Sudharshan Inbaraj runs Meta Ads campaigns for e-commerce businesses, sharing campaign results and discussing creative testing, targeting, and optimization. They have also explored AI tools for producing video ads.
Most useful for Meta Ads campaign management and reporting for e-commerce and digital product sellers.
Best for
Digital product sellers wanting a modest budget Meta Ads test judged on cost per purchase.
Not for
Consumer brands seeking sponsored content series with large reach or brand awareness campaigns.
Key takeaways
One post reports 51 purchases at ₹55 each after adding a Catalog Ad.
Another reports 37 purchases over seven days using strong creatives and simple targeting.
A 16-day eBook store campaign had ₹143 cost per purchase.
Check first: Ask for recent results on comparable campaigns and any audience demographics before contacting.
Main ideas
Creatives and iteration do the work, not detailed targeting · Report results as numbers, and compare them to the market · Build on a campaign that already works instead of rebuilding it
Sudharshan Inbaraj shares practical Meta Ads work for e-commerce businesses, especially campaign results and the choices behind them.
The posts show a focus on purchase outcomes, creative testing, simple targeting and ongoing optimization.
They also show an interest in AI tools for producing video ads.
This gives readers a concrete view of how Sudharshan describes campaign work, though the available material does not establish a broader career history or signature project.
The clearest recurring strength in these posts is making campaign reports easy to compare: Sudharshan gives spend, purchase counts and cost per purchase, then points to the factors they believe mattered.
One post reports 51 purchases at ₹55 each after adding a Catalog Ad to an existing ad set; another reports 37 purchases over seven days and credits the result to strong creatives, simple targeting, a conversion objective and consistent optimization.
An exception is the 16-day eBook-store campaign, whose ₹143 cost per purchase is higher than the shorter tests, a reminder that the results are not uniformly low-cost or directly comparable.
A post about Creatify AI shows interest in speeding up creative production, but reports no campaign outcome from using the tool.
Readers may find these posts useful for seeing how Sudharshan frames performance experiments; the figures are self-reported, and the posts alone do not verify results or establish that the same approach will transfer to another account.
Sudharshan Inbaraj's recurring argument is that Meta Ads performance comes mainly from the creative and the optimization loop, not from precise audience construction, and that results should be shown as plain numbers — spend, purchases, cost per purchase — checked against comparable campaigns.
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The posts are short campaign reports from e-commerce and digital-product stores, plus one note on testing an AI video tool to speed up creative production.
Creatives and iteration do the work, not detailed targeting
Across campaigns, Sudharshan Inbaraj credits creative quality and steady optimization rather than audience research or layered targeting settings.
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The claim is consistent: when a campaign performs, they point to the ad itself and to adjusting from results.
In the catalog-ads post they state outright that no detailed targeting was used — the outcome came from the creatives, proper optimization, and letting Meta find the right audience. The seven-day campaign lists the same four causes: scroll-stopping creatives, simple targeting with no over-complication, a clear conversion objective, and consistent optimization.
Report results as numbers, and compare them to the market
Every campaign post is shaped the same way: a short results table followed by the factors credited.
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The recurring numbers are website purchases, total spend in rupees, and cost per purchase. Two posts go further and cite Meta's own comparison against similar ad sets, which frames a result as relative rather than just good in isolation.
The seven-day run for an e-commerce brand reported 37 website purchases on ₹4,030 spend at ₹108 per purchase, with Meta showing that cost as 80% lower than similar ad sets.
A three-day digital-books campaign reported 21 purchases at ₹62.48 per purchase and cited a comparison 84% lower than similar ad sets.
The comparison figures come from Meta's in-platform reporting, not an independent audit.
Build on a campaign that already works instead of rebuilding it
The stated preference is incremental: keep the ad set that is producing purchases and add another ad inside it, rather than starting a parallel campaign.
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This is a testable contrast with accounts built around separate, audience-layered campaigns.
