Managing Data Challenges in Custom Shopify Apps

The cleanest way to structure product data in Shopify is to use metafields for taxonomy and tags for merchandising. If we were starting from scratch on Shopify today, we’d structure our product data using metafields for every attribute that matters - material, style, fit, size, use-case - and reserve tags purely for merchandising or internal campaign logic. This separation creates clarity. Metafields give you structured, indexable data that can power collections, filtering, navigation, and content. Tags give your team the flexibility to run promos, launch seasonal bundles, or manage display rules in the theme layer. Each has a purpose, and they shouldn’t need to overlap. But the reality is not usually so neat. Metafields are a relatively recent addition to Shopify, and most established stores were built before they were introduced. Instead, what you often find is a tangled system of colon-based tags - things like style:classic or colour:navy - serving double duty as both taxonomy and frontend logic. That works to a point, but it creates problems at scale. Indexable collections become hard to manage. Tag-based filtering introduces inconsistencies. And any custom logic built into the theme or feed starts to fall over. When we restructure a catalogue, one of the first things we look at is whether it’s possible to migrate key taxonomic data into metafields, even if that means doing it gradually, collection by collection or category by category. It’s not always feasible, but it’s a worthwhile aim. A well-structured catalogue makes everything easier: building subcategories, feeding LLMs, improving shopping feeds, simplifying GA4 tracking, and managing site structure as the business grows.

So you've decided on Shopify Plus for your #B2BeCommerce. Good choice. I've been implementing it since 2017. Here's what I'd focus on. Three pillars. Get these wrong and your project stalls. Get them right and you scale. 1. Client data B2B isn't anonymous traffic. You know your customers. They have negotiated terms, credit limits, shipping preferences, sales rep assignments. Where does this live? Your #ERP? #CRM? Both? Before you touch #Shopify, map it out. Customer creates account → how do they get approved? → how does their pricing show up? → who owns the relationship? Most projects skip this. Then sales complains the portal "doesn't work for our customers." 2. Product data If you have 50,000 SKUs with specs, certifications, and regional variations — Shopify alone won't cut it. You need a source of truth. Could be a PIM like Akeneo - Could be a well-structured spreadsheet if you're small. Clean it first. Then push to Shopify. I've seen companies try to manage product data inside Shopify. Six months later they're drowning in spreadsheets and their catalog is a mess. 3. Transactional data Pricing. Inventory. Orders. This is where B2B projects die. Your agency will say "we'll handle ERP integration in phase 2." Phase 2 never comes. Nail down before launch: What's the source of truth for inventory? How do orders flow back to ERP? Customer-specific pricing — does it live in Shopify or get pulled real-time? If you can't answer these, you're not ready to launch. I'm taking on Shopify Plus and product data projects right now. If you're mid-implementation and stuck, or planning a build and want to avoid the usual traps — DM me.

“We want all our Shopify orders to flow into Business Central—without manual intervention.” A client came to us with this seemingly simple request. But as anyone who’s worked on ERP-eCommerce integrations knows—real-world complexity doesn’t live on the spec sheet. The Goal: Seamless, two-way integration between Shopify (the storefront) and Microsoft Business Central (the ERP). Key Requirements: - Auto-create Sales Orders in BC from every Shopify order - Sync inventory updates from BC to Shopify in near real-time - Reflect cancellations and refunds from Shopify back into BC accurately Our Approach: - Built custom APIs and middleware to validate and route incoming orders - Aligned tax, pricing, and discount data with BC’s business logic - Leveraged Power Automate and custom AL extensions for sales order creation, customer syncing, and item ledger updates - Enabled real-time inventory sync using BC webhooks Challenges We Tackled: - Bridging Shopify’s flexible data model with BC’s structured master data - Managing high-volume orders during peak sales without lag - Preventing duplicate orders from retry loops during sync failures The Outcome: - 90% reduction in manual data entry - 70% faster order processing - Real-time visibility for finance and fulfillment teams across systems The best part? We laid a scalable foundation ready to support future sales channels—Amazon, warehouse apps, and more. Thinking about ERP-eCommerce integration? Here’s our takeaway: Don’t just install a connector. Understand your processes first. That’s where real transformation begins. Have you worked on similar integrations? I’d love to hear what worked (or didn’t) in your experience. #BusinessCentral #Shopify #ERPIntegration #DigitalTransformation #eCommerce #TechStrategy

