eCommerce Podcast

ChatGPT once invented a 20% advertising budget for a founder who hadn't made a single sale yet. Marissa Rodriguez explains why that happens, and what she does differently.
Marissa Rodriguez has spent twenty years building and scaling direct-to-consumer brands, first as a fractional CMO and COO, now as the founder of Through Experience, a mentorship platform for ecommerce founders. She talks to Matt Edmundson about the risk of handing serious decisions to generic, industry-agnostic AI tools, and why she thinks the real opportunity lies in training AI on the judgement of people who have actually done the job. She shares how her own email marketing training module grew from six videos to fifty once she tried to capture everything she actually knows, and the moment a founder's P&L came back with a 20% advertising budget that ChatGPT had invented from nothing. Along the way, they cover decision fatigue, shiny object syndrome, and where Marissa tells founders to focus first when they're starting out with AI in their business.
08:22 - Why Generic AI Puts First-Time Founders at Risk
15:40 - Training AI With Her Own Frameworks
29:33 - Decision Fatigue and Shiny Object Syndrome
40:06 - ChatGPT Invented a 20% Ad Budget

Why Generic AI Puts First-Time Founders at Risk (08:22)
Matt asks Marissa the question she gets most often, what's going wrong for people right now with AI. Her answer isn't about a specific tool or tactic. It's about who's giving the advice. Most ecommerce founders are first-time founders with no deep domain expertise to fall back on, and a generic AI tool doesn't know what it doesn't know. It won't ask the qualifying questions a human expert would, and it will hand over confident answers regardless.
Marissa has watched this happen with financial models and P&Ls built in ChatGPT, where the tool makes assumptions about costs and budgets without ever checking whether they're right for that particular business.
"Having a generic AI working with you, I think you're gonna end up in a bad place."Marissa RodriguezHer point isn't that AI is the problem. It's that AI without operator judgement built into it is the problem, and that gap is exactly what she thinks founders need to close.
Training AI With Her Own Frameworks (15:40)
Marissa began mapping her own expertise into an AI the same way Matt describes doing with SAM, Aurion's internal AI layer built on their own playbooks. She assumed it would be straightforward. She already had a structured curriculum from years of building courses and digital products.
It wasn't straightforward. One email marketing module alone, originally six videos and forty five minutes, grew into fifty videos once she tried to capture every exception, every variation by AOV (average order value) or customer path to purchase, every moment she'd normally break her own rule because the context called for it.
"It went from six videos to fifty videos, because there was so much more than I anticipated that I actually had to sit down and give it the context for understanding."Marissa RodriguezThat's the gap most generic AI tools skip. The judgement isn't a setting you switch on, it's decades of decisions that nobody writes down until someone asks them to.
Decision Fatigue and Shiny Object Syndrome (29:33)
Founders are surrounded by noise, Marissa says, a constant stream of other people's wins and warnings on LinkedIn and Instagram, most of it stripped of the context that would tell you whether it actually applies to your business. Her advice is to narrow the focus deliberately rather than chase every new tactic.
"There's only three actions a person can take on the internet that matter to an ecommerce business. Click, subscribe, and buy."Marissa RodriguezEverything else, in her view, is in service of those three actions. Paid ads and content drive the click, email marketing earns the subscribe, and the operations behind the scenes, inventory, reporting, unit economics, make the buy possible. She'd rather be great at a handful of things that compound predictably than good at everything and consistent at nothing.
ChatGPT Invented a 20% Ad Budget (40:06)
A founder came to Marissa with a P&L that ChatGPT had built for them. It looked complete. It wasn't. The advertising line assumed 20% of revenue, with nothing behind the number, for a business that was pre-revenue and hadn't launched yet.
"ChatGPT just made up the paid advertising line. ChatGPT just decided like 20% is what you need for paid advertising."Marissa RodriguezMarissa's challenge back to the founder was simple, on what basis. A properly trained financial model, in her experience, knows which questions it can answer from publicly available information and which ones it has to ask the founder directly, the weight of a product before it estimates shipping, the likely return rate for that specific category. Generic AI skips the asking and goes straight to the guessing.
About Marissa Rodriguez
Marissa Rodriguez is the Founder & CEO of Through Experience, a mentorship platform for ecommerce founders. She has spent 20 years in DTC and ecommerce, including close to a decade as a fractional CMO and COO scaling brands across fashion, wellness, CPG and lifestyle. She now builds AI trained on her own operator frameworks for founders and small teams.
Website: throughexperience.com
LinkedIn: Marissa Rodriguez
Instagram: Marissa_ThroughExperience

