ANONYMISED CASE STUDY · AUTOMATION & INTEGRATIONS
A growing store had automations spread across separate tools and scripts, data in many places and an email agency already running its main flows. The work brought the automations onto one monitored platform, added marketing, multilingual and reporting workflows around the agency's own, and made every run visible.
Anonymised client project · Automation & Reporting
Illustrative automation monitor listing scheduled jobs such as the low-stock alert, back-in-stock detection, competitor catalogue scan, customer tagging, translation sync, flow performance sync, order attribution sync and the watchdog, each with its schedule and a completed status.
Illustrative example · layout, not a client screenshotThis is an ongoing engagement rather than a single fix. The aim was to give the store one dependable place for its automations, then build marketing, multilingual and reporting workflows on top, each one tested before launch and monitored after it.
Anonymised client project: Anonymised multilingual Shopify retailer. Selected projects are anonymised to respect client confidentiality.
What we found
The store's automations had grown up across no-code workflows and standalone scripts, so nobody could see at a glance what had run, what had failed or what depended on what. Store, email, search and social data lived in separate tools and did not always agree. An external agency already ran the main email flows, so any new automation had to add to theirs rather than compete with them.
Workflows had grown up across no-code tools and standalone scripts, with no single record of what had run or failed.
Store, email, analytics and social data sat in separate tools, and order counts in analytics did not always match the store's own records.
An external agency ran the main email flows, so new automation had to fill gaps in their programme rather than duplicate it.
Other-language pages were served through a third-party proxy rather than the store's own markets and translations.
Before changing anything
We started by listing every existing automation: what triggered it, which system it wrote to and who owned the result. We mapped which emails the agency already sent, so new triggers would fill gaps instead of duplicating them, and compared order records in analytics with the store's own orders to see where reporting disagreed.
Simplified architecture: the Shopify store, email platform, analytics, search data, social channels and public competitor catalogues connect to one platform with a worker, job queue, database and run log, which produces alerts, email trigger events, translated content and reporting views.
Move what exists onto one platform first, then add new workflows one at a time, each with its own test and sign-off.
What exists
One platform
One workflow at a time
Every run
Read-only views
The work completed
The platform sends server-side events to the existing email platform, so lifecycle emails such as a win-back for top customers, a second-purchase nudge and back-in-stock messages run alongside the agency's flows without replacing them. On product pages, a sold-out size now offers a sign-up in the shopper's language that feeds a daily restock check and a back-in-stock email.
Back-in-stock workflow: a shopper signs up for a sold-out size on the product page in their language, the sign-up is stored, a daily job checks for restocks, and an event triggers the back-in-stock email.
The store's pages are translated natively, with a daily sync so new and changed content is translated too. A language selector and restructured markets put each language on the store itself, and the old proxy addresses redirect permanently to the native pages. New articles are also translated for a second-language audience as they are published.
Translation workflow: new or changed store content is translated into each store language by a daily sync and published on the store's own markets, while old proxy addresses redirect permanently to the native pages.
Email flow performance, social activity and order attribution are synced into reporting views behind a read-only client login. A data clean-up matched analytics orders against the store's own records and set out reporting rules, agreed with the client in writing.
Illustrative reporting layout with figures removed: email flow performance, social activity, order attribution and the run log, behind a read-only client login.
A daily low-stock alert covers the store's own-made lines, back-in-stock changes are detected daily, and a weekly scan of public competitor catalogues records price changes. An AI shopping assistant on the storefront was rolled out to every visitor only after a controlled holdout test.
In development
These are under way or planned. None of them is part of what this case study describes as delivered.
Weekly trend lists drawn from public competitor catalogues, in a trial with the client. The runs are still started by hand.
A data warehouse bringing ad spend, attribution and margin together, planned as later phases of the data work.
A webhook receiver is in place, but no store events are connected to it yet; the jobs above still run on a schedule.
Evidence
Each automation was checked before it went live and is watched after. The case study describes what was built and how it is monitored; it does not claim a revenue, conversion or time-saving figure, because none has been established for publication.
| Area | Status | Note |
|---|---|---|
| Scheduled jobs | Verified | Every run is recorded in a run log, and a watchdog emails when a job errors or is overdue. |
| Email triggers | Verified | Flows stay in draft until written client sign-off; discount codes are created in the store and redeemed in a test first. |
| Back-in-stock sign-up | Verified | Tested end to end, from the product page sign-up to the email. |
| Order attribution | Checked | An audit found one data lane recording nothing; it was fixed and rechecked. |
| Backups | Verified | Encrypted nightly off-site backups, with a restore tested. |
| Revenue impact | Out of scope | Not established for publication; no revenue, conversion or time-saving figure is claimed. |
The records confirm which automations are live, how they are tested and how they are monitored. They do not establish a revenue or conversion uplift, and none is claimed.
A single monitored platform in place of scattered scripts, with a record of every run.
Marketing triggers and multilingual workflows that add to the agency's programme rather than duplicating it.
Read-only reporting views and written rules for how the numbers are counted.
What you receive
Automation work is reviewed in writing: what runs every day and how reliably, the time it saves your team, and what is falling short, in plain terms. This sample uses a fictional store, so every name and figure in it is invented.
Sample report · fictional store · invented figures
3 pages · A4 · opens in a new tab
Automations that run quietly in several places tend to fail quietly too. Putting them on one platform with a run log and alerts means a missed job is noticed the same day, the store's team can see what ran, and new work can be added without upsetting what an agency or another tool already does.
Project details are anonymised. Work described as delivered is live in production; work still in development is shown separately and is not presented as delivered.
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