Andrew Vathanakamsang

If you're running NetSuite as your ERP and Shopify as your storefront, you've probably encountered Celigo. Maybe you're new to the team and you've spotted "integrator.io" in the Shopify apps. You're wondering: how exactly does this sync orders, inventory, and customer data between our systems?

For some brands, Celigo is infrastructure that just works. For others, it's a annual expense that creates as many problems as it solves. The difference comes down to understanding what Celigo actually does, what it costs beyond the sticker price, and whether your operation genuinely needs it.

Here's how to make that call.

## What Celigo Actually Does

Celigo is an integration platform as a service (iPaaS) designed to move data between applications without writing custom code. Think of it as centralized plumbing for your tech stack—instead of building separate integrations between Shopify and NetSuite, Shopify and Klaviyo, NetSuite and your warehouse management system, you manage all those connections in one place.

The platform works through **flows**—automated processes that move data from a source endpoint to a destination endpoint. A flow might listen for new orders in Shopify via webhook, transform that data into the format NetSuite expects, and create a sales order record. Another flow might sync product data from NetSuite to Shopify every hour, ensuring your website inventory stays current.

Celigo provides several tools to build these flows:

- **Pre-built connectors** for popular applications (particularly strong for NetSuite + Shopify)
- **Visual mapping tools** to transform data from one schema to another
- **Filters and branching** to route data based on conditions
- **Lookup functions** to enrich data from additional sources
- **JavaScript hooks** for custom logic when visual tools aren't sufficient
- **Error handling and monitoring** with audit logs and alerting

The interface resembles Shopify Flow—visual, configuration-driven, accessible to non-developers. The difference is scale and sophistication. While Shopify Flow handles simple automation within the Shopify ecosystem, Celigo manages complex, bi-directional data flows across enterprise systems.

## The NetSuite Advantage

Celigo's strongest use case is NetSuite integration. The company has deep ties to NetSuite (their offices are reportedly close to each other), and their marketplace includes pre-built integration bundles for NetSuite + Shopify that handle common scenarios out of the box: order sync, inventory sync, customer sync, and fulfillment updates.

These pre-built integrations are both Celigo's biggest strength and a potential limitation.

On the strength side: they work well for standard use cases, getting you operational quickly without custom development. A typical flow might sync product data managed in NetSuite to your Shopify storefront, or push new Shopify orders into NetSuite for fulfillment. The integration handles the heavy lifting—API authentication, rate limiting, data transformation, error handling—without requiring deep technical expertise. And Celigo maintains these integrations as APIs change, which means you're not scrambling to fix broken integrations every time Shopify or NetSuite releases an update.

On the limitation side: these pre-built integrations are black boxes. You install an app, configure settings, and trust that the underlying logic works correctly. You don't have full transparency into how data flows or how edge cases are handled. When you need customization beyond what the pre-built integration supports—and most brands eventually do—you're layering custom code on top of an abstraction you don't fully control.

For brands running standard NetSuite + Shopify operations, this trade-off often makes sense. The pre-built integration handles 80% of requirements immediately, and you can customize the remaining 20% as needed. For brands with complex, highly customized workflows where the integration itself is a competitive advantage, the black box becomes a constraint.

## The Real Costs Beyond the Price Tag

Celigo's pricing is a function of the number of endpoints (applications you connect) and flows. Connect Shopify, NetSuite, Klaviyo, a CRM, and a marketplace, and you're looking at multiples of that base price.

Beyond licensing costs, there's operational overhead that doesn't appear on the invoice but directly impacts whether Celigo delivers value:

- **Ownership and expertise.** Despite marketing that positions Celigo as a no-code solution for business users, complex integrations still require technical expertise. You need someone who understands API rate limits, data transformation, error handling, and the quirks of each connected system. That person needs to read documentation, debug cryptic error messages, and make architectural decisions about data flow. The visual UI makes simple things easier, but it doesn't eliminate the need for technical competence. You're not avoiding development work—you're doing development work in a different interface.
- **Testing limitations.** Unlike custom code with unit tests and CI/CD pipelines, testing Celigo flows is primarily manual. You can use sandbox environments and clone flows for testing, but you're essentially testing in production. When you embed JavaScript hooks within flows—a common pattern as integrations grow more complex—that code typically isn't covered by automated tests. For teams accustomed to test-driven development and code review processes, this feels like a step backward. The ease of making changes in a visual interface means changes happen without the rigor you'd apply to custom code.
- **Change management and sprawl.** The ease of building flows is a double-edged sword. When anyone can create a new flow to solve a problem, flows multiply without oversight. Someone builds a new flow, the old flow gets turned off but never deleted, and six months later nobody remembers why either exists. Without discipline around documentation, review, and cleanup, Celigo becomes a sprawl of undocumented integrations that's harder to maintain than well-structured custom code. And because you're charged per connection, unused flows that remain active are literally costing you money for nothing.
- **Vendor lock-in.** The more flows you build in Celigo, the more committed you become to the platform. Migration costs grow with every integration. This isn't unique to Celigo—it's true of any iPaaS—but it's worth considering upfront. You're not just buying a tool; you're committing to an ecosystem. If Celigo's pricing changes, if the platform doesn't scale with your needs, or if you need capabilities Celigo can't provide, extracting yourself becomes expensive and time-consuming.

## When Celigo Is the Right Choice

Despite these considerations, Celigo is the right choice for many e-commerce brands. You'll know you're a good fit if:

- **You're using NetSuite.** If your ERP is NetSuite and you need to sync data with Shopify, Celigo's pre-built integrations are hard to beat. The alternative is building and maintaining custom NetSuite integrations, which is expensive and time-consuming. NetSuite's API is powerful but complex, and keeping integrations current as both platforms evolve requires ongoing development effort. Unless you have highly specialized requirements that the pre-built bundle can't accommodate, Celigo will get you operational faster and cheaper than custom development.
- **You have multiple integration points.** The value of a centralized integration platform grows with the number of connections. If you're syncing data between Shopify, NetSuite, Klaviyo, a CRM, multiple marketplaces, and a warehouse management system, consolidating those integrations in one place provides real operational value. Monitoring, error handling, and maintenance all happen in one interface instead of scattered across multiple systems. When an integration fails, you have one place to check. When you need to understand how data flows through your stack, you have one source of truth.
- **You have budget and dedicated ownership.** Celigo isn't a casual purchase, but for brands doing $50M+ in revenue where integration failures directly impact operations, the cost is justifiable. Equally important: you need someone to own the platform. This doesn't have to be a full-time role, but it needs to be someone's responsibility to maintain flows, review changes, monitor errors, optimize connections, and audit what's actually being used. Without ownership, Celigo becomes expensive shelfware that creates more problems than it solves.
- **Pre-built connectors exist for your stack.** Celigo's marketplace includes over 700 application connectors. If the vendors in your tech stack are well-represented—particularly if you're using popular e-commerce tools like Klaviyo, NetSuite, various marketplaces, and common CRMs—you benefit from pre-built integrations that Celigo maintains. If you're connecting to niche systems or building everything with universal HTTP connectors, you're doing custom development anyway. At that point, the value proposition diminishes significantly.

## When Custom Integration Makes More Sense

Conversely, Celigo is probably the wrong choice if your requirements, team structure, or workflows don't align with what the platform does best:

- **Your integration needs are simple.** If you're only syncing data between two or three systems with straightforward requirements, custom integrations or lighter-weight tools may be more cost-effective and easier to maintain. Paying for infrastructure you could build and maintain with a few hundred lines of code doesn't make economic sense.
- **You have strong in-house development capability.** Teams comfortable building and maintaining API integrations, implementing queue-based job systems, and writing comprehensive tests may find custom integrations more flexible and easier to reason about than visual flows with embedded JavaScript. If your engineering culture prioritizes code quality, test coverage, and version control, Celigo's visual interface and limited testing capabilities will feel like a constraint rather than an enabler.
- **Your workflows are highly customized.** Pre-built integrations work best for standard use cases. If your business processes are differentiated and complex—if the way you handle orders, inventory, or customer data is a competitive advantage—you'll end up customizing so heavily that you're essentially building custom integrations within Celigo's framework. At that point, the framework becomes overhead rather than value. You're paying for abstraction that gets in your way.
- **Testing and code quality are non-negotiable.** If your engineering culture requires comprehensive test coverage, code review, and version control for all business logic, Celigo's primarily manual testing approach and visual interface won't meet your standards. The JavaScript hooks you embed in flows aren't easily testable. Changes to flow configuration don't go through pull requests. You're trading control and rigor for convenience, and that trade-off may not be acceptable.

## Making Celigo Work For You

If you decide Celigo is right for your operation, success depends on treating it like critical infrastructure rather than a set-it-and-forget-it tool. Apply the same rigor you'd use for custom code:

- **Establish ownership.** Assign someone—whether an internal team member or an external partner—to own the platform. This person reviews all changes, maintains documentation, monitors errors, and ensures flows follow consistent patterns. Without ownership, Celigo becomes a free-for-all where flows proliferate without oversight.
- **Implement change management.** Don't let the visual interface create a culture where anyone can modify production flows without review. Require review before flows go live, maintain documentation for each integration, and create regular backups using Celigo's snapshot feature. Treat flow changes like code commits—document what changed, why it changed, and what the expected impact is.
- **Audit regularly.** Review active connections quarterly. Disable flows that aren't being used. Delete deprecated integrations. Connection count drives cost—keeping unused connections active is literally paying for infrastructure you don't need. We've worked with brands running 40+ active flows where only 12 were actually necessary. The rest were duplicates, abandoned experiments, or workarounds that outlived the problems they solved. Each represented both technical debt and unnecessary licensing costs. Regular audits prevent this waste.
- **Plan for testing.** Accept that testing will be more manual than with custom code. Use sandbox environments where possible, clone flows for testing changes, and maintain detailed runbooks for validating integrations after updates. When you embed JavaScript in flows, test that code in isolation before deploying it. Recognize that you're essentially testing in production, and plan your deployment process accordingly.
- **Monitor proactively.** Configure alerts for critical flows. Review error logs regularly. Integration failures are often silent—data stops syncing but no one notices until inventory is wrong or orders are missing. Proactive monitoring catches issues before they impact operations. Set up notifications for flow failures on mission-critical integrations like order sync or inventory updates.

## Making the Build-vs-Buy Decision

The build-versus-buy decision for integrations isn't primarily technical—it's strategic. Celigo works best when your competitive advantage lies elsewhere. If data synchronization is necessary infrastructure but not a differentiator, buying a platform lets you focus resources on what actually distinguishes your brand in the market.

But if your integration requirements are unique, if your workflows are a competitive advantage, or if you have the capability to build and maintain custom integrations efficiently, Celigo may add complexity without commensurate value.

The wrong answer is choosing based on what seems easiest in the moment or because NetSuite + Shopify brands just use Celigo. The right answer requires understanding your actual requirements, your team's capabilities, your budget constraints, and your long-term technical strategy.

Trying to decide whether Celigo's annual fixed cost and ongoing maintenance cost makes sense for your operation? Dealing with flow sprawl in an existing implementation? Or evaluating whether custom integration would serve you better? We help e-commerce brands make this evaluation based on their actual requirements, team capabilities, and strategic priorities. [Let's talk!](https://nebulab.com/contact-us)


Before You Buy: What Shopify Brands Need to Know About Celigo

Andrea Vassallo

I want to start with a confession. As a consultant, there is nothing easier than saying yes.

