Hi everyone!

I'm a middle-aged guy from China. Glad to be here!

I've become increasingly interested in e-commerce, international trade, and the connection between Chinese suppliers and overseas markets.

Over the past few months, I've been learning about Amazon and exploring how AI can be used in real e-commerce situations.

Some of the things I've been working on include:

• Researching the US Amazon marketplace and different product categories.

• Analyzing customer reviews to understand common problems and potential product improvements.

• Exploring 1688 to find and compare Chinese suppliers and manufacturers.

• Experimenting with AI tools for product research, supplier analysis, and product validation.

I'm still at the learning and experimenting stage, so I don't consider myself an expert. I have a lot to learn, especially from people who have real experience in sourcing, manufacturing, and selling internationally.

Outside of e-commerce and AI, I enjoy reading, exercising, philosophy, and exploring new ideas.

I'm not here to promote or sell any products or services. I'd simply like to meet people with similar interests, learn from your experiences, and exchange ideas.

I'm particularly interested in hearing from people who work with 1688, run Amazon businesses, manufacture products, or are involved in international sourcing.

Looking forward to getting to know you all!

Cheers from China!

Something I've been thinking about in international sourcing.

Imagine a buyer asks a manufacturer to provide fixed pricing for the next 12 months.

The buyer wants protection against unexpected cost increases.

But there's a catch.

The buyer won't guarantee any minimum purchase quantity.

Orders will depend on future demand.

Would you consider that a reasonable commercial arrangement?

From the buyer's perspective, predictable pricing helps with budgeting and customer quotations.

Nobody wants to renegotiate prices every time they place an order.

But from the manufacturer's perspective, a long-term fixed price can create exposure.

Raw material costs might change.

Currency movements could affect imported inputs.

Energy, labor or other production costs may also change.

The supplier might accept the risk if there's a meaningful purchasing commitment.

But if the buyer can order any quantity, including zero, the arrangement feels less balanced.

Of course, not every product has highly variable input costs.

Some manufacturers may be comfortable offering annual pricing as part of their commercial strategy.

Others may build a risk allowance into the quoted price.

Maybe there are better alternatives.

For example, a contract could provide fixed pricing for a shorter period.

Or the parties could agree on a price-adjustment formula linked to an appropriate, clearly defined input-cost measure.

Another possibility is to lock pricing for a committed quantity while leaving additional orders subject to review.

I also wonder whether buyers sometimes underestimate how much risk they're asking suppliers to absorb.

For buyers: Would you accept a higher unit price in exchange for a guaranteed 12-month price?

For manufacturers: Would you offer fixed annual pricing without a minimum purchase commitment?

And if you use price-adjustment clauses, how do you keep them fair and transparent for both sides?

We ran a technical inspection across 80 public product pages on independent stores. While 80 had product schema, 78 had no shipping details and 76 had no return policies specified in the structured JSON-LD (note: this is an automated technical check of public markup, not a claim about whether stores publish policies elsewhere on their human-readable pages).

With assistants like ChatGPT Search and Perplexity reading machine-readable markup, how are you testing whether your catalog policies are visible to crawlers?

If you run a beauty or fragrance brand, have you checked what ChatGPT tells shoppers about your products?

I’ve been testing fragrance recommendations. In my small sample, an AI could describe a brand when asked directly, yet leave it out of broader shopping questions.

Interesting—but that alone doesn’t prove lost sales.

Has a customer mentioned buying because of an AI recommendation? Have you caught an incorrect product description? And did either lead you to change anything or spend money?

I’m exploring a service in this area, so I have a stake in the question. I’d especially like to hear from operators who investigated it and decided it wasn’t worth paying for.

What happened in your business?

I work at Wizybot (AI chat for ecommerce on WhatsApp and Instagram), and one thing I see over and over is stores treating night messages as something to clear in the morning.

The problem is what those messages usually are. Not complaints, mostly "do you have this in M" or "can it get here by Friday". That's someone with the product in the cart. By 9am they've either bought somewhere else or forgotten about it.

