Product Marketing, GTM & AI Systems - London
Product marketer based in London - five years turning customer insight into positioning, and positioning into measurable revenue. Repositioned a NASDAQ-listed platform out of the cashback category, led the GTM for its NASDAQ direct listing under SEC constraints, and built the launches, enablement and production AI systems that made it stick. Most useful where the marketing function is being built, not inherited.
Short on time? The whole story below, condensed to 11 slides - positioning, the listing GTM, and the research that overturned an assumption.
Open the deck ↗Benchmarked five major competitors and identified a critical messaging convergence - every player was positioning on cashback rate. Repositioned WeShop onto equity ownership as a structurally incomparable value proposition. Battle cards adopted across commercial teams at 75% adoption rate.
The Problem
WeShop was competing in a market where TopCashback, Quidco, Rakuten, Ibotta, and Upside all positioned primarily on cashback rate. The implicit assumption was that WeShop needed to compete on the same dimension - and win. The strategic problem: competing on cashback rate is a race to the bottom against platforms with larger partner networks and more negotiating leverage.
The Intelligence Work
Benchmarked all five competitors across messaging, value proposition structure, merchant acquisition positioning, and consumer-facing narrative. The finding was consistent across all five: every competitor led with rate as the primary hook. Not one positioned on ownership, equity participation, or long-term value accumulation.
The Repositioning
Built the case for repositioning WeShop not as a better cashback platform but as a fundamentally different category - one where shopping builds equity in a publicly traded company. This frame makes rate comparison irrelevant: you cannot compare equity ownership to a cashback percentage because they are not the same type of value.
Produced battle cards and objection handlers built around this frame, specifically addressing the moment when a merchant or commercial conversation defaults to cashback rate comparison. The objection handler sequence reframes rather than defends - moving the conversation from rate to ownership value before the comparison can be made.
The Outcome
75% adoption across commercial teams. The repositioning frame became the default language across partner acquisition, merchant onboarding, and press materials including the NASDAQ listing narrative. The equity ownership angle that appeared in Forbes coverage was a direct output of this intelligence and repositioning work.
Renamed WeShop's core reward mechanic from "Shares/Shareback" to "WePoints" across every user-facing surface - emails, FAQs, help centre, in-app copy - ahead of the NASDAQ listing, with Product and Legal on terminology and compliance claims. Classified honestly: a positioning and messaging exercise, not a product launch. The job was one consistent mental model for the reward mechanic at the moment of maximum public scrutiny. Comprehension and support-ticket impact are the defined success measures; reading them is the open follow-up.
Public record: WeShop's customer announcement of the rename, 1 Oct 2025 ↗
Ownership stated honestly throughout: owned end to end where it says so, GTM-only where Product owned the build. The lesson carried across all of them - no launch closes without a recorded baseline and a named measurement owner.
Owned the full product marketing lifecycle for WeShop's first precision-targeting capability, concept to production launch in the week commencing 14 July 2026 - the first time content-placement targeting moved beyond "show to everyone" to precision-targeted audiences.
Led the full go-to-market strategy for WeShop's NASDAQ direct listing - a three-phase, eight-channel programme built under SEC compliance constraints - all copy reviewed and approved against SEC standards before deployment, no communication went out without Legal and Compliance sign-off - resulting in Forbes coverage and a 400%+ stock surge in the week following the listing.
The Challenge
WeShop's NASDAQ direct listing on 14 November 2025 was a first-of-its-kind moment for a B2C rewards platform. The GTM had to work across two audiences simultaneously - retail investors and existing users - while operating inside SEC compliance constraints that limited what could be said, when, and how. There was no playbook for this.
The Approach
I designed a three-phase programme: pre-listing momentum, listing-day activation, and post-listing community amplification. Each phase had distinct channel mixes, message hierarchies, and compliance checkpoints.
What Made It Work
The decision to separate the investor narrative from the user narrative - and then reconnect them at the listing moment - created a coherent story that worked for both audiences without diluting either. The 7.54% email CTR reflected how well the user-facing message landed. The 4,580 UGC posts post-listing reflected genuine community energy, not manufactured engagement.
What I Learned
Compliance constraints are a forcing function for clarity. Every sentence that couldn't be said pushed the work toward what could be said more precisely. The best-performing creative in the campaign was the simplest - the pieces where the constraint removed all the noise.
Public record
Forbes on the listing ↗ · Listing-day announcement ↗ · NASDAQ: WSHP ↗
Two Product-owned launches where I owned the go-to-market - stated plainly, because ownership clarity matters more than inflated claims. Product and Engineering built the capability; I built how the market met it.
