leadership audiences about the operational friction behind a revenue plan, and distinguish a miss from a surprise.
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Aleman focuses on hiring, capacity planning, board-level risk signals, ramp math, and rep territory effects. Schuck focuses on pipeline coverage, stage velocity, and win-rate and loss diagnostics.
The content may suit a campaign aimed at GTM or sales leaders, but the posts don’t identify who follows or sees them. Before booking, request recent audience composition for the target market and documented survey results or another source measuring professional roles or seniority.
The four LinkedIn posts by Heather Schuck contain no sponsorship announcement, brand-partnership disclosure, media kit, rate card, or inquiry route.
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They feature first-party content a brand could attach to: a six-part revenue playbook, "Where to Cut Without Killing Growth," distributed through a lead-capture link, and a "Closed Won Deal Analysis Tool" that readers are directed to run on their own numbers.
Both are their own offers to revenue leaders, not third-party sponsorship formats, and neither includes terms a buyer could plan against.
No post labels another company as a sponsor or partner or names a contact channel for commercial proposals.
A buyer should ask whether Schuck takes on partnerships, how to contact them, and, if so, for terms covering placement, deliverables, approval steps, and lead time.
Strong fit on subject matter and demonstrated approach.
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Brief
A revenue-intelligence or sales-process-maturity vendor — in pipeline analytics, CRM hygiene, forecast inspection, or revenue-operations services — wants a creator-led campaign for GTM and revenue leaders arguing that most pipeline and forecast numbers mislead.
Reasons
The campaign’s central claim is one Heather Schuck already makes and teaches with arithmetic: 4x pipeline coverage with 30% stale deals is closer to 2.8x, and the gap between the headline number and the real one causes missed quarters. A vendor can attach to an argument the audience is already being given, rather than introduce it.
They work in diagnostics rather than encouragement. The stage-velocity argument tells readers to set a baseline for how deals move through their own cycle, then read departures from it as causes — qualification at discovery, pricing or stakeholder alignment at proposal, a competing internal priority during negotiation. That gives a campaign a sequence to demonstrate instead of a “more visibility” claim.
Loss analysis is framed as a product question, not a rep problem: aggregate by deal size and segment, and a low win rate points to the stage where the product stops solving the buyer’s problem. Vendors selling win-loss research, positioning work, or product marketing can use that framing directly.
The creator already publishes in a resource-led format. “Where to Cut Without Killing Growth” is a six-part playbook whose sections each come with a calculator readers run on their own numbers, and the posts point readers to a Closed Won Deal Analysis Tool. A partner can support or build alongside that format rather than invent one.
The AI-readiness post gives the campaign a second entry point, with a clear gate: standardise qualification criteria, clean CRM data, define exit criteria for every stage, agree cross-functionally on what “qualified” means, and build one source of truth for pipeline reviews — before buying another tool.
Limits
No sponsorship announcement, media kit, rate card, or commercial contact appears in Heather Schuck’s posts. Ask whether a partnership route exists, and confirm placement, deliverables, approval steps, and lead time before planning.
The posts establish subject matter and approach, but not audience composition. Ask for audience data before making a buying decision based on who follows or sees the content.
The posts are all written LinkedIn posts. Ask whether Heather Schuck can deliver video, audio, or long-form articles before planning a campaign that depends on those formats.
When to choose a specialist
Poor fit with the available posts, and not plannable as described.
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Brief
A consumer or lifestyle brand — or a fintech app targeting general professionals — wants a broad awareness push in short-form video, with a guaranteed reach number and a fixed set of sponsored placements.
Reasons
All four posts address revenue and sales leaders, covering pipeline quality, deal velocity, loss reasons, and AI readiness. A campaign whose value depends on reaching a general audience calls for a different creator.
The reviewed set contains no video, audio, or visual work. Ask whether Heather Schuck can produce short-form video before specifying it as a deliverable.
The posts contain no audience-composition data. Ask for reach evidence before committing to a guaranteed-reach number.
The posts contain no sponsorship disclosure, media kit, rate card, or inquiry channel. Ask whether a commercial route exists before setting placements, deliverables, or lead times.
The content is diagnostic and specific. The posts offer no example of a product demo or walkthrough of a sponsor’s interface; the closest format is a self-run calculator or worksheet linked from a post.
Limits
Reframe the brief around revenue-leadership diagnostics and a written or worksheet-based deliverable, and the same creator becomes plausible. The poor-fit judgment concerns the brief as written, not the creator’s reach or standing.
A worked campaign example
Angle
“Pipeline reality check.” Lead with the arithmetic the creator already uses, then tackle the step most teams skip: subtracting deals that are not really in play and rereading the pipeline by stage.