A single-image creative had already produced acceptable purchase results, then one more ad was added to the same ad set using the Catalog Ads method. The combined result was reported as 51 website purchases on ₹2,829 spend at ₹55 cost per purchase.
The post credits the added catalog ad with performing but does not separate the two ads' individual costs, so it shows the test was run without establishing that the catalog ad beat the original creative.
The same structure carries over to digital products
Two of the campaigns concern selling digital books rather than physical goods, and Sudharshan Inbaraj applies the same conversion-focused structure and the same reporting shape.
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This is a useful reference for clients selling downloads, ebooks, or courses, where the purchase path differs from retail.
A 16-day eBook store campaign reported 144 website purchases on ₹20,596 spend at ₹143 per purchase, credited to the right audience, creative strategy, and optimization. A three-day run for a digital books platform reported 21 purchases at ₹62.48 per purchase on ₹1,312 spend.
The longer, higher-spend eBook run came in at a higher cost per purchase than the short digital-books test, so the larger budget was not simply a scaled version of the cheaper result.
AI video generation as a way to cut creative production cost
Sudharshan Inbaraj treats creative volume as the input everything else depends on, and watches tools that could increase it cheaply.
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The interest is in faster, cheaper production of ad variants to test, not in a performance method with results behind it.
They describe exploring Creatify AI, which turns a product link into multiple video ad creatives with AI avatars, presenter videos, quick transitions, and different ad styles within minutes — framed as cutting production time and enabling rapid testing for marketers, e-commerce brands, and agencies. No campaign numbers are attached to the tool.
This is the most complete single example of how Sudharshan Inbaraj works: it shows the budget, the outcome, the cause they credit, and the market comparison in one post.
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A newcomer who reads only one item should read this one, because it answers what a campaign from this person actually looks like and how they explain the numbers.
The post reports a one-week campaign for an e-commerce brand, April 29 to May 5: 37 website purchases, ₹4,030 total spend, and ₹108 cost per purchase.
Meta's own comparison reported the cost per purchase as 80% below similar ad sets, so the result was not only good in absolute terms but ahead of comparable campaigns on the platform.
Sudharshan credits four things: strong scroll-stopping creatives, simple targeting with no over-complication, a clear focus on the conversion objective, and consistent optimization based on performance.
Notably, none of the credit goes to audience research or detailed targeting settings.
For brands planning a campaign
Useful as a benchmark for what a small, short test budget can produce and as an argument for keeping targeting broad and putting effort into creative instead. A hiring agency could ask them to replicate this structure on a client account and judge the result against the same cost-per-purchase comparison.
It answers a specific, common decision: should you leave a working ad set alone or add to it?
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This post shows one answer in numbers, and it also demonstrates how they test incrementally rather than rebuilding a campaign from scratch.
The campaign began with a single image creative that produced acceptable purchase results.
Sudharshan then added one more ad to the same ad set using Catalog Ads, and it also performed: 51 website purchases on ₹2,829 spend, a ₹55 cost per purchase.
They note that no detailed targeting was used at all — the outcome came from the creatives, the optimization, and letting Meta find the audience.
For brands planning a campaign
Points toward testing catalog or feed-based creative additions inside a proven ad set, and toward broad targeting as a default rather than a fallback. Worth checking whether the client's product catalog is clean enough for Catalog Ads before committing to this route.
This is the cheapest and shortest test in the set, so it answers what the minimum viable experiment looks like and how quickly they expect a read.
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It also shows the kind of product they run — digital goods sold through a storefront.
A three-day campaign for an e-commerce digital books platform spent ₹1,312 and produced 21 website purchases at ₹62.48 per purchase.
The post states the cost was 84% lower than similar ad sets.
The scale is small, but the structure is the same as the larger campaigns: spend, purchases, cost per purchase, and a comparison against comparable ad sets.
