A beauty brand whose small team spent hours each day on product-fit questions and manual recommendations. The work was repetitive, hard to scale, and left little first-party data behind to act on.
Beauty brand
A beauty brand whose small team spent hours each day on product-fit questions and manual recommendations. The work was repetitive, hard to scale, and left little first-party data behind to act on.
Repetitive support tickets and manual product recommendations were eating the team’s time.
Built a product-recommendation quiz, a dashboard to read the data, and an email automation flow to follow up — reducing manual triage.
04 — Diagnostic
What the audit would surface.
- 01Repetitive ‘which product is right for me’ tickets answered manually.
- 02No structured first-party data captured from those conversations.
- 03Follow-up emails sent ad hoc, not triggered by behaviour.
05 — Proposed architecture
How we’d build it.
- 01A product-recommendation quiz capturing structured customer inputs.
- 02A dashboard to read quiz data and segment customers.
- 03An email automation flow triggered by quiz results, with human-reviewed templates.
06 — Implementation roadmap
Scope
Define quiz logic, data model and the decisions the dashboard must support.
Build
Quiz app, storage and reporting dashboard.
Automate
Behaviour-triggered email flow with human-in-the-loop drafts.
Potential impact & risks
Estimated impact: richer first-party data and less support friction. (Illustrative scenario, not a measured client result.)
- — Quiz logic must stay genuinely helpful, not a dark-pattern funnel.
- — Email automation needs consent and clean unsubscribe handling.
- — Recommendations should be reviewed so they stay accurate as the catalogue changes.
This is a example build — prototype scenario. Figures are illustrative and not a guaranteed result — your numbers will depend on your store, traffic and market.
Want a build like this — measured for your store?
Start with an audit. We’ll tell you whether the opportunity justifies the build.