What Actually Breaks When You Pick the Wrong eCommerce Automation Tool

What Actually Breaks When You Pick the Wrong eCommerce Automation Tool

ByUS Stacked

1. Mapping Shopify order data between Zapier and Make

Okay, first off — Shopify order payloads are deceptively inconsistent. You’d think a customer’s email or line items array would behave identically across platforms, but no. I once set up a simple flow to send order data from Shopify into Airtable using Zapier and Make side-by-side, just to verify which platform handled it better. They both choked — differently.

Zapier pulled in perfectly fine test data during setup but failed when a real order came in with a blank discount code field. It just skipped the whole line. Make, meanwhile, triggered twice on a single order if the customer used Shop Pay. Like, exact same payload, two triggers.

The actual data differences? Zapier flattens everything — and I mean aggressively — so arrays like line_items[0].title become line_items_0_title. Make preserves the array. That sounds good until you try to map anything into a Google Sheet using dynamic rows, and suddenly half your automation is just regex functions trying to extract values.

“line_items_info.description” did not exist in payload, but no errors thrown — just a silent skip.

Aha moment: the real culprit was me using a Shopify test order that didn’t include taxes. That changed the total line count, which broke Make’s iterator completely. Neither platform warns about this upfront.

Quick tips when mapping Shopify orders

  • Always test real orders, with real discounts/taxes
  • Use Zapier if you want flattened one-to-one column mapping
  • Use Make if you’re comfortable handling deep arrays
  • Create a separate path for “test mode” flows
  • Log incoming webhooks to a sheet before sending downstream

2. Handling abandoned cart emails with conditional logic

I tried doing this with Zapier Paths, then with Make’s router logic. In both cases, the preview emails looked fine. But live runs? Most abandoned cart emails were being sent for orders that had actually been completed — just with a few seconds’ delay.

That happened because Shopify doesn’t always send the abandoned checkout webhook immediately, and in some stores (especially with currency conversion apps installed), the completed order fires first.

What worked better? Adding a 90-second delay in the automation directly, then re-checking the checkout status via Shopify’s API before sending. The catch? Only Make let me re-pull the object mid-flow. Zapier can’t easily do a “GET object again” call without exhausting task volume.

If pixel events were enabled, the webhook fired before the checkout status updated in the admin. No documentation on that anywhere.

By the way, if you ever see abandoned cart emails going to customers who already got receipts, this is why. Most customer support teams think it’s a Klaviyo issue. It’s not.

3. Syncing WooCommerce subscriptions with Airtable for reporting

Oh I absolutely regret saying yes to this one. WooCommerce’s REST API returns subscriptions as custom post types, not products or orders. That means you can’t catch them cleanly unless you add one of three different plugins — two of which expose data differently. Good luck.

Zapier doesn’t even list subscriptions as a trigger unless you’re using a webhook, and even then, the structure is partial. Make can technically scrape it if you use a raw HTTP module with authentication headers, but you’re on your own mapping user IDs to actual email addresses. I had to cross-reference the WordPress user ID and then use a secondary call to /wp-json/wp/v2/users. Brutal.

{
  "subscription_id": 3145,
  "user_id": 92,
  "status": "active",
  "product_id": null
}

That null product ID? It comes from older subscriptions that were migrated from a different plugin. There’s zero fallback mapping.

Ended up storing everything in a staging table in Airtable, manually tagging rows with product names until we could normalize the data.

4. Filtering duplicate events from Stripe on payment succeeded

Once Stripe switched to synchronous webhook retries, I started seeing double entries in Google Sheets when sending via Zapier. The trigger fired once when the event showed up, then again a few minutes later. But only if the customer was international. I have no airtight explanation.

I ended up tagging events with the Stripe event ID and storing them in a dedupe table before pushing them into outputs. Make has better support here — their webhook module lets you parse headers more cleanly, so I could use X-Stripe-Signature to validate and store known events.

Zapier has zero memory unless you stitch together a cache using Storage by Zapier or send data to a separate retention app like Airtable. Even then, conditional filtering is brittle — Zap filters often don’t match unless you flatten the entire payload first.