Public record: Founders programme, live ↗ · Launch press release, Mar 2026 ↗
Following WeShop's NASDAQ direct listing (14 November 2025), led the end-to-end GTM for the US market - a cold start with zero brand recognition.
Public record: US launch announcement on nasdaq.com ↗ · TD Cowen conference press release ↗
Led the GTM for the T-Mac G3 - a next-generation IoT gateway platform unifying metering, monitoring, and control into a single intelligent building system. Targeting enterprise facilities management and commercial property buyers with long sales cycles and high technical proof requirements. Clients included a data-centre and storage operator, a major mixed-use estate (700+ tenants), and an investment firm.
Designed and executed the full launch sequence across a 3-person team: pre-launch strategy, webinar programme, event activation, landing page, and sales enablement materials for enterprise retail and facilities management segments. Alongside the Segmentation Builder and the listing, this is the launch pattern repeated in a third context: pure B2B, long sales cycle, technical buyer.
Underneath the launch: built the ICP and a competitor analysis of the BMS / IoT-metering space to steer positioning, produced an ROI / energy-savings calculator so sales could quantify payback for facilities and property buyers, and ran account-based marketing into named enterprise targets - the data-centre / storage operator and the 700+ tenant estate above.
Public record: t-mac.co.uk, the G3 platform live ↗ · Iron Mountain case study ↗ · Camden Market case study ↗ · ASN Capital case study ↗
Ran a 15-interview VoC programme designed to stress-test, not validate, an existing positioning hypothesis. Found the real friction was attribution confidence, not fraud prevention. Rebuilt merchant onboarding and positioning around that insight - doubling activation rates and influencing the product roadmap.
The Starting Point
The commercial team believed low merchant activation was driven by fraud prevention concerns. The existing positioning leaned into security and protection. I wasn't convinced - so before committing to a GTM built on that hypothesis, I designed a research programme to test it properly.
The Research Design
15 structured interviews with active and lapsed merchants. Deliberately designed to stress-test the fraud hypothesis, not confirm it. The interview guide avoided leading questions and created space for merchants to surface their real objections unprompted.
From Insight to Execution
Rebuilt the onboarding sequence around the attribution confidence insight. New messaging led with transparency on how transactions were tracked and attributed - not with cashback rates or partner network size. Battle cards and objection handlers were rewritten around the real objection, not the assumed one.
The research findings also went to the product team. Transaction verification was elevated to Q2 roadmap priority as a direct result - closing the gap between what we were promising in positioning and what the product was actually delivering.
The Outcome
Activation moved from 20% to 45% - a shared outcome, with the repositioning and enablement rebuild as the primary PMM-attributable levers. The more significant outcome was structural: the business now had a research methodology it could repeat, and the product roadmap had a commercial brief attached to the next development cycle.
What I Learned
The most dangerous assumption in positioning is the one everyone agrees on. Fraud prevention felt intuitively right to the commercial team - it was coherent, it was defensible, and nobody had tested it. Research designed to confirm finds confirmation. Research designed to challenge finds the truth.
Six steps run continuously - feeding the equity-vs-cashback repositioning and every campaign rate decision.
Co-created A Taste for Football with Humphrey - a podcast series exploring football scouting and talent identification. Humphrey hosted and interviewed. I owned all production: guest outreach and relationship building, scheduling, audio editing, design, copy, and episode marketing.
Guests included scouts, regional recruitment coordinators, heads of academy recruitment, chief scouts, talent identification leads, and performance analysts from professional clubs and the FA. The conversations produced a consistent intelligence picture of how professional clubs evaluate new technology - the trust objection (my network is my value), the workflow objection (I already have a process), and the value objection (cost vs return) - which directly informed the positioning and GTM strategy for the Player ID platform.
Also worked alongside a football analyst for two years at LionHeart Football, adding a data and performance analysis layer to the scouting and recruitment intelligence base.
Public record: The series on Listen Notes ↗ · LionHeart Football, live site ↗
Enablement measured by adoption, not production volume - and rebuilt when research changed the story.
Built from 15 structured discovery interviews and three rounds of commercial team review. Four layers: feature comparison, positioning comparison, objection handling (5 handlers), and win/caution signals. Went through two major revision cycles - Shareback model explainer added after enterprise procurement contacts asked "how does this actually work?", campaign performance data added after commercial asked "how many users will see my brand?", sustainability objection added and tested successfully in the third commercial conversation.
Designed and delivered a structured enablement and training programme so the commercial team could run the CRM, analytics and design workflows independently. Built as a repeatable, documented system rather than ad hoc handovers - live sessions backed by a completed tutorial video series for on-demand reference.
