Ladie Pabillar shares practical advice on ecommerce paid advertising, including how to assess ad performance, decide what to scale, and develop creative angles from competitor research.
Useful for step-by-step paid social diagnosis, scaling checks, and competitor ad angle research.
Best for
Fits DTC ecommerce brands spending six figures monthly on Meta and TikTok with media buying and creative teams.
Not for
Does not fit consumer brands seeking short-form video content, unboxing, routine, or testimonial for broad shopper reach.
Key takeaways
Check click rate first, then conversion rate, CPM, frequency, and view-through conversions before scaling.
Group competitor ads by angle, not format, then test three to five concepts.
Track new reach weekly; below 20% signals need to test new angles.
Check first: Confirm commercial terms, deliverables, pricing, usage rights, and audience breakdown before contacting.
Main ideas
Diagnose what changed in the account before scaling or cutting · Steal the thinking from competitors, never the ad · Aim at people who do not yet know they have a problem
Ladie Pabillar shares practical ecommerce advertising analysis, especially how to find the cause of weak or misleading ad results before changing budgets.
Their posts also explain how to source creative angles from competitors and how customer-awareness stages affect audience choice.
A separate post summarizes K2O’s creator-marketing launch as described by its CEO on a podcast.
Pabillar’s strongest material turns broad advertising advice into ordered checks.
In their scaling walkthrough, they contrast a 3.2x ROAS ad that was scaled with a 1.8x ad that was still finding new customers, then separate possible causes: creative, landing page or offer, audience costs and frequency, and attribution.
The post helps readers decide what to investigate before moving budget.
Their competitor-research playbook is similarly concrete: collect ads, group them by angle rather than format, choose concepts to test, and check whether they fit the brand’s own customers.
Its closing distinction—borrow the thinking, not the ad—sets a clear limit on imitation.
The material also shows a broader interest in how brands reach audiences: a post on customer-awareness stages argues for considering people earlier in the buying process, while the K2O recap distinguishes celebrity-led awareness from trust built through creators using a product.
The recap retells a podcast discussion; it does not describe a programme they ran.
Readers looking for practical paid-social diagnosis and creative research can follow them for these step-by-step explanations.
Ladie Pabillar writes about paid social advertising for ecommerce brands.
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Across the posts, one premise recurs: before changing an ad account, work out what actually changed.
ROAS alone can mislead, so the posts give a fixed sequence for checking whether the cause is the creative, landing page or offer, audience exhaustion, or how sales are counted.
The same instinct shapes the creative advice: find new test angles through a structured weekly review of competitors' ads, not guesswork.
On audience strategy, the recommendation is to reach people earlier in the buying process.
The posts are operational rather than analytical, with numbered steps, named tools, explicit thresholds and prompts readers can paste into a model.
Two positions are reported from other practitioners rather than presented as Pabillar's tested work; the sources are named.
Diagnose what changed in the account before scaling or cutting
Pabillar treats falling ROAS as a symptom to trace, not a verdict.
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The method starts with a daily tracking sheet that pulls spend, impressions, clicks, CPM, click-through rate, AOV, purchases, conversion value, ROAS and CPA, each colour-coded against target.
Checks then run in a set order to isolate possible causes: click rate and ROAS falling together point to the creative; click rate holding while conversion rate drops points to the landing page or offer; a CPM spike with a strong click rate points to paying more to reach the same audience too often; frequency per ad shows whether a modest-ROAS ad at low frequency is still finding new customers; and healthy platform metrics alongside falling store revenue point to attribution, which the post addresses by switching to 7-day click attribution.
Weekly new reach below 20% signals audience exhaustion; the recommended response is to test new angles rather than pull budget.
Pabillar credits the approach to Marin Istvanic.
The post opens with a contrast: an ad at 3.2x ROAS is scaled while one at 1.8x ROAS is killed, even though the lower-ROAS ad is the only one still finding new customers. That contrast drives every check that follows.
Pabillar's answer to where new creative angles come from is a seven-step weekly process.
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Build a list of 10 to 15 brands in the category and cut it to five; search an ad-spy tool by brand, keyword and country, sorting by shares rather than by longest-running; pull one product's active ads and extract their angles; break those ads down in Meta Ads Library, looking for patterns in formats, types of people used, hooks, messaging and editing styles; save 10 to 20 ads to a swipe file grouped by angle rather than format; test three to five concepts against your own product; then check that the angle fits your best customer.