When a stakeholder comes to us with a tight deadline and a massive feature list, saying yes makes everyone smile. The contract gets signed, the kick-off meeting is a celebration, and the adrenaline starts pumping. It feels like momentum.

But over the years, I’ve learned that a quick yes is often the precursor to a slow, painful failure.

We work with some of the most ambitious consumer brands in the world. I talk to the stakeholders and C-level executives every day, and believe me—I know the pressure you are under. You have board members asking about Q4 projections, competitors launching new features, and a team that is already stretched thin. When you come to an agency, you aren't looking for friction; you are looking for relief.

However, the industry is full of Yes-Men agencies that will agree to impossible timelines and flashy solutions just to close the deal. They solve your immediate anxiety, but they often create a much bigger problem down the road: a fragmented customer experience, spiraling maintenance costs, and a website so bogged down by technical debt that innovation becomes impossible.

At Nebulab, we take a different approach. It’s an approach that often surprises our clients initially, but it’s also the reason they stay with us for years.

## Who's Running Your Website?

In too many brands, the digital experience is everyone's playground and no one's responsibility. The idea of a dedicated "CEO of the Website" who orchestrates this entire dance sounds great in theory, but in practice, that role is often split across five people who already have full-time jobs.

Here's what a typical week can look like when working with a fast-paced brand:

- Monday morning, the CMO pushes for a site-wide redesign of product pages—cleaner layouts, bigger imagery, more white space to elevate the brand's premium positioning.
- Tuesday, the CRO proposes an A/B test to add "Frequently Bought Together" modules and size recommendations to those same product pages to boost AOV.
- Wednesday, the CEO comes back from a board meeting: "We need to integrate our new virtual try-on tool on product pages before the investor update. They want to see us innovating with AI."

Each request makes perfect sense from the perspective of each executive. But what's completely missing is a systems-wide view of the website.

That's where we step in. Rather than jumping head-first into design and coding, we ask, "Does this ladder up to a coherent digital strategy?".

## The Trap of the Big Redesign

Let’s look at a concrete example of where a Yes-Man agency can hurt you.

Clients often come to us asking for a redesign. Perhaps their conversion rate is not where they'd like it to be, or a competitor just launched a brand new website and the FOMO is eating them alive.

For a typical agency, this is the holy grail of projects: huge scope, fat retainer, flashy portfolio piece.

Unfortunately, redesigns are also terrible for your risk profile: the more you change, the more you increase the chances that something will go wrong. And with no data to understand the impact of individual changes, it's very hard to understand what's causing the underperformance.

So, when a client asks for a new website, we lead with curiosity instead. We dig into the data, we talk to stakeholders, we talk to their customers.

What we often find is that uprooting their existing experience is too expensive and unnecessarily risky. In such cases, we advocate for a more scientific approach where we shift your budget from a high-risk gamble to a series of incremental experiments.

While certainly more boring, this approach minimizes risk while maximizing your shots-on-goal, and often yields better ROI than a six-month redesign.

## Pushback is Love

This brings me to the core of how we work. For us, consultancy is the art of benevolent pushback.

But let me be clear: this isn't about being obstructionist, and it certainly isn't about taking control of your company. You are the decision-maker. Our job is to ensure you make those decisions with your eyes wide open.

When we challenge a request, we are laying out the trade-offs:

* We warn you if a shortcut creates "technical debt" that will slow you down next year.
* We highlight if a feature bloat distracts from your core value proposition.
* We advise against rigid plans when the market data tells us to pivot.

But once we have presented the risks and offered our best counsel, it's still up to you to make the call.

And should you still decide to move forward with your original plan, well... We're happy to disagree and still commit. There's a time to challenge and there's a time to execute.

## Stewards, Not Vendors

I understand why C-levels are wary of agencies. You’ve likely been burned by vendors who over-promised and under-delivered, or who required so much hand-holding that it felt like you were working for them.

We aim for the opposite. In fact, we want to be stewards of your business.

When you hire Nebulab, you aren't just hiring a pair of hands to design pixels and write code. You are hiring a partner who will lose sleep over your conversion rates. You are hiring a team that has the experience to look you in the eye and say, "I know how much you want this feature, but if we rush it, it will break your Black Friday sale. Let’s postpone it instead."

It takes humility to serve, but it takes courage to lead. We strive to do both.

If you are looking for someone to just say "Yes," there are plenty of other agencies out there who will happily oblige. But if you are looking for someone to help you navigate the chaos, clarify your vision, and build a resilient business—[we should talk.](https://nebulab.com/contact-us)


What Your E-Commerce Agency Won’t Tell You, But Should

Michela Frecchiami

Would you buy a $2,000 cashmere sweater without understanding how the fabric drapes on your body? Would you commit to a $5,000 handbag if you couldn't see the grain of the leather or how it sits on your shoulder? For most customers, the answer is no—and that's the fundamental challenge luxury brands face online. A poorly rendered material or unrealistic fit doesn't just lose a sale—it damages brand perception in ways that take years to rebuild. 

AI-driven virtual try-on is changing this equation. Augmented reality technology has existed for over a decade, but it's only now—thanks to its combination with advanced AI—that it's reached a quality level suitable for luxury. What's changed is that recent advances in AI, particularly generative models and diffusion algorithms, have transformed AR from a novelty into a genuinely useful tool. Today's systems can render materials with near-perfect accuracy, understand body types and movements, and account for real-world lighting conditions. 

But here's the critical insight: only a seamless, intuitive, and realistic experience can actually deliver benefits. The key is **realism over perfection**—virtual try-on needs to look authentic, not artificially polished. When rendering feels too perfect or synthetic, it triggers skepticism. AR models that slow down navigation, feel clunky to use, or produce artificial-looking results create a boomerang effect—damaging brand perception instead of enhancing it.

## The Business Case: Numbers That Actually Matter

Luxury and jewelry e-commerce typically sees conversion rates of around 1.0%–1.2%, which is significantly lower than the global average of 1.4%, according to Shopify's 2024 benchmarks. Yet retailers implementing AI-powered virtual try-on and 3D/AR experiences see consistent improvements: Shopify reports show that interacting with a 3D model increases purchase likelihood by 27%, while AR interactions can drive up to a 65% lift in conversion rates. 

More importantly, returns decrease measurably—Shopify case studies report 5–10% lower return rates on products using AR visualization, while third-party aggregated studies note reductions of up to 30–40% in specific categories. For a brand selling €800 shoes or €3,000 handbags, this doesn't just reduce logistics costs—it preserves inventory value and the ability to resell items at full price. 

Consider a brand doing €50M in annual e-commerce revenue. A 40% conversion lift on a 1.0% baseline brings you to roughly 1.4%—that's potentially millions in additional revenue. A 20–35% reduction in returns on high-ticket items can save hundreds of thousands of dollars in reverse logistics and preserve margins by ensuring products are sold at full price instead of being returned, reprocessed, and potentially discounted.

## How Leading Brands Are Implementing Virtual Try-On

The brands getting this right understand that virtual try-on must embody the same principles of realism over perfection that define their physical products. Here's how leading luxury houses are approaching implementation.

![](https://cdn.sanity.io/images/kqgmf0g2/production/ac99fdc24e43fdd74d8305bb7676cff3de11dc95-1999x1070.png?fit=clip)

**Gucci** has enabled sophisticated try-on across sneakers, accessories, watches, and beauty products with meticulous attention to material authenticity. Their AR rendering accounts for ambient lighting in real-time without looking artificially perfect—the leather on a Gucci loafer reflects light differently across its surface, just as it would in person. For beauty, Gucci offers virtual try-on of lipsticks and makeup through its dedicated beauty VTO platform on mobile devices.

**Chanel** focuses on beauty and accessories, enabling customers to virtually apply lipsticks and eyeshadows with remarkable color accuracy across different skin tones and lighting conditions. This is realism over perfection in practice: Chanel's renderings preserve real skin texture rather than over-smoothing it into synthetic perfection. For eyewear, their try-on feature captures a precise fit for different face shapes—critical in a category where how frames sit on your nose bridge or temples can make or break a purchase decision. 

**Luxottica** has explored multiple angles across its eyewear portfolio, including mobile apps, in-store kiosks, and smart glasses partnerships. Eyewear is particularly well-suited for virtual try-on, as customers need to see how frames complement their face shape, skin tone, and personal style. Their in-store kiosks let customers rapidly try on dozens of frame styles, demonstrating that digital try-on doesn't replace physical retail—it enhances it.

## What It Takes to Implement Successfully

The brands that succeed with virtual try-on share three non-negotiable principles: material fidelity that captures how products actually behave, interface design that feels native to the brand, and seamless integration across the entire customer journey. These aren't optional refinements—they're the foundation that separates implementations that strengthen brand equity from those that damage it.

Rendering quality separates successful implementations from those that damage brand perception. This requires extensive 3D modeling using photogrammetry techniques that capture products from hundreds of angles. Creating these digital assets properly requires significant upfront investment: specialized equipment, skilled 3D artists, and weeks of work per product line. The goal isn't perfection—it's fidelity. 

![](https://cdn.sanity.io/images/kqgmf0g2/production/9539e7101760bf9573222e41c2f94e2d9a5301dd-1600x1052.jpg?fit=clip)

Over-polished lighting, uniform textures, and idealized proportions tend to look synthetic. Imagine a cashmere sweater rendered with perfectly even lighting and no variation in the knit—it looks like CGI, not luxury. Strong implementations capture how products behave in the real world: the way leather reflects light differently across its surface, the natural movement of knitwear on the body, and the visible nuance of shades across different skin tones. That credibility is what distinguishes luxury-grade virtual try-on from mass-market implementations.

These digital assets become valuable inventory that can be reused across try-on, product pages, and marketing campaigns. However, there's no shortcut—low-quality 3D models produce experiences that are worse than having no try-on at all.

### Interface Design Must Align with Brand Language

High-quality 3D assets are necessary but insufficient. The interface itself must translate brand codes into interaction patterns that feel native to the luxury experience—not bolted on as an afterthought.

The interface should translate brand codes into the practical mechanics of try-on:

- **For minimalist brands**: Restrained UI with discreet prompts, few controls, and visual focus on the product
- **For more expressive houses**: Editorial layers and styling context, provided they don't distract from the core decision
- **Path to action**: One-tap camera activation, effortless switching between sizes, colors, and finishes, and simple save-to-wishlist or share-to-consultation flows
- **Continuity with the rest of the site**: Typography, tone of voice, motion, and microcopy should all feel native
- **Interaction patterns**: Mirror what customers already know from your PDPs and checkout to reduce cognitive load
- **Trust signals**: Explicit permissions for camera access, visible loading feedback, and gentle haptics that confirm key actions without breaking immersion

Above all, be selective: features like social sharing or gamification should only appear when they advance the brand's intent. In luxury, sophistication is often about what you remove; keeping the surface area clean allows the craftsmanship, materials, and fit to take center stage, making the experience feel effortlessly premium.

### Mobile Performance Is Mission-Critical

Mobile drives the majority of luxury e-commerce traffic. Performance is non-negotiable. Virtual try-on on mobile presents unique challenges: smaller screens, varied device capabilities, inconsistent camera quality, and suboptimal lighting conditions. The experience must be even more refined on mobile than on desktop, with rendering quality that cannot degrade—a luxury customer browsing on an iPhone expects the same material fidelity they'd see on a desktop.

