Moonpig's AI play shows personalization sells: what sales teams can copy now
Moonpig just put numbers behind something most reps feel in their gut: personalized experiences move revenue. In the six months to 31 October, sales rose 6.7% to £169m, with a swing back to profit at £26.6m (vs a £33.3m loss a year earlier). The lift comes as AI designs more cards, helps customers customize messages, and handles support.
About half of all purchases now use AI-led features like stickers, photos, or custom handwriting-up from roughly 2% two years ago. That combo of speed, relevance, and ease translated into more orders and higher spend per order.
Key numbers worth your attention
- Revenue: £169m for the half-year, up 6.7%.
- Profitability: £26.6m pre-tax profit vs a £33.3m loss last year.
- Adoption: ~50% of purchases include AI features (from ~2% two years back).
- Support: AI chat resolves ~33% of queries with higher satisfaction scores than human-handled ones.
How they did it (and why it worked)
- Personalization at scale: Shoppers can tweak any design for age, relationship, or purpose with a few clicks. Less friction, more relevance, better conversion. See supporting research on personalization and revenue.
- Human-in-the-loop quality: "AI is now designing a lot of cards for us," said CEO Nickyl Raithatha, while stressing that in-house teams still review designs to keep them on-brand and fresh. No generic spam clogging the catalog.
- Rep productivity boost: Instead of crafting one or two designs a day, AI suggests 50+ options for a person to edit or curate. Same logic applies to sales content: more first drafts, faster iteration, higher output.
- Faster support, happier buyers: The AI chat resolves a third of tickets and gets better ratings. That means lower wait times and fewer blockers before a purchase.
Sales takeaways you can put to work this quarter
- Personalize every touchpoint: Use dynamic fields (role, company, use case) in email, chat, and proposals. Start simple: age, occasion, relationship-style prompts worked for Moonpig-your equivalents are industry, pain point, and priority.
- Ship AI-assisted content: Give reps tools to auto-generate 10-20 outreach angles, call openers, or proposal sections. Keep a human editor to approve tone and claims.
- Stand up an AI help layer: Let a bot solve FAQs, order status, pricing basics, and hand off the rest. Track resolution rate, time to first response, and CSAT.
- Guardrails > guesswork: Create prompt templates, compliant language blocks, and brand glossaries. Review outputs before they go live-Moonpig's team does.
- Measure like a hawk: A/B test personalization vs control. Watch conversion rate, average order value, and ticket volume. Keep what lifts revenue, cut what adds noise.
Leadership and stability
Raithatha emphasized this isn't a jobs-cut story. AI multiplies creative options; people curate and improve them. He steps down at month-end, with Catherine Faiers (from AutoTrader) taking over-momentum on AI and growth appears set to continue.
Macro note: recent budget changes haven't shifted customer behavior, and peak-season trading started strong. Translation for sales teams: demand is steady; execution quality decides who wins share.
If you're building your own playbook
- Skill up the team on prompts, review workflows, and measurement. Practical, job-specific training helps you avoid detours. See AI courses by job.
- Want a structured path for go-to-market teams? Try this AI certification for marketing and sales.
Bottom line
Moonpig showed that practical AI-personalization, faster support, and human-reviewed outputs-drives real revenue. Copy the system, not just the tools: clear guardrails, human review, and relentless testing.
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