Episode link: https://www.ecommerce-podcast.com/ai-for-small-business-why-generic-ai-fails-you

AI will not run your ecommerce store on autopilot. Cyril Golub joins Matt Edmundson to explain how to actually put it to work, as a supervised assistant you give a clear goal, not a magician you hand the keys to. It is a practical, often funny conversation about where AI genuinely helps online sellers and where it just burns tokens.
Summary
Cyril Golub has been building ecommerce software since 2003, so he watches the AI wave with the eye of someone who has seen a few hype cycles come and go. In this episode he and Matt dig into what AI changes for online sellers and what it quietly leaves exactly the same. Cyril's thesis is that "ecommerce" is turning into "AI commerce", where by early 2027 both buyers and sellers have their own agents, and "how do I start selling online?" becomes a single sentence typed to an agent.
Along the way they get practical. Most AI startups are building for buyers, leaving the seller side wide open. Marketplaces own your customer data, and on Amazon a buyer is one click from a competitor. Matching the right model to the job matters more than reaching for the biggest one every time. And competitor monitoring at scale lets you act alongside rivals rather than react to them.
Key points covered in this episode:
Who's building AI for sellers, not just buyers (10:25)
Who really owns your customer data (17:41)
Which AI model you should actually use (26:55)
Using AI to monitor your competitors at scale (31:43)

Who's Building AI for Sellers, Not Just Buyers (10:25)
Cyril points out something surprising about where the money is going. Roughly three quarters of AI-native ecommerce startups are building buy-side tools, helping shoppers find bargains and streamline their buying. The seller side, by contrast, is far less crowded, which he finds odd given how much easier it usually is to sell to businesses than to chase millions of individual consumers.
That gap raises a harder question for any small founder building in the space. If a tool genuinely helps sellers increase their margin, who gets to build it? The giant sitting on terabytes of data, a mid-size player, or a small third party with neither the data nor the audience?
"The main question that early-stage investors are asking to startup founders, what if, then put the name of the huge company. What if Amazon will develop it on their own, directly in-house, and then roll out to all of their hundreds of thousands of customers?" — Cyril GolubMatt's hope is that the answer turns out to be the small operator in the garage rather than another platform giant, which is exactly the kind of story this show roots for.
Who Really Owns Your Customer Data (17:41)
The storefront, Cyril argues, is a habit inherited from the offline world, the digital echo of goods on a shelf. But not every visitor wants to browse a shelf. Some arrive with a clear intention and want to reach checkout in two or three clicks. Others arrive to explore, unsure what they want yet. The problem is that most sellers know almost nothing about which is which, often only the channel or the ad that brought someone in, and rarely anything that ties through to the actual purchase.
That blindness is the real cost of not owning your data, and on a marketplace it gets worse.
"When we are talking about Amazon sales, your buyer is just one click away from buying from your competitor. That's a huge problem." — Cyril GolubHis practical steer is to pull as much data as you can out of whatever platform you sell on, through the richest API access available, so you can connect your own agent and ask it for actionable insight rather than just advice.
Which AI Model You Should Actually Use (26:55)
This is where the conversation gets genuinely useful for anyone watching their AI bill. Cyril's view is that you should match the model to the job, the same way you would match a hire to a task.
"It might be like if you are hiring a PhD to just calculate simple numbers in your Microsoft Excel sheet. You must choose the exact type of model that is quite enough, but not more, for your particular goal." — Cyril GolubHe gives a concrete number. Connecting Claude to an Amazon store through the SP-API to process an inventory of around 2,000 SKUs costs roughly 4 million tokens, mostly input. That is far too simple a job to hand a frontier model. His answer is to think in layers:
A deterministic layer that prepares the data quickly. If a task is fully deterministic, it is just software, no AI needed.
A cheap supervisor model to decide which parts of the data actually matter for the goal at hand.
A frontier model reserved only for the work that genuinely needs judgement.