Mobile-first design means rethinking the entire flow:

- Load times under two seconds
- Seamless camera access
- Flawless one-handed navigation
- Fewer steps to activate the camera
- Larger touch targets
- Simplified controls
- Aggressive optimization for bandwidth constraints

Most implementations fail here—the technology works beautifully in controlled desktop environments, but feels clunky and slow in real-world conditions on actual mobile devices.

### The Customer Journey Must Be Unified

Virtual try-on doesn't exist in isolation—it needs to connect seamlessly across every touchpoint where customers make decisions or seek reassurance.

Virtual try-on should sit exactly where decisions are made and then carry through the journey without friction:

- **On product pages**: Entry point should be prominent but unobtrusive, with states and preferences saved to wishlists so customers can resume their shopping later or discuss their purchases with a sales associate
- **During virtual consultations**: Associates should be able to reference the same try-on view to offer precise fit and styling advice, recreating the intimacy of a boutique experience at a distance
- **In-store**: Pre-built shortlists from at-home sessions can accelerate fittings and boost confidence
- **Behind the scenes**: Tying try-on engagement to personalization, size guidance, and CRM—always with explicit consent—turns a single interaction into a coordinated, cross-channel experience that feels like a natural extension of the brand

### Accessibility Is Non-Negotiable

Credible try-on must perform consistently across diverse customers—not just the narrow demographic often used in technology testing. This isn't just ethical; it's essential to building confidence and reducing returns across your entire customer base.

Requirements for inclusive virtual try-on:

- **For beauty**: Rigorous shade and undertone modeling that accounts for different lighting and camera conditions, with calibration that avoids over-smoothing and preserves real texture
- **For eyewear and jewelry**: Precise scaling, fit guidance, and clear, non-technical explanations that help users understand how size and proportion translate from screen to real life
- **Multiple modes**: Camera-on self try-on, try-on on models with similar features, and accurate static previews for customers who prefer not to use live AR
- **Navigation support**: Ensure each mode can be navigated with keyboard and screen-reader support
- **User control**: Transparent controls to adjust lighting, skin tone matching, and fit markers, so users can fine-tune results to their context rather than accept a one-size-fits-all output

Investing here is both the right thing to do and good business: it reduces friction, broadens reach, and builds trust with audiences often underserved by generic experiences, while improving confidence in purchase decisions and, ultimately, lowering return rates.

## Measuring Success Beyond Conversion Rates

Evaluating the success of virtual try-on requires a connected narrative that ties commercial impact to experience quality and brand equity. In luxury, it can be the right trade-off to accept slightly lower short-term conversions if the experience feels more authentically premium and strengthens brand equity over time. This is the most important tension to understand: **brand perception and conversion aren't always aligned, and in luxury, brand perception often matters more.**

Start with the financials to confirm tangible value—conversion rate lift, return-rate reduction, and AOV growth—then use experience signals to explain why those outcomes occur. Post-interaction surveys should assess whether the experience elevated brand perception, not just whether it drove conversions. 

Track the metrics that reveal whether virtual try-on is reinforcing your luxury positioning:

- **Engagement depth**: Time spent in try-on, number of products and variants explored, completion of key actions such as saving to wishlist or adding to cart
- **Downstream behaviors**: Do users who engage with try-on have higher LTV, lower return rates, or greater repeat purchase frequency?
- **Cross-channel continuity**: In-store visits that reference an at-home try-on shortlist demonstrate how digital confidence translates into physical conversion
- **Brand outcomes**: Pair pre/post brand perception scores (quality, innovation, trust) with sentiment from social and customer service that explicitly references try-on
- **Material authenticity correlation**: Analyze whether improvements in perceived fit and material authenticity correlate with reduced returns and higher AOV

When these layers align—stronger brand scores, deeper engagement, and better unit economics—you have the connective tissue that shows virtual try-on is not merely lifting conversion, but reinforcing the luxury positioning that sustains pricing power and long-term value.

## Technology Should Serve the Brand, Not Define It

AI-driven virtual try-on isn't a trendy feature—it's a tool to increase customer confidence, reduce returns, and strengthen brand positioning. But only when implemented with the same attention to detail that luxury brands apply to every other customer touchpoint. The brands succeeding with virtual try-on share a common principle: technology should serve the brand experience, never define it. The technology should feel invisible, allowing the product and brand to remain center stage. 

The gap between leaders and followers will only widen as technology continues to evolve. Brands that invest strategically now in high-quality implementations will establish competitive advantages that become increasingly difficult to replicate. But implementation quality matters far more than speed. A poorly executed virtual try-on can do more harm than not having one at all. 

The key is finding the right balance between innovation and brand integrity, between cutting-edge technology and timeless luxury values. When you strike that balance, virtual try-on becomes more than a feature—it becomes an extension of what makes your brand special. 

**Need help implementing a virtual try-on experience that actually serves your brand?** Let's discuss creating an experience that maintains your luxury positioning while delivering tangible business results.

Why Luxury Brands Are Betting on AI-Powered Virtual Try-On

Alessandro Desantis

Your potential customers are beginning to have conversations about your products with AI assistants like ChatGPT, Gemini, and Perplexity. When someone asks “What are the best wireless earbuds under $200 for running in all weather?” they’re getting instant recommendations complete with comparisons, specs, and reasons why.

[Nearly 60% of consumers](https://news.darden.virginia.edu/2025/06/17/nearly-60-use-ai-to-shop-heres-what-that-means-for-brands-and-buyers/) now use AI to help them shop, with [over half of Gen Z already using AI for product discovery](https://www.salesforce.com/news/stories/consumer-shopping-ai-trends-2025/).

This shift creates a new challenge for e-commerce brands: Generative Engine Optimization (GEO), the discipline of increasing your visibility within AI-powered discovery tools. Unlike traditional SEO, where you can pull up Search Console and see exactly where you rank, AI discovery is opaque. There’s no dashboard tracking how often ChatGPT mentions your brand, and no confirmed ranking factors. In other words, no one really knows what makes one brand show up over another.

Yet some brands consistently appear in AI responses while others don’t, and the industry is starting to make educated guesses about why that is.

In this post, we’ll focus on the high-level strategies for improving your AI visibility across platforms. We’ll explore the foundational tactics that work today and are most likely to continue to work tomorrow, regardless of how AI commerce evolves.

(Note: We’ll dive deeper into ChatGPT’s Instant Checkout feature and what it specifically means for Shopify brands in a separate, more technical post soon—stay tuned!)

## Setting Realistic Expectations About AI Commerce

Before diving into tactics, it’s important to understand where AI-powered commerce actually stands today versus where industry hype suggests it might be.

While nearly 60% of consumers report using AI to help them shop, usage doesn’t yet translate to transactions. A [comprehensive study of 973 e-commerce websites](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5585812) analyzing 12 months of data (August 2024 — July 2025) with $20 billion in combined revenue reveals that organic LLM traffic currently:

- **Represents less than 0.2% of total e-commerce traffic**—roughly 200 times smaller than Google’s organic search
- **Converts at lower rates than all traditional channels except paid social**, including underperforming organic search, paid search, email, and direct traffic
- **Generates lower revenue per session** than established channels across the board
- **Shows favorable engagement signals** (like bounce rate, indicating relevance) but **shorter session durations and fewer page views**, suggesting users are still learning to trust AI recommendations

**The good news:** The same research shows conversion rates from AI traffic are improving over time, suggesting the channel is maturing even if slowly. The performance gap is narrowing, though [projections indicate it won’t reach parity with organic search within the next 12 months](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5585812). This is a trend that will only accelerate with ChatGPT’s recent release of Instant Checkout.

In other words, GEO should be viewed as a long-term investment in future discoverability, not an immediate revenue channel. You’re building positioning for when—not if—AI-powered shopping reaches mainstream adoption. The brands that establish authority now will have significant advantages when the channel matures, but don’t expect meaningful traffic or conversion impact in 2025.

Think of this as 2005-era SEO: the fundamentals you build today will compound as the channel matures. The brands that act now—while the channel is still developing and the playing field is relatively open—will likely dominate their categories once adoption reaches critical mass.

## Measuring Your Share of Answer

Before you can improve your AI visibility, you need to understand where you currently stand. But unlike with Google’s Search Console, there’s no dashboard showing how often your brand appears in AI responses.

The solution is a surprisingly trivial “Share of Answer” approach. Here’s how it works.

Start by creating a comprehensive list of 25-50 prompts that represent how customers discover and research products in your category. If you’re struggling to generate this list, consider enlisting an LLM to help: you can provide context about your product category, target audience, and typical customer journey stages, and ask it to generate relevant queries. These should span different stages of the shopping journey:

- For product discovery, include queries like “Best laptop for graphic design students” or “What mattress should I buy for back pain?”
- For comparison and research, test prompts such as “[Brand A] vs [Brand B] comparison” or “Alternatives to Popular Product X.”
- For problem-solution matching, try “What helps with back pain while working from home all day?” or “How to choose the right standing desk.”

Once you’ve built your query set, it’s time to test your LLM by asking it each of the queries you’ve come up with. We recommend testing with ChatGPT, which [holds over 60% of the AI assistant market](https://gs.statcounter.com/ai-chatbot-market-share) and [ranks among the top 10 most visited websites globally](https://www.similarweb.com/website/chatgpt.com/). While it’s worth periodically checking other platforms like Gemini, Perplexity, and Claude to understand the broader landscape, your measurement and optimization efforts should center on where your customers actually are—and right now, that’s predominantly ChatGPT.

Calculate your Share of Answer score: the percentage of relevant queries where your brand receives at least one mention. The scoring breaks down like this:

- **Below 20%:** You’re invisible to the AI. Most queries in your category don’t mention your brand at all.
- **20-40%:** Limited presence. You appear occasionally, but competitors dominate most conversations.
- **40-60%:** Competitive visibility. You’re in the mix, showing up regularly alongside other established players.
- **60-80%:** Category leadership. You appear in most relevant queries, often with favorable positioning.
- **Above 80%:** You dominate AI discovery and are the go-to reference in your space.

When testing each prompt, don’t just check whether your brand appears. Document where it appears in the response, how it’s described, which competitors are mentioned alongside you, and what sources the AI cites. Context matters because it shapes how people perceive your brand.

This baseline becomes your benchmark. Without it, you’re making changes without knowing what’s working. With it, you have a clear starting point and a way to track progress over time.

## Prioritize Product Information Completeness

AI systems strongly prefer brands with detailed product specifications, clear imagery, accurate pricing and availability, comprehensive size guides, and compatibility information. Products with incomplete information are frequently skipped entirely, even from well-known brands.

Think of product data as table stakes. Without structured, complete information, LLMs have a much harder time understanding or recommending your products. To fix that:

- **Complete your specifications.** Every product should have comprehensive details covering dimensions, materials, compatibility, and performance characteristics. Include all details that help customers (and chatbots!) make confident decisions. As an example, if you sell a a standing desk, that means not just height range, but weight capacity, motor type, noise level, and cable management features.
- **Improve your imagery.** High-quality images showing products from multiple angles and in use help both customers and AI systems understand what you’re selling. Include lifestyle shots that demonstrate use cases, detail shots that highlight key features, and size comparison images that provide context.
- **Ensure pricing transparency.** Accurate product pricing, taxes, shipping costs, and availability clearly stated helps AI systems recommend products that customers can actually purchase.
- **Add detailed size and fit information.** For apparel, furniture, and any product where fit matters, comprehensive size guides are essential. Include measurements, fit guidance, and ideally customer photos showing the product on different body types or in different spaces.

