Selected Case Studies

Contents

Apple

One live view replaced eight tabs and cut weekly debates over the numbers

The Opportunity

Conflicting apps left Apple sales teams with no single trusted view of performance

Apple's global sales organisation ran on a patchwork of tools. Teams across major regions were jumping across eight apps to answer basic performance questions. Figures did not always match, field work slowed, and nobody had one live view that tied hardware, services, and channel together.

The Solution

One app pulled fragmented data into one trusted place for the field

We designed a mobile-first sales analytics app so fragmented data lived in one place for field teams. Dashboards drew the same numbers for every product line, region, and channel; analytics layers supported quarterly planning; channel views let account executives see distributors, carriers, and retail side by side; reporting replaced hand-built spreadsheets with up-to-date figures for spotting gaps. It worked with Apple's existing CRM, with offline modes for people working across regions.

The Impact

One live view replaced eight tabs and cut weekly debates over the numbers

One app replaced the habit of checking eight systems and disagreeing on the figure. Account executives saw the same real-time picture as leadership, so demand conversations and channel plans started from shared data. Partner briefings matched what executives saw, without reconciliation drag. Less time matching spreadsheets meant more time on planning and execution, and field plans stayed aligned with quarterly goals.

Google

More publishers finished setup, and Auto Ads started earning for them at scale

The Opportunity

Two million publishers were leaving money on the table without Auto Ads

Auto Ads used ML to place ads and find inventory site owners missed, but most publishers never turned it on. The product was one snippet, yet it felt opaque for non-technical users. Manual placements were familiar; the ML piece sounded like a black box. Google's bet on automated monetization only paid off if people finished setup, and competitors were ready to win users who stalled.

The Solution

Emails that explained Auto Ads plainly and kept onboarding moving

We used email to close the gap: segmented sends so each publisher saw copy that matched where they were stuck: first what Auto Ads actually did, then how to paste the tag, then what to check in reporting. Side-by-side examples showed ML placements next to manual ones; follow-up messages walked through the code with screenshots. Publisher stories with real revenue lifts went out as proof, and deeper mails covered mobile anchor ads, AMP, and cross-device setups for people ready for more.

The Impact

More publishers finished setup, and Auto Ads started earning for them at scale

Completion rates went up: people finished the flow once the emails explained Auto Ads in plain steps, and support tickets dropped. More publishers switched Auto Ads on, so Google's models could surface inventory that manual setups skipped. AdSense kept its lead, and publishers spent less time managing placements and more time on their sites.

Vodafone

Less rear-view billing, more steering while the period is open

The Opportunity

Spend swung for real reasons; teams only saw it after close

On Vodafone’s IoT platform, spikes came from rollouts, misconfigured devices, roaming, firmware shifts, and fleet usage, not one-off bugs. After billing closed you could see what happened; you still could not reliably forecast the bill, set guardrails, or catch drift in time to act. That gap hurt in three ways:

• Bill shock eroded trust, and finance slowed expansion sign-offs.
• Ops burned time on spikes without clean attribution or a fast path to root cause.
• When setup and docs were hard to use, automations never shipped and good controls stayed slideware.

The Solution

Plain-language forecasts, caps with a next step, operator-first alerts, docs that matched the API

Forecasting answered "What will we likely spend this period?" in language finance teams use. Breakdowns followed how teams already group fleets and sites; weak signals were labelled clearly instead of presented as exact. Caps mirrored real ownership: warn before a hard stop, show "what next" when a threshold is crossed, and keep a simple log of who changed what. Anomalies stayed short and clear, with links to the slices teams already use to investigate. Developer docs used the same words as the UI (quota, threshold, alert), with quickstarts and copy-paste examples so integrations shipped with fewer…

The Impact

Less rear-view billing, more steering while the period is open

Teams shifted from "what happened last month" to what's coming, where caps apply, and what changed. Finance could plan earlier, ops could act before close, and programme leads could grow fleets with fewer invoice surprises. Clearer signals and shared vocabulary trimmed escalation noise and sped fixes, often before the billing period ended.

EE

New nav ended confusion and increased conversion

The Opportunity

Broken navigation blocked 54% of users from basic shopping tasks.

EE UK's site was a £2.8B shopfront, but the nav fought the customer. In testing, everyone flagged Shop as overloaded and poorly grouped; fewer than half of wayfinding tasks ended in the right place. With most telecom sales starting on mobile and rivals offering cleaner paths, the IA was leaking real money.

