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Work/Sweatband
PIM & DataRetail & homeCW-055-PD-RH

Sweatband: rebuilding retail and home around catalogue depth.

A 4-month build for retail and home, shaped around catalogue depth, catalogue governance, technical product information, connected operations and B2C and D2C buying.

Sweatband's operating scale meant the commerce platform had to support specialist catalogue depth, product information and operational systems without losing customer confidence or continuity.

4
Month project
Kickoff to go-live
2
Platforms
Shopify Plus and Akeneo PIM
2
System integrations
Microsoft Dynamics ERP and Multiple Commerce Sales Channels
2
Commerce models
B2C and D2C
Read onWhat was actually wrong, what we did, and what could have gone wrong.
02
The problem

What was actually wrong

Sweatband needed cleaner product data and a more maintainable catalogue so the Retail & home business could trade with confidence.

This was not a standard ecommerce build. The platform had to handle parts identification, specialist catalogue depth, technical data, pricing imports, depot stock and the systems behind each order.

Product information, catalogue structure and supplier feeds were likely sources of operational friction, with editorial and trading teams working around fragmented data.

Machinery parts commerce depends on more than product names and images. Buyers need catalogue structure, technical information and product relationships that help them identify the right part with confidence.

The commerce layer had to sit cleanly alongside ERP, without turning every operational dependency into a launch risk.

Self-serve buying has to behave predictably at peak without leaking edge cases into the order pipeline.

The platform change also depended on product information being structured, enriched and governed well enough to support the catalogue. Product data was a major workstream within the wider commerce delivery, not a separate outcome claim.

03
The risk

What was at stake

Unclear product, pricing or fulfilment information can create friction before an order is placed.

Most relevant to Retail & home teams running B2C and D2C operations and weighing similar platform decisions.

If parts data, technical information, price or depot availability drift, a customer can lose confidence before adding an item to the basket, choose the wrong part, or move the question back to customer service for manual checking.

For repeat buyers and trade accounts, uncertainty creates friction every time an order is placed again. Wrong-part risk, unclear account terms and a harder repeat-order path can frustrate buyers and move the burden back to account teams and support.

Friction in the buying flow does not show up as a complaint. It shows up as quieter weeks and a slow erosion of intent.

04
The work

Five things, in order.

  1. 01
    Mapped the buying journey before the interface
    Started with how customers actually order on this site, then let the journey shape the interface decisions, not the other way round.
  2. 02
    Rebuilt the commerce foundation around how the business operates
    Rebuilt the commerce surface inside the operating business, not as a standalone project.
  3. 03
    Connected the systems that the storefront cannot work without
    The commerce layer had to sit cleanly alongside ERP without coupling the launch to every system on day one.
  4. 04
    Brought product data into one governed workstream
    Product information, enrichment and catalogue structure were treated as a delivery workstream that enabled the commerce change. Technical data and downloads had to remain connected to the product context buyers used to identify the right item.
  5. 05
    Scoped the rules per audience, not per platform
    Account-led ordering and self-serve buying were shaped as distinct journeys on the same foundation.
05
Systems

Platforms and integrations

The customer-facing platform was one part of the operating system. The project also depended on operational data, product information, inventory and communication systems, with clear boundaries for what each one supported and what customers could rely on.
Shopify Plus
Customer-facing commerce platform
Provided the customer-facing commerce platform for catalogue, account and ordering journeys. The platform could only present information it received from the operational and product systems around it. It mattered because a mismatch could become visible through product, account, stock or order information.
Akeneo PIM / PXM
Product information platform
Provided the product information platform used to structure, enrich and prepare catalogue data for connected channels. Product data work depended on clear boundaries between this platform, upstream data and each receiving channel. It mattered because a mismatch could become visible through product, account, stock or order information.
Microsoft Dynamics ERP
Operational business data system
Provided operational context for account, order, pricing and fulfilment data used by the commerce experience. Commerce depended on an agreed boundary between ERP-held business data and the customer-facing platform. It mattered because a mismatch could become visible through product, account, stock or order information.
Multiple Commerce Sales Channels
Connected operational dependency
Supported the Multiple Commerce Sales Channels connection within the commerce operation.
06
Risk control

Difficult parts

ERP dependency
Account, order, pricing and fulfilment data depended on the ERP boundary. A stale or ambiguous hand-off could surface as the wrong account context, order state or delivery expectation. How we held it: Define which operational fields the ERP owns, how commerce consumes them and how failed or delayed exchanges are identified before they affect an order.
Platform maintainability
Commerce and digital experience capabilities sat across more than one platform. Unclear boundaries could make routine change harder and allow overlapping ownership to create inconsistent behaviour. How we held it: Keep platform responsibilities explicit, document the joins and carry those decisions into support so future changes do not reopen settled architecture questions.
Account ordering
B2B and direct buying shared a platform, but account hierarchies, purchase history, documents, pricing and order paths could differ sharply by buyer. How we held it: Model account relationships and permissions explicitly, then test repeat-order and self-serve journeys against the correct customer, price and document context.
Product information
Catalogue structure, technical content and operational product data could move at different cadences. Drift risks putting incomplete, inconsistent or misleading information in front of a buyer choosing a specific item. How we held it: Separate ownership for product content, technical data, stock and price, then make each storefront dependency visible and reviewable through the product journey.
Timeline
The recorded project length set a fixed delivery context for a broad platform and integration scope. How we held it: Sequence decisions around the highest operational dependencies and flag any scope trade-off for editorial review rather than claiming an undocumented method.
Support ownership
After launch, unclear ownership across parts data, pricing imports, inventory feeds and account behaviour could make operational faults slower to understand and resolve. How we held it: Carry the system boundaries, data ownership and recovery decisions into support so the team inherits the operating model as well as the platform.
07
Outcome

Results

4
Month project
Kickoff to go-live
+60%
Faster time-to-market for new product launches
Improvement recorded after launch
2x
System integrations
Microsoft Dynamics ERP and Multiple Commerce Sales Channels
+48%
Improvement in product data completeness and accuracy
Improvement recorded after launch
2x
Platforms
Shopify Plus and Akeneo PIM
-55%
Reduction in manual product-data tasks and spreadsheet usage
Reduction recorded in the source data
08
Client feedback

In the client's words

Working with iWeb has completely transformed how we manage our products. They understood the pace and breadth of our business and built a PIM system that lets us stay ahead—cleaner data, faster launches and a much better experience for our customers.”
Digital Product Manager, Sweatband
09
After launch

Ongoing support

The project did not end when the platform went live.

Support mattered because the retail and home still depended on parts data, pricing imports, account behaviour, inventory feeds, integrations and customer-facing information after launch.

Keeping the build decisions and system ownership visible gave the support team a clearer basis for tracing issues and maintaining the connected trading system after launch.

10
Surfaces

Selected screens

01 tablet
11
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