Start with an audit of your top products. Where are the gaps? What information is missing or outdated? Prioritize fixing the products that drive the most revenue or that represent your brand in competitive searches. Then expand systematically across your catalog.

## Create Educational Content That Demonstrates Expertise

Educational content demonstrates genuine expertise in ways that product pages can’t. When you publish in-depth guides, detailed comparisons, and thoughtful how-to content, you signal to LLMs that you understand your space deeply, and you’re more likely to show up in long-tail queries.

- **Write comprehensive buying guides.** Address the real questions customers have when choosing products in your category. Not just “which product is best” but “what should I consider?” and “how do different features impact performance?”
- **Answer technical questions thoroughly.** If customers in your category wonder about compatibility, installation, maintenance, or optimization, create content that addresses these topics with real depth. Surface-level answers won’t do, as LLMs tend to prioritize sources that go deep.
- **Create comparison content.** Develop honest comparisons that help customers understand tradeoffs between different product types or approaches. When you can fairly compare alternatives in your category, you build credibility with both humans and machines.
- **Document use cases and applications.** Show how products work in real situations. Case studies, setup guides, and application-specific content help AI systems understand not just what you sell, but how and when customers should use it.

Start with your most frequently asked questions. What do customers want to know before buying? What problems are they trying to solve? What comparisons do they make? Create content that answers these questions comprehensively, and you’ll naturally build the educational foundation that AI systems look for.

## Build Authority Through Authentic Engagement

The tough truth about GEO is that your website is just one data point. What matters more is what the rest of the internet says about your brand.

LLMs train on and reference content from across the web. Even though [ChatGPT is citing Reddit less nowadays, ](https://www.investors.com/news/technology/reddit-stock-chatgpt-advertising-google/?utm_source=chatgpt.com) Reddit content is likely to still carry heavy weight in core model training. The same goes for YouTube videos showing your products in action, or social media content that demonstrates real customer satisfaction with your brand.

When your brand shows up consistently across Reddit, YouTube, industry publications, social platforms, and other sources, ChatGPT notices. It’s looking for patterns of trust across multiple sources. One mention is just noise. Ten consistent mentions across different platforms are signal.

- **Encourage authentic customer sharing.** Make it easy and natural for satisfied customers to share their experiences. Send post-purchase emails with prompts like “How are you using your new standing desk? We’d love to see your setup!” Include shareable moments in your product experience—unique unboxing, thoughtful packaging notes, or surprising product features that make people want to post.
- **Participate meaningfully in community discussions.** Find where your customers congregate online (e.g., industry forums, Reddit communities, Facebook groups) and find ways to amplify those channels. In some cases, this might mean contributing directly. In others, it will mean fostering the organic contributions that already happen (e.g., Reddit is notoriously hostile to brands hijacking the conversation).
- **Create shareable resources.** Develop tools, calculators, guides, or templates that others want to reference and link to naturally because they’re genuinely useful. An ergonomic desk setup calculator, a home office lighting guide, or a remote work productivity toolkit: these resources get shared, cited, and linked to organically, building the distributed presence that LLMs will recognize.

In other words, focus on building genuine value where your customers already spend time. The visibility will follow naturally.

## Monitor Your Progress and Iterate

Just like SEO, GEO isn’t a one-time project, but rather an ongoing practice that requires regular measurement and iteration. The AI landscape evolves quickly, and what works today may need adjustment tomorrow:

- **Re-run your Share of Answer analysis quarterly**. Track how your optimization efforts are actually impacting AI visibility. Use the same query set you established in your baseline, but don’t be afraid to add new queries as customer behavior evolves or as you expand into new product categories.
- **Track quality, not just quantity.** Look not just at whether your Share of Answer score is improving, but at how the nature of your mentions is changing. Are you appearing earlier in responses? Are the descriptions more accurate? Are you being mentioned alongside stronger competitors?
- **Test new approaches systematically.** When you hear about a new tactic or strategy, don’t just implement it everywhere. Test it on a subset of products or categories, measure the impact on your Share of Answer, and expand only if you see positive results. A disciplined approach prevents wasted effort on unproven tactics.
- **Document what you learn.** Keep a log of changes you make, results you observe, and patterns you notice. Over time, you’ll build institutional knowledge about what works for your specific brand, category, and customer base. This documentation will be invaluable when new team members join or when you need to justify continued investment.

Rather than implementing every possible tactic, you’ll want to systematically improve your Share of Answer score over time while building assets that serve customers across all discovery channels.

## Building AI Visibility Is Building Future-Proof Discovery

As covered in the expectations section above, AI-powered product discovery is still early-stage but showing clear momentum. The trajectory is undeniable: [Gartner predicts that traditional search engine volume will drop 25% by 2026](https://www.gartner.com/en/newsroom/press-releases/2024-02-19-gartner-predicts-search-engine-volume-will-drop-25-percent-by-2026-due-to-ai-chatbots-and-other-virtual-agents), and conversion rates from AI traffic are [steadily improving over time](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5585812).

This creates a strategic window. The brands that build AI visibility now—while the channel is still developing—will likely dominate their categories once adoption reaches critical mass.

To do that, start with your baseline measurement, then systematically work through product data completeness, educational content creation, and authentic engagement. Finally, monitor quarterly and iterate based on what you learn. The approach is straightforward, but it requires commitment—a trait that is quite rare in an industry that tends to be very short-term oriented.

Need help navigating this transition? At Nebulab, we work with mid-market and enterprise e-commerce brands to build the technical infrastructure that drives visibility across all discovery channels—including emerging AI channels. We take a pragmatic, data-driven approach to GEO that balances long-term positioning with near-term ROI from your core acquisition channels. We can help you measure your baseline, prioritize optimization opportunities, and build a systematic approach to GEO that integrates with your broader e-commerce strategy. [Get in touch](https://nebulab.com/contact-us/) to discuss how we can help your brand show up in the conversations that matter most.

The Early Mover’s Guide to AI Product Discovery

Alberto Vena

Replatforming is one of those moments that can either unlock a step-change in organic growth—or erase years of SEO equity overnight. 

A new commerce stack can improve speed, flexibility, and long-term maintainability. It most likely also breaks URL structures, buries content, and confuses search engines at scale. A 20–40% traffic dip in the first months after launch isn’t unheard of when fundamentals are missed, and the revenue impact can be painful. The upside, though, is equally real: treat SEO as a first-class citizen throughout the migration and you can come out with faster pages, cleaner architecture, and a stronger growth engine than you started with. 

As we repeat as a mantra, and it applies to every aspect of a migration, especially with SEO, to try to change as few things as possible during the migration, and patiently wait until the improvements are easy and safe to implement.

This guide blends hard-won lessons from complex e-commerce migrations with practical checklists you can use right now. We’ll cover the technical foundations *and* the organizational dynamics that make or break replatforms. Because SEO fails less often in code editors than in project plans.

## Pre‑Migration Audit & Benchmarking

### Establish a Baseline Before You Move Anything

If you can’t describe exactly how your site performs today, you can’t diagnose what changed tomorrow. Start with a thorough SEO audit and a shared benchmark doc that the stakeholders agree to protect.

At minimum, benchmark:

- **Traffic & revenue baselines:** Segment by channel (organic/non-brand vs. brand), device, market, and key templates (home, category/PLP, product/PDP, content hubs, blog, help center). Tie organic sessions to revenue where possible.
- **Rankings and visibility:** Track head terms and long-tail queries per template, Capture average ranks and note which terms drive conversions.
- **Crawl data & index footprint:** Export from your crawler and Google Search Console. Inventory indexable vs. non-indexable pages, canonicalized duplicates, parameterized URLs, pagination, and index bloat.
- **Top-performing pages and link equity:** Identify the top 10–20% of URLs that drive 80% of organic value. Map internal links to these pages and log external links you must preserve.

Document technical debt you plan to fix on the new stack: render-blocking scripts, slow templates, thin content, overlapping intent, and legacy parameters. Include dates on every report to ensure post-launch comparisons are apples-to-apples.

## URL Mapping & Redirect Strategy

### One-to-One Redirects Are Non‑Negotiable

Your URL structure is your site’s skeleton. Change it without a precise redirect plan and the body collapses. For any URL that changes, implement **one-to-one 301 redirects** from the old canonical to the new canonical. Avoid chains and loops; every additional hop dilutes signals and slows users down.

**Homepage and category dumps are rank-killers.** Blanket-redirecting old PDPs to the homepage (or to their parent category) is a fast path to irrelevance. Search engines treat irrelevant redirects like soft 404s—you don’t preserve equity, and users bounce because intent isn’t met.

### Practical Tips for Redirect Excellence

- **Build the map early.** Start from your top-value URLs and work down. Include query parameters that resolve to unique content (e.g., `/size=xl` may be ignorable; `/color=navy` may map to a canonical color page if that’s how your new platform works).
- **Normalize everything.** Decide on trailing slashes, case sensitivity, and default file extensions. Enforce consistently to prevent duplicate paths.
- **Keep UTMs safe.** When implementing your redirects, be sure you are not removing UTMs (or any other query params) from the URLs, otherwise you might compromise attribution tracking.
- **Test before launch.** Validate your redirect file in staging with a crawler. Check for 200/301/404 distributions, chains, loops, and relevancy (old product → closest new product or guide).
- **Deploy at the edge.** If possible, run redirects at your CDN/edge layer for speed and reliability.

### Treat Content as the Oxygen of SEO

During migrations, content gets “cleaned up” or rewritten. That can be good—but accidental pruning is common. Titles, H1s, product descriptions, and alt text are easy to strip when templates change. Structured data is often left behind or auto-generated with gaps. If there are content mistakes that can be addressed along with the migration, stop doing it and take a note for later. Again, it’s better not to change too many things together; otherwise, it will be hard to understand where SEO issues originate. 

### What to Preserve—and Improve

- **Metadata parity:** Ensure title tags, meta descriptions, H1s, and canonical tags carry over. Where templates change, confirm variables resolve correctly (e.g., brand + model + key attribute).
- **On-page copy and attributes:** Preserve unique product copy, comparison tables, FAQs, and reviews. Standardize units and naming conventions to avoid duplicate content from near-identical variants.
- **Media and alt text:** Maintain image URLs where feasible, or redirect them. Keep descriptive alt text for accessibility and image search.
- **Structured data:** Recreate JSON-LD for Product, Breadcrumb, Review, Article, FAQ, and Organization. Validate with Google’s Rich Results Test and ensure parity with visible content.

**Manual review is mandatory.** Pick the top 50–200 URLs by organic value across templates and review them page-by-page in staging. Look for lost copy chunks, missing schema, and new UX that hides or defers critical information. If something feels “lighter,” it probably is.

## Technical Considerations

### Replatforming Is a Chance to Upgrade, Not Just Replicate

Perfect preservation isn’t realistic. Your new platform will bring its own SEO opinions, often better than what you had before, and some of them are non-negotiable. Shopify’s URL structure, as an example, is fixed. That’s not a threat; it’s a constraint you can leverage. Treat these standards as a built-in upgrade path: use the move to clean up crawl paths, modernize rendering, and lift Core Web Vitals where possible. The key here is balancing touching only things that have a real impact or are in a no-risk zone. Some examples:

**Robots & index controls:**

- Align **noindex** and **canonical** rules to your new architecture. If you consolidate filters or change pagination, update rel=“prev/next” strategies and canonicals accordingly.