For brands planning a campaign
Useful as an argument for short, low-cost validation before scaling a digital-product campaign, and for setting expectations about how fast this operator looks for a signal. A client with a slow sales cycle or high-consideration product should not assume the same timeline transfers.
This answers what happens beyond a single week and at higher spend, on a product category close to the digital-products work above.
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It shows the same method over a longer window and lets a newcomer compare cost per purchase across campaign sizes.
Over 16 days, a campaign for an eBook store generated 144 website purchases on ₹20,596 of ad spend, at ₹143 cost per purchase.
Sudharshan attributes the consistent sales to the right audience, creative strategy, and optimization.
The cost per purchase here is higher than in the shorter digital-products test above, which suggests the longer or larger spend was not simply a scaled version of the cheaper result.
For brands planning a campaign
A reminder to compare cost per purchase across campaign sizes rather than assuming the best small test scales linearly. Useful when setting budget tiers for a client or deciding when a working campaign has reached diminishing returns.
It answers what this person watches and explores beyond client reporting.
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The post is an early impression rather than a finished case study, but it shows how they think about creative production cost, which is the input that everything else in their work depends on.
Sudharshan describes testing Creatify AI for video ad generation and being impressed that a product link alone can produce multiple ad creatives with AI avatars, product presenter videos, quick transitions, and different ad styles within minutes.
They frame the value as cutting video production time and enabling fast testing of multiple ad concepts for marketers, e-commerce brands, and agencies.
No performance numbers or client results are attached to this tool.
For brands planning a campaign
Signals openness to AI-assisted creative production to feed the high-volume creative testing their Meta campaigns depend on. Treat it as interest, not a proven method — there is no reported campaign outcome here, so it should not be cited as evidence of results.
Creative-led delivery with deliberately simple targeting2
Digital products: ebook and digital-book stores2
Benchmark comparison drawn from Meta's own reporting2
Catalog Ads as an additive test inside a working ad set1
AI video ad generation as an adjacent interest1
Meta Ads results reported with hard numbers
Every campaign post leads with an outcome table rather than a description of the work: number of website purchases, total spend in rupees, and cost per purchase.
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Examples span a three-day digital-books campaign (21 purchases, ₹1,312 spend, ₹62.48 cost per purchase), a 16-day ebook store campaign (144 purchases, ₹20,596 spend, ₹143 cost per purchase), and a seven-day run for an eCommerce brand (37 purchases, ₹4,030 spend, ₹108 cost per purchase).
Posts campaign performance as a snapshot with spend, purchase count, cost per purchase, then a short bullet list of contributing factors.
For brands planning a campaign
Agencies can expect reporting in this shape and can use it as the template for client updates: one screen, three or four numbers, a short list of what was changed between runs. The rupee spend levels shown are small to mid-size budgets, so the numbers are most comparable to accounts in that range rather than large-scale spend.
Creative-led delivery with deliberately simple targeting
Across campaigns, the stated lever is the creative and the optimization loop, not audience construction.
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The catalog-ads post says plainly that no detailed targeting was used and that the result came from creatives, optimization, and letting Meta find the audience. The seven-day campaign lists simple targeting, no over-complication, a conversion objective, and consistent optimization as what worked.
Runs broad or lightly targeted conversion campaigns and treats creative plus ongoing optimization as the main controls.
For brands planning a campaign
This is a testable contrast for agencies whose accounts are built on layered interest and lookalike sets. It suggests trying a broad, conversion-optimized ad set with creative variation as the main variable, and judging it on cost per purchase over a defined window.
Catalog Ads as an additive test inside a working ad set
One post describes adding a second ad to an existing ad set using the Catalog Ads method after a single-image creative had already produced acceptable results.
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The added catalog ad also performed, and the combined result was reported as 51 website purchases at ₹55 cost per purchase on ₹2,829 spend.
Adds catalog ads to an existing ad set alongside the original creative rather than creating a parallel campaign.