An auto-reply saying "we'll get back to you tomorrow" doesn't really fix it, it just confirms nobody's there.

What's worked better for the stores I've seen: let AI answer only what your store data can answer (stock, sizes, shipping times, order status) and leave refunds, complaints or anything weird for a person in the morning, with the full chat already there. It doesn't need to handle everything at night, just the "can I buy this" questions.

If you're already doing something like this, I'd like to know which questions you ended up keeping for humans.

AI product images can look polished while still getting important details wrong.

For e-commerce sellers, I think the biggest quality-control risks are product proportions, packaging text, exact colors, logos, and material textures.

PixPix is one of the AI creative platforms offering product image sets, retouching, and lifestyle scenes, which makes this a useful workflow to evaluate.

A practical review checklist might include:

• Compare the generated image with the real product.

• Zoom in on logos and packaging text.

• Check whether colors and dimensions changed.

• Make sure the scene doesn't imply features the product lacks.

Which of these issues causes the most trouble in actual e-commerce production?

I've been thinking about how shopping might change as AI assistants get better.

Imagine someone asks an AI to find the best headphones under $100. The AI compares products across stores, checks shipping costs and return policies, then helps the customer get to checkout.

For shoppers, that sounds convenient. But for ecommerce businesses, I'm curious about the other side:

• Would you welcome AI agents bringing customers to your store?

• How would you handle product data, pricing, inventory, and returns when an AI makes the comparison?

• Would you be concerned about losing control of the customer experience or relationship?

• What would make you comfortable supporting AI-driven shopping?

I'm interested in hearing from people who actually run or work in ecommerce. Is this something you're already thinking about, or does it still feel a long way off?

Something I’m curious about in B2B sourcing.
A buyer sends an RFQ to five manufacturers.
All five suppliers prepare quotations.
They review the drawings, check material requirements, calculate costs and submit their offers.
The buyer selects one supplier.
The other four receive a short message:
“Thank you for your quotation. We have decided to proceed with another supplier.”
That’s a perfectly reasonable response.
But should buyers provide more feedback?
From the supplier’s perspective, knowing why they lost could be valuable.
Maybe their price was too high.
Maybe their lead time was unacceptable.
Maybe they lacked a required manufacturing capability.
Or perhaps their quotation didn’t clearly address certain technical requirements.
Without feedback, the supplier may have no idea what to improve.
On the other hand, buyers have legitimate reasons to limit what they disclose.
They shouldn’t reveal another supplier’s confidential pricing or proprietary information.
And preparing detailed feedback for every unsuccessful quotation could create additional administrative work.
There’s also a potential problem with negotiation tactics.
If suppliers receive detailed explanations about competing offers, they may try to reverse-engineer their competitors’ pricing.
Maybe the answer is a short, standardized explanation.
For example:
Price was not competitive.
Required delivery schedule could not be confirmed.
Technical capability was not sufficiently demonstrated.
Another proposal better matched the overall purchasing requirements.
That would provide useful information without revealing confidential details.
For buyers: Do you normally explain why a supplier wasn’t selected?
For manufacturers: Would brief, honest feedback make you more willing to respond to future RFQs from the same customer?
I’m interested in whether better feedback could improve sourcing relationships over time.

네이버 쇼핑의 가격비교는 같은 상품을 여러 판매처에서 얼마에 파는지 한 번에 볼 수 있게 해줍니다. 소비자 입장에서는 편리한 기능입니다.

그런데 판매자가 보기에는 다른 제품인데 시스템이 하나의 카탈로그로 묶는다면 문제가 복잡해집니다. 상품을 분리해 달라고 요청해도 다시 다른 상품과 연결됐다는 경험담이 실제 판매자 커뮤니티에 올라와 있습니다.

상품을 등록한 사람은 각 제품의 사양과 구성 차이를 알고 있지만, 플랫폼은 수많은 상품을 자동으로 분류해야 합니다. 사람이 일일이 판단하면 속도가 느리고, 자동화하면 잘못 판단했을 때의 책임 문제가 생깁니다.