The Solution

User research simplified EE's nav to three clear sections

We ran tree tests and hybrid card sorts with ten people and rebuilt the nav around what they actually looked for: a three-bucket mega-nav (My EE, Shop, Help) instead of the old sprawl. Brands sat together in Shop; "Added benefits" moved to My EE because every participant looked there first; help content landed next to the tasks it supported.

The Impact

New nav ended confusion and increased conversion

After the change, tasks finished more often and people found products faster. Nobody had looked for "Added benefits" under Why EE; everyone expected handset brands under Shop. Odd buckets like "Good As New" and "EE TV" had confused two in five people; those labels got untangled. The point was simple: match the mental model, stop the drop-off.

John Lewis

35% faster product discovery drove £8.2M annual revenue

The Opportunity

John Lewis customers loved the brand but got lost among 400K+ products

John Lewis runs about £15B online across 400,000+ SKUs. Loyal shoppers still got stuck in category pages: filters didn't surface the right stock fast enough, and Quick View stopped short of a confident buy. More than half of visits were on phones; next to Amazon and sharp DTC brands, small friction meant real money left on the table.

The Solution

Smarter filters, a serious Quick View, and clearer listing and product pages

We focused on three pressure points in the shopping flow. Filtering: live counts, saved combinations, and suggestions from browse history so people narrowed the catalog without starting over each time. Quick View: size, colour, delivery, and checkout from the overlay instead of a dead-end preview. Listing and product pages: faster images, clearer hierarchy, and cross-sell blocks (including "Style with it") that nudged basket size without clutter.

The Impact

35% faster product discovery drove £8.2M annual revenue

Filtering and Quick View cut time-to-product by about 35%; listing and product page work lifted engagement and basket size. The programme landed around £8.2M incremental revenue in the year we measured, with better checkout completion on mobile. Same brand promise: just fewer dead ends in a crowded online retail market.

Aviva

Strong uptake, real savings, and a clear story for the brand

The Opportunity

Car insurance punished safe drivers with demographic stereotypes instead of rewarding actual driving behaviour

UK car insurance still leaned on broad demographic buckets: safe drivers often paid like risky ones because price was not tied to how people actually drove. Most people think they drive better than average, so flat stereotypes felt unfair and missed real risk. Aviva needed a fairer signal than postcode alone: behavior from the road, not a label on a form.

The Solution

Turns every smartphone into a driving coach that rewards safer driving

Aviva shipped the UK's first smartphone-based telematics app to price cover from how people actually drive. The app used GPS to log acceleration, braking, cornering, speed, and phone use over 200 miles. Drivers got a 1 to 10 score, quick feedback, optional sharing, and a direct path into a quote, so risk moved off postcode shortcuts and onto behavior.

The Impact

Strong uptake, real savings, and a clear story for the brand

Aviva Drive passed 1M downloads in about six weeks, well above the annual plan, picked up Apple's "App of the Week," and held around 4.5/5 in the store. Roughly four in ten users scored 7.1 or above and saved about £101 on average; most people in the programme qualified for some discount. The business case turned positive inside two years, and perception of Aviva as inventive moved up by 33 points, so telematics felt like a practical next step, not a campaign gimmick.

Lloyds Banking Group

Time to resolution fell by about 75%, and satisfaction rose sharply.

The Opportunity

High volume, high costs, growing pain.

The UK's largest bank, Lloyds Banking Group, saw heavy strain in home insurance call centres: high call volume, rising operating cost, and unhappy customers. Data showed that three call types ("Amendments," "Cancellation Calls," and "Policy/Cover Queries") consumed nearly 75% of total handling time and over 250 FTEs. Average resolution ranged from 359 to over 1,000 seconds, leaving teams little room for good service.

The Solution

Move the busiest jobs online so staff and customers spend less time on the phone.

As design director, I led a ten-person team across Lloyds Bank, Halifax, Bank of Scotland, and MBNA. We aligned on one design system, prototyped quickly, and tested with 200+ people to ship clear, rule-based flows for amendments and cancellations. Self-serve for policy queries reduced phone volume without pushing complexity onto customers, and agents stayed free for complex cases.

The Impact

Time to resolution fell by about 75%, and satisfaction rose sharply.

Lloyds cut cost and frustration by moving the heaviest insurance jobs into digital self-serve, the same routes that had consumed three-quarters of handle time. Average resolution time fell by about 75%; digital self-serve crossed 60% within six months. Satisfaction climbed as waits and transfers shrank. Savings went back into better service and simpler upsell paths, on infrastructure the group could extend to the next wave of products.