The post credits the catalog ad with performing well but does not separate the two ads' individual costs, so it does not establish that catalog ads outperformed the original creative.
For brands planning a campaign
For eCommerce clients with a product catalog already feeding Meta, adding a catalog ad alongside a proven static creative is a low-effort test worth including in a proposed plan. It also shows a preference for incremental tests in the same ad set rather than rebuilding campaigns from scratch.
Two of the five posts concern selling digital books rather than physical goods: a 16-day ebook store campaign and a three-day campaign for a digital books platform.
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Both report purchase counts and cost per purchase in the same format used for the physical eCommerce example.
Applies the same conversion-objective campaign structure to digital-product stores as to physical eCommerce.
For brands planning a campaign
This is a relevant reference point for clients selling downloads, courses, or other digital products, where the purchase action and return path differ from physical retail. Ask for the comparable digital-product account before assuming the same cost-per-purchase benchmarks transfer to a physical-goods brief.
Benchmark comparison drawn from Meta's own reporting
Two posts cite the platform's ad-set comparison to frame results: cost per purchase described as 80% lower than similar ad sets in the seven-day campaign, and 84% lower in the three-day digital-books campaign.
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For brands planning a campaign
These figures come from Meta's in-platform comparison, not an independent audit, so they are useful as a directional signal about the account's relative standing but should be re-checked against the client's own reporting before being repeated in a proposal. If the agency wants a verifiable performance claim, it should ask for account-level screenshots or a read-only reporting view.
One post reports exploring Creatify AI, a tool that turns a product link into multiple video ad creatives with AI avatars, presenter videos, and varied styles in minutes.
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The post frames the appeal as cutting video production time and enabling rapid testing of multiple ad concepts for marketers, eCommerce brands, and agencies.
For brands planning a campaign
If the client brief needs a high volume of video variants for creative testing at low production cost, this is a stated area of interest to discuss. It reads as exploration rather than a described client engagement, so treat it as a capability question to confirm rather than a delivered case study.
Sudharshan Inbaraj's posts show two named commercial connections and several unnamed client campaigns.
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The clearest brand link is Creatify AI, which they describe testing for video ad generation.
A campaign report for an eBook store carries the hashtag #ChantoSolutions, but the posts never say whether Inbaraj owns, works for, or runs campaigns for that business.
Campaigns reporting purchase results for e-commerce and digital-book clients do not name those clients.
No post discloses a sponsorship, affiliate arrangement, or paid partnership.
Creatify AI
Mention
Inbaraj says they recently explored Creatify AI for video ad generation and calls the experience impressive, noting that a product link automatically produces multiple ad creatives with AI avatars, presenter videos, and quick transitions.
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The post recommends the tool to marketers, eCommerce brands, and agencies for faster video production and concept testing. No payment, sponsorship, or partnership is disclosed.
A post reporting 144 purchases over 16 days for an eBook store ends with the hashtag #ChantoSolutions, and the post refers to "our eBook Store" and "our digital book collection."
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The posts do not state whether Inbaraj owns this business, is employed by it, or runs campaigns for it, so the relationship is unstated.
Sudharshan Inbaraj publishes short Meta Ads campaign reports for ecommerce and digital-product stores.
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The posts follow one shape: a few numbers (purchases, spend, cost per purchase), then a plain list of what was done and what changed, with the argument usually landing on creative quality and steady optimization rather than detailed targeting.
To explore the work, start with the campaign reports that match your product type — a physical-goods run and a catalog-ad test for retail, and two digital-book campaigns for downloads, ebooks, or courses.
Then read for the method rather than the headline figures: how each result is attributed, and where the platform's own comparison against similar ad sets is doing the framing.
One further post is exploratory, covering an AI video tool for producing ad variants faster.
Readers who want the ideas behind the numbers should follow the posts that explain why a change was made, not only the ones that report an outcome.
Both work on paid acquisition for ecommerce and DTC brands and both teach by walking through a campaign and naming the levers that moved it.
