Every first purchase is different

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🧐Three Things a VIP/Active/Lapsed Tier Report Can't Actually Tell a Beauty Brand

RFM tiers treat every first purchase the same, which is exactly backwards for a multi-category beauty catalog. 

A customer whose first order was a $28 lip product and a customer whose first order was a $120 skincare set land in the same "new customer" bucket, even though repeat behavior between those two categories diverges hard.

Add order value and category breadth at first purchase as a signal RFM ignores entirely. 

A customer whose first order spanned three categories behaves differently afterward than one who bought a single item, and that distinction is invisible to recency, frequency, and spend alone. This is buildable manually from existing order data without any new tooling.

Track cross-category graduation paths, not just repeat purchase within the same category. 

Lip balm buyers who go on to buy full skincare sets follow a specific, findable pattern in the order data, a genuine upgrade path most brands only notice after it's already happened organically. 

Marketing that path proactively to customers showing the early signal, rather than waiting for them to discover it themselves, turns a passive pattern into an active revenue lever.

Segment by attribute affinity, not product category. 

Two customers buying the identical SKU can have completely different underlying motivations, fragrance-free for sensitive skin, vegan for values, acne-prone for a specific concern. 

Tagging purchases by the attribute that actually drove the decision, where that data exists in reviews, quiz answers, or product metadata, produces a segmentation layer category-based tiers can never see, since two people in the same category bucket are being marketed identically for reasons that don't actually apply to both of them.

Building order-value segmentation, mapping graduation paths across a full catalog, and tagging attribute affinity across historical orders are each genuine data projects, the kind of work static RFM was specifically built to avoid needing in the first place. 

Connect Omnisend to Claude or ChatGPT, and it sees exactly what's driving revenue by campaign, by segment, by send, building the order-value segmentation in strategy one directly; the graduation-path and attribute-affinity models in strategies two and three are deeper analysis worth validating with an analyst before treating them as a one-prompt deliverable. You can get started with ready-made prompts here.

Check whether the current tiering is actually predicting anything, or just describing what already happened to a customer who's already moved on to something the report never saw coming.

Partnership with Omnisend

See What Your Email Account Needs in 30 Seconds 

You probably know your open rate. The harder question is what needs fixing next, and finding that answer can take longer than the campaign itself.

Omnisend just made checking in a 30-second habit. It connects straight to Claude and ChatGPT, so you can ask your account what's going on, and Omnisend AI does the fixes for you without leaving the chat.

You don't even need to write the prompts yourself. There's a full library already built, sorted from beginner to advanced. 

• Beginner: Get an instant health snapshot, rank campaigns by revenue, or check deliverability.

• Intermediate: Compare weekly performance, analyze subject-line patterns, locate automation revenue leaks, and do the fixes.

• Advanced: Score the complete account, build a re-engagement campaign for you, or replace two hours of monthly reporting.

Built on feedback from 150,000+ brands, Omnisend users generate an average of $79 for every $1 spent. 

Switching takes zero effort since Omnisend migrates you for free within 5 days, and your bill drops up to 35%.

Connect your AI and run your first prompt today.

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👻 Snapchat partnered with HubSpot to streamline lead generation campaigns, giving advertisers automated CRM syncing, improved conversion tracking, and clearer ROI measurement through native lead ads. 

📈 Meta reported $59.4 billion in Q2 ad revenue, up 27% YoY, while saying its AI-powered ad tools are driving industry-leading growth despite rising concerns over AI spending and weaker Q3 guidance. 

🧠 A geoSurge study found AI models are 3.2× more likely to search for brands they already know, with 63% of brand-specific searches favoring one of each model’s five most familiar brands. 

📊 Google launched Structured Data Files v10.1 for Display & Video 360, adding AI transparency labels, digital out-of-home campaign support, and new bulk campaign management features.

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