BUY-2924: US Market Engagement List
Status: Draft
Channel: BuyWhere LinkedIn Company Page
Goal: Engage 20 US e-commerce/AI commerce accounts during US launch (April 2026)
Engagement Targets (20 Accounts)
Tier 1: Major US Retailers (Engagement Priority: High)
| # | Account | Handle | Why Target | Engagement Approach |
|---|
| 1 | Amazon | @amazon | Largest US e-commerce; AI commerce leader | Comment on AIcommerce posts, share Amazon Deals content |
| 2 | Walmart | @walmart | #2 US retailer; massive catalog | Comment on marketplace posts |
| 3 | Target | @target | Disruptive pricing; tech-savvy audience | Comment on Bullseye AI posts |
| 4 | Best Buy | @bestbuy | Electronics specialist; agent commerce relevant | Comment on smart home/AI posts |
| 5 | Shopify | @shopify | Developer-focused; app ecosystem | Engage on partner/API content |
Tier 2: AI Agent / Developer Communities
| # | Account | Handle | Why Target | Engagement Approach |
|---|
| 6 | Nvidia AI | @nvidia | AI infrastructure; gaming/prosumer | Comment on AI agent posts |
| 7 | Anthropic | @anthropicai | Claude/MCP focus | Comment on Claude use cases |
| 8 | OpenAI | @openai | GPT agents; largest AI community | Engage on tool use posts |
| 9 | LangChain | @langchainai | Agent framework; dev community | Comment on LangChain tutorials |
| 10 | CrewAI | @crewai | Agent framework; growing community | Engage on multi-agent posts |
Tier 3: Developer Advocates / Influencers
| # | Account | Handle | Why Target | Engagement Approach |
|---|
| 11 | Product Hunt | @producthunt | Dev product launches; launch platform | Engage on launch posts |
| 12 | Dmitri Tucker | @dabitari | AI commerce influencer | Engage on commerce AI posts |
| 13 | Austin Sharp | @austinsharp | E-commerce operator | Engage on affiliate commerce |
| 14 | Rob Wormald | @robwormald | Dev advocate; ex-Google | Engage on developer content |
| 15 | Swyx | @swyx | AI/devrel; IP callback architect | Engage on AI agent architecture |
Tier 4: E-commerce Operators / Affiliate
| # | Account | Handle | Why Target | Engagement Approach |
|---|
| 16 | Costco | @costco | Bulk retailer; deal seekers | Comment on value posts |
| 17 | HomeGoods | @homegoods | Home category; lifestyle | Engage on home commerce |
| 18 | Nordstrom | @nordstrom | Fashion; premium tier | Engage on fashion tech |
| 19 | Macys | @macys | Department store; broad catalog | Comment on trends |
| 20 | Kroger | @kroger | Grocery; everyday commerce | Engage on grocery tech |
Engagement Templates
Template A: AI Commerce Angle
Interesting approach to [topic]. We're seeing more developers build shopping agents with structured product data APIs instead of scrapers — the data layer is where agents break down.
BuyWhere gives AI agents clean access to 600K+ Amazon US products via MCP or REST. Might be useful for [specific use case they mentioned].
Would love to compare notes on how you're solving the product data problem.
Template B: Developer Tools Angle
This is a great example of [use case]. For AI agents that need live product data, we've been working on an API-first approach — structured responses, MCP integration, and affiliate-tracked purchase links.
Happy to share more if you're exploring this space. The MCP setup takes about 5 minutes.
Template C: Affiliate Commerce Angle
Great content on [topic]. If you're working on affiliate commerce, the product data layer is often the bottleneck — inconsistent pricing, stale data, anti-bot issues.
We built BuyWhere specifically for AI agents that need clean, real-time product data with affiliate tracking built in. Commission rates on Amazon Associates go up to 10% for qualifying categories.
Template D: Price Comparison Angle
Solid breakdown of [comparison topic]. For anyone building price comparison into AI agents, the key insight is separating data retrieval from reasoning — let the API handle product data, let the agent handle the decision.
600K+ products, real-time prices, Schema.org JSON-LD. MCP compatible.
Template E: Quick Comment
This resonates. The product data problem in e-commerce is real — especially for AI agents that need structured, real-time data. We've been focused on exactly this at BuyWhere.
Engagement Tracking
| Account | Handle | Date Engaged | Type | Notes |
|---|
| Amazon | @amazon | | | |
| Walmart | @walmart | | | |
| Target | @target | | | |
| Best Buy | @bestbuy | | | |
| Shopify | @shopify | | | |
| Nvidia AI | @nvidia | | | |
| Anthropic | @anthropicai | | | |
| OpenAI | @openai | | | |
| LangChain | @langchainai | | | |
| CrewAI | @crewai | | | |
| Product Hunt | @producthunt | | | |
| Dmitri Tucker | @dabitari | | | |
| Austin Sharp | @austinsharp | | | |
| Rob Wormald | @robwormald | | | |
| Swyx | @swyx | | | |
| Costco | @costco | | | |
| HomeGoods | @homegoods | | | |
| Nordstrom | @nordstrom | | | |
| Macys | @macys | | | |
| Kroger | @kroger | | | |
Weekly Engagement Goals
- Week 1: Engage with 5 accounts (retailers focus)
- Week 2: Engage with 5 accounts (AI/dev tools focus)
- Week 3: Engage with 5 accounts (influencers focus)
- Week 4: Engage with 5 accounts (remaining accounts)
Notes
- All engagement should be substantive — add value, don't spam
- Personalize each comment to the specific post
- Use UTM on any links shared:
?utm_source=linkedin&utm_medium=social&utm_campaign=us-launch-apr26
- Track engagement in the table above for weekly reporting