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Nehal Kazim's account of a brand stuck at $200K per month covers platform diversification, creative volume, and audience structure — the same territory of scaling paid spend that Sudharshan Inbaraj addresses in single-channel campaign reports.
Nehal Kazim writes long, staged case studies that span several months and multiple ad platforms, and frames the lesson as infrastructure for scaling spend.
Sudharshan Inbaraj stays with Meta, reports one campaign at a time, and shows the numbers in rupees with cost per purchase as the main measure.
A reader following Nehal Kazim gets a multi-channel scaling narrative; following Sudharshan Inbaraj gets compact, repeatable Meta campaign results.
Both write practical Meta Ads guidance for ecommerce brands, and both treat the creative as the part worth getting right.
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Skylar Kraatz's post on testimonial ads argues that the review chosen matters more than the format, and that testing on a small static budget finds the messaging that scales — a close match to Sudharshan Inbaraj's emphasis on scroll-stopping creatives and simple, conversion-focused setup.
Skylar Kraatz teaches through lists of mistakes and format advice, especially around holiday planning and testimonial creative, and rarely publishes account-level spend or purchase figures. Sudharshan Inbaraj does the reverse: the posts lead with spend, purchases, and cost per purchase from named campaigns, and the method is inferred from those numbers.
Both build performance creative for ecommerce and DTC brands and both look past Meta alone.
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Stephanie Brown sets out a three-part creative framework for AppLovin at a DTC agency, covering hooks, interactive end cards, and long-form video, and promotes full-funnel creative, landing pages, and conversion-rate work — the same creative-and-conversion focus Sudharshan Inbaraj applies inside Meta.
Stephanie Brown's posts centre on AppLovin as an alternative to crowded Meta and TikTok auctions, and read as agency service positioning for creative and CRO packages. Sudharshan Inbaraj stays on Meta and on individual campaign outcomes, and the posts are reports of runs rather than an offer of services.
The only measured audience figure in the reviewed evidence is Sudharshan Inbaraj's LinkedIn following of about 1,800 people, recorded on 9 October 2026.
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No demographic, geographic, seniority, or engagement data came with it.
That is a small base for a paid placement whose value depends on reach, so the decision here is about whether the following is the right professional crowd, not how big it is.
The five reviewed posts (1 May to 19 June 2026) show what the content is written for: Meta Ads campaign results for e-commerce and digital-product sellers — spend, purchase counts, and cost per purchase for a digital book store and an e-commerce brand — plus one post on using an AI tool to generate video ad creatives.
Those posts read as material for performance marketers, e-commerce operators, and agencies; campaign figures are given in rupees, which suggests an India-centred advertiser audience, though that is inferred from currency rather than from any audience record.
What the posts do not show is who actually reads Inbaraj's updates, how many of them, or what roles they hold.
For an agency placing him with eCommerce clients, the topical fit is visible and the audience composition is unproven.
Concrete next step: ask Inbaraj for recent results on campaigns comparable to your clients' products and ad formats, along with any platform audience-demographics export and any audience survey he holds; that is the evidence needed to test reach, geography, and whether the followers are the buyers and operators you want to influence.
LinkedIn followers
1,801
LinkedIn profile listing for the linked account · 2026-10-09
The reviewed posts do not document any sponsorship or partnership offer from Sudharshan Inbaraj.
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They are campaign case notes and tool commentary: Meta Ads results for e-commerce clients, catalog ad testing, and an exploration of Creatify AI for generating video ad creatives.
Nothing in this material describes sponsor policies, ad-format specifications, rates, deliverable terms, or a route for brands to request a paid collaboration.
That is a gap in the reviewed evidence, not a statement that he never accepts sponsorships.
For the agency brief in question, this distinction matters: the posts show a practitioner who runs paid social campaigns on behalf of e-commerce businesses, so a commercial relationship is more plausibly a service engagement for campaign work than a purchase of sponsored content slots.