Matt notes he is seeing this layered thinking take hold more widely as people wake up to what AI actually costs. Cyril adds a warning about context too, in that the right answer moves over time. What was true a week ago, around a season, a sale or a competitor's campaign, can be wrong the next week.
Using AI to Monitor Your Competitors at Scale (31:43)
Cyril's most vivid example is competitor monitoring. Imagine your store suddenly does half the usual sales on running shoes. Nothing has changed in your store or your traffic, so as a human, or using older tools, you have no idea why.
Now imagine an AI tool watching not two or five rivals but a hundred of them, with insider accounts inside each. It can spot a competitor's loyalty campaign quickly, map it to your own inventory, and launch a comparable campaign at the same time, sometimes before the rival's first email even reaches most of its audience.
"It's a real game changer because you are not reacting. You are acting at the same time on the same issue, same field as your competitors are doing right now." — Cyril GolubMatt's follow-up is the honest one every founder should sit up for. If it has never been easier to monitor your competitors, it has also never been easier for them to monitor you.
The Closing Advice for an Overwhelmed Founder
Matt ends by asking what a roughly million-pound ecommerce brand, feeling swamped by it all, should do. Cyril's answer is the line the whole episode hangs on.
"Think about AI like your assistant that must be supervised and must be clearly set with the goal and the expected outcome. Don't think that it is kind of a magician or wizard that will do everything without you and without your supervision." — Cyril GolubToday's Guest
Cyril Golub is the founder of Jinnify (jinnify.ai) and has been building ecommerce software since 2003. He spent years in early-stage venture as an angel investor and brings a builder's eye to where AI genuinely helps online sellers.
LinkedIn: Cyril Golub
Jinnify: jinnify.ai

Cyril likes to connect with like-minded people on LinkedIn. Reach out and say hello, especially about all things AI.
Keep Listening
Find the full archive of episodes, the free monthly eCommerce Cohort meetups, and everything else at ecommercepodcast.net. If you enjoyed this one, make sure you like and subscribe in your favourite podcast app or on YouTube so you never miss a show.
Episode link: https://www.ecommerce-podcast.com/ai-in-ecommerce-what-actually-works

Ecommerce Merchandising, and How to Stop Advertising Sold-Out Stock
Most stores are quietly leaking sales, and ad spend, to products customers literally cannot buy. In this episode Matt Edmundson talks merchandising with Leon Bart-Williams, co-founder and CEO of Akikumo, a merchandising-intelligence app for Shopify brands. Before Akikumo, Leon built the first shoppable-TV commerce experience at NBCUniversal and helped hundreds of brands scale on Amazon, so this is a proper operator conversation about the part of ecommerce almost nobody owns.
Summary
Merchandising is the most overlooked driver of conversion, and it is far more than shop-window mannequins. Leon defines it as putting the right product in the right place at the right time in front of the right audience, with the right inventory to satisfy demand. It is the process of turning inventory into sales.
The conversation works through where stores go wrong. Out-of-stock products sitting in the prime slots of a collection. Ads pointing at sold-out or partially sold-out products. A failure to understand which variant is actually driving the sales of a given product. From there Leon and Matt move into curation over volume, why more choice tends to sell less, A/B testing collections rather than just the checkout, and inventory planning that starts with sales velocity at the variant level. Practical throughout, with real brands named and a ten-minute daily habit any store can start tonight.
Merchandising Is the Job Nobody in Your Store Is Doing
Merchandising usually gets wrapped in grandiose language. Leon's definition is plain.
"Merchandising is really the process of taking your inventory and converting it to sales." — Leon Bart-WilliamsThe point most brands miss is that merchandising applies to the ecommerce storefront, not just the physical store. The first positions in a collection do the same job as the front window of a shop.
"Think of the first 10 slots of your website as the moment people walk through the door of a physical store and see a mannequin with nothing on." — Leon Bart-WilliamsOut-of-stock products in the first ten positions of bestsellers or new arrivals are the fastest, most common leak.
The problem starts biting at around 400 to 500 SKUs, not tens of thousands, especially in apparel where size, colour and style variants multiply.
It is almost always human error and broken team communication, not strategy. As Leon's co-founder Jared (previously a principal engineer at Figs and Chewy) puts it, most of ecommerce is just fixing human errors and self-inflicted wounds.