**Sitemaps:**

- Generate clean, modular XML sitemaps (≤50k URLs per file) by template and locale. Exclude noindex/redirecting/404 URLs. Host them on the primary host and reference them in `robots.txt`.

**Rendering & performance:**

- Prefer SSR or SSG for content-critical templates; hydrate interactivity progressively. Ensure your SPA routes are indexable and expose canonical, meta, and schema on initial HTML;
- Use the migration to improve **Core Web Vitals**. Compress and lazy-load media, reduce JS bundle size, and adopt modern image formats. Measure template-by-template rather than sitewide averages.

**Internationalization:**

- If you operate in multiple locales, validate `hreflang` across the new structure. Misaligned language-region pairs or missing return tags can throttle international visibility.

## Pre‑Launch Testing

### Treat Staging Like Google

Your staging environment is where good intentions meet reality. Crawl it with enterprise-grade tools and with Googlebot-like settings. The goal: catch anything that could impair discovery, understanding, or measurement. 


> In Shopify, crawling a store has been historically a problem, as the platform was failing to distinguish genuine and controlled crawls and malicious bots. They recently released an update to allow SEO specialists to use [Web Bot Auth](https://datatracker.ietf.org/doc/html/draft-meunier-web-bot-auth-architecture) to authorize tools to crawl the website and complete audits. 

**What to QA before launch:**

- **Canonical tags:** Are they self-referential and stable? Watch for environment URLs sneaking into canonicals.
- **Meta and headers:** Confirm `noindex`, `nofollow`, and caching headers are correct by template.
- **Structured data:** Validate JSON-LD at scale. Check Product offers/prices/availability match the page.
- **Internal links:** Ensure navigation, breadcrumbs, related products, and in-content links point to production paths (not staging) and use consistent trailing slash rules.
- **Pagination & filters:** Verify canonical strategies and indexability of facets. Decide which filters deserve indexation and which should be `noindex`.
- **Analytics & tags:** Confirm GA/GTM (or your stack), conversion events, and ecommerce schema fire correctly. Deduplicate events to avoid inflated revenue.
- **Sitemaps & robots:** Generate production sitemaps from staging, then disable staging in `robots.txt`. Dry-run the production `robots.txt` to avoid surprises.

## Post‑Launch Monitoring

### The First 4–6 Weeks Are the Danger Zone

Launch day isn’t the finish line; it’s when the real work begins. Search engines will revisit, recrawl, and reinterpret your site. Tiny issues can compound into big problems if you’re not watching closely.

**Daily (and weekly) checks:**

- **Crawl errors:** Monitor 404/5xx spikes in logs and Search Console. Prioritize legacy URLs with external links.
- **Redirect health:** Watch for chains and high-latency routes. Patch mismapped redirects quickly.
- **Indexation & coverage:** Track valid vs. excluded pages. Investigate sudden swings in “Duplicate, Google chose different canonical” or “Crawled – currently not indexed.”
- **Rankings & traffic:** Compare to pre-migration baselines by template and market. Watch non-brand first—brand often masks issues.
- **Sitemaps:** Ensure new URLs are submitted and rising in “Indexed” count. Remove retired sitemaps.
- **Core Web Vitals:** Check for regressions by template. Tracking scripts and A/B tools often bloat JS unexpectedly.

## Ongoing Optimization

### The Migration Is the Beginning of a New SEO Cycle

With the new stack live, resist the temptation to “set and forget.” The best-performing teams set a 90-day post-launch roadmap to convert the migration into momentum.

Focus on:

- **Internal linking refinement:** Use crawl and click data to push equity toward high-value pages—new collections, seasonal hubs, or evergreen guides. Revisit nav and breadcrumbs with real user behavior.
- **Backlink reclamation:** Outreach to update important external links that still point to redirected URLs. Every updated link removes a hop and strengthens relevance.
- **Content re-optimization:** Compare pre- and post-launch rankings. Refresh thin or underperforming pages. Expand FAQs based on emerging queries. Build content around new capabilities the platform unlocks (e.g., better comparison components or dynamic bundles).
- **Schema enrichment:** Add missing structured data types and test eligibility for new SERP features.
- **Technical hardening:** Keep pruning parameter cruft, improve caching, and continue shrinking JS. Treat Core Web Vitals budgets as part of your web performance SLOs.

## Final Takeaway: Paranoia and Precision Protect SEO

Replatforming is equal parts engineering project and change-management exercise. The teams that win keep SEO at the table from discovery to post-launch, blend caution with ambition, and treat baselines as a contract with the business. Precision in redirects, content parity, and technical configuration prevents avoidable losses. Paranoia—healthy skepticism and relentless verification—catches the edge cases that otherwise compound.

Most importantly, view replatforming as the start of a stronger SEO engine. Take advantage of all these tasks to clearly understand what’s needed to fix crawl waste, speed up templates, consolidate content, and build richer experiences once you are on the other side. Done right, you won’t just protect your rankings—you’ll future‑proof your visibility and open new lanes for growth.

If you’re planning a replatform or recovering from one and want an experienced partner to guide the process, **let’s talk**: [https://nebulab.com/contact](https://nebulab.com/contact).

How to Ensure SEO Integrity During a Replatform

In the first three articles of our series on Experience-Led Commerce, we explored [why customer experience is emerging as a powerful lever for sustainable growth](https://nebulab.com/blog/ecommerce-growth-customer-experience), [how to think about customer experience for your brand](https://nebulab.com/blog/what-is-ecommerce-cx), and what it takes to [operationalize CX investments across your business](https://nebulab.com/blog/ecommerce-digital-roadmaps). Now it’s time to face a tougher question:

If customer experience is so important, why do so many brands still struggle to act on it?

For digital-native startups, weaving customer feedback and iteration into their DNA is almost second nature. But for established, complex commerce organizations, the friction is real. It often boils down to this: being "data-driven" sounds great in theory, but is much harder in practice.

In this article, we’ll look at what actually holds teams back from building data-driven CX programs—and how to evolve your approach without grinding your business to a halt.

## The Two Biggest Barriers to a Data-Driven Approach

Adopting a data-driven mindset when it comes to CX requires overcoming two very common (and very human) challenges.

### 1. Short-Term Goals Override Long-Term Investments

For most commerce organizations, especially when they’re under pressure from economic headwinds, the priority is hitting the week’s revenue target. CX investments often feel too abstract, too slow to return, or too far removed from the conversion funnel to warrant resourcing.

But the reality is that neglecting CX can sabotage long-term performance. A short-term focus might get you a few wins, but it often comes at the expense of brand equity, retention, and customer lifetime value.

And yet, the solution can't just be telling brands to be more strategic. Brands need to balance the long-term nature of customer experience investments with short-term business constraints, finding quick wins through fast and measured experimentation.

### 2. Decisions Are Made by Guts, Not Data

All the brands we’ve worked with over the years have claimed to be data-driven. In practice, many are still reliant on hopes, intuitions, and the occasional good HiPPO, the Highest-Paid Person’s Opinion.

While it’s easy to point fingers, the reality is that this approach doesn’t stem from ignorance, but rather from the fast-paced nature of the average consumer brand. By the time you're done running your three-month audit, consumer sentiment has shifted, results are nowhere to be seen, and leadership is asking how fast you can launch that new sitewide sale.

As a result, in the best-case scenario, most leaders and operators end up relying on their gut. In the worst-case scenario, they cover their personal opinions under a thin layer of spurious numbers only meant to reinforce their original assumptions.

Again, simply trying to bash through and tell brands that they should be more data-driven is not helpful, and is often met with understandable nods and eye rolls.

## Building Trust One Step at a Time

For XLC to be adopted, you need to make the data collection process manageable for a busy team and build trust in the data.

Luckily, the Hierarchy of Experimentation Evidence provides exactly that: a way to improve how decisions are made, without requiring your organization to grind to a halt. First designed by experimentation expert Ronny Kohavi, this framework helps brands evaluate the strength of the data behind their decisions.

We can visualize it as a pyramid with five levels. From bottom to top, we have:

- **Anecdotes and expert opinions.** Observations, gut feelings, HiPPOs, UX/UI audits are all useful data sources for generating hypotheses on friction points and improvement opportunities, but they are not reliable evidence.
- **Observational studies.** These encompass dashboard analytics, funnel analysis, user behavior patterns, etc. They can help identify issues and suggest potential data correlations, but they cannot be used to prove causality.
- **Uncontrolled experiments.** These are pre-post analyses (i.e., rolling out a change and verifying its impact) and other kinds of experiments that are vulnerable to confounding factors (e.g., seasonality, marketing campaigns, pricing adjustments).
- **A/B tests.** The gold standard for establishing causality. We have published two entire articles on [what makes a good A/B test](https://nebulab.com/blog/ecommerce-ab-testing) and [how to avoid the most common types of biases when A/B testing](https://nebulab.com/blog/ecommerce-ab-testing) (it’s harder than you think), so we encourage you to read up on the matter!
- **Meta-analyses.** Aggregating results across tests, either through systematic reviews or sequential testing methods, is the strongest form of evidence, but it’s also exceedingly rare in digital product development and e-commerce (and, in many cases, unnecessary).

As you have probably guessed, the data at the top of the pyramid is the most reliable, but it’s the hardest to collect; the data at the bottom of the pyramid is the least reliable, but it’s the easiest to collect.

## How To Use the Pyramid

You can use the pyramid to map your data collection maturity. In doing this, it’s important to distinguish between data you *collect* and data you *trust*:

- Understanding what data you collect is easy. In our experience, most brands stop at observational studies or uncontrolled experiments. While A/B testing is common in e-commerce, it’s rarely done with the rigor required to produce useful insights.
- Understanding what data you trust is trickier and more political. As an example, if you collect granular data on the performance of your merchandising strategy, but then you throw it all away when the CEO sends you a Slack message, that data is being collected but not trusted.

Once you have this understanding, your goal should be to graduate to the next level of trust.

But that doesn't mean flipping a switch overnight. The transition from one layer to the next is less about chasing perfect alignment and more about creating structured opportunities to challenge your assumptions with more reliable evidence.

For instance, imagine your analytics suggest that customers are dropping off at the shipping step of your checkout. A pre-post analysis might show a lift after simplifying that step, but it's hard to isolate the cause. So you run an A/B test that confirms the shorter form improves conversion—but only on mobile. That result contradicts a prior assumption that desktop was the problem, and it gives your team stronger footing to explore mobile-first optimizations.

**The point isn't to confirm what you already believe. It's to upgrade the quality of your decision-making, even when the results surprise you.**

By doing this, you’re allowing your organization’s approach to customer experience to mature more gradually. That means your team doesn’t have to pause all work for a six-month analytics overhaul. Instead, they can start small, run a focused A/B test, or build a hypothesis from behavioral data, and incrementally build momentum.

When CX leaders adopt this model, they gain the credibility to win time and budget for long-term projects (solving the short-termism problem), and Gget everyone making decisions based on the facts, not opinions (solving the trust problem).

Over time, organizations become more data-driven, trusting rigorous evidence over intuition and making faster, more confident decisions.

## Small Steps, Compounding Impact

The brands that win on customer experience aren't necessarily the ones with the biggest budgets or the flashiest tech stacks. They're the ones that build feedback loops, run smart tests, and act on what they learn, week after week and quarter after quarter.

You don’t have to wait for a replatform or an executive offsite to start. Identify your current level of evidence maturity. Run one better experiment. Make one stronger decision. Then do it again.

This is how transformation happens: not with a bang, but with a rhythm.

