Product strategy / Operating model / Omnichannel retail

Designing a digital BAU operating model

A research-backed product case study that turns an omnichannel retail strategy into a practical system for prioritisation, delivery, measurement, and continuous improvement.

Role: Product Manager
Period: June 2026

Operating-model artefact from the Woodie’s product case study, connecting intake, prioritisation, delivery, and learning.

4 operating principles

3 stakeholder groups

4-week proposed release cadence

90-day adoption plan

Team: Cross-functional retail stakeholders
Product stage: Strategy and operating model
Customer: Retail customers and internal teams
My ownership: Research, model, measures, adoption plan
Key constraint: Protect run while funding change

The brief was operational. The real challenge was strategic.

The Woodie’s brief asked for an effective business-as-usual operating model that could support reliable day-to-day delivery and long-term strategic objectives. I treated that as a product design problem: how should work enter the system, earn priority, move safely into production, and create evidence for the next decision?

My research connected the brief to Woodie’s published digital ambition. The business was planning to triple digital’s share by 2030, grow the digital channel by 130%, and advance four growth drivers across range expansion, loyalty, retail media, and mobile commerce.

This work was created in response to a Digital Product Executive case brief. The research, prioritisation choices, operating model, measures, and implementation plan are my own.

Research turned strategy into operating constraints.

I reviewed Woodie’s public strategy, omnichannel customer evidence, delivery context, and seasonal retail cycle. Four constraints shaped the model.

Omnichannel value

Customers using stores, home delivery, and Click & Collect spend five times more than in-store-only customers. BAU must protect the full journey, not only the website.

Seasonal demand

Garden, decorating, and Christmas peaks change the acceptable level of release risk.

Shared capacity

Campaigns, incidents, supplier onboarding, and strategic work compete for the same team.

Cross-team delivery

Commercial, technical, and retail operations must make one coordinated product decision.

Grafton Group Capital Markets Event 2026 Woodie’s and Virtualstock partnership

One intake protects both stability and progress.

Every request enters one visible intake. Work is ranked by customer value, commercial impact, urgency and risk, then effort. A separate fast lane is reserved for genuine live incidents so that an emergency does not distort normal prioritisation.

Prioritisation model showing one intake and a proposed split between operational and strategic work — The 65/35 run and change split is a starting hypothesis. I would validate it against real demand, capacity, incident volume, and the trading calendar.

Four principles govern the queue

Protect stabilityImprove the platform and the delivery process.
Prioritise valueRank impact instead of request volume.
Protect run and changeServe today without sacrificing tomorrow.
Follow the journeyImprove the experience across online and store.

A four-week release needs stronger control, not more ceremony.

A longer release cadence concentrates more change into each production event. I designed a light delivery engine with explicit readiness, testing, release ownership, rollback planning, and post-launch monitoring.

Define readyStories, acceptance criteria, and dependencies

Build togetherClear handoff, change control, and review

Pass the gateFunctional testing and business UAT

Release and learnRollback readiness and analytics

My quality decisions were informed by prior delivery evidence: 95% accurate document processing on Bill Reader and 97% automated test coverage at Mastercard. Those are examples from my earlier work, not Woodie’s outcomes.

See the Bill Reader evidence

Measurement makes the model accountable.

The operating model uses two layers of evidence. Operational health shows whether delivery is dependable. Strategic progress shows whether protected change capacity is moving the business toward its published ambition.

Operational health

Release success and rollback frequency
Defects reaching production
Time to resolve critical issues
Backlog readiness and blocked work

Strategic progress

Conversion and average order value
App activation and loyalty sign-ups
Extended catalogue coverage
Digital share of total sales

These measures define how I would evaluate the model. They are not presented as achieved results.

The model flexes with the retail calendar.

A useful BAU system cannot allocate capacity identically throughout the year. Release risk should tighten around major trading peaks, while quieter windows create room for strategic change and platform improvement.

Spring and summer

Protect garden trading and schedule larger changes around peak demand.

Autumn

Support decorating demand and prepare the Christmas release plan.

Winter

Use a Christmas code freeze, then reset priorities for the new year.

Strategy enters the same machine

An app and loyalty launch enters as a 0-to-1 change initiative. Supplier onboarding becomes repeatable BAU work. Retail media depends on a performant, measurable digital estate. The model provides one decision path without pretending that every type of work is identical.

Implementation begins with evidence from the real team.

I designed the first 90 days to strengthen the existing system before changing it. The plan moves from observing real work, to improving ticket and measurement quality, to owning the BAU stream and supporting a strategic initiative.

Thirty, sixty, and ninety-day implementation plan for adopting the operating model — The adoption plan starts with workflow observation, stakeholder interviews, analytics review, and a store visit before proposing wider process change.

What I would validate first

Where work currently enters and where decisions stall.
Actual team capacity, incident demand, and release constraints.
Which metrics are trusted and which require a new baseline.
How trading, marketing, technology, and stores define a successful release.

The proof is the quality of the decisions.

This case study demonstrates how I research a business, identify the operating tension, turn strategy into prioritisation rules, make capacity trade-offs explicit, define success measures, and plan adoption with the people who will use the system. The result is a complete, testable operating model with clear criteria for future validation.

What I would improve next

I would test the model against six months of real requests and incidents. That evidence would show whether the initial capacity split is realistic, where work waits longest, and which quality checks prevent the most expensive failures.

Next experiment

Run a four-week shadow cycle without changing the current process. Compare intake age, priority changes, blocked work, release defects, and strategic capacity against the proposed model before adopting it.

Next case study

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