Are You Running Ads to Products People Can't Buy?
A costly and common mistake is ad spend hitting stock that is already gone. Leon describes checking a friend's brand, Johnny O, in Meta's Ad Library and spotting a top-selling product with three quarters of its variants sold out, sitting in the top ad slot. One email to their ads team fixed it.
The subtler version is the partially sold-out product, and this is where the 80/20 rule matters at the variant level.
"The dress in blue, size 2, is driving 80% of the sales of this specific product. You need to know that, and you need to at least be fully stocked in that product." — Leon Bart-WilliamsRoughly 20% of a product's variants drive roughly 80% of its sales.
If that core variant is out, the listing can look in stock while the ad is effectively pointing at a sold-out product.
When a product is gone but the creative still works, point the ad to the collection, or to the category, rather than a dead page. When in doubt, point to the collection.

Why More Products Means Fewer Sales
Leon and Matt agree that curation beats volume, and the science backs it up. Matt recounts the jam experiment, where a display of six jams sold well and a display of thirty-six exotic jams saw sales fall. More choice created paralysis, not more buying.
"More does not mean more sales." — Leon Bart-Williams"People want fewer options. People want to be told what to do." — Leon Bart-WilliamsKeep collections tight. Around 50 SKUs or fewer per collection is manageable and tends to convert better.
Curation is part art, part science. The science is how many products. The art is telling a story around a hero product, the dress plus the handbag plus the shoes, and writing content around it.
Brands Leon rates for this include Cara Cara NYC, and his customer Brooke at Simply Moab and Desert Soul in Moab, Utah, who matches her ecommerce experience to her physical stores.
Endless scroll with no curation is the opposite of this, and it kills conversion.

Test Everything, and Plan Inventory at the Variant Level
Testing should not stop at the checkout. Leon argues for A/B testing collections, layouts, price points and new products, each with a hypothesis and a finite goal.
"You can't optimise your way to success all the time." — Leon Bart-WilliamsThe counter-example is Vince at the Texas golf brand Bogey Brothers, who built sale pages by hand from memory, guessed the pricing, launched, and never tested how they performed.
Inventory planning is merchandising too. Understanding sales velocity at the variant level before cutting a purchase order stops a brand restocking the wrong sizes and colours that then sit on shelves. Leon's customer Brooke spends 20 to 30 hours a week with pencil and paper walking her stores to work out what to reorder. Matt frames the stakes plainly.
"Stock on the shelves is cash not doing anything." — Matt EdmundsonMatt's own examples make the point across business models. His beauty business carried seven days of stock because the supplier held the risk, while his supplement business runs 18-month lead times, so the data has to be dialled in because a single mistake locks up cash for a year and a half.
Leon's rule number one sits underneath all of it.
"Don't piss off the customer." — Leon Bart-WilliamsWhere to Start This Week
Leon's advice for anyone starting out, limited by time, space and money, needs no tool at all.
Go into your top collections tonight and make sure they are fully stocked.
Keep collections tight and curated.
Archive products that are not selling.
Push out-of-stock items to the bottom of the page.
Do it every single day. It takes intentionality and diligence, not software.

Today's Guest
Leon Bart-Williams is co-founder and CEO of Akikumo, a merchandising-intelligence app for Shopify brands that handles collection sorting, collections intelligence, variant-level analytics and inventory planning, giving small, lean teams the power of an entire merchandising team. Before Akikumo he worked in business development at Comcast and NBCUniversal, was recruited by Amazon in 2014 to help brands scale on the marketplace, and returned to NBCUniversal on the founding team that built its shoppable-TV and livestream-shopping commerce product. Akikumo offers a 30-day free trial.
Website, akikumo.com
Shopify App Store, apps.shopify.com/akikumo
LinkedIn, in/leonbartwilliams
Email, leon@akikumo.com

About the eCommerce Podcast
The eCommerce Podcast is hosted by Matt Edmundson, who has run his own online businesses since 2002 and still ships products all over the world. Each week Matt talks with ecommerce experts and founders from around the world, digging into the questions that help all of us run our online stores better. With more than 300 episodes in the archive, you can find every show, the show notes, the newsletter and the cohort groups at ecommercepodcast.net.