Need help getting your team in the right rhythm? [Let’s talk.](https://nebulab.com/contact-us)

Why Your Brand Is Not Really Data-Driven (And What to Do About It)

In the first two posts of our series on Experience-Led Commerce, we laid the foundation for why customer experience (CX) is one of the most powerful (and most underutilized) levers for sustainable e-commerce growth.

We saw that in an environment of rising acquisition costs and changing consumer behaviors, [brands that invest in customer experience can outperform their peers across virtually every metric](https://nebulab.com/blog/ecommerce-growth-customer-experience): lower CAC, higher LTV, and improved conversion rates. We introduced *Experience-Led Commerce* (XLC) as a structured approach to making that investment count.

[We also explored how customer experience spans three dimensions](https://nebulab.com/blog/what-is-ecommerce-cx): when, where, and why customers interact with your brand. Combined together, these three angles provide a clearer and more actionable framework than traditional customer personas.

But with the foundation laid out, we are now faced with a critical question: where should you start if you want to start investing into your brand’s customer experience? With today’s [“messy middle,”](https://www.thinkwithgoogle.com/consumer-insights/consumer-journey/navigating-purchase-behavior-and-decision-making/) so many touchpoints to optimize, and so many technologies to adopt, even the most sophisticated brand operators can end up feeling overwhelmed, which can lead to bad investments or analysis paralysis.

This article is our answer to that conundrum. We’ll outline the exact process we follow at Nebulab to go from unstructured data to structured insights to prioritized opportunities. By the end of the article, you’ll have a modular framework that you can easily replicate for your own brand based on your resources and level of maturity.

Without further ado, let’s take a look!

## Step 1: Collect the Right Data

To improve your customer experience, you first need to understand what it looks like today—not just in theory, but in the messy, real-world interactions your customers have with your brand across channels, platforms, and moments in time.

As you may imagine, this understanding can only come from data, both qualitative and quantitative. By continuously collecting and analyzing data about the state of your customer experience, you can start forming a picture of the state of play.

The data that we typically collect during a CX audit includes:

- **Anecdotes:** Strategic priorities, internal assumptions, and departmental pain points. The stuff that your gut tells you to be right. For some organizations, this is unfortunately the only data source that matters. Others will scoff at this in favor of a pure-play data-driven methodology. We think neither approach is correct, and we’ll see why in a second.
- **Expert heuristics**: UX/UI audits, competitor benchmarking, and other heuristic reviews to spot points of strengths, points of friction and gaps in your customer experience. [Baymard](https://baymard.com/) is the state of the art when it comes to e-commerce UX. We rely on their library extensively, but we also combine it with our own expertise and the client’s business/operational context.
- **Market research**: Consumer confidence trends, macroeconomic factors, pricing sensitivity, and industry forecasts. These will give you an idea of what consumers are looking for, what trends to ride and what ships to abandon. At the same time, you should be mindful that market research can only give you a map, and the map is not the territory.
- **Voice of the customer**: As the name suggests, this is quite literally what your customers are telling you about your brand and experience. Customer interviews, first- and third-party product reviews, on-site surveys, social media comments all fall under this category. However, you should know that people will often lie about their intentions and desires, even when they don’t realize they’re doing so.
- **Behavioral analytics**: Not what your customers are saying, but rather what they’re doing. This includes funnel analytics, session replays, heatmaps, return rates and reasons, and the results of your A/B test experiments. While more reliable, behavioral analytics are inherently partial (they tell you what, not why) and backwards-looking (they analyze the past and cannot predict the future).

One common pitfall you may run into here is to go too deep on one data source at the expense of all the others: collecting one year of customer reviews may not be as important as collecting the latest three months of customer reviews and pairing those with quantitative data about the return rates and return reasons of different product categories. This is one of those cases where variety matters more than volume.

Furthermore, data has an expiration date. Market research from two years ago may be generally interesting, but it’s likely the economy and your industry have shifted significantly since that report was produced. This means that going too deep on one data source isn’t just useless: it can actually be counterproductive by polluting your data set.

This may sound hard already, but collecting data is actually the easy part. The real challenge lies in making sense of it.

## Step 2: Turn Data Into Insights

The second step in your data-driven journey is synthesis. This is where you look at the data and start poking at it to answer questions such as:

- Where do stakeholder perceptions align (or not) with customer sentiment?
- Which complaints show up in product reviews, focus groups, and support tickets?
- Where are users dropping off in the funnel, and what are they saying when they do?

Good synthesis is more art than science, and it requires both pattern recognition and critical thinking. It involves looking for friction and flow—moments that either propel users forward or send them backward. With one client, for instance, we observed that users were often confused by the site's taxonomy. This friction was surfaced in the UX audit, confirmed in session replays and bounce rates, and ultimately traced back to a lack of backend tagging standards that also hampered merchandising and CRM—something that was lamented in stakeholder interviews.

Artificial intelligence can be a powerful ally in this phase, especially when you're dealing with large, complex datasets:

- For qualitative data, AI can cluster open-ended responses, extract sentiment trends from hundreds of reviews, or summarize transcripts into digestible themes.
- For quantitative data, it can automatically flag anomalies in metrics, identify statistically significant shifts in conversion rates, or correlate changes in performance with external factors like promotions, ad spend, or inventory levels.

Used well, AI helps you explore multiple hypotheses quickly, highlight non-obvious connections, and surface the signals buried in the noise. What it *shouldn’t* do is replace human interpretation. Think of it as an accelerator—it helps you go faster, see broader patterns, and zoom in on what matters, all so you can focus on decisions, not data wrangling.

Here are a few example prompts you might use to synthesize insights:

- "What are the top 3 recurring complaints in these reviews?"
- "Summarize this interview transcript by theme and tone."
- "Group these support tickets into top 5 issue types with example quotes."
- "Highlight any statistically significant drop-offs in this conversion funnel."
- "Which campaigns had the highest impact on AOV last quarter?"

When running audits, we follow a battle-tested AI-assisted workflow that involves progressive summarization and distillation of the data:

1. We store the raw data from each data source in an internal database.
2. We ask the AI to summarize the key insights from each datapoint.
3. We ask the AI to further cluster the summaries by each data source.
4. Finally, we use the AI to produce a single executive summary.

At every step, we store the AI’s output as its own artifact, which allows us to either drill down into the individual datapoint or get the ten-thousand foot view by reading the executive summary, which helps us have informed discussions for different purposes and at different levels of granularity. It also allows us to find areas of alignment and contradictions between datapoints or data sources (more on how to handle these in the paragraphs below), which helps us validate the integrity, quality and thoroughness of our research.

By this stage, you should have an executive summary that clearly outlines the state of your current customer experience from multiple different perspectives and viewpoints.

The next step is to understand what opportunities those insights open for your business.

## Step 3: Turn Insights into Opportunities

This is the phase where a strategy finally begins to take shape.

At this point, you’re not just looking at what’s broken—you’re figuring out how to fix it. To do that, you need to bridge the gap between what the data tells you and what action you can take. For each insight you've uncovered, ask yourself: what can we do about this?

For example:

- If your qualitative research shows customers consistently complain about a confusing checkout flow, your opportunity is to redesign the checkout—possibly starting with small experiments like simplifying the shipping form.
- If analytics reveal a high return rate on a specific product category, the opportunity may lie in improving product detail pages, sizing information, or the product itself.
- If reviews and support tickets highlight unmet expectations around delivery timelines, the opportunity could involve improving operations or setting better expectations through copy and UI.

Each problem statement then becomes a candidate opportunity. But not every opportunity is worth tackling immediately. That’s where the **ICE framework** (Impact, Confidence, Effort) comes in. You should assess each opportunity by its:

- **Impact**: How much value could this create for your users and your business?
- **Confidence**: How confident are you that this change will solve the problem?
- **Effort**: How complex or costly is the solution?

Scoring each opportunity across these three dimensions allows you to build a prioritized backlog. Quick wins (high impact, high confidence, low effort) go to the top and will usually be executed right away. High-effort or low-confidence ideas go into your long-term roadmap.

Turning insights into opportunities is where both intuition and industry expertise become critical. Matching symptoms to solutions is not enough; you need to understand what’s *really* driving the friction and how it plays out in your specific context.

A DTC supplement brand notices that many customers cancel their subscription after the first month, citing “didn’t see the benefits.” A less experienced team might respond by offering deeper discounts or extending the trial period. But a closer look reveals that customers often don’t take the supplements consistently—or aren’t sure what to expect. The real opportunity is to design an onboarding flow that educates users on dosage, timing, and expected outcomes, paired with reminders and habit-building tips.

This is why cross-functional collaboration, paired with seasoned judgment, often makes the difference between good ideas and transformative ones.

But even experienced operators are not infallible, which is why we don’t just blindly roll out our solutions. Instead, we generate multiple hypotheses and validate both our assumptions about the root cause and the potential effectiveness of each solution. We do that by borrowing another concept—this time, it’s called Lean Validation.

## Step 4: Execute, Measure, and Iterate

Lean Validation comes from the Lean Startup methodology, where the goal is to maximize learning while minimizing waste. Instead of committing fully to a solution, you test it as early and cheaply as possible. The aim is to understand whether the direction you’re heading in is sound and reduce the risk of spending significant resources on something that doesn’t work.

You can validate ideas leanly with:

- A/B tests on key journeys
- Unmoderated user tests on wireframes or prototypes
- Email experiments or landing pages to test messaging or demand
- “Fake door” tests to gauge interest before building the full solution
- Clickable prototypes to observe usability without writing production code

If you’ve paid attention, you’ll notice that lean validation is where the loop closes: you’re effectively returning to the data collection stage, except this time your goal is not to understand the original problem, but rather to validate whether your proposed solution is effective. In technical terms, your research has now shifted from generative to evaluative. Then, equipped with more data about the validity of your solution, you can decide whether you should roll it out or go back to the drawing board and try a different approach.

To learn more about this mindset and how to operationalize it, we recommend reading our post on [applying digital product management to e-commerce](https://nebulab.com/blog/ecommerce-product-management), where we explore how to build and evaluate solutions through rapid experimentation and continuous discovery.

## Putting It All Together

Experience-Led Commerce is a playbook that helps brands move beyond discounting wars, beyond channel fragmentation, and beyond gut-feel roadmaps. It gives you the clarity to see what your customers need, the structure to act on it, and the systems to keep learning as you grow.

Now you’ve got the blueprint—one that scales with your brand as you grow. But if you want help building it out, you don’t have to do it alone. [Let’s talk.](https://nebulab.com/contact-us)

From Data to Growth: A Framework to Build Your E-Commerce Roadmap

*This is the second article in a series about enterprise e-commerce replatforms. The first article is about [running discovery workshops](https://nebulab.com/blog/replatform-discovery-workshops), which lay the foundation for a successful migration strategy.*

Replatforming is one of the riskiest moves an enterprise e-commerce team can make. For brands processing thousands of orders an hour, the thought of switching platforms conjures visions of downtime, lost sales, and panicked operators scrambling to fix errors. It’s no surprise that many CTOs and e-commerce managers hesitate to move to Shopify Plus, even when their legacy platform is holding them back.

But it doesn’t have to be that way. At Nebulab, we’ve developed a phased, risk-mitigated approach to Shopify replatforming that allows enterprise brands to make the switch incrementally—without downtime, feature freezes, or disruptions to daily operations. The key? A zero-risk rollout strategy that begins with a comprehensive Discovery Workshop.

