What is a new-to-brand shopper acquisition campaign?

A new-to-brand shopper acquisition campaign specifically targets households who have never purchased your brand before, using targeting data — often retailer loyalty history — to identify competitor buyers or category non-buyers and message them without assuming any prior familiarity. This skill builds that campaign end to end: segments, message framework, trial offer, channels, and a funnel with KPIs tied to genuinely new households.

How is this different from the Lapsed Shopper Win-Back Campaign skill?

Acquisition targets shoppers who have never bought your brand at all — the targeting data comes from competitor or category purchase history, and the message has to introduce the brand from zero. The Lapsed Shopper Win-Back Campaign targets people who used to buy and stopped — the targeting data comes from your own purchase history showing a lapse, and the message references a prior relationship rather than starting cold. They require different data, different messages, and typically different budgets.

Which AI models does this prompt work with?

Any capable chat model — ChatGPT, Claude, or Google Gemini. The prompt is model-agnostic, so use it directly in a chat, save it as a Custom GPT, or store it as a team skill so every acquisition push follows the same segment-first discipline.

What targeting data do I need before running this?

Ideally, retailer loyalty or panel data that can distinguish competitor buyers from true category non-buyers, since they convert at different speeds and need different messages. If you don't have that level of data yet, say so — the skill will still build a campaign structure, but it will flag broad targeting (like general demographic proxies) as a weaker substitute for real purchase-history data.

Shopper Brand

New-to-Brand Shopper Acquisition Campaign

Build a campaign that recruits shoppers who have never bought you.

What is the New-to-Brand Shopper Acquisition Campaign?

The New-to-Brand Shopper Acquisition Campaign skill is a free AI skill that builds a campaign to recruit shoppers who have never purchased your brand before, distinct from re-engaging lapsed buyers. You give it your brand, the retailer or channel, who you believe is buying competitors or nothing in your category, and your budget; it returns targeting criteria built for a cold audience, a message framework that earns attention without assuming prior familiarity, a trial-offer mechanic designed to lower first-purchase risk, a channel and tactic mix, and a funnel from awareness through trial to early repeat. It is built for shopper and brand marketing teams whose growth depends on actual household penetration gains, not just deeper spend from existing buyers. Because it targets people with no purchase history rather than a lapsed segment, both the targeting data and the message start from zero familiarity. Grounding the trial offer in live food and beverage demand data shows which flavor or benefit story is most likely to earn a first try.

Who it's for

Shopper marketing managers driving household penetration growth
Brand managers focused on trial among non-buyers
E-commerce and growth marketing managers building acquisition funnels
Category development managers expanding a brand's buyer base

What you get

Targeting criteria built for a cold, never-bought audience
A message framework that assumes zero brand familiarity
A trial-offer mechanic that lowers first-purchase risk
A channel and tactic mix for reaching non-buyers
An awareness-to-trial-to-repeat funnel with KPIs
Risks and watch-outs specific to acquiring cold shoppers

How to use it

1Paste the prompt into ChatGPT, Claude or Gemini.
2Fill the bracketed inputs (brand, retailer, target non-buyer profile, budget).
3Answer its clarifying questions about what data defines 'never bought.'
4Ask it to build a variant message for a specific competitor-buyer segment.

See it in action

Example input

Brand: fictional 'Ferro & Finch Cold Brew', sold in US grocery and convenience. Target: shoppers who currently buy a competitor's cold brew or no cold brew at all, identified through retailer loyalty data. Retailer: a national grocery chain. Budget: $65,000 over 8 weeks. Goal: acquire 5,000 new-to-brand households.

Example output

New-to-brand acquisition campaign — Ferro & Finch Cold Brew

Targeting criteria (cold audience)
Two distinct non-buyer segments, since they need different messages:
1. Competitor cold brew buyers — identifiable via retailer loyalty data as households buying a rival cold brew SKU but never a Ferro & Finch UPC. These shoppers already believe in the cold brew occasion; the job is winning the brand choice, not the category.
2. Category non-buyers — households buying regular hot coffee or none, never any cold brew UPC. These shoppers need the cold brew occasion sold to them before the brand does, a harder and slower conversion.
Recommend weighting the $65,000 budget toward segment 1 first — it is the faster path to your 5,000-household goal, since the category-conversion job in segment 2 typically takes longer than an 8-week window to show results.

