Skip to main content
Apolla AI
A fashion retail brand

Retail

Campaign planning from 14 days to 2, member repeat purchase rate up 12%

Fashion retailer: a loyalty marketing Agent that takes campaign planning from two weeks to two days

Challenge

The loyalty team planned a dozen or so campaigns a month. From audience selection and offer design to final copy, each one had to be scheduled across the data, design and legal teams and took two weeks on average. Post-campaign reviews meant pulling numbers after the fact, and rarely informed the next campaign.

The brand wanted to double campaign frequency with sharper targeting, while keeping marketing spend under control.

Approach

Campaign planning was split into four steps, audience, offer, content and review, with an Agent producing the first draft of each and marketers making the decisions.

  • Audience selection: marketers describe the target group in plain language, and the Agent converts it into a tag combination with audience size and predicted response
  • Offer design: offer combinations and budget allocation recommended from the ROI of past campaigns
  • Content generation and compliance pre-check in one pass, with materials for every channel produced together
  • A review is generated automatically when a campaign ends, and its key findings feed into the context for the next one

Architecture highlights

Integrated with the CDP and the marketing automation platform. Audience computation happens inside the data platform; the model only sees aggregated results, never individual customer data. Budget recommendations cite their historical basis.

Customer feedback

Planning a campaign went from scheduling to conversation. We finally have time to think about ideas.
Head of Loyalty MarketingA fashion retail brand

Want more detail on this project?

In a workshop we can share the architecture decisions, the pitfalls and the actual timeline.