"Shopify POS is a powerful foundation, but it’s not a turnkey solution for every retail scenario. The platform's maturity is a moving target, and understanding its ecosystem gaps is crucial for strategic planning and preventing costly misalignments."

Joseph Brown, Operations Director, Kubix

Are you considering Shopify POS for your retail operations but unsure about its strengths and limitations?

You're not alone.

As one of the leading ecommerce platforms, Shopify has rapidly expanded into the retail point-of-sale space, but its product is still evolving. In this pod, we explore the realities of implementing Shopify POS backed by expert insights from Joe Brown, Operations Director at Kubix, who has direct experience implementing POS for different business models.

Whether you're running a standalone store or extensive retail estate, this episode has practical advice, product limitations and decisions that can make or break your omnichannel strategy.

The reality…

Shopify POS has become more capable for retail, especially in multistore environments with enhanced permissions and faster workflows. However, it still has capability gaps that can surprise retailers, and requires careful planning, discovery and customisation.

Key discussion points

1. Deep discovery is critical

A recurring theme is the importance of thorough discovery when planning POS projects. Retailers should map existing workflows, identify edge cases (like made-to-order products, custom packing, or complex stock movements) and assess how Shopify’s platform supports or complicates these processes.

Rushing into implementation without understanding detailed workflows can lead to costly rework or operational issues down the line.

2. Inventory management limitations

One of Shopify's gaps is in inventory management for complex use cases. For example, handling stock exchanges between stores where products are unavailable locally remains problematic.

Shopify currently supports split fulfillment orders but lacks native support for multi-quantity line items or real-time transfer workflows, which can frustrate larger or more nuanced operations.

3. Hardware cost & compatibility

POS selection is more than a software decision; hardware investment is foundational. Some issues arise when existing custom integrations, like bespoke receipt printers or scanner setups, are incompatible with new POS hardware or updates.

Testing hardware thoroughly before rollout is essential, and technical teams need to validate network setups, peripherals and existing workflows.

Practical tip: Shopify's recommended hardware kits may not suit every store. Custom hardware may be necessary, but it can add complexity and cost.

4. Ecosystem maturity and functional gaps

While Shopify's ecosystem is growing, certain functionality including multi-currency gift cards, B2B support or advanced inventory tracking, lag behind expectations.

Retailers with complex order workflows may need to integrate third-party apps or custom solutions to fill these gaps.

Chapters

[00:45] Introduction to Shopify POS and Its Evolution

[03:30] Market Positioning and Retail Challenges

[06:40] Discovery Process in Retail POS Implementations

[09:25] Hardware Considerations for Shopify POS

[12:20] Custom Development Needs in Retail

[15:10] Omnichannel Experience and Customer Journey

[17:55] Integration Challenges with Legacy Systems

[20:50] Inventory Management and Workflow Complexities

[23:15] Future Improvements and Wish List for Shopify POS

“In just an hour, I built a UI to interrogate my data, and it handled most of the heavy lifting for a client project."

Chris Marshall, Director & Co-founder, OnState.

Optimising Ecommerce Data with AI: Real-World Applications

Yes, we talk about AI a lot on the podcast. It's inevitable, AI is weaving its way into so many ecommerce processes and tasks.

This episode is highly practical.

We cover real-world examples of how AI tools are being used to speed-up product data tasks whilst reducing the need to rely on expensive licences for specialist tools.

Summary

Ecommerce businesses are increasingly turning to AI to enhance their data management processes. The pod explores how AI tools are being used to clean, enrich, and structure product data, providing real-world examples that highlight their practical applications.

The Build vs. Buy Dilemma Is Being Reframed

Businesses often face the decision of whether to build custom solutions or purchase existing platforms.

In the context of AI for product data, building allows for tailored solutions using tools like Google Sheets and AI models such as ChatGPT for tasks including data transformation and HTML cleaning.

On the other hand, buying involves using specialized AI-enabled tools or outsourcing, which can save time but may incur higher costs.

Practical AI Strategies Discussed:

1. DIY data cleaning: AI models can automate data cleaning tasks, such as reformatting unstructured HTML and standardising attributes, saving significant manual effort.
2. Automating data structure: AI can analyse complex datasets, infer attribute types, and suggest categorisation rules, streamlining the setup of dynamic product groups.
3. Hybrid approaches: combining DIY methods with outsourcing can optimise resources, allowing businesses to handle unique projects efficiently.

Tune in to hear how AI is transforming data migration and management by automating previously manual tasks, increasing speed and allowing for continuous learning.

Chapters

[00:30] The Build vs. Buy Debate in AI Data Management

[03:20] AI in Data Migration: Practical Use Cases

[06:15] Transforming Data with AI Tools

[09:20] The Role of AI in Content Management

[12:20] Engaging with Data Structures

[15:00] Building Custom AI Tools for Specific Needs

[17:45] Tactical Middleware: A New Approach

[20:35] Speeding Up Data Transformation Processes

[23:20] Validating AI Outputs and Managing Expectations

[26:15] The Future of AI in Ecommerce Data Management

"It's not just about selling more; it's about selling smarter and more sustainably."

Oscar Rundqvist, CEO, eComID.

Summary:

High return rates are a significant challenge in fashion ecommerce, leading to financial losses and environmental harm. This podcast explores how innovative technology like eComID is helping the industry by reducing returns, personalising customer experiences and promoting sustainability.

The problem...

Returns are costly; retailers face expenses from shipping, inspecting and repackaging returned items. Additionally, unsellable returns often end up in landfills, contributing to environmental damage. It's estimated that returns cost retailers between $21 to $46 per item, highlighting the need for effective strategies to minimise them.

Change is needed

Retailers need better metrics to understand customer behaviour.

Traditional metrics like average order value (AOV) can be misleading. Instead, brands should focus on "kept order value," which reflects the revenue retained after returns. This shift in focus can lead to more accurate assessments of profitability and customer loyalty.

Emerging solutions like eCom ID offer innovative ways to tackle return issues. By creating a digital shopping passport, eComID stores individual preferences related to size, style and fit. This enables precise pre-purchase guidance and personalised size recommendations, reducing the likelihood of returns.

Data from post-purchase behaviour and return reason codes are invaluable for improving size accuracy and product fit. Brands like H&M have learned that systemic issues, such as inconsistent sizing, drive return rates. By incentivising detailed feedback, brands can refine their offerings and policies.

Innovative brands are experimenting with personalised return fees based on shopper history. This approach encourages mindful purchasing and supports sustainability by reducing unnecessary returns.

Reducing returns is not just about cutting costs; it's a step toward a more sustainable fashion industry. By utilising personalised, data-driven tools, brands can enhance customer experiences and build lasting loyalty.

Chapters:

[00:30] Introduction to Tech and Sustainability in Fashion

[02:40] Understanding eCom ID and Its Solutions

[07:10] Insights from H&M: The Returns Challenge

[12:45] Data Accuracy in Returns Management

[15:35] The Role of Size and Fit in E-commerce

[20:20] Measuring Success: Average Kept Order Value

[24:15] The Environmental Impact of Returns

[29:50] Future of Ecommerce: Agentic Commerce and Virtual Try-Ons