Consultancy

A reporting service for advertisers selling on Amazon, their own website and in store. I ingest their advertising data through each platform's official API into their own data warehouse, alongside their sales, stock and margin, and build the reporting on top: one view of what the advertising costs and what it returns. Reporting only: I don't run or change campaigns.

• Warehouse ingestion: daily, automated feeds from Amazon Ads (Sponsored Products, Brands and Display), Google Ads (including Shopping), Meta and Microsoft Advertising into the client's data warehouse: spend, impressions, clicks, conversions and attributed sales by campaign, ad group and product, kept as history beyond each platform's own retention.
• Return on ad spend you can trust: ROAS, ACoS and TACoS by channel, campaign and product, set against actual orders, revenue and margin from the client's ERP, store and Amazon seller data, so profitable spend is told apart from spend that only looks good in the ad console.
• Product-level performance: Sponsored Products and Shopping results per SKU beside stock, sell-through and contribution after marketplace fees, to show where advertising is driving profitable sales and where it is spending on lines that can't be replenished.
• Budget allocation & pacing: spend split across Web, Amazon and Retail by the client's own campaign naming, tracked against budget and last year, with unallocated spend surfaced rather than lost.
• How access works: read-only, authorised by the advertiser through each platform's own consent screen, revocable at any time; data stays on the client's own infrastructure. See the privacy policy.

A small number of bounded engagements alongside my in-house role. Two modes, depending on what you need:

• Build: hands-on delivery — I'll stand up your warehouse, BI stack, or analytics pipeline myself. Code, infra, dashboards, the lot.
• Advise: strategic input for founders, CTOs, or boards — architecture review, hiring, due diligence, separating AI hype from leverage.
• Scope: NDA-friendly, non-compete with my employer's sectors (beauty, tattoo, pet grooming supply). Anything else is fair game.
• Intro: Mostly via referral — if you've landed here directly, mention who pointed you my way. Async by default; calls evenings or weekends.

Grounded in what I've actually built, not theory. These are the areas where I can compress years of trial-and-error into weeks of advice.

• Data system design from scratch: end-to-end architecture for companies with no data function yet — what to build, in what order, what to skip. Hourly syncs from NetSuite, Shopify, Google, and friends into a centralised warehouse using Python, SQL, Polars, DuckDB, and Docker.
• BI & reporting from zero: replacing legacy spreadsheets and disconnected tools with a unified Streamlit (or equivalent) stack — SSO, role-based access, dashboards covering exec, ops, finance, and marketing. Pragmatic alternative to per-seat BI licences.
• Analytics & commercial modelling: CAC modelling and optimisation, pricing & shipping margin analysis with statistical significance testing, basket & bundle intelligence, discount dependency, vendor spend, inventory and OTIF diagnostics, EOS L10 scorecards.
• AI enablement: embedding LLM-assisted development (Claude Code) into data and engineering teams to compress pipeline and dashboard delivery. Helping leaders separate hype from where AI actually moves the needle.
• Self-hosted local LLMs: standing up private, zero-marginal-cost models for sensitive workloads — review sentiment, SEO copy analysis, product description harmonisation, classification — with prompts tuned per task. No data leaves your network.
• Hiring & team build: what a first data hire should look like, when to add the second, how to structure a small high-leverage team. Drawn from building and leading a team of PhD-level scientists and engineers.
• Technical due diligence: independent reviews of data & AI stacks for investors, acquirers, or boards considering an investment.

Nexus Brands Group. I built the data function from scratch across 18 businesses in beauty, tattoo, and pet grooming supply — warehouse, BI, analytics, AI, and the team to run it. Selected outcomes:

• +25% OTIF improvement from unified inventory and order visibility.
• ~1% of revenue recovered that was previously lost to out-of-stock.
• £Xm in excess & obsolete inventory identified and surfaced to leadership (figure withheld).
• Designed and built FLARE — a self-hosted warehouse + dashboard platform replacing legacy reporting across the group. Zero per-seat cost, every employee has access. Anonymised public demo →
• Built and lead a team of PhD-level data scientists and engineers supporting senior leadership across the group.
• Evidence base underpinning a major global restructuring and optimisation programme.

UK multi-channel retailer (shops, web and Amazon), consultancy. Warehouse, BI and nightly management reporting live; advertising reporting from Amazon Ads, Google Ads, Meta and Microsoft into the same warehouse in delivery.