A contract inside an FCA-regulated bank: content governance across around 600 branches, with brand content and comms contribution inside a live national multichannel campaign ("Make Money Work For You", launched Feb 2023). Multi-stakeholder Compliance and Legal sign-off throughout.
Public record: Barclays "Make Money Work For You" ↗
Beyond the words on the page, I own the pages themselves. Across three roles I have built and run WordPress sites hands-on - full builds, plugins, SEO, e-commerce and integrations - as the person responsible for what users see and how it performs.
Public record: LionHeart Football, live site ↗
AI assistants are the new front page. AEO is the discipline of getting a product understood and cited by answer engines - ChatGPT, Perplexity, Google AI Overviews - not just ranked by classic search. I designed and shipped a full AEO and SEO system on Kelo, the invoice-recovery product I build and own, so assistants describe and cite it accurately. Hands-on, as the founder - not a strategy deck.
llms.txt and llms-full.txt - structured markdown that gives AI answer engines a clean, canonical description of what Kelo is, who it is for, and its key pages, so they cite it correctly.Public record: live llms.txt ↗ · gokelo.com ↗
Product marketing runs on content. A curated selection of public-facing work - proposition and product pages, launch campaigns, solution guides, thought-leadership and earned media - still live or preserved in the web archive. WeShop pieces are produced as sole marketer; EIC/Monarch pieces were produced and managed across the group's brands during my 2022 tenure.
WeShop (2023 to present, sole marketer)
EIC / Monarch Partnership group (2022) - content produced and managed across the group's brands; Monarch's own domain is now retired, so these are preserved in the web archive.
As sole marketer at a listed company, every workstream runs through a defined stakeholder lane - each with a different currency of trust. Roles, not names; this is the standing operating map.
PMM as the bridge between what the market says and what Product ships - with the receipts running in both directions.
AI systems designed and deployed in production - not as experiments, but as operational infrastructure delivering measurable impact - all under a human-in-the-loop governance model.
The most valuable PMM work is invisible in a metrics table: the launch-breaking flaw caught before go-live. It has happened often enough to be a pattern, not luck - a standing pre-launch logic and risk review across everything shipped.
A public, runnable Model Context Protocol server extracted from Kelo's live production endpoint - it lets an AI assistant read what a business is owed, prioritise who to chase, and draft the chase email. It cannot send anything. Every tool either reads or returns a draft, and that boundary is enforced in code, not requested in a prompt: there is no send tool to call, and a test fails the build if anyone adds one. The same human-in-the-loop principle Brian runs on at WeShop, written down as code anyone can read and run in 30 seconds.
"Reversibility, not risk appetite, is the line." - from the design notes. Read the code and the full reasoning at github.com/solomonsalako/kelo-mcp ↗
Designed and implemented a systematic retailer scoring and placement framework that replaced ad hoc affiliate decisions with a data-driven allocation model - generating £2.91M in cumulative revenue over 18 months.
The Problem
Affiliate placement decisions were being made on instinct - which retailers felt right, which rates were highest, which categories were trending. There was no scoring model, no audience-fit validation, and no seasonal allocation logic. Revenue was inconsistent as a result.
The Framework
I built a retailer scoring model that weighted commercial rate at 60% and demographic relevance at 40% - deliberately structuring it so that rate alone couldn't win a placement without audience fit. The model included:
The Commercial Logic
The most important design decision was the 60/40 weighting. A purely rate-driven model would systematically favour unfamiliar high-commission retailers over trusted lower-commission ones - exactly the wrong trade-off for a platform where user trust is the core product. Rate as a tiebreaker, not a primary signal, was the principle that made the model commercially sound rather than just financially optimised.
Results
£2.91M cumulative revenue over 18 months. Monthly peak of £260K+. CTR improved 25% against pre-framework baseline. Placement conversion improved 15%. The framework is now the operating model for all affiliate decisions - not a one-off exercise.
Designed WeShop's CRM programme from scratch on a platform operating with zero paid digital acquisition - no PPC, no paid social, no influencer spend. The CRM programme is not a supporting channel alongside a paid acquisition machine; it is the primary scalable acquisition and retention mechanism. Board-level business case, 6-phase roadmap, and 3-scenario revenue model - the board approved the programme and funded the full infrastructure build.