The guardrail: a competitor's customer is not automatically yours, so validate borrowed angles before running them.
The closing rule: steal the thinking, never the ad.
The process rests on the premise that one product's ad library holds six to eight distinct angles.
It narrows the field quickly: 10 to 15 candidate brands become five, then 10 to 20 saved ads become three to five concepts worth testing.
Pabillar says they run this process every week, bringing in new angles and dropping weak ads.
Aim at people who do not yet know they have a problem
Pabillar reports on a call with marketer Lachezar Voynov and attributes the argument to Eugene Schwartz's Breakthrough Advertising.
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Most ecommerce brands, the post argues, spend against the wrong stage of customer awareness, concentrating on people who already know the product exists and are ready to buy—the smallest and most expensive audience to reach.
The alternative is to address earlier stages, first creating awareness of the problem and then offering a solution.
Pabillar presents the five-stage awareness model as the rationale.
The five stages run from unaware through problem aware, solution aware and product aware to most aware.
Stage four and five buyers are described as the audience most brands compete for.
The result offered is Voynov's brand growing from $100k to $700k in seven months.
Both the 90%-of-brands figure and that growth result are Pabillar's account of one conversation, not measured benchmarks.
Go deep with fewer creators instead of sampling thousands
Reporting on an episode of the Chew on This podcast featuring Jay Hunter of K2O, the post sets out a five-part creator-led launch sequence: put TikTok Shop ahead of Shopify or Amazon and let the website catch spillover traffic; replace mass-sampling thousands of creators with fewer, deeper relationships, on the premise that loyal creators post more and better; use a celebrity name to open the door while creators who genuinely use the product close the sale; design an achievable incentive; and protect top performers with priority and early payment.
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Pabillar's takeaway: most brands do not have a celebrity name to open doors, but any brand can go deep with creators and treat them well.
The incentive is concrete: creators post daily in a 60-day challenge to earn $1,000, with no follower-count or view minimums. More than 10,000 creators are in the programme; top performers still get priority and early payment.
Turn the advice into a routine that runs every week
Across the posts, cadence matters more than tips.
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The scaling diagnostic uses a daily tracking sheet and a weekly new-reach check; the competitor-research playbook runs every week to replace weak angles. The advice is built for reuse: short numbered playbooks with named tools, explicit thresholds and, in the competitor-research posts, prompts ready to paste into Claude.
The weekly new-reach threshold of 20% is the clearest example: it sets a decision—test new angles—rather than leaving the call to judgment. The competitor post spells out the routine: run it every week, bring in new angles and drop weak ads.
Answers the question media buyers face every morning: something moved in the account, but was it the creative, the offer, the audience or the way sales are counted?
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The post lays out a fixed order for checking each possibility before changing the budget.
The post opens with a concrete contrast: an ad at 3.2x ROAS was scaled while an ad at 1.8x ROAS was killed, even though the lower-ROAS ad was the only one still finding new customers.
That example sets up the argument.
The method starts with a daily tracking sheet for spend, impressions, clicks, CPM, click-through rate, AOV, purchases, conversion value, ROAS and CPA, colour-coded against targets.
Checks run in a set order: click rate first, because a simultaneous drop in click rate and ROAS points to the creative; conversion rate separately, because a drop there while click rate holds points to the landing page or offer; CPM against click rate, where a spike in CPM with strong click rate signals over-reaching the same audience; frequency per ad, where a low-ROAS ad at low frequency may be doing more work than a high-ROAS ad farming the same people; and view-through conversions, where falling Shopify revenue alongside healthy platform metrics prompts a switch to 7-day click attribution.
The final check is weekly new-reach tracking.
The post claims that below 20% means the audience is exhausted, and recommends testing new angles rather than pulling budget.
It credits Marin Istvanic as the source of the approach.
For brands planning a campaign
A brand scaling paid social can use the check order as a weekly account-review routine, especially when attribution and frequency effects distort headline ROAS. The method requires daily data collection and new-reach tracking, so brands without that instrumentation should build it first.
Answers how a creator or affiliate programme can be structured when awareness and trust do separate jobs.