Before committing
Ask whether a commercial contact or partnership route exists; none appears in Heather Schuck’s posts.
Ask for audience composition in the target market; Heather Schuck’s posts establish subject matter, not audience makeup.
Agree on deliverable formats in writing. The posts are written and contain no video or audio work.
Brief
A pipeline-analytics vendor wants a campaign showing revenue leaders why their coverage numbers overstate reality, with a working asset the audience can run on their own data.
Next step
If the commercial route checks out, start with the coverage reality-check post plus the stage-velocity worksheet. Together, they reuse the creator’s strongest, most concrete published arguments and give the sponsor a measurable interaction point.
Sequence
Opening post: restate the diagnostic in the creator’s own terms — 4x coverage, a share of deals that have not moved in 60 or more days, deals blocked by supply, deals that will need repricing — and show the recalculated number. Close with the sponsor’s calculator or worksheet as the tool for running the subtraction on the reader’s own pipeline.
Second piece: a stage-velocity baseline worksheet. Readers record how deals normally move through their cycle, given their product, price and team, then mark where the current pipeline departs from that baseline. The sponsor’s role is instrumentation and routing: turning a stage-level signal into a specific coaching or process action, not a generic dashboard.
Third piece: a loss-reason aggregation template, grouped by deal size and segment, that surfaces the stage where deals consistently die. It supports the argument that the failing stage signals a product-market-fit problem rather than a rep problem.
Fourth piece: an AI-readiness checklist used as a gate. It pairs each issue—inconsistent CRM data, reps qualifying deals differently, stages that do not reflect how deals move, a process that cannot be explained in under two minutes, an untrusted forecast—with a foundational fix. This positions the sponsor’s product behind process hygiene, not ahead of it.
Why it holds together
Every element restates or extends a position the creator already publishes, making the campaign a continuation of their argument rather than a sponsor script.
The format matches the creator’s demonstrated habit of pairing a diagnostic question with a calculation readers perform—the structure of the posts here.
The campaign asks the audience to check its own numbers before buying anything, consistent with the creator’s view that tooling amplifies an existing process rather than creating one.
Choose Omprakash for a campaign about applying AI to account-based marketing and stalled deals.
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In his post on AI skills for ABM, he describes using account, meeting, website, and CRM data to prioritize accounts and diagnose deal stalls. His post on B2B outbound distinguishes between AI surfacing risks and buying-committee gaps and people deciding which accounts deserve action.
His examples focus on AI-enabled ABM and mid-funnel deal intelligence. Heather Schuck’s are a closer fit for broader revenue decisions, such as adjusting pipeline coverage for stale deals or using stage-level patterns to diagnose risk.
Choose Jennifer for a campaign about testing whether pipeline opportunities are genuinely qualified and giving managers concrete coaching criteria.
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In her pipeline qualification example, she describes comparing later-stage deals with milestones such as buyer engagement, documented decision criteria, measurable outcomes, and stakeholder coverage.
She reports that the exercise reduced a stated $14 million pipeline to $4 million.
Her sales execution post contrasts repeatable revenue processes with vague follow-up.
Jennifer’s examples emphasize qualification milestones and sales execution. Heather’s are a closer fit for campaigns about stage velocity, stale-deal adjustments, and using loss patterns to investigate revenue risk.
Choose Jose for a campaign about making growth plans and forecasts operationally credible.
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In his post on hiring plans and revenue capacity, he explains why quota-based headcount math can miss ramp time, pipeline capacity, manager coverage, and territory effects. His post on forecast surprises recommends reading deal behavior—such as repeated close-date pushes or missing meetings—instead of relying only on a rep’s risk label.
Jose’s examples center on RevOps planning, sales capacity, and board-level forecast visibility. Heather’s are a closer fit for campaigns about recalculating pipeline coverage, locating stage friction, or connecting loss patterns to product fit.
This page is built from four written LinkedIn posts by Heather Schuck: a pipeline-coverage reality check, an argument that revenue leaders cannot steer by lagging finance indicators, a win-rate and loss-reason analysis, and a sales-process readiness test for AI.
The similar-creator comparison draws on two LinkedIn posts each from three other creators writing on adjacent revenue topics.
No video, audio, or long-form article work was available, so nothing here describes those formats.
The posts carry no publication dates, so the page maps current themes and methods rather than a chronology or a career history.
No audience-composition data, sponsorship announcement, media kit, rate card, or commercial contact appears in the posts here, so the page describes what the posts argue and how they teach, not reach or a documented partnership offer.
Two accounts are on record for this creator, a LinkedIn profile and an Instagram account; only the LinkedIn posts were reviewed as text.