-- Onboarding performance vs benchmark (mock schema, real outputs)
SELECT sequence_step,
ROUND(100.0 * SUM(opened) / COUNT(*), 2) AS open_rate,
ROUND(100.0 * SUM(clicked) / COUNT(*), 2) AS click_rate
FROM email_events
WHERE campaign_type = 'onboarding_v2'
GROUP BY sequence_step; -- Step 2 returns 43.25 / 2.34The EIC role sat inside the Monarch Partnership group of energy brands (Monarch Partnership, EIC, t-mac Technologies, National Utility Hub, Welcome Energy, ESS, SafeSwitch Utilities - since merged into EIC Partnership). Beyond the T-Mac G3 launch, the remit was rebuilding the marketing function across the group.
Public record: SafeSwitch at launch, Aug 2022 archive ↗ · t-mac rebrand-era site, 2022 archive ↗ · The webinar programme, still running ↗ · Group structure ↗
Packaging and proposition work across two very different markets: a consumer reward mechanic at WeShop, and productised, tiered B2B service families across the EIC Partnership energy group. The through-line is the same - turn a capability into a proposition a buyer can choose from.
Public record: t-mac Micro BMS tier ↗ · WeShop "Shopping Starts Here" ↗ (EIC service-pillar URLs retired after the 2024 group consolidation into EIC Partnership).
At EIC Partnership I owned the account-based marketing motion for the T-Mac G3 platform - an intelligent building energy management system targeting enterprise manufacturing clients. The challenge: a technically complex product, no existing relationships, and multi-department buying committees with competing priorities across five functions.
For a global industrial manufacturer, I built a company-specific pain hypothesis around rising energy costs, carbon reporting obligations, and process optimisation. The first outreach was not a product pitch. It was a personalised insight on their manufacturing footprint with a concrete reason to talk now. I then supported the commercial team through five-department navigation: Operations Director, Site Manager, Technical Manager, Maintenance Manager, and Finance Manager - each requiring a different narrative and objection handler. Common objections handled: production disruption risk, savings accuracy, internal resource requirements, data access, and prior site experience.
Post-win: packaged the case study, ROI framing, pilot narrative, and ESG positioning for wider rollout. Same ABM motion applied to a precision-manufacturing group with multiple sites and a process-efficiency angle.
Beyond the T-Mac G3 motion, enterprise marketing across the group spanned a 20+ named-account portfolio - Pizza Hut, Stansted Airport, thyssenkrupp Materials, NHS trusts and independent schools among them - marketed through per-account, case-study-led campaigns and sector verticals (schools, NHS, public sector, airports), run with the Head of Marketing using per-account campaign tracking.
Architected and validated a point-in-time football projection engine spanning five English tiers, from the Premier League to the National League, with AI-assisted implementation. The build followed one rule borrowed from quantitative trading - measure first, adopt only what survives, reject with evidence. Several plausible signals were thrown out because the data did not support them.
Identified a market gap - founder-led service businesses (trades, clinics, agencies) have no clear view of revenue health across jobs, invoices, and client relationships - and built Kelo to close it. Specified, built, and deployed to production in under four weeks through AI-assisted development using Claude Code, with every feature specified commercially before a single line was written.
Before Kelo had a single paying client, four structural pieces of commercial strategy were built - each feeding the next, none forced by a client.
The investor review surfaced a genuine product gap - the original ICP would qualify retainer-billed businesses the product structurally can't serve well - which triggered the ICP rebuild. The same review caught an unprovable causal claim in early pricing language ("savings Kelo generated") and pushed it toward a defensible reframe.
July 2026 phase-gate: product strategy narrowed to a Cash Recovery MVP - invoice-first, with a two-number hero UI ("£X is stuck. £Y is chaseable this week") and a transparent, rules-based chase engine. UK market sizing confirmed: TAM £366M, SAM £89M, SOM £1.55M at year five. Contingency recruitment identified as the profit-opportunity leader: 33x ROI on Core plan. Market validation informed by a structured competitive deep-dive that produced an adopt/reject framework now embedded in the product architecture.
Product marketing and GTM practitioner based in London, with five years building positioning, CRM infrastructure, and commercial revenue programmes at early-stage and growth companies across fintech, financial services, B2B SaaS, and payments.
Work sits at the intersection of customer insight, product positioning, and commercial revenue accountability - research-led repositioning, regulated GTM execution, Agentic AI deployed in live production. Most useful where the marketing function is being built or repositioned, and where the brief is to move fast without losing rigour.
Currently at WeShop as sole marketer - owning the full scope from GTM strategy and CRM architecture through to AI production systems and audience segmentation. 160K+ opted-in CRM subscribers. Brian AI intelligence system in soft launch with CEO daily use. Audience Segmentation Builder live in production from July 2026.
Open to Product Marketing Manager and GTM roles at AI, fintech, and B2B SaaS companies. Based in London.