Drew Morgans spent 15 years and around $50,000 building an AI trained on his own work, and it now helps run his Amazon agency.
Summary
Drew Morgans, founder of the Amazon and marketplace agency Marknology, returns to the eCommerce Podcast for a conversation that is all about AI in the real world rather than the hype. Drew explains how he fed 15 years of his own work into an AI system, roughly 500,000 emails, around 300 podcast episodes, blogs, SOPs, playbooks and won and lost proposals, to build something that thinks the way he does. The pair get into the practical build behind it, why context is the hard part, how he keeps his running costs sane by using cheap local models for ingest and expensive frontier models only for client-facing work, and why he chose a single "super agent" over a crowd of specialist bots. Matt and Drew also compare notes on being founders who came up through code, stepped away, and have now picked it back up. It is a generous, concrete look at where AI actually pays off for an ecommerce operator, and where it does not.
Turning 15 Years of Work Into an AI That Thinks Like Him
Drew describes the shift from using AI for one-off tasks to building a system trained on his own history. He ingested roughly 500,000 of his own emails, around 300 podcast episodes, blogs, SOPs, playbooks and old proposals, turning past work into present value.
"I really needed a copy of me." - Drew Morgans"The real meat's in the emails." - Drew MorgansThe starting point was helping himself first, then rolling the tools out to his bookkeeper, design team and wider team.
Old work he had already paid for in time and energy became a source of ongoing value, including 500 real examples of how he handled objections over the years.
A QuickBooks-approved app now auto-drafts invoices from incoming email, always human-gated, never sent automatically.

Why Context Is the Real Problem
Drew argues the genuine bottleneck is not the model, it is context. AI works sequentially, checking one source then the next, and never holds everything at once the way a close colleague would. His answer was to build the context in before the model ever runs.
Drew IQ is a set of around 350 operator rules, a series of "if this, then this" checks that mirror what he already knows as an operator, plus tags that give each product context such as whether a SKU is a launch.
WCS is a context engine that surfaces everything relevant to a brand in about half a second, locally, so the model arrives with the full picture already in front of it.
Winston is his always-on system, built to be model-agnostic with fallbacks, so if one provider went down he could switch and keep running. He built it on OpenClaw, an open-source always-on agent framework, rather than tying himself to a single vendor.

Cheap Model In, Expensive Model Out
One of Drew's most useful principles is matching the model to the job. He runs cheap local models via Ollama for the overnight grind of ingesting emails, transcripts and Amazon data, and reserves the expensive frontier models for anything client-facing or "trying to be me".
"Cheap model in, expensive model out." - Drew MorgansAll the pre-work, the rules, the routing and the context surfacing, happens on his own machine, so it is effectively a free call.
By the time the reasoning model is called, it is doing only the final, high-value layer, which keeps costs in check without dumbing down the output.
Both Drew and Matt land on the same conclusion about tool-switching, that the opportunity cost of constantly jumping between systems is real, and there is more value in committing to one setup and getting genuinely good at it.

One Super Agent Beats Twenty Specialists
Drew tried the popular approach of building a crowd of named specialist agents, a CFO agent, a design agent and the rest, all chatting and arguing in Discord. It did not work as well as the alternative.
"I cut down the jungle, my team manicures the lawn." - Drew MorgansInstead of managing twenty different personalities, he invested his time in one super agent that knows where all his information lives and can genuinely help.
Drew's view on where things are heading is that mediocre roles are in trouble, trades will grow in value, and strong operators will be fine, with a parallel movement back towards authenticity and in-person work.
On Amazon specifically, he sees AI helping him do the deep, non-scalable work an agency normally cannot afford at scale, cross-referencing ranking, margin, inventory, reviews, Reddit and search query gaps to work out why a listing is not converting.

