Evidence · Commercial AI Transformation

Results from running the business, not from advising on it.

Twenty-five years of commercial operating responsibility across mobile, retail, subscription software, financial services and technology, much of it using data science and machine learning to make commercial decisions, with the P&L on the line.

Formalus applies that experience to commercial AI transformation for mid-market mobile, retail and membership organisations, with financial cases tested against client evidence and results measured against an agreed baseline.

Michael Williamson, founder of Formalus
Michael Williamson, founder of Formalus. Read his background →
Operator evidence

Twenty-five years accountable for commercial results.


25years

Senior commercial operating roles, including full P&L responsibility at O2.


£24bnrevenue

Cumulative revenue within the scope of GM, VP and C-suite roles, from full P&L to functional commercial accountability.


4th → 2ndin six months

UK share of new customer acquisition volume at Vodafone.


JD Poweraward

Award for customer experience at consumer scale.

Commercial transformation track record

Five commercial transformations, and what each one delivered.

These examples come from executive operating roles and independent consulting work. Each shows commercial decisions, customer processes and the way of working being changed before the supporting technology was built.

Operator evidence Mobile · O2 UK · 12M customers

JD Power award for customer experience

Context. General Manager of O2 Pay & Go, with end-to-end accountability for a £1.8bn P&L at the UK’s largest mobile operator.

What changed. The end-to-end digital customer experience was defined across web, mobile and IVR before the platforms were built to deliver it.

Result. Self-service for 12M customers, 2.5M loyalty opt-ins, 4M acquisitions, and a JD Power award for customer experience at consumer scale.

Operator evidence Mobile · Vodafone Group · four markets

UK share of new customer acquisition volume moved from fourth to second

Context. Global General Manager for Commercial Growth, responsible for customer acquisition and distribution.

What changed. The acquisition architecture for prepaid SIM-only was defined across four markets first: fraud, registration, provisioning and triggered communications. It was then built on one platform.

Result. In the UK, share of new customer acquisition volume moved from fourth to second in six months, with 2M customers added across Europe.

Operator evidence Retail · Staples Europe · around 800 stores · 19 countries

Digital +45% year on year

Context. Vice President of Marketing and Analytics for Europe, with marketing, analytics and promotions across around 800 stores and 19 European websites. Nineteen country teams, 165 people and a €110M budget across €2.5bn of regional sales.

What changed. Nineteen country teams were centralised into a single hub, with 30,000 segments run from it, and the European CRM platform was specified and built to serve that model.

Result. €400M+ online sales, digital up 45% year on year, and the fastest growth in seven years.

Operator evidence Subscription renewal · Symantec / Norton EMEA · 12 sites

Eleven quarters of decline reversed

Context. Vice President of Marketing for EMEA. 110 people, $650M of sales, 71M customers, and eleven straight quarters of decline.

What changed. The full customer-lifecycle engine was designed before the platforms, and $390M was migrated off legacy retail to e-commerce.

Result. Eleven quarters of decline reversed into eight of growth, with 5M customers added.

Independent transformation engagement DHL Service Logistics · EMEA and Asia Pacific

Changed how DHL resolved customer issues across EMEA and Asia Pacific

Context. Customer experience transformation focused on how customer issues were resolved across EMEA and Asia Pacific.

What changed. The resolution journey was mapped with 28 cross-functional leaders and validated with customers before the digital investment was defined.

Result. Two programmes were delivered, with the customer-validated journey used to direct the digital investment.

Delivered as an independent senior transformation consultant.

The same discipline now applied to mid-market mobile operators, multi-site retailers and membership organisations: decide what should change and what it is worth before the technology is chosen.

Machine learning

Machine learning used to keep customers, set prices and decide credit.

Operator evidence Mobile · O2 and Vodafone

Churn scoring and automated customer value management

Quantitative methods applied to churn and customer value inside a £1.8bn P&L at O2, and across Vodafone’s commercial growth remit.

Operator evidence Retail · Staples Europe

Machine-learning promotions and real-time dynamic pricing

A team of 42 data scientists assessed the return on retail and online promotions and built the machine-learning models behind real-time dynamic pricing across 19 European websites.

Operator evidence Financial services · Equifax Europe

Machine-learning credit and fraud risk decisioning for HSBC, Santander and Barclays

As Chief Products and Marketing Officer for Europe, built the decisioning services and the European go-to-market around them. The models sat inside live credit and customer decisions. Around 11% of European revenue came from the new launches, with $5.7M of new enterprise business won. Those decisions were only as reliable as the data and identity controls behind them, a lesson that now shapes how Formalus designs every AI-enabled process.

In each case the commercial case was defined before the model, the points where a person still decides were designed deliberately, and commercial accountability sat with the operator.

In their words

What leaders in mobile, retail and membership say.

Operator evidence · Mobile

“Michael is a strong, seasoned leader with excellent customer vision – and able to turn that into tangible commercial plans. Transformed Vodafone’s thinking about prepay mobile in a very short timeframe. Made a major impact across Vodafone’s operations across EMEA. One of the very best.”

Saj Arshad, Global Chief Marketing Officer, Vodafone Group
Operator evidence · Retail

“Michael has one of the strongest strategic minds that I have come across in business. He demonstrated a great ability to analyse and understand customer needs and experience particularly in a complex multichannel environment.

Michael is also a great listener to not only customers but his teams and colleagues which made him a trusted colleague.”

Gordon Glover, President, Staples Europe
Operator evidence · Membership

“The work identified and prioritised the biggest commercial opportunities, helping us focus on the areas with the greatest potential business impact. Michael then helped us shape the high-level commercial design for the future operating model, working back from the business outcomes we wanted to achieve so the commercial requirements were defined before technology decisions were made.”

Paula Bates, Head of Digital Transformation, Greater Birmingham Chambers of Commerce

Read Michael’s background →

Evidence status

How sure we are of each finding.

Each finding is labelled Research-backed, Practitioner-validated, Client-validated or Measured.

Research-backedA pattern supported by research or sector evidence.
Practitioner-validatedTested with experienced operators or practitioners.
Client-validatedConfirmed against this client’s people, processes and evidence.
MeasuredObserved against a real baseline.

Numbers are labelled separately. Illustrative shows what an output could look like. Modelled uses client data and stated assumptions. Measured is observed against a real baseline. Operator evidence refers to prior operating experience.

A financial case is a forecast until the results are measured. The Value Blueprint establishes the baseline and benefit measures, and your board locks them when it approves the investment. Value Assurance protects the approved decisions through the build, and the Value Ledger measures the value after go-live. Your management team owns the result, your technology partner owns technical delivery, and results are credited to the change only where the evidence supports it.

Which result would you improve first?

Start with a Free AI Opportunity Assessment: a free 60-minute executive discussion, followed by a concise written assessment. We’ll discuss the commercial result you want to improve, where changes to decisions and processes may help, and whether there is an opportunity worth investigating.

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