Industries

Retail

Storefronts, checkout and order operations that hold up on the busiest day.

Retail teams do not need another channel. They need the catalogue, the stock position and the customer to agree with each other, in the same second, across web, app and store. We work on the unglamorous middle — product data, checkout, fulfilment — because that is where conversion and margin actually leak.

Book A Call Run the numbers first

Google Cloud for retail

We lean on Vertex AI for Search and recommendations, BigQuery for the customer and stock model, and Cloud Run for the seasonal spikes you cannot pre-provision. The stack is broad; we scope the two or three pieces that move your number.

01 · Capture

CDP on BigQuery · Apigee

Store, app and marketplace behaviour resolved into one customer record.
02 · Discover

Vertex AI Search

Search and recommendations tuned on your own conversion data.
03 · Convert

Cloud Run · Gen AI for Marketing

Checkout and campaign pages that hold up on the sale day.
04 · Fulfil

Inventory model on BigQuery

Stock, split shipment and returns read from the same model.

63.5%: of customers cite targeted promotions as a top reason to buy — the bar personalisation has to clear; Source: Industry benchmark, paraphrased

SASB CG-MR — Multiline & Specialty Retailers, with CG-AA for apparel

SASB standards ↗

Retail’s environmental weight sits upstream in the supply chain and in packaging, not in the store’s electricity bill — so scope 3 and packaging carry more weight than the energy line.

CG-MR-150a.1
Energy management in retail

total energy (GJ), % grid, % renewable

Store and DC meter data joined to the estate model, so a retro-fit business case is built from your meters.
CG-AA-430a.1
Supply chain emissions

scope 3 upstream emissions (tCO2e)

Supplier-level factors attached at the product record, which is also where your PIM already lives.
CG-AA-410a.1
Packaging lifecycle

% of packaging recyclable, reusable or compostable by weight

Packaging spec per SKU, rolled up automatically at range review.
CG-MR-220a.1
Customer privacy

% of PII used for secondary purposes

Purpose-scoped consent records, exportable per customer for a subject access request.

Sustainability disclosures, their SASB code, the metric and how we would instrument it
Standard	KPI	Metric	How we instrument it
`CG-MR-150a.1`	Energy management in retail	total energy (GJ), % grid, % renewable	Store and DC meter data joined to the estate model, so a retro-fit business case is built from your meters.
`CG-AA-430a.1`	Supply chain emissions	scope 3 upstream emissions (tCO2e)	Supplier-level factors attached at the product record, which is also where your PIM already lives.
`CG-AA-410a.1`	Packaging lifecycle	% of packaging recyclable, reusable or compostable by weight	Packaging spec per SKU, rolled up automatically at range review.
`CG-MR-220a.1`	Customer privacy	% of PII used for secondary purposes	Purpose-scoped consent records, exportable per customer for a subject access request.

Where we help

Catalogue truth: One product model feeding web, marketplaces and in-store systems, with enrichment and image generation applied before publish rather than as a manual clean-up afterwards.
Checkout and fulfilment: Payment, tax, returns and split-shipment logic that a promotions calendar cannot break. Built to be load-tested against the sale, not the average Tuesday.
Discovery that earns its keep: Search and recommendations tuned on your own conversion data, with generative assistance where it measurably lifts reply or add-to-cart rates — and switched off where it does not.

Working on something in Retail?

A 30-minute call is usually enough to work out whether this is a quick win or a rebuild — and we will tell you if it is the second one before you spend anything.

Book A Call

Calculator

What is slow checkout costing you?

Conversion falls measurably for every second of load time past 2.5s. Put in your sessions, conversion and basket size and we will size the revenue sitting behind that.

Indicative

Retail

Revenue per month

$714k

Orders per month: 8,400
Modelled conversion uplift: 4.9% %
Revenue recovered / year: $420k

Monthly sessions 400 k

5 k 3000 k

Current conversion rate 2.1%

0.2% 6%

Average order value 85 $

Mobile LCP, 75th percentile 3,200 ms

1000 ms 8000 ms

Model assumptions

Applies a 7% conversion impact per second of load time above a 2.5s LCP, capped at 35%.
Assumes the uplift is additive on existing traffic — no change in media spend.
Excludes checkout-step abandonment separately modelled in the audit.

An order-of-magnitude model, not a quote. We will send the version with your real baselines, the assumptions replaced, and the implementation cost netted out.

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