Industry

Retail & Ecommerce

Storefronts, supply chains, and unit economics that hold up

I help retail and ecommerce teams build storefronts that convert, OMS and inventory systems that survive Black Friday, and a unified customer view that actually drives lifetime value rather than just dashboards.

At a glance
Regulations
5 frameworks
KPIs tracked
6 core metrics
Reference stacks
5 patterns
Services
4 engagements
Case studies
2 published
Perspective

How I think about retail & ecommerce

The architecture, the trade-offs, and where I push back on conventional wisdom.

Retail and ecommerce is the industry where every millisecond and every basis point compounds into a number the CFO recognizes. A 100ms slower hero image on a product detail page shows up in conversion rate. A 0.5% inventory accuracy gap shows up in cancellations and customer-service tickets. My retail work starts from the unit economics and works backwards into the architecture, not the other way around.

Headless commerce is the dominant pattern for any retailer who outgrew Shopify Plus or Salesforce Commerce Cloud's templating layer. Hydrogen, commercetools, Saleor, and Medusa each have legitimate reasons to exist, and the right answer depends on your team's stack, your product complexity, and how much of the merchandising experience you want to own. I help teams choose and migrate without losing organic traffic, which means careful URL preservation, redirect mapping, and a phased rollout that doesn't tank Lighthouse scores during the cutover.

Performance is the silent feature that drives retail revenue. Core Web Vitals are now ranking signals, and edge rendering is the lever that moves them most. I build storefronts on Next.js with edge-rendered product detail pages, ISR for category pages, and aggressive Cloudflare or Vercel CDN caching tuned to inventory cadence. Image optimization (AVIF/WebP at the right sizes), font subsetting, and JS bundle discipline are non-negotiable. The goal is sub-1.5s LCP on the median mobile device, not a perfect Lighthouse score on a fiber connection.

Inventory and order management is where the back-end gets harder than the front-end. Real-time accuracy across DTC, Amazon, eBay, retail stores, and 3PLs requires an OMS that treats inventory as an event-sourced ledger, not a snapshot. I design OMS systems where every reservation, allocation, and fulfillment is an event, with reconciliation jobs that catch the inevitable drift between systems. Black Friday traffic patterns aren't just higher load, they're different load: read-heavy on browse, write-heavy in waves on checkout, with a 10-second SLA on cart-to-confirmation that decides whether the page is a sale or an abandonment.

Payments and PCI scope reduction are the parts most teams underinvest in until the audit. Tokenization through Stripe, Adyen, or Bolt should be the default, and the merchant systems should never see a raw PAN. I build checkout flows with proper SCA handling for European traffic, network-tokenized card-on-file for repeat purchases, and fraud orchestration through Signifyd, Forter, or Riskified that doesn't accidentally reject high-LTV customers. See a recent ecommerce optimization or start a project if your storefront needs to actually move the conversion rate this year.

Challenges

What teams struggle with

The recurring problems I see on retail & ecommerce engagements.

  • 1Black Friday and product-launch traffic spikes (10-50x baseline)
  • 2Inventory accuracy across DTC, marketplaces, retail, and 3PLs
  • 3PCI-DSS scope on payment flows and tokenization
  • 4Headless commerce migrations from legacy SaaS platforms
  • 5Customer identity and CDP unification across channels
How I help

Capabilities I bring

Concrete engineering work that resolves the challenges on the left.

  • Headless commerce on Shopify Hydrogen, commercetools, or Saleor
  • OMS and inventory systems that hold real-time accuracy at scale
  • Edge-rendered, Core Web Vitals-tuned storefronts
  • Personalization and recommendations grounded in first-party data
  • Composable checkout with Stripe, Adyen, or Bolt
Metrics

What teams measure

The KPIs leadership obsesses over in this sector. Most tie back to performance and architecture decisions made years before the dashboard was built.

01

Conversion rate (CR)

The North Star; mobile CR typically 1.5-3%, desktop 2-5%, every 10bps is real revenue.

02

Largest Contentful Paint (LCP)

Sub-2.5s mobile LCP correlates directly with conversion; 1.5s is best-in-class.

03

Cart-to-confirmation latency

p95 under 3s end to end is the bar; abandonment spikes sharply past it.

04

Inventory accuracy

Real-time accuracy across channels; >99.5% is achievable with event-sourced OMS, lower triggers cancellations.

05

Average order value (AOV) and AOV per session

Bundling, upsell, and recommendation effectiveness measured directly.

06

Black Friday peak headroom

Capacity tested against 10-50x baseline; the wrong number gets you a meme on Reddit.

Reference stacks

Stacks I see most often

Patterns I reach for first when scoping a retail & ecommerceengagement. I don't pick technologies for novelty - read more about how I choose.

1

Shopify Hydrogen + Oxygen for DTC, with Sanity or Contentful for editorial

2

commercetools or Saleor + Next.js storefront on Vercel, with Algolia or Typesense for search

3

Postgres + Redis + Kafka for OMS event streams, fronted by Go or TypeScript services

4

Stripe, Adyen, or Bolt for tokenized checkout, with Signifyd or Riskified for fraud

5

Snowflake + Hightouch or Census for reverse-ETL personalization off a unified customer table

Technologies

Tools of the trade

The platforms and frameworks I lean on for retail & ecommerce work.

References

Primary sources & further reading

Public regulator pages, standards bodies, and vendor docs I rely on for retail & ecommerce work. For my own writing on these topics, see Insights.

Building for Retail & Ecommerce?

Let's discuss your specific challenges and how technology can help you ship safely, sleep well, and keep regulators happy.