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This case breakdown follows a launch sequence, showing newcomers the decisions in order.
The post reports a sequence that Jay Hunter of K2O described on the Chew on This podcast.
K2O launched on TikTok Shop before Shopify or Amazon, then added its website to catch spillover traffic.
The company moved from mass-sampling thousands of creators to building fewer, deeper relationships, reasoning that loyal creators post more and post better.
Celebrity name recognition is presented as opening the door, while working creators close the sale.
The incentive design is specific: a 60-day challenge in which creators post daily to earn $1,000, with no follower or view minimums.
With more than 10,000 creators, the programme still follows the 80/20 pattern, giving top performers priority and early payment.
The post ends by asking whether Kylie Jenner's name or the treatment of creators mattered more.
For brands planning a campaign
Useful as a reference for brands building a creator seeding or affiliate programme, especially when weighing deep relationships against mass sampling and deciding whether to remove follower minimums from an incentive.
The post retells a podcast conversation about a brand with unusual celebrity backing.
Before adopting the approach, ask for results from a programme without that backing.
Answers which audience a campaign should target first: people already ready to buy, or people who do not yet know they have a problem.
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The post explains the reasoning behind that targeting choice.
The post recaps a 30-minute call with Lachezar Voynov, who is described as having scaled an ecommerce brand from $100k to $700k in seven months.
Its central claim is that 90% of ecommerce brands target the wrong stage of customer awareness, focusing on the smallest and most expensive audiences: people who already know the product exists and are ready to buy.
Voynov is described as taking the opposite approach: targeting people who do not yet know they have a problem, establishing the problem first and then offering a solution.
The post credits Eugene Schwartz's Breakthrough Advertising and lists five stages of customer awareness, from unaware through problem aware, solution aware and product aware to most aware.
For brands planning a campaign
The five-stage list is a straightforward audit tool for a brand's prospecting mix: it forces a clear choice about how much budget goes to ready-to-buy audiences versus earlier-stage ones. The 90% figure and scaling result come from the poster's account of a single call, so treat them as a framing argument, not benchmark data.
The “ad heist”: turning competitor ads into test angles2
Diagnosing ad performance before deciding what to scale1
Choosing an audience by customer-awareness stage1
Creator-led launch sequencing1
Diagnosing ad performance before deciding what to scale
For ad accounts spending over $1 million a month, Pabillar lays out a check sequence that starts with a daily tracking sheet importing spend, impressions, clicks, CPM, click-through rate, AOV, purchases, conversion value, ROAS, and CPA, color-coded against targets.
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The checks help isolate the cause: falling click rate alongside falling ROAS points to the creative; steady click rate with falling conversion rate points to the landing page or offer; a CPM spike with a strong click rate suggests the audience is being reached too often.
Frequency per ad shows whether a lower-ROAS ad at low frequency is still finding new customers.
Weekly new reach below 20% signals a need to test new angles rather than pull budget.
When platform numbers look fine but Shopify revenue is down, the post recommends 7-day click attribution for a clearer read on whether an ad is working.
Build a daily tracking sheet, then check click rate, conversion rate, CPM against click rate, frequency per ad, view-through conversions, and weekly new reach—in that order.
The post notes that a 3.2x ROAS ad was scaled while a 1.8x ad was killed, even though the lower-ROAS ad was the only one still finding new customers.
For brands planning a campaign
A DTC brand scaling paid social can use this as a daily or weekly triage routine. It argues against making scaling and cutting decisions on ROAS alone.
Reporting on a call with Lachezar Voynov, Pabillar says most ecommerce brands target the wrong audience by focusing on the last two of Eugene Schwartz's five awareness stages: people who already know the product exists and are ready to buy.
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These are described as the smallest and most expensive audience.
The alternative is to target stages one to three—people who do not yet know they have a problem—by creating the problem before offering the solution.
Schwartz's Breakthrough Advertising is credited as the source of the five-stage model.
The account of this advice comes secondhand from a call and a book, not from Pabillar's own account data.
For brands planning a campaign
For a creative and audience testing plan, this supports building cold-audience and problem-aware creative rather than leaning on retargeting. The brand should validate the approach against its own funnel economics.