## How to Roll Out Shopify Without Risk

One important tradeoff to consider with this approach: while it may slightly increase the overall project timeline, it significantly shortens your time-to-value. Because you're launching a lean implementation early in the process, your team begins learning, iterating, and delivering value from day one—long before a traditional big-bang launch would go live.

Our rollout strategy begins by identifying a part of the business that can be safely isolated. This might be a low-volume country, a non-core sales channel, or a product line with minimal business impact. We then build a lean Shopify implementation for that segment, prioritizing reliability over feature parity.

Once live, we run both platforms in parallel. To maintain data consistency, we implement bi-directional sync mechanisms between Shopify and the legacy platform. Orders, inventory updates, and customer data are pushed in near real-time via middleware or a shared OMS. For instance, when an order is placed on Shopify, it is instantly mirrored on the legacy platform, and vice versa. This ensures fulfillment, accounting, and analytics teams can continue operating without change.

Depending on the systems involved, we may use webhooks, direct API integrations, or tools like Matrixify—especially when data is already formatted for its use. Where necessary, we normalize data structures across platforms to avoid inconsistencies and edge-case failures. The result is a seamless flow of information, so business teams don’t need to alter their workflows.

We also implement robust monitoring and logging for every sync operation. This allows us to detect and resolve any data mismatches or delays before they become operational problems. By this stage, Shopify is already live in production—but the real value lies in observing its performance with real traffic and users.

Let’s break down the phases of a hypothetical rollout plan:

## Phase 1: Run Shopify Side-by-Side

In the first phase, we establish a dual-platform setup.

![](https://cdn.sanity.io/images/kqgmf0g2/production/61212d99d708984bdd92ed04f5614daa3bf15178-2572x1408.png?fit=clip)

Here’s how it works:

* We launch Shopify for a single, low-risk market or sales channel. This might be a small international market or a limited product line.
* Users in that segment interact solely with Shopify, while other markets continue using the legacy platform. A useful trick here: leveraging Shopify’s new Customer Accounts. These accounts use one-time passwords, so we can migrate only the relevant users’ orders without needing password data. Later, we can connect accounts with a custom [identity provider](https://help.shopify.com/en/manual/customers/customer-accounts/new-customer-accounts/identity-provider) if needed.
* Shopify and the legacy platform are tightly integrated. Products and collections sync from the legacy platform to Shopify, preserving PIM processes. Orders placed on Shopify sync back to the legacy system or a shared OMS, so downstream integrations and operations (fulfillment, finance, support) remain unchanged.

This setup lets us test Shopify in a real-world environment without disrupting existing operations. If needed, rollback is simple: we redirect traffic back to the legacy platform without affecting back-office systems.

## Phase 2: Scale Up, One Market at a Time

The second phase is about expanding Shopify’s footprint through multiple, incremental iterations.

![](https://cdn.sanity.io/images/kqgmf0g2/production/ad05fda4084d4ded3afcb43eab002a0db60c5252-2558x1442.png?fit=clip)

We do this by:

* Selecting the next market or sales channel with lower business impact.
* Identifying gaps between current Shopify features and that market’s requirements—such as shipping methods, currencies, or languages.
* Filling those gaps to prepare Shopify for increased traffic.

For example, we might help a brand expand from a single EU market to several, simply by iterating on localization and shipping options. As each iteration proves successful, we redirect traffic and repeat the process, steadily building confidence.

Eventually, all segments are running on Shopify—a sign that the migration is functionally complete.

## Phase 3: Shut Down the Legacy Stack

Now that all traffic has moved to Shopify, we begin dismantling the old platform.

![](https://cdn.sanity.io/images/kqgmf0g2/production/e2a91030b561cf780cfba72648c210221d07946f-1874x1438.png?fit=clip)

This phase is essential for reducing technical overhead and cost. Rather than replacing all integrations at once, we decouple each one individually, each with its own rollback plan. This reduces the risk of introducing widespread issues.

A typical sequence might start with analytics, followed by CRM and other integrations. Each step gives us a chance to verify behavior and performance before moving on to the next.

By the end, every integration runs directly through Shopify, and we can fully decommission the legacy system with confidence.

## What This Looks Like in the Real World

With a parallel architecture in place, we expand Shopify’s role country by country, channel by channel. Each new launch is reversible. If issues arise—UX bugs or operational hiccups—we simply reroute traffic to the old system.

In one recent project, we started with a few low-impact countries. The initial Shopify build handled basic operations but deliberately omitted full feature parity. This allowed rapid testing and iteration. As each market validated the setup, we introduced localized features and content. Eventually, the platforms became so similar that migrating the core markets felt like a natural final step.

Another client, using a headless setup, took a slightly different route. They began by migrating their image CDN—a feature included in any Shopify plan. This alone offset the cost of their plan. Next, they moved PDPs to Shopify’s Storefront APIs. PLPs are next, still powered by Algolia, which remains synced with the old platform. Bit by bit, they’re building a safer, modular migration path.

## When Is a Phased Rollout Worth It?

This phased, dual-system approach is ideal for large, complex e-commerce operations. A full cutover is too risky: even a single hour of downtime can cost tens of thousands and cause long-term brand damage. With deep integration needs across ERPs, OMSs, CRMs, and analytics platforms, maintaining operational continuity is non-negotiable.

Running both platforms in parallel creates a safety net that justifies the extra engineering effort.

One important tradeoff to consider: while this approach may slightly extend the overall project timeline, it significantly shortens your time-to-value. Because you're launching a lean implementation early, your team starts learning, iterating, and delivering value much sooner than with a traditional big-bang launch.

Smaller brands may not need this level of complexity. If your tech stack is simpler, with fewer integrations and lower order volume, a direct switch might be more efficient. At Nebulab, we help clients choose the right strategy based on their scale, risk tolerance, and operational maturity.

## Replatforming Without Regret

Traditional replatforming aims for full feature parity before launch. That usually leads to long timelines, bloated requirements, and fragile go-lives that can’t be reversed.

Our approach is different. We start small, test early, and scale with intention. With robust data sync, meticulous planning, and real-world validation, we make each phase of migration safe, reversible, and rooted in operational reality.

Enterprise replatforming doesn’t have to be an all-or-nothing gamble. With the right strategy, you can migrate to Shopify Plus in a safe, phased, and fully reversible way. [Let's do it together!](https://nebulab.com/contact-us)

A Dual-Run Strategy for Zero-Downtime Shopify Replatforms

As executives at enterprise and mid-market brands increasingly turn to more modern commerce platforms for their flexibility and ecosystem advantages, the stakes have never been higher. A failed replatform doesn't just mean cost overruns—it means lost revenue, damaged team morale, and potentially catastrophic business disruption.

The solution isn't more sophisticated technology or larger budgets. It's a disciplined approach to discovery that surfaces what truly matters before a single line of code is written.

## Why Most Agencies Get Discovery Wrong (And Why That's Expensive)

Here's the uncomfortable truth: most agencies treat discovery as a checkbox exercise. They'll send a brief questionnaire, schedule a few calls, and call it done. The reason is simple—*thorough discovery doesn't scale*. It requires senior talent, takes weeks to execute properly, and can't be easily templated across clients.

But this shortcuts approach creates predictable problems:

- **Misaligned stakeholders** discover fundamental disagreements three sprints into development.
- **Hidden technical debt** surfaces when legacy systems refuse to integrate cleanly.
- **Scope creep** becomes inevitable when requirements weren't properly captured upfront.

The financial impact is severe. We've seen brands burn through millions (yes, really) in development costs before realizing their initial assumptions were wrong. One client came to us after three failed projects in which different agencies had attempted to migrate the brand's custom subscription model to Shopify—failures that could have been prevented with a proper discovery process.

## The Executive Case for Deep Discovery

Consider discovery an insurance policy for your replatforming investment. A properly executed discovery workshop costs 5-10% of your total project budget but can prevent 50-80% of the risk factors that typically derail implementations:

- **Risk mitigation** is the primary value proposition. Discovery workshops identify integration challenges, stakeholder misalignments, and technical constraints before they become expensive problems.
- **Accelerated time-to-market** follows naturally. Teams aligned on clear requirements move faster than teams constantly having to change course.
- **Improved team dynamics** emerge from collaborative planning. Discovery workshops reveal communication patterns, decision-making processes, and internal politics that will impact your project's success.

## Nebulab's Discovery Framework: A Five-Phase Approach

Our discovery process has evolved through countless replatforming projects over the years. It's designed to surface critical information and design an implementation plan that minimizes risk and maximizes alignment.

### Phase 1: Stakeholder Alignment and Baseline Setting

Before any workshop begins, we identify the right participants. This isn't about including everyone—it's about including the *right* people. We need at least one representative from each department that will either execute the replatform or be impacted by its results.

**The stakeholder selection process matters more than most teams realize.** Include too few people, and you'll miss critical requirements. Include too many, and the workshop becomes unwieldy. We typically aim for 6-8 participants maximum.

Each stakeholder receives a comprehensive questionnaire covering their role, current systems, performance metrics, and concerns. The survey is tailored to uncover the specific information we'll need for productive workshop discussions.

The questionnaire serves two purposes: it gives us baseline context and starts stakeholders thinking strategically about the project. When people arrive at the workshop, they're already mentally prepared for deeper discussions.

### Phase 2: Envisioning Success and Failure

Every workshop begins with alignment on what we discovered during stakeholder interviews. We present our findings and ask: "Does this accurately reflect your situation?"

**If everyone agrees, we proceed. If there's disagreement, we've identified a critical issue.** Internal misalignment often masquerades as technical complexity. Better to surface and resolve these conflicts in week one than discover them in month six.

Our first collaborative exercise involves **envisioning success.** Each stakeholder writes down what their ideal end state looks like—not just technical specifications, but business outcomes. Fast-forward to six months post-launch: what does success look like for your specific role and responsibilities?

**The counterpoint exercise, a Pre-Mortem, follows immediately:** if everything goes horribly wrong, why did it fail? This approach consistently surfaces risks that wouldn't emerge through traditional requirements gathering.

These exercises reveal stakeholder priorities and potential conflicts. When the marketing director envisions "flexible campaign landing pages" but the IT director fears "maintaining custom functionality," we know there's a conversation to be had.

### Phase 3: Architectural and User Journey Mapping

**Understanding your current state is prerequisite to designing your future state.** We map your existing technical architecture, focusing on data flow and system integration. We answer questions such as:

- What are the third-party systems you integrate with?
- How do inventory updates flow between those systems?
- What happens when a customer initiates a return?

We then conduct a user journey mapping exercise, **giving equal dignity to both customers and internal users.** Your operations team, customer service representatives, and marketing coordinators all have their own user journeys, with their own points of friction that impact business performance. A replatform that optimizes for end-customer experience while creating (or failing to address!) friction for internal users is a failed replatform.

The architectural and journey mapping exercises run in parallel, with insights from one informing the other. Technical limitations often explain customer experience friction, while user behavior patterns reveal why certain integrations are critical or what integrations are missing that would facilitate the user's life.

### Phase 4: Solutioning

In the fourth part of the workshop, we move into solutioning, where we design the new reality.

**In doing this, we typically aim to be very conservative with the consumer's user journey:** unless there's compelling business justification for dramatic change, the post-replatform consumer UX should closely mirror the existing one. This might seem counterintuitive: after all, isn't the whole point of the replatform to _improve_ the user experience?