Message framework (zero familiarity assumed)
Do not lead with the brand name as if it's already known. Lead with the occasion and the specific reason to switch or try:
- For competitor buyers: a head-to-head reason to switch — a specific taste or format difference stated plainly, not vague superiority language.
- For category non-buyers: an entry-level education message — what cold brew is, why it's different from iced coffee, positioned as approachable rather than a coffee-snob category.

Trial-offer mechanic
A first-purchase-only discount or rebate, gated to loyalty accounts with no prior Ferro & Finch purchase history — this keeps the offer's cost concentrated on genuinely new households rather than subsidizing existing buyers' next purchase. [Insert your actual offer depth once your margin allows you to set it.]

Channel & tactic mix
- Retailer loyalty-targeted media: the most precise way to reach the two named segments directly, since it uses the same purchase-history data that defined them.
- Social and video: awareness-building for the category-non-buyer segment specifically, where the job is bigger than a single ad impression.
- In-store secondary placement near competitor cold brew or near hot coffee, depending on which segment a given store's shopper base skews toward.
- Sampling, if budget allows a modest allocation: cold brew is a taste-driven category-non-buyer decision, so this is a plausible allocation to test alongside retailer media rather than reserving all budget for the paid channels.

Funnel & KPIs
- Awareness: impressions and reach against the two defined non-buyer segments specifically, not total category shoppers.
- Trial: first-purchase rate among targeted non-buyers, tracked via loyalty-account redemption of the trial offer.
- Early repeat: second purchase within 30-45 days of trial, since trial without repeat is a weak signal that the 5,000-household goal is durable rather than a one-time discount response.

Watch-outs
- If the trial offer isn't gated correctly, existing Ferro & Finch buyers redeeming it will inflate your reported "new household" number without any real acquisition happening — confirm the gating logic with the retailer's media team before launch.
- An 8-week window favors segment 1 (competitor buyers) heavily; if segment 2 underperforms, that is expected, not a signal the message failed — category conversion typically needs a longer runway.

Want me to build the segment-1-only version of this brief, sized to the full $65,000 budget, since it's the faster path to your household goal?

The prompt

Here's the start of the prompt. Download the free bundle for the full, ready-to-paste version — plus the installable Claude Skill and Custom-GPT instructions.

# Role
You are a senior shopper marketing strategist who builds acquisition campaigns aimed at shoppers with zero purchase history on the brand. You separate never-bought audiences from lapsed buyers, and you build messages that assume no prior brand familiarity.

# Context I'll provide
- Brand: [BRAND]
- Retailer or channel: [RETAILER / CHANNEL]
- Who we believe is buying competitors or nothing in this category: [TARGET NON-BUYER PROFILE]
- How non-buyers can be identified (loyalty data, panel, none yet): [TARGETING DATA]
- Budget: [BUDGET]
- Campaign window: [TIMEFRAME]
- Acquisition goal: [GOAL e.g. new households, trial units]

Frequently asked questions

What is a new-to-brand shopper acquisition campaign?: A new-to-brand shopper acquisition campaign specifically targets households who have never purchased your brand before, using targeting data — often retailer loyalty history — to identify competitor buyers or category non-buyers and message them without assuming any prior familiarity. This skill builds that campaign end to end: segments, message framework, trial offer, channels, and a funnel with KPIs tied to genuinely new households.
How is this different from the Lapsed Shopper Win-Back Campaign skill?: Acquisition targets shoppers who have never bought your brand at all — the targeting data comes from competitor or category purchase history, and the message has to introduce the brand from zero. The Lapsed Shopper Win-Back Campaign targets people who used to buy and stopped — the targeting data comes from your own purchase history showing a lapse, and the message references a prior relationship rather than starting cold. They require different data, different messages, and typically different budgets.
Which AI models does this prompt work with?: Any capable chat model — ChatGPT, Claude, or Google Gemini. The prompt is model-agnostic, so use it directly in a chat, save it as a Custom GPT, or store it as a team skill so every acquisition push follows the same segment-first discipline.
What targeting data do I need before running this?: Ideally, retailer loyalty or panel data that can distinguish competitor buyers from true category non-buyers, since they convert at different speeds and need different messages. If you don't have that level of data yet, say so — the skill will still build a campaign structure, but it will flag broad targeting (like general demographic proxies) as a weaker substitute for real purchase-history data.

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