Pabillar summarizes a five-part launch sequence Jay Hunter of K2O described on the Chew on This Podcast for the Kylie Jenner brand: launch on TikTok Shop before Shopify or Amazon, with the website catching spillover traffic; work deeply with a smaller set of creators instead of mass-sampling thousands; use the celebrity name to create awareness while creators who actually use the product close the sale; make the incentive achievable through a 60-day challenge to post daily and earn $1,000, with no follower or view minimums; and protect top performers, who get priority and early payment even across a creator pool of over 10,000.
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Pabillar adds that most brands do not have a celebrity name to open doors, but any brand can build deeper creator relationships.
For brands planning a campaign
Useful for a creator-seeding brief or TikTok Shop launch, this provides a sequencing model for channel order, creator depth, incentive design, and payment terms. Ask for measured results before using it as a performance benchmark.
The posts are short, numbered playbooks with arrow-bulleted sub-steps, specific thresholds, tool names, and, in two cases, ready-to-paste Claude prompts.
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The competitor-research post describes a process run every week. These are operational guides, offering sequences and decision rules rather than performance data or client stories.
Several steps rely on paid tools and prompt-enabled workflows that a team must set up before adopting the process as written.
For brands planning a campaign
A brand's growth or creative team can use the posts as checklists and prompt templates, but should supply its own data, tooling, and review before treating the sequences as a campaign plan.
These posts disclose no sponsorship, ownership, employment, client, affiliate, or investment relationship.
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Pabillar points readers to a paid competitor-ad research tool and an AI assistant as parts of their ad research, and recommends two practitioners by name. Ask Pabillar to confirm whether any commercial relationship applies to these mentions.
AdSpy
Mention
Step two of the competitor-ad playbook is to go to AdSpy, search a brand name with keywords and country, and sort by shares rather than longest-running ads.
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They present AdSpy as a tool they use and recommend to readers.
Pabillar's ad-heist playbook has readers feed competitor ads, then their own product details and reviews, into Claude to list the angles behind an ad and test whether an angle fits their customer.
After a 30-minute call, Pabillar writes that they have followed Voynov for years across his LinkedIn posts, newsletter, and other resources, and credits his customer-awareness approach with scaling an ecommerce brand from $100k to $700k in seven months.
Ladie Pabillar’s work covers three connected threads: diagnosing Meta ad performance before changing spend, turning competitors’ ads into original test angles, and choosing creative for different customer-awareness stages.
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Start with their scaling checklist for the account-level decision sequence, then read the ad-heist playbook for a repeatable source of creative tests.
For a different view of the same DTC growth problem, Ujla Shakoor focuses on creative fatigue and keeping a pipeline of new ads; Shahbaz Akbar explains how creator content and product demonstrations shape TikTok Shop buying.
Amir Faizan Malik offers a useful comparison on audience-specific copy and landing-page messaging rather than paid-media diagnostics.
Both discuss DTC Meta advertising and creative decisions.
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Shakoor warns that relying on one winning ad can leave an account exposed when that creative wears out, complementing Pabillar’s advice to diagnose performance and keep testing new angles.
Shakoor’s posts emphasize creative fatigue and maintaining a pipeline of new ads; Pabillar offers a more detailed account-diagnostic and competitor-research process.
Both write about DTC ecommerce growth through paid or social content.
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Akbar’s TikTok Shop posts explain how problem-solution content, demonstrations, and creator reviews can move a customer from attention to trust and purchase.
Akbar concentrates on TikTok Shop and creator-led commerce, while Pabillar focuses on Meta ad diagnostics, customer awareness, and finding test angles in competitors’ ads.
Both address how marketing messages connect with a specific customer and how testing can improve results.
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Malik’s examples show audience research shaping landing-page copy, email, and ad creative.
Malik’s work focuses primarily on copywriting and conversion messaging across landing pages, email, and ads; Pabillar’s centers on paid-social account decisions and competitor-derived creative angles.
For a DTC ecommerce brand scaling paid social, the available audience information supports a cautious test of Ladie Pabillar for LinkedIn-first education aimed at ecommerce operators and media buyers—not a guaranteed run of DTC purchase decision-makers.
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Their posts target that professional audience, with examples including a step-by-step method for scaling $1M+/month ad accounts and a competitor-angle playbook called the ad heist.
The only published audience metric is a LinkedIn follower count of 7,478, observed 2026-10-09.