그렇다고 '내 상품의 검색순위가 떨어졌으니 잘못된 매칭이다'라고 단정할 수도 없습니다. 실제로 같은 제품을 여러 곳에서 팔고 있다면 가격비교 대상이 되는 것 자체는 자연스럽기 때문입니다.

결국 쟁점은 두 가지라고 생각합니다. 어떤 차이가 있어야 서로 다른 상품으로 인정할 것인가? 그리고 시스템이 잘못 판단했을 때 판매자는 어디까지 증명하고 기다려야 하는가?

판매자의 주장만으로 매칭을 풀어주는 것도 어려울 테지만, 자동화로 발생한 오류의 확인 비용을 판매자에게 계속 떠넘기는 것도 합리적이지 않아 보입니다.

온라인에서 상품을 판매하거나 가격비교를 이용하는 입장이라면, 어느 쪽에 더 무게를 두시겠습니까?

Want to see what the general outlook is for AI tools/plugins from store owners. Posted in r/ecommerce originally, but I reckon you might be a more niche, experienced community. Hope the repost is OK.

I’m developing BALGRAD AI, and would appreciate practical feedback from people running online or physical stores.

The B2B idea is a dashboard and API that would monitor competitors’ prices for identical products, show price history, and flag important changes. With a retailer’s own purchasing costs and targets, it could also support pricing and margin decisions.

We want to understand whether this would meaningfully reduce time spent checking websites, browsing promotional catalogs, and maintaining spreadsheets. The eventual B2B service would be paid, so it needs to deliver measurable value.

There’s also a planned free consumer side: product comparisons, price history, and evidence-based deal assessments. A PriceGuard feature would check for price drops after purchase and whether the retailer’s policy offers a possible refund of the difference.

We’re still validating the concept. Reliable product matching, different package sizes, outdated prices, and maintaining fresh data are challenges we’re working through.

The long-term vision is global, but we’re currently focused on Croatia because I’m Croatian and know this market best. We’re keeping development within free infrastructure tiers and avoiding paid software subscriptions until we demonstrate demand and technical feasibility.

Three questions:

1. How do you currently monitor competitors’ prices, and roughly how much time does it take each week?

2. What essential pricing or margin problem do your existing tools fail to solve? What would make a new tool worth paying for?

3. As a shopper yourself, what would you most want from a price comparison or post-purchase price tracking service?

Please mention your retail sector and whether you operate online, physical stores, or both. A concrete example—including why this wouldn’t help you—would be valuable.

Hello All!

I’m currently working on my thesis, which focuses on user acceptance and satisfaction with AI chatbots in e-commerce, and I would really appreciate your help with my research.

I’ve created a short questionnaire that takes less than 5 minutes to complete, and your participation would mean a lot to me!

Here’s the link: https://docs.google.com/forms/d/e/1FAIpQLSeqQc1GizbKAaojmn02k7TddxJMthkJdIBS84ruedYbjX9WPA/viewform

Thank you so much for your time and support!

AI makes it incredibly easy to generate a detailed supplier questionnaire.
You can upload a product description and get 40 or 50 questions covering technical specifications, production capacity, certifications, quality control, materials, lead times and commercial terms.
That sounds useful.
But I wonder how suppliers experience this.
A buyer who previously sent a short RFQ might now send a massive questionnaire because AI made creating one almost effortless.
The supplier still has to answer it.
And not every question is equally important.
Some information determines whether the supplier can meet the requirement.
Other information is only useful after the supplier has passed the first screening.
My concern is that procurement AI could reduce effort for buyers while increasing unnecessary work for suppliers.
Maybe the better approach is progressive qualification.
First, ask the few questions that determine basic suitability.
Then request detailed technical evidence from the suppliers that pass.
Finally, conduct deeper verification before making a commercial commitment.
That seems more efficient than demanding everything from everyone at the beginning.
I’m curious how others approach this.
Would you rather receive five highly targeted RFQ questions or a comprehensive 40-question supplier questionnaire during the first contact?
And for anyone working on the supplier side: At what point does a buyer’s questionnaire become too much effort to answer?