Real programmes. Real projects. Always anonymised.

A seasonal retailer's abandoned-basket rate was climbing, with no automated recovery in place beyond a single generic reminder email.

Designed and built a full abandoned-basket lifecycle automation, triggered directly off real browse and cart behaviour rather than a blanket schedule.

A major home-improvement retailer had rich on-site browse data going unused — customers who explored products in depth but didn't buy received no tailored follow-up at all.

Designed two connected programmes from scratch: a lightweight "browse module" surfacing personalised picks from a customer's most-engaged category inside regular campaigns, plus a standalone automated programme for high-intent browsers, hero-ing their most-viewed product alongside a curated best-sellers set. Built the qualification and exclusion logic (recency, frequency capping, stock and cart checks) to keep it relevant rather than repetitive.

A loyal customer base was under-monetised: repeat purchase rates sat below sector benchmarks despite strong brand affinity.

Built a personalised customer contact strategy that maximised existing CDXP (customer data & experience platform) data to time messages around real purchase cycles.

Customer engagement was front-loaded around the initial booking, with almost no structured contact between purchase and travel date.

Mapped and automated pre- and post-trip journeys covering the full travel lifecycle, from booking confirmation through to post-trip cross-sell.

Lifecycle campaigns were built around a single generic segment, largely disconnected from how customers actually behaved.

Rebuilt segmentation from the ground up using data-led journey mapping across every key lifecycle moment.

A department store retailer opened its Black Friday sale a week ahead of its rivals, in a period contributing up to three quarters of annual online revenue — with no way to follow up the shoppers who browsed products but never reached a basket.

Built a browse-abandonment programme deliberately distinct from basket abandonment: qualifying visitors who viewed product pages without engaging the buy button at all, then serving a single large hero image of their most-viewed item with context-sensitive recommendations beneath it, varied by recipient.

A high-value, considered-purchase category ran on a slow, sparse nurture programme with wide gaps between touchpoints. It wasn't clear whether a tighter cadence would help or simply add noise.

Ran a proper test-and-learn: one group on a fast cadence (four sends over nine days), one on a slow cadence (four sends over twenty-one days), and a true holdout control that received nothing. Measured results across email, online and in-store spend, not just click-throughs.

Ahead of a major seasonal trading event, the business needed to know whether its peak-period email campaigns were driving real incremental sales, or just reaching people who would have bought anyway.

Built a genuine control-group methodology, holding back a comparison audience from campaign sends, then measured the emailed group against that control on shopper conversion, revenue per customer and basket value — separately for each event in the trading window. Ran a parallel win-back push targeting lapsed and lapsing customers over the same period.

Replenishable beauty products were all sent reminder campaigns on the same fixed schedule, regardless of how quickly each one actually got used up.

Ran a structured test comparing multiple fixed replenishment windows against a custom predictive algorithm, product line by product line, tracking opens, clicks and conversion for each scenario rather than assuming one timing model would fit the whole range.

A meaningful pocket of lapsed and lapsing customers had, at some point, genuinely engaged with the brand but were being contacted the least — an obvious reactivation opportunity nobody had properly tested.

Segmented the lapsing base by engagement risk to find the lowest-risk group to target, then designed a three-strand test — interactive-only, offer-only, and interactive-then-offer combined — against a genuine 10% holdout control, with success measured on open rate, click rate, conversion, revenue per customer and average order value.

Open rates had been declining year over year, and it wasn't clear how much of that was falling engagement versus a change in how opens were even being measured.

Ran a full diagnostic across six marketing-strategy categories, analysed the subject-line patterns separating top from bottom performers within each, and quantified how Apple's Mail Privacy Protection was inflating measured opens through automatic pre-loading.

A product-recommendations module had been added to two major email programmes, but nobody had rigorously checked whether it was actually earning its place.

Ran a proper with/without comparison across both programmes, tracking click-through, conversion, average order value and revenue per 1,000 emails rather than relying on gut feel.

A beauty brand's email open rate had jumped +68% year-on-year, sitting well above the 23% industry benchmark — on the surface, a huge win to celebrate.

Dug beneath the headline number and traced the spike to Apple's Mail Privacy Protection inflating measured opens, not genuine engagement growth. Cross-checked against click-to-open rate — the more reliable signal — which had actually dropped 39% over the same period. Used that clearer picture to prioritise a full personalisation architecture across the customer lifecycle, mapping specific product-recommendation logic (best-sellers, cross-sell, browse-based, birthday-based) to each stage from first visit through to win-back.

A national health and fitness group's marketable database was growing fast — up more than 25% year on year — and almost all of it was being mailed on the same footing. Nobody had asked how much of that growth was real.

Segmented the entire mailable base by recency of engagement — opened in the last 60 days, 61–120 days, 120-plus days, and never opened at all — then layered on year-on-year subscribe and unsubscribe trends and a full time-of-day and day-of-week send analysis.

A homewares brand was performing well on paper and wanted a proper annual review rather than another set of flattering headline numbers.

Benchmarked open, click, click-to-open and unsubscribe rates against the sector, tracked how customers migrated between engagement segments across seven months, and split open rates by inbox provider to separate a content problem from a delivery problem.

A trade retailer ran a full suite of loyalty and project-based trigger emails, each reported on in isolation. Nobody had put them side by side to see which were actually earning their place in the programme.

Reviewed all twenty trigger campaigns on identical footing — unique open, click, click-to-open, conversion, revenue per email and average order value — so they could be ranked against each other rather than each against itself.

A digital marketing team was spending roughly 80% of its time manually building three separate weekly campaigns, leaving almost no room for testing, personalisation or strategic work.

Designed a practical "good, better, best" transformation roadmap: starting with a modular template delivered manually, then moving to a decisioning engine that automatically pulled business, seasonal, loyalty and behavioural content from a shared content bank, so no customer saw the same message twice.

Customer data and messaging were organised by channel — web, email, stores, call centre — rather than around the person, making it hard to build one coherent view of any individual customer or time contact around what they actually needed.

Mapped a full customer-centric journey model spanning every lifecycle stage from prospect through to loyal and disengaged, then built persona-level data profiles scoring each customer on explicit product interest, channel preference and commercial value — using real behavioural data, not survey guesswork.

Post-purchase messaging was planned and delivered channel by channel, with no shared view of where a customer actually sat in their relationship with the brand — making it hard to time contact around what they needed next.

Built a phased omnichannel transformation roadmap across multiple customer life-stages (from just-bought through to lapsing), following a structured Discovery → Strategy → Execution → Measure process, with three test-control models designed (global, life-stage and use-case) to properly isolate what the omnichannel approach itself was actually adding.

Peak-trading campaigns were being sent as broad, largely undifferentiated blasts — treating a loyal VIP, a one-time past shopper and a browsing lapsed customer all the same way during the busiest, highest-stakes weeks of the year.

Built a fully segmented peak-trading framework spanning seven distinct audiences — previous seasonal shoppers, VIPs, active browsers, category buyers, lapsing customers, geographic and gift-focused segments — each with its own message and offer strategy, informed by structured cross-industry benchmarking of what genuinely earns attention during peak.