E-commerce
Data for online brands that have outgrown their spreadsheets.
E-commerce is where most of our products started. We build especially for Shopify and other online stores: one warehouse for your shop, ads, email and stock data, with tested rules, dashboards and alerts on top.
Selling in person as well? Pop-ups, markets and shop floors feed into the same customer and stock data.
- 1Your storeShopify, WooCommerce and more
- 2One warehouseBigQuery
- 3Tested rulesdbt
- 4AnswersLooker Studio and alerts
Shopify first, other platforms too
- Shopify
- WooCommerce
- BigCommerce
- Squarespace
- Wix
- Adobe Commerce
- Custom stores
What online brands bring us
Six problems, and what we build for each.
“Shopify, Google Analytics and the ad platforms all give different sales figures.”
We bring every source into one warehouse, reconcile it against your store and agree one set of rules, so there is one number per question.
“Repeat customers keep showing up as new ones.”
Guest checkouts, typos and old addresses get matched, and each real person gets one Master Customer ID and one order history.
“Best-sellers run out while slow lines sit in the stockroom.”
We age your stock to find dead lines and forecast demand for each product from your own sales history.
“Nobody can say which ads or emails actually paid for themselves.”
Each campaign, channel and email is tied to the orders and revenue that followed it, and you get an alert when spend or refunds jump.
“Good customers are drifting away and nobody notices until they’ve gone.”
Every customer is scored 0 to 9 on recency, frequency and spend, and you get a weekly list of high-value customers who are slipping.
“Thousands of product pages, reviews and tickets nobody has time to read.”
AI writes consistent product copy and attributes from your catalogue, and reads reviews and support tickets to surface the top issues.
The method
Four layers, shaped by online retail.
Customers, segments, stock and spend: the four places value most often goes missing in an online store’s data.
- I
The Fidelity Layer Identity resolution
A truer count of your customers, and one history for each of them.
- II
The Decile Engine 0–9 RFM scoring
You see your “999” Brand Champions, and the big spenders who have gone quiet, so effort goes where it counts.
- III
The Velocity Layer Inventory and demand
Clear reorder numbers, and less money tied up in stock you can’t sell.
- IV
The Illumination Layer Marketing attribution
You can see which spend earns its keep, and which to cut.
The Decile Engine, at a glance
Every customer scored 0–9 on recency, frequency and spend. The “999”s are your Brand Champions.
Where online brands usually start
Twelve products for e-commerce. Buy one, or a few.
- Start here · 7 days
Diagnostic report
A seven-day data audit. We check what you have, where it can’t be trusted and where revenue is leaking, then hand you a written list of fixes in the order we would tackle them.
Data warehouse build
Your sales, customer, marketing and operations data in one BigQuery warehouse, so every report starts from the same numbers.
Dashboards
Custom dashboards built around the questions you ask: a Looker Studio command centre for you, plus live screens for the office or warehouse showing today’s orders, stock alerts and targets.
- AI
Churn alerts
A weekly list of your high-value “999” customers who are starting to drift, each with a suggested win-back.
- AI
Reorder forecasts
Predicted stock needs for every product, so cash isn’t stuck in slow lines.
- AI
Anomaly watch
An alert when sales, refunds or ad spend suddenly jump or drop for no obvious reason.
- AI
Ask-your-data assistant
Ask “who were my best customers last month?” in plain English and get the answer from your own warehouse.
- AI
Review and support mining
AI reads your reviews and support tickets and tells you the top complaints and product issues.
- AI
Product copy and tagging
Consistent product descriptions and attributes, generated straight from your catalogue.
- AI
Automated personal emails
On-brand emails that go out when a customer does something that matters, like an enquiry, an order or going quiet, written to sound like you rather than a template.
Pick-and-pack checks
Every order is scanned at packing, so wrong items are caught before they ship.
Pop-up and market sales capture
Sales from events and markets land in the same customer and stock data as your online store.
Every product is price on request.
See it built
A typical build, and one of our own stores.
- Worked example · made-up brandE-commerce
Alder and Fern: an online tea brand
A one-minute interactive walkthrough of a typical e-commerce build. A made-up tea brand goes from four tools that disagree, through the 7-day Diagnostic, to a joined-up pipeline with live dashboards, win-back lists and anomaly watch.
- Diagnostic findings
- Data pipeline
- Dashboards
- Win-back list
- Our own ventureDigital products · e-commerce
WovenQR and FormeQR
Two QR artwork brands where the pattern of the code itself forms a customer’s logo, initials or name. Both run on one generation engine we built, with separate storefronts, checkout and protected delivery.
- QR generation engine
- Two storefronts
- Checkout and delivery
- Marketplace service
Start with seven days in your store’s data.
The Diagnostic ends with a written list of fixes, in the order we would tackle them, and a walkthrough. Or just ask us a question.
Not an online store? See where else we fit