Any partnership terms would need to be established directly, and no official inquiry route for partnerships was located in the reviewed material.
Brief: a seller of digital products — an ebook store, a course, or downloadable guides — wants new customers from Meta Ads in a two-to-three-week test on a modest budget, judged on cost per purchase.
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This is the closest match in the reviewed evidence.
Sudharshan Inbaraj's posts are conversion-objective campaign reports for exactly that product type: a 16-day eBook store run (144 purchases, ₹20,596 spend, ₹143 cost per purchase), a three-day digital-books test (21 purchases, ₹1,312 spend, ₹62.48 per purchase), and a catalogue-ad test that produced 51 purchases at ₹55 per purchase.
They describe the method in testable terms: one strong creative, deliberately simple targeting, a conversion objective, and ongoing optimisation, with the catalogue post stating plainly that no detailed targeting was used.
The reporting shape — purchases, spend, cost per purchase, plus the few changes credited with the result — suits an agency that has to justify spend to a client each week.
The small test budgets also fit a client who wants a cheap read before committing further.
When to choose a specialist
Brief: a consumer brand wants a sponsored content series — paid posts or videos delivered to a large audience with a reach target — rather than campaign management.
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The reviewed evidence does not support that.
No sponsorship offer, rate, format specification, or partnership enquiry route appears in the five posts; they are case notes about running Meta campaigns, so a commercial relationship here is more plausibly a service engagement than a purchase of sponsored slots.
Reach is also unproven: the only audience figure is about 1,800 LinkedIn followers, with no demographics, geography, engagement or seniority data, and no evidence about who reads the posts.
Nothing in the material covers brand-awareness or upper-funnel work, long-form or video content production, or non-Meta channels; the one video post is an early impression of an AI creative tool with no campaign results attached.
A brief built on audience scale or sponsored-placement deliverables would rest on assumptions the material does not support.
A worked campaign example
Brief worth testing: an ebook and online-course seller wants new purchases at a defined cost per purchase and will start small.
Proposed angle: hire Sudharshan Inbaraj for a paid Meta Ads engagement, not a sponsored post, structured in three steps.
Step one is a three-day validation run at roughly ₹1,300–₹1,500, one image creative, conversion objective, broad targeting rather than layered interest or lookalike sets.
Compare the result against Meta's similar-ad-set benchmark, the comparison cited in two of the reviewed campaigns, and treat it as a directional signal rather than an audit.
Step two: if cost per purchase lands near the reported ₹55–₹108 range, add a catalogue ad inside the same working ad set instead of rebuilding the campaign — the approach described in the catalogue test, where a second creative was added alongside a proven image.
Step three: extend to two to three weeks at a mid five-figure rupee budget, with the client's product catalogue already feeding Meta.
Ask for read-only account access or screenshots before repeating any figure publicly, and ask for results on campaigns comparable to the client's product and ad format.
Keep the reporting format the posts already use — purchase count, spend, cost per purchase, and the two or three changes made between runs.
Expect cost per purchase to drift upward as spend grows: the short digital-books test reported ₹62.48 while the longer eBook store run reported ₹143, so do not assume the cheapest result scales linearly.
Choose Stephanie Brown over Sudharshan Inbaraj when the brief is wider than a low-budget Meta-only test and the client also needs creative and page-conversion work.
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The reviewed posts set out a three-part creative framework for AppLovin — a five-second hook built for a skippable feed, interactive end cards treated like a small landing page, and sixty-second video that agitates the problem and stacks social proof — and describe an agency offering media buying across Meta, TikTok, Google and AppLovin, conversion-rate optimisation, affiliate management, branded UGC and landing pages. That makes a better case where Meta cost per purchase is already climbing and the brand wants a second channel, or where the client would rather buy campaign management, creative production and landing-page work from one supplier than run a single-channel test.