While the answer is yes, that doesn't mean you should do it immediately. Postponing the UX improvements reduces development complexity, contains implementation costs, and minimizes the risk of alienating your users with a completely new UX. We've discussed this (somewhat counterintuitive) approach at length in [our article about replatforms:](https://nebulab.com/blog/ecommerce-replatforms) **time and time again, we've seen a more conservative approach outperform big-bang replatforms.**

Architectural changes, however, can and should be more transformative: during this phase, we redesign your e-commerce infrastructure to support your business objectives and enable future evolution. This means dropping legacy tools, improving how you use them, or adopting new ones, with the two-fold goal of streaminling your internal operations and enabling future evolution for your consumer UX.

### Phase 5: Launch Strategy and Risk Mitigation

In the fifth and final stage, we have a good understanding of where you are and where you want to be. All that's left to figure out is how to get there.

To do that, we design your launch strategy, and we do it **based on your scale and risk tolerance.** While smaller brands might opt for a coordinated launch across all channels and markets, enterprise clients typically require phased rollouts, beta testing with select customer segments, or market-by-market deployment.

The launch strategy is usually a mix of business considerations (e.g., will team members have to operate two systems until we're fully switched over?) and technical considerations (e.g., how are we going to keep data in sync between different systems during the interregnum?).

We also go deep on risk mitigation: what can go wrong at different stages of the project, how are we going to minimize that risk, and what is our recovery process for the worst-case scenario?

This phase concludes the workshop and produces your implementation roadmap, complete with timeline, resource requirements, and risk mitigation strategies.

## What Discovery Workshops Do (And What They Don't)

As you have seen, discovery workshops have several benefits:

- **They set the stage for a successful replatform.** Clear requirements, aligned stakeholders, and realistic timelines create conditions for successful partnerships, preventing friction and second-guessing.
- **They generate returns that extend far beyond replatforming projects.** The architectural diagrams, user journey maps, and stakeholder alignment achieved during discovery become valuable assets for future initiatives.
- **They improve your internal communication and decision-making processes.** The skills and frameworks learned during discovery transfer to other strategic projects.

However, a discovery workshop is not a panacea! No matter how thorough your workshop, you will never be able to capture your full spectrum of requirements, and you will still be left with unanswered questions. That's alright: the purpose of the workshop is not to eliminate uncertainty, but rather to identify the areas of biggest uncertainty so that you can adress them head-on during the project rather than pretend they don't exist.

If you're looking to replatform and could use some help running a discovery workshop, [reach out!](https://nebulab.com/contact-us) We've run countless workshops over the years and would love to help make your replatforming project a success.


Better E-Commerce Replatforms Require Better Discovery Workshops

It's that time of the year! After spending considerable time going through [Shopify's Summer 2025 Edition](https://www.shopify.com/editions/summer2025) and experimenting with some of the features that became available over the last few months, we are excited by the platform's latest releases: Shopify continues to push the boundaries of what's possible in e-commerce, with remarkable new features and expanded capabilities.

Still, while the overall direction is encouraging, we've also identified some implementation challenges, particularly for the type of mid-market and enterprise client we serve.

Let's take a look at what's changed, what it means for developers and merchants, and where we think Shopify should focus next!

## Horizon: Who's Thinking About the Developers?

![](https://cdn.sanity.io/images/kqgmf0g2/production/2c1d263d8905534742e7b134c45f61f38d02d978-800x450.webp?fit=clip)

[Shopify's new Horizon theme](https://www.shopify.com/news/summer-25-edition-merchant) created a lot of excitement, especially with the no-code/low-code crowd: with the new nested blocks and [AI-powered block generation](https://www.shopify.com/blog/expanding-your-ai-horizons-summer-edition-25), merchants have more flexibility than ever.

While we appreciate all the love that went into [Horizon](https://themes.shopify.com/collections/horizon-themes), our team also had some concerns:

- "What happens to Dawn now?" was the recurring question. With Horizon being the new standard, we expect Dawn to be left behind, forcing agencies and merchants to adjust.
- As most developers learn at some point, giving non-technical users more content management flexibility often comes at the expense of UX and brand consistency. Could Horizon be _too_ flexible for a merchant's own good?
- While Horizon is great, it primarily addresses Shopify's SMB merchants. Meanwhile, developers still need to build custom CI/CD pipelines if they want a robust theme management workflow.

The Shopify theme ecosystem is already renowned for its friendliness towards non-technical users. As Shopify gains traction in the mid-market and enterprise segment, we were also expecting the platform to invest in more robust primitives, tooling, and workflows for theme development—especially since larger merchants are increasingly choosing Liquid over headless in an effort to simplify (more on this below).

As an example, an improved GitHub integration could streamline theme development significantly, and it would be the perfect complement to Shopify's marketing- and simplicity-centered narrative.

## Markets: Segmentation Evolves Beyond Geography

![](https://cdn.sanity.io/images/kqgmf0g2/production/4224e88863540e39b12e9556cf23fbba6c15ba13-1280x720.jpg?fit=clip)

With the latest updates, Shopify has turned Markets from an internationalization tool into a more comprehensive segmentation platform. Merchants can now [segment markets by customer type (B2C vs. B2B), geography and channel (online vs. retail)](https://www.shopify.com/blog/markets), all from a unified interface.

However, despite the new experience finally putting an end to the discrepancies between B2B, POS, and the old Shopify Markets, we also think there's room for improvement:

- The new Markets interface requires a learning curve—the parent-child market relationships aren't immediately intuitive, which is understandable given the complexity of the problem Shopify is solving.
- While Shopify has fixed a number of bugs related to Markets and New Customer Accounts, some were left unaddressed. As an example, the "Buy Again" button still defaults to your primary market rather than the purchase market.

We're also a little concerned by the state of the ecosystem around Shopify Markets: in our experience, very few third parties are able to integrate with Markets at the moment.

Alberto from our team raised an important consideration: "With the new Markets, the ecosystem will need time to align. It took three years for some apps to start supporting the current Markets architecture, and these updates may require them to redo some of the work."

## Next-Gen Dev Platform: So Far So Good

![](https://cdn.sanity.io/images/kqgmf0g2/production/1a7cee517c85cff09117135aa8d89a57347ac715-800x450.webp?fit=clip)

[Shopify's next-gen developer platform](https://shopify.dev/beta/next-gen-dev-platform) shows real promise, with local development capabilities, declarative metaobjects/metafields definitions, monitoring and logging, and much more.

The direction is encouraging, even though some core challenges remain:

- The new localhost architecture is exciting and could really speed up the development of custom apps. Unfortunately, it doesn't support webhooks or Shopify Flows yet—both of which we use heavily. We do recognize these are tough problems to solve, but we're also confident Shopify can figure them out!
- Declarative data definitions are an interesting solution to the ancient problem of Shopify schema management. However, because the resulting metaobjects and metafields are app-owned, they can't be used across different apps, which is problematic in large builds where merchants have multiple custom or third-party apps that need to be integrated.
- The [unified Polaris Web Components](https://shopify.dev/beta/next-gen-dev-platform/polaris) are incredible, but will require yet another migration. Alas! At least there's [an MCP server](https://github.com/Shopify/dev-mcp) to help with the job.

As we excitedly wait for the next batch of improvements, we'll continue to complement Shopify's built-in capabilities with some of our custom tooling, such as [Shopify Toolkit](https://github.com/nebulab/shopify_toolkit).

### AI: One MCP Server to Rule Them All

![](https://cdn.sanity.io/images/kqgmf0g2/production/f98633309e66df5cc87c4530e5fee392ddf07c81-2840x2130.webp?fit=clip)

Quite frankly, the early iterations of Shopify Sidekick were underwhelming to say the least: the agent would hallucinate more often than not, unable to answer even the most basic queries.

Today's version tells a different story entirely. With sophisticated reasoning capabilities and deep platform integration, [Sidekick](https://www.shopify.com/blog/expanding-your-ai-horizons-summer-edition-25#3) now delivers usefulinsights on conversion metrics, bounce rates, and sales performance, and can even take action on behalf of the merchant. While we don't see larger brands dropping more sophisticated analyticsa and BI tooling for Sidekick, it is undeniably come a long way.

But Shopify is not using AI just for operational efficiency. With Perplexity, ChatGPT, and Google all recently announcing their AI shopping features, the platform is investing heavily in standardized and interoperable data access. As a couple of examples:

- The [Global Catalog](https://changelog.shopify.com/posts/introducing-shopify-catalog) allow LLMs to retrieve structured, real-time catalog access optimized for AI consumption. Also, Global Cart and Checkout should be launching soon, allowing LLMs to enable end-to-end conversational checkout experiences.
- The [Storefront](https://shopify.dev/docs/apps/build/storefront-mcp/servers/storefront) and [Customer Accounts](https://shopify.dev/docs/apps/build/storefront-mcp/servers/customer-account) MCP servers open new possibilities for AI-driven shopping experiences: think a wine sommelier chatbot with full inventory awareness and transactional capabilities.

While most of these products are in their very early stages, we'll keep experimenting over the next weeks and months to get a sense of all the potential use cases, so stay tuned!

## The Road Ahead

While this Edition was impressive as always, here's _what else_ we would have liked to see but didn't!

### Search & Discovery Needs Some Love

First of all, Shopify's Search & Discovery continues to lack advanced capabilities:

- Empty collections appear prominently in search results with no built-in way to hide them, creating a poor user experience that directly impacts conversion rates, especially for international merchants that might only sell certain collections in specific markets.
- The inability to use metafields for search indexing forces merchants into awkward workarounds—like copying metafield data into product descriptions just to make it searchable.

When merchants invest heavily in rich product data through metaobjects and metafields, that investment becomes worthless if customers can't discover products through that enhanced information.

As a result of some of these issues, one of our clients recently switched back to their custom search solution after finding Shopify's native search inadequate for their catalog complexity.

For a platform now positioning itself as enterprise-ready, we're hoping to see more investment flow into Search & Discovery to fully integrate it with the rest of Shopify's ecosystem.

### Whatever Happened to Hydrogen?

Perhaps the most interesting omission from the Summer 2025 Edition was the near-complete absence of Hydrogen updates, which might seem even more puzzling considering how much attention it has received in [Summer '24](https://nebulab.com/blog/shopify-editions-summer-2024) and [Winter '25](https://nebulab.com/blog/shopify-boring-edition).

However, given what we're seeing in the market, we were not too surprised that Hydrogen is taking a backseat: the reality is that most brands are currently choosing Liquid themes over headless solutions, prioritizing simplicity and reliability. Even brands that initially launched on Hydrogen are migrating back to Liquid themes, finding that the promised flexibility doesn't justify the additional overhead in the current economic climate.

To be fair, Shopify did release [Storefront Web Components](https://shopify.dev/docs/api/storefront-web-components), allowing merchants to embed the Shopify experience off-site and anywhere they can use HTML—we have some demos coming soon!

All in all, we're confident that we'll see more news about Hydrogen in the upcoming months, but for the time being, the market—and Shopify with it—have focused elsewhere.

## What This Means for You

Shopify's Summer 2025 Edition brought meaningful progress, although some annoying gaps persist. While we expect Shopify's future updates to address some of these pain points, brands should also strive to understand what Shopify does exceptionally well and where additional investment may be necessary to meet their specific business requirements.

At Nebulab, we're excited to help clients leverage these improvements while building complementary solutions where needed. If you need any help integrating Shopify into your business, [don't hesitate to reach out!](https://nebulab.com/contact-us)

The Nebulab