It shows LinkedIn reach but not followers’ job roles, employers, seniority, industries, geographies, ages, or genders.
Ask for audience survey results or another documented professional breakdown, plus recent reach and engagement for posts on paid-social scaling and competitor-angle research, to assess delivery against the intended DTC ecommerce audience.
Ladie Pabillar's LinkedIn posts offer how-to advice on paid social and creator marketing, not a media kit, rate card, or availability statement.
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They cover scaling decisions on seven-figure ad accounts, a competitor-ad research playbook, a beauty brand's creator launch, and another marketer's resources.
No sponsorship or partnership offering, inquiry route, formats, deliverables, usage rights, pricing, timelines, or booking terms are listed.
For a paid engagement, approach them through the LinkedIn profile listed in the identity section and ask whether they offer partnerships and what terms apply.
A DTC ecommerce brand spending six figures a month on Meta and TikTok wants to scale paid social profitably, and its media buying and creative teams need both a decision routine and a supply of new test angles.
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Pabillar's published work maps onto those needs directly.
The scaling post sets out a fixed check order for accounts above $1M a month: click rate first, then conversion rate separately, then CPM against click rate, frequency per ad, and view-through conversions.
If Shopify revenue diverges from platform reporting, switch to 7-day click attribution.
Track new reach weekly; below 20% signals a need to test new angles rather than cut budget.
The post also specifies the daily tracking sheet columns.
The competitor-ad playbook covers the other half: a weekly loop that narrows a category to five brands, pulls active ads, extracts the six to eight angles a library holds, groups 10 to 20 saved ads by angle rather than format, and reduces them to three to five concepts tested against the brand's own product and best customer.
The posts offer operator-facing advice rather than consumer content, so the fit is with the brand's strategic and creative process.
Treat this as a consulting, workshop or advisory engagement; ask directly about commercial terms, deliverables, pricing and usage rights.
Assess the method, not reach: the only audience figure available is 7,478 LinkedIn followers observed 9 October 2026, with no engagement or role breakdown.
When to choose a specialist
A consumer packaged goods brand wants short-form video content from a creator—an unboxing, routine or testimonial—distributed to a broad shopper audience on TikTok and Instagram, judged mainly on reach and assisted conversions among Gen Z buyers.
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Ladie Pabillar's posts do not support that need.
All five posts are written for ecommerce operators and media buyers, not shoppers: they are numbered how-to sequences about diagnostic order, competitor ad research, audience awareness stages and a creator-programme case study.
The posts show no consumer-facing content production, on-camera work or performance data for a consumer audience.
The audience picture is equally thin for this purpose: a follower count on one professional network does not show role, seniority, industry, age or geography, so it cannot confirm a consumer composition.
The posts document no sponsorship formats, deliverables, timelines, usage rights or booking terms, and no inquiry route was located.
Ask directly whether a paid content format is available and, if so, how to scope and price it before including them in a reach-led plan.
Even where the topic touches creator programmes, the contribution is analysis of someone else's launch, not a personal content offering.
A worked campaign example
Worked campaign angle for a six-week advisory engagement for a DTC ecommerce brand spending six figures a month on Meta and TikTok: pair account diagnostics with a creative-angle pipeline, run as a fixed weekly rhythm rather than a one-off audit.
Week one sets the instrumentation.
Build the daily tracking sheet from the published column list—spend, impressions, clicks, CPM, click-through rate, AOV, purchases, conversion value, ROAS and CPA—colour-coded against the brand's own targets.
Without that baseline, the later checks cannot be run.
Weeks two to five run the competitor loop once a week.
Each session narrows 10 to 15 category brands to five, searches an ad-spy tool by brand, keyword and country, sorted by shares rather than longest running, pulls one product's active ads, extracts the angle list, breaks them down in Meta Ads Library for repeated patterns across formats, people, hooks and editing styles, and saves 10 to 20 ads grouped by angle.
Each week, produce three to five concepts, each validated twice: against the brand's product details and reviews, and against its best-customer profile.
That second check guards against copying a competitor's customer along with its ad.
The worked angle: take the awareness-stage argument and build a problem-creation concept for cold reach, aimed at people who do not yet know they have the problem the product solves, rather than another testimonial aimed at buyers already aware of the brand.