Best discovery here: Stripe’s retry logic is less aggressive if your webhook returns a 2xx fast. Slowing down your response actually triggers more retries. Not documented anywhere.

5. Using custom webhooks for inventory sync between platforms

Airtable inventory sheet, Shopify storefront using SKU variants, Amazon listings through MCF — syncing all these sounds manageable until you realize there’s a 2–3 minute delay before quantities sync across Amazon accounts. Meanwhile, Shopify is instant. So if two customers check out back-to-back, one order cancels due to stock unavailability. Doesn’t matter which automation tool — this is physics.

The only workaround I found was to lock the SKU in Airtable using a “Processing” status cell during the webhook chain, then release the lock once all three platforms confirmed the update. But that meant I had to pause certain flows mid-execution, and Make allowed this using delay + webhook wait. Zapier can’t wait for an incoming webhook once a Zap starts — it just times out.

Edge case: if an inventory update webhook was delayed more than three minutes, Make assumed the webhook failed and dropped the second path execution. No logs, no retries.

I now pipe every webhook output to Notion just so I have some observable log when things go sideways. Not beautiful, but fast.

6. Adding AI-generated product descriptions without breaking formatting

This sounds simple: use OpenAI to write a quick paragraph based on product tags or bullet lists, then dump it directly into Shopify or WooCommerce. But markdown support? HTML stripping? It’s chaos.

Here’s what broke it:

  • Double quotes turned into HTML entities mid-flow
  • Bold tags from OpenAI wrapped line breaks unpredictably
  • Zapier sanitizes markdown if you use Email actions, but not Webhooks
  • Make lets it through, including broken <br> tags

One workaround: ask OpenAI to return plain text with pipe characters between bullets. Then manually convert these in Make using a Text Split + Reassemble. Tedious but safe.

The biggest snag was that WooCommerce rejected updates with malformed HTML — but didn’t say so. It returned a 200 OK, but the field just didn’t update.

Airtable was oddly well-behaved here. Used it as staging to preview the text before pushing live — worth it just to avoid weird line breaks in production listings.

7. Matching eCommerce product variants across systems with no IDs

Short version: if your Shopify SKUs do not match exactly in WooCommerce or Etsy, forget clean automation. Product names aren’t consistent, and neither Etsy nor WooCommerce exposes internal variant IDs in webhook payloads unless you go digging.

So what worked? A messed-up combination of fuzzy matching via Make’s [Set Variable] + OpenAI module. I passed the description and asked the model to predict the matching SKU, then cross-checked it against a lookup array in Airtable.

“Given: Red Canvas Bag – Medium. Find closest match among: Red Bag – Medium Canvas, RedBag-M, Canvas Bag Medium Red”

It’s a hack, and sometimes wrong — but less wrong than relying on users to fill in variant codes manually. Did expose one bug in Make: if OpenAI fails, the error gets swallowed entirely unless you explicitly enable error routing per module.

This section of the flow took three times longer to build than our actual shopping cart. Welcome to multi-platform eCommerce.

8. Which platform actually fails less during flash sales

Here’s my very scientific benchmark: I watched three client stores during a flash sale that lasted six minutes. All were running the same logic — trigger when order amount exceeds a threshold, mark as “VIP Buyer” in Airtable, send a Slack ping to ops.

  • Zapier missed 2 out of 9 qualifying orders — reason unknown
  • Make captured all 10, but delayed the last one by 4 minutes
  • A custom n8n setup captured 8/10 but failed silently on one webhook delivery (payload too large)

The only reliable signal: Make queued up Slack notifications when their outgoing limits hit, whereas Zapier just dropped the events without queuing. Again, no warning. Just a shrug emoji in the logs.

After that I started buffering inbound data aggressively. Every webhook writes raw JSON into S3 first. Then downstream automations pull from that archive rather than rely on real-time triggers. Adds latency, but at least it happens.

Because I really don’t want to explain to a client that their highest-spending customer didn’t get tagged because “the Zap timed out but didn’t error.”