The reviewed material contains no purchase counts, spend figures or cost per purchase, so the case for moving to another channel is argued rather than shown with a client result.
You would be buying a broader multi-channel and creative scope than the single-channel Meta test the other option is known for, and nothing in these posts supports a prediction about cost per purchase on an ebook or course catalogue.
Ask which parts are handled in-house and which are subcontracted, and ask for AppLovin and Meta results on a comparable product at a comparable budget before treating the framework as a replacement for a tested small-spend run.
Choose Nehal Kazim when the client is already spending enough that the problem is a scaling ceiling rather than a first read on creative.
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One reviewed post walks through a six-month direct-to-consumer health and wellness engagement taken from $200K to $1.2M per month while holding target ROAS, by launching TikTok and YouTube as funded channels rather than tests, moving from eight to thirty-five new creatives a week using a modular system where hooks, bodies and calls to action are interchangeable, and splitting audiences into cold, warm and hot with separate creative and bid strategies. Choose this over Sudharshan Inbaraj when the brief is about adding channels and creative volume to spend that is already working, not proving a conversion objective on a small budget.
The numbers are monthly revenue and ROAS for a wellness brand, not cost per purchase for digital products, and the spend level described sits far above a two-to-three-week test of a few thousand rupees.
The second reviewed post promotes a live session with a measurement partner rather than reporting a campaign, so evidence for this specific buyer decision is thinner than the first post suggests.
Ask for cost-per-purchase results at the client's budget, and ask what the modular creative system costs to run — tripling testing volume has to be paid for somewhere.
Treat the scaling playbook as relevant only once the client's own conversion economics are stable.
Choose Skylar Kraatz when the brief is a seasonal plan or a specific creative-format question rather than day-to-day campaign running.
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One reviewed post gives a Black Friday and Cyber Monday checklist: start campaigns earlier than intuition suggests, update the copy and call to action on ads that already performed instead of testing new concepts during the peak, and shift budget toward website visitors, engagers and email subscribers rather than cold prospecting, with a giveaway or leads campaign to build the retargeting pool.
Another argues for testimonial ads — start with static versions on a smaller budget, find the reviews that actually resonate, then scale the winning messaging into other formats.
Choose this over Sudharshan Inbaraj when the client needs a second opinion on holiday timing and budget split, or a social-proof creative format diagnosed, and intends to run the campaigns in-house.
Both posts are advisory.
There are no purchase counts, spend figures or cost per purchase in either, and no service offer or enquiry route, so this reads as a consulting or content input rather than a documented hands-on engagement.
The holiday guidance is tied to a seasonal window and the testimonial argument covers one creative type; neither explains why a client's current campaign is underperforming or what to change first.
Ask for a worked example with real numbers on a comparable account before treating the advice as a substitute for someone managing the account.
This profile is built from five LinkedIn posts published by Sudharshan Inbaraj, each read in full: four Meta Ads campaign reports (a seven-day e-commerce run, a catalog-ad test, a three-day digital-books test, and a 16-day eBook store run) and one post on testing Creatify AI for video ad generation.
That account is the only channel supplied.
Publication dates were not provided; one post states an April 29 to May 5 campaign window, so claims cannot be placed on a timeline.
The campaign figures are self-reported, and the ad creatives, audience definitions, client names, and client-side confirmations behind them were not available, so no result is treated as independently verified.
Two named brand links appear: Creatify AI, described as a tool the author tested, and Chanto Solutions, which appears only as a hashtag on one eBook store post without stating the relationship.
Similar-creator comparisons draw on two saved LinkedIn posts each from Nehal Kazim, Skylar Kraatz, and Stephanie Brown; those posts are export text rather than verified complete posts, and none of the three comparators' full bodies of work was reviewed.
Missing evidence, listed here as actionable gaps rather than filled in: biographical and career detail, publication dates, ad creatives, audience definitions, account-level confirmations, audience demographics, and any partnership terms or enquiry route.