"It's helping scale the unscalable." - Drew MorgansToday's Guest
Drew Morgans (Andrew Morgans) is the founder and CEO of Marknology, a full-service Amazon and marketplace agency based in Kansas City, working across Amazon, TikTok Shop, Walmart and Shopify. He also runs a warehouse and 3PL operation and a portfolio of brands. This is his second appearance on the show.
Website: marknology.com
LinkedIn: in/amorgans
Instagram: @andrewmorgans

About the eCommerce Podcast
The eCommerce Podcast helps you deliver eCommerce WOW, with real talk about building, growing and running online stores. Hosted by Matt Edmundson, each episode brings on experts and founders who have built the stores and learned the hard way, so you do not have to. New episodes every week.
Website: ecommerce-podcast.com

Episode link: https://www.ecommerce-podcast.com/ai-for-ecommerce-personal-ai

How do the best brands actually win on TikTok Shop, and where does AI genuinely earn its keep in the process?
Summary
Matt Edmundson talks with Sohun Sanka, founder of Clankers and former head of growth at the TikTok Shop outreach tools Euka and Reacher. Having audited more than 2,000 affiliate programmes and seen the backend of over 150 agencies and 1,000 brands, Sohun has a clear read on what separates the brands that win on TikTok Shop from the ones quietly burning through samples.
The conversation moves from the biggest mistake most of us make with AI (never showing it what good looks like) to the practical mechanics of creator commerce. Sohun explains why top affiliates post 15 to 21 times a week, why lighting matters more than copy in the first two seconds of a video, and how the best brands treat their affiliates like a sales team rather than a mailing list. On the AI side, he walks through Claude Code plan mode, turning repeat tasks into skills, and the single connection almost nobody has switched on that would save agencies 15 or more hours a week.
03:14 Who Sohun is and what Clankers does
07:36 The biggest mistake we are all making with AI
08:33 Why you have to show AI what good looks like
15:06 First steps on TikTok Shop before you touch AI
17:42 Using AI to analyse video, lighting and hooks
25:03 Frontier models versus the agents that connect them
28:27 Claude Code plan mode and turning tasks into skills
32:38 The hundred dollar versus two thousand dollar task
44:12 Why consistency and volume win on TikTok Shop
48:25 Treating your affiliates like a sales team
50:43 The window fans affiliate making a million a year
55:38 Where to begin with TikTok Shop and AI

Show AI What Good Looks Like
08:33
The first mistake Sohun sees is that almost nobody has their processes written down clearly, and their data is rarely clean enough for AI to learn the business. Feed a tool like Claude Code or Codex nothing, and it hands back what he calls AI slop.
"You don't even have a folder on what good looks like, your brand colours and stuff. You're not feeding it anything." - Sohun SankaHis fix is to onboard AI the way you would onboard a person. A new employee gets 90 days, a meticulous onboarding guide, multiple calls a week and time to shadow. Most people give AI two sentences and then conclude it does not work.
"Think about how much context a human gets when they're signing on for a new job, and then they give nothing to AI." - Sohun SankaWorking out what good looks like is uncomfortable precisely because it forces you to define processes most of us keep vague. As Matt put it, the reason we do not tell AI what good looks like is often that we are not sure ourselves.
Win on TikTok Shop Before You Touch AI
15:06
Plenty of the early wins have nothing to do with being AI-forward. Sohun's first moves are the unglamorous ones.
Pull the copy from your highest performing Meta ad angles and landing pages, then feed it into a creative testing brief for affiliates.
Mine your last 30 days of Amazon reviews for one-liners customers actually use, and hand those to creators.
Use social listening to find creators already talking about your product, then reach out personally rather than starting a slow sampling process.
Benchmark against comparable brands. Tools like Kruva scrape and index every video and its KPIs daily, so you can pace yourself against five similar brands and use that as a working definition of good.