I've worked with more than 750+ eComm brands on their data connection between Shopify and Meta/Facebook. There's tons of problems I've found, but these are the top 5 data & tracking issues brands have (and don't even realize). ❌ Landing Pages drop tracking code - there's a ton of excellent third-party landing page platforms out there. Most people don't realize that they drop tracking code and lead to data gaps. (You need custom code that properly passes tracking code from the Landing page to the Shopify Checkout) ❌ Click data missing from Checkout - lots of customers need multiple web sessions to go from ad click to product purchases. Most people don't realize this leads to dropped click data and purchases that look like direct traffic (but should be attributed to ad clicks). (You need code that matches sessions and stitches the data together to ensure click data is included with all Purchase events, when available) ❌ Over-counting from non-web orders - A lot of brands have Shop orders, subscription renewals, and offline/draft orders get processed through the Shopify checkout. A basic CAPI connection will send Purchase events for these orders, which leads to misattribution and over-counting. (You need code that is smart enough to see the order source and re-route non-web orders to separate events) ❌ Light payloads with low EMQ - Most brands and most developers don't realize just how much data you can send in any given payload. If your data payloads are missing external id, FBP, and phone info, it leads to low EMQ scores and limits the performance of your ads. (You need an advanced CAPI connection that sends the upper limit of all data, ensuring maximum data coverage) ❌ Data volume too low - Many brands fail to hit the minimum volume of 50 conversions per ad set per week. Under this threshold, Meta simply isn't getting enough data to exit the learning phase and will optimize to clicks instead of conversions. (You need to either increase spend or consolidate your campaigns to ensure you have 50+ weekly conversions) --- If you're using the free/native Shopify CAPI connection, you likely have 3 or more of these issues. Even brands using paid CAPI solutions usually have 1 or more of these issues. If you need help assessing and/or fixing your data and tracking setup, comment below or shoot me a DM.

[ 👀 Long-read but worth reading] Post-purchase upsells can wreck your data. Quietly & Fast. Most Shopify post-purchase up-/cross-sell apps do one of two things: - Some do it right. They “hold” the same order and let the customer add items to it. - Others do it wrong. They create a second order right after the first one. That sounds small. It is not! Because the moment you use your data “as is”… everything breaks. 1. Your customer counts get corrupted: A simple split like: has placed order 1 time = first time customer   has placed order 2 or more times = returning customer   Now fails. A first-time buyer who takes a post-purchase upsell becomes a “returning customer” in your tools. Even though they never came back. They just said yes one more time, five seconds later. 2. Your RFM model becomes fiction: RFM depends on clean order history. • Recency gets distorted because the “last order date” becomes a few seconds after the first order   • Frequency inflates because one checkout becomes two orders   • Monetary value gets chopped into two smaller orders instead of one real order value  So your “best customers” list starts filling up with people who simply clicked an upsell once. And your “new customers” segment shrinks. 3. Your time-between-orders metric becomes useless: Marketing teams love “days between purchases.” It drives: • replenishment flows   • winback timing   • VIP thresholds   • lifecycle reporting  But if the upsell creates a second order instantly: Time between order 1 and order 2 becomes minutes. So your averages crash. Your medians crash. Your cohorts lie. Your retention curve looks better than reality. Then you scale spend based on fake confidence. 4. Your Klaviyo segmentation starts misfiring: This is where it gets painful. Because Klaviyo does not know intent. It only knows events. So you end up with: • first-time buyers skipping your welcome and education flows   • “returning customer” campaigns hitting people who never returned   • VIP logic triggering too early   • post-purchase flows double-firing because there are two orders  Even basic reporting like revenue per recipient gets messy. Because the same person now has two “Placed Order” events back-to-back. → The fix is not a spreadsheet. The fix is choosing the right app. The best setup keeps the upsell inside the same order. So your order count, customer status, RFM & segmentation stays real. So far, the best options I have seen: 1. Rebuy Engine 2. Order Editing With option 2 sending very interesting events to Klaviyo with post-purchase windows  Choose your apps wisely. Because bad data does not look broken. It looks believable.

Frequent issue when working with Shopify apps, including Flow: Race conditions. When events are sent to different apps that need to process them, and one may affect the other. For example, order creation. You may have: One flow that tags the order. A fulfillment app that receives it and creates a label with a carrier based on order tags. The problem? There's no guarantee that the flow (1st app) will be executed before or after the import by the fulfillment app (2nd app). All apps subscribe to Shopify webhooks. These offer very few guarantees, and from these, none work well in configurations with multiple apps. How is this solved? - Use apps with proper sync features They need to be updated every time there's an update to the original data. - Add a delay on one app A significant delay on one of the apps (if it has this feature), will make it act after the other app has finished acting. - Make the resource have the right data upon creation For example, instead of having a form where the customer clicks a checkbox and then using Flow to tag them, have the tag within the form's HTML so it's already added when the customer is created. If you can't do the above: When the apps worked a few times in a certain sequence, don't think that this will always be the case. #shopify #shopifyplus #ecommerce