Pair it with one product-aware variant so the two can be compared on the same product and offer.
Set decision rules at the start, not after the results.
Read each test in the published order: if click rate and ROAS fall together, the creative is the problem; if click rate holds and conversion rate falls, look at the landing page or offer before touching targeting; if CPM rises while click rate stays strong, the audience is being reached too often; if platform numbers look healthy while Shopify revenue drops, re-check on 7-day click attribution.
Track new reach weekly and treat anything below 20% as a signal to brief the next angle set rather than pull budget.
End deliverables: the tracking sheet template, the heuristic order as a written checklist, a swipe file grouped by angle, the three to five tested concepts with validation notes, and a short brief for the next test round.
Put one boundary in writing: the playbook's own rule is to steal the thinking, never the ad, so concepts ship as angles and prompts for the brand's team to turn into its own creative.
This proposal is based on Pabillar's posts, not an agreed scope; check availability, pricing and rights terms directly before budgeting.
Choose Shakoor when the brand's main risk is dependence on a single winning Meta ad.
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Their published work is built around the point where a proven creative tires: CPA climbs, ROAS drops, and the account has no next test waiting.
They argue for a standing pipeline of hooks, angles, customer problems, and formats instead of chasing the current winner, and frame the job as always asking what is being tested next.
That is a better fit than Pabillar when the immediate problem is creative-pipeline fragility rather than a check order for an already-large account.
Shakoor's posts are narrower on the scaling decision itself.
Pabillar's posts do not set out a diagnostic order across click rate, conversion rate, CPM, frequency, view-through conversions, and weekly new reach, nor a competitor-ad research loop that narrows a category to five brands and extracts angle sets.
A brand that needs a full account diagnosis and a sourcing method for competitor angles would still need that layer.
Choose Malik when the weak point is after the click: the landing page, the offer, or the message-market fit.
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His published work focuses on conversion copy, including landing-page headlines that speak to one specific person with one specific problem and a rewrite that moved conversion from 2.1% to 8.4%.
He also describes email sequences, sales pages, and ad creative built from deep audience research and then tested against metrics.
That is a better fit than Pabillar when creative is earning clicks but the page or offer is failing to convert, or when the brand needs a copy-testing programme rather than a media-buying diagnostic routine.
Malik does not cover the account-level scaling and competitor-angle research that Pabillar's posts cover.
They do not include a decision order for ROAS, CPM, frequency, or new reach, or describe an ad-spy or Meta Ads Library process for extracting competitor angles.
Malik's posts cover conversion-copy project work across email, landing pages, and ad copy, so they complement Pabillar rather than replacing the media-buying side.
Choose Akbar when the campaign decision is TikTok Shop-led rather than Meta-led.
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His published work argues that TikTok Shop can create demand instead of only capturing it, with content, creators, community, reviews, and product working together before a shopper ever searches.
He breaks winning content into problem-solution, product demonstration, and creator review, and describes the sequence as problem creating attention, demonstration building understanding, and creator trust driving purchases.
That is a better fit than Pabillar when the brand wants a TikTok Shop content system and creator-led trust, not a diagnostic order for an existing paid-social account.
Akbar's posts are channel-specific.
They do not cover Pabillar's Meta account checks for click rate, conversion rate, CPM, frequency, view-through conversions, or weekly new reach, or offer a competitor-ad research playbook for finding and grouping angle sets.
A brand that needs to scale a Meta account profitably or source ad angles would still need that layer.
This page covers five LinkedIn posts published by Ladie Pabillar.
The posts cover a scaling diagnostic for ad accounts spending more than $1 million a month, two near-identical versions of a competitor-ad research playbook, a recap of a call with Lachezar Voynov on customer-awareness stages, and a summary of K2O's creator-led launch as described by its CEO on a podcast.
Two of those posts report other practitioners' views rather than Pabillar's own measured work, and they are presented that way.
The posts have no publication dates, so their order and recency cannot be established; the follower count was 7,478 on October 9, 2026, with no engagement, seniority, industry, or geography breakdown.
The posts do not identify a signature book, show, or business, and no sponsorship formats, deliverables, pricing, or usage rights appear in them.
Comparisons to other creators draw on posts cited in those sections and cover only what those posts show.