The context most people miss is scale. There are roughly 4.5 million TikTok Shop sellers in the US, and only about 1.6% are making a couple of hundred dollars in the last 30 days. That gap is an education problem, and it is exactly where AI can help.
Use AI to Read Your Video, Not Just Your Copy
17:42
TikTok affiliates are not polished UGC creators. They often miss the softer craft, like lighting and camera setup, and on a fast-scrolling feed a slightly off frame can cost you the view before a word is spoken. Sohun uses Gemini to break a video into frames and analyse the first two seconds, pulling out the lighting style so creators can be coached with real examples rather than vague advice.
Cost is the catch, because generating a transcript and classifying every hook frame by frame gets expensive across a thousand videos. Tools like Kruva already pass through transcript, hook and lighting analysis, which lets him reduce the Gemini spend by three to five times by only analysing the final slice of each video with his own framework.
The payoff is reuse. Inside GMV Max you can pull the two-second and six-second video view rates, find every creative clearing a 33% hook rate, analyse what those openings share, and splice those hooks back into your Meta ads or even Amazon sponsored display.
Plan Mode, Skills and the Connection Nobody Switches On
28:27
Sohun's view is that prompt engineering is already on the way out.
"You don't need to learn how to prompt anymore." - Sohun SankaClaude Code plan mode interviews you instead. Ask for a report and it works through which brand, which KPIs, what output format, whether it should run as a cron job, and whether it lands in Slack for review or goes straight to a client. When you find yourself doing that 10 to 15 times a week, you turn it into a skill and run it on demand.
The bigger miss sits underneath all of it. Most brands and agencies have never connected their TikTok Shop tool to Claude as a connector, even though these tools expose around 110 MCP tools. One connection turns the model into an expert on the platform, yet agencies are still spending 15 to 17 hours a week on manual reporting that could be automated.
This ties back to how Sohun runs his own day. He puts a dollar value on his time and sorts tasks into hundred dollar, five hundred dollar and two thousand dollar an hour work, then pushes the cheap tasks to AI or to someone on Upwork.
Treat Your TikTok Shop Affiliates Like a Sales Team
44:12
On TikTok Shop the follow-through is human. Where a performance marketer lets the ad mechanism do the work, here someone has to sit with creators, coach them, share the data and keep them posting.
"The top affiliates are posting 15 to 21 times a week per brand." - Sohun SankaVolume matters because the algorithm is getting very good at matching niche interests to the right moment, and there simply is not enough content in the US to feed it (China's feed runs at roughly 60% shoppable content by comparison). The brands getting this right build real communities. Javy Coffee grew TikTok Shop revenue from $300K to $1M a month with 15 community managers, each owning a book of affiliates and rewarded on the GMV growth they drove, making sure new affiliates post 30 times in their first 30 days.
Then there is the story that anchors the whole episode. One of Sohun's friends is a top-100 affiliate who prints around $1M a year in commissions selling window fans. He runs a dedicated account posting the same fan under different lighting gradients, hundreds of videos, purely to A/B test lighting, then scrapes the top-100 videos each month into a Claude Code testing pipeline.
"We A/B test copy. And I was like, nah, we need to A/B test lighting, because on TikTok Shop, the lighting matters so much." - Sohun SankaMatt recognised the pattern from his own son, a nutritionist with a large Instagram following who tests everything down to the camera angle, and once found that a tea towel over his shoulder lifted engagement. It is the same instinct ecommerce operators already have for split-testing headlines, applied to video.
Where to Begin with AI on TikTok Shop
55:38
Sohun's single starting move is to connect your Claude or ChatGPT connector to your TikTok Shop outreach tool. That tool holds every affiliate activation you are running, so one connection lets the model learn all of it. From there he asks it to lay the whole flow out on a FigJam board, note what should be switched on and off, and show where the leaks are. You will still need TikTok Shop domain knowledge to sense-check what it says, but it is a fast way to see what is actually going on.
Sohun's question for Matt's audience: what is the thing that scares you most about getting started with AI? Matt will be answering it on social, so come and join the conversation.
Today's Guest
Sohun Sanka is the founder of Clankers and previously served as head of growth (employee number one) at the TikTok Shop outreach tools Euka and Reacher. With a background in influencer and affiliate marketing, he now helps brands and agencies build custom in-house software with Claude Code and become genuinely AI-enabled across their TikTok Shop and creator commerce operations.
Website: clankersapp.com
LinkedIn: linkedin.com/in/sohun-sanka

About the eCommerce Podcast
The eCommerce Podcast helps you deliver eCommerce WOW. Every Thursday, Matt Edmundson talks with experts and founders who have built the stores and learned the hard way, so you can grow, scale and eventually exit your own business.
Website and newsletter: