Retail Media Search & Keyword Strategy
Build the search and keyword bidding tactics inside a retail media plan.
What is the Retail Media Search & Keyword Strategy?
The Retail Media Search & Keyword Strategy skill is a free AI skill that builds the search and keyword bidding tactics inside a retail media account — the layer beneath the overall campaign plan. You give it your product, the retail media network, your objective, and your budget and margin; it returns keyword segmentation across branded, category, competitor, and long-tail terms, a match-type and bidding approach per segment, a negative-keyword approach to stop budget bleeding, a search-term harvesting workflow that promotes proven terms to exact match, a budget split across keyword tiers, and the measurement framework to judge performance. It is built for shopper and e-commerce media managers who already have a retail media budget approved and need the actual keyword-level tactics to spend it well. Because it works one layer beneath the overall media plan, it goes deeper on search and bidding than a broader plan typically covers. Grounding the keyword segmentation in live food and beverage demand data shows what shoppers are actually typing into the search bar.
Who it's for
- Shopper and e-commerce media managers running retail media search
- Retail media specialists managing keyword-level bidding
- Brand digital marketing managers funding sponsored search
- Agency retail media planners building keyword strategy for clients
What you get
- Keyword segmentation: branded, category, competitor, long-tail
- A match-type and bidding approach per segment
- A negative-keyword approach
- A search-term harvesting and graduation workflow
- A budget split across keyword tiers
- A measurement framework tied to margin-based breakeven
How to use it
- 1Paste the prompt into ChatGPT, Claude or Gemini.
- 2Fill the bracketed inputs (product, network, objective, budget, margin).
- 3Answer its clarifying questions about current keyword performance.
- 4Ask it to build the actual keyword list for the category segment.
See it in action
Example input
Brand: fictional 'Quill Valley Granola'. Platform: a major retail media network. Objective: grow the hero SKU and stop losing brand-term impressions to a competitor bidding on our name. Budget: a modest monthly test budget. Margin: [insert]. Problem: broad-match terms are converting poorly and eating most of the budget.
Example output
Retail media search & keyword strategy — Quill Valley Granola Keyword segmentation - Branded (defend): "Quill Valley," "Quill Valley granola," and close misspellings. Small volume, cheapest clicks, highest conversion — and currently being encroached on by a competitor bidding your name. - Category: "granola," "healthy granola," "granola clusters," "breakfast granola" — your biggest volume opportunity and the segment worth the most testing budget. - Competitor: named competitor granola terms, used cautiously — a defensive/opportunistic play, not a volume driver; expect a lower conversion rate here than on your own branded terms. - Long-tail: "gluten-free granola clusters," "low sugar granola," or whatever specific attributes your hero SKU actually carries — lower volume per term but often the cheapest, highest-intent clicks once identified. Match-type & bidding approach - Branded: exact match, aggressive bid — this is the cheapest defense against the competitor bidding on your name, and losing this auction is the highest-priority problem to fix first. - Category: a mix of phrase and exact on proven terms; start a small broad or auto component specifically to discover new converting long-tail terms, then graduate winners out of broad and into exact. - Competitor: exact match only, modest bid, monitored closely for conversion — cut fast if it's not converting within a defined test window. - Long-tail: exact match once identified through the harvesting process below; these terms usually support a lower bid for a similar or better conversion rate. Negative-keyword approach Apply negatives aggressively on the category broad/auto campaign specifically — any search term that has spent budget across a meaningful sample size without converting should be added as a negative rather than left to keep bleeding spend. Review this weekly while the discovery campaign is active, since this is where a "modest monthly test budget" gets wasted fastest if left unmanaged. Search-term harvesting workflow 1. Let the category broad/auto campaign run and report actual search terms. 2. Weekly, pull the search-term report and identify terms converting above your target rate. 3. Add winning terms to a dedicated exact-match campaign at a bid you control. 4. Add non-converting terms as negatives in the broad campaign so it stops re-learning the same lesson. Budget split (directional) Given the stated brand-term defense problem, weight budget toward branded defense first despite its low overall spend need — it's inexpensive to defend and expensive to lose. Roughly: majority to category (your growth engine), a meaningful minority to branded defense, a small test allocation to competitor terms, with long-tail folded into category once harvested. [Insert your actual monthly budget to set real dollar caps per segment.] Measurement - Primary: ROAS against a breakeven tied to your actual margin — [insert margin] to calculate this precisely. - Brand-term impression share and auction win rate — the direct measure of whether the competitor-bidding problem is resolving. - New-to-brand order share on category and long-tail terms, since branded-term conversions are largely existing buyers, not growth. - Search-term report health: declining wasted spend on non-converting broad terms over time is a sign the harvesting workflow is working. Want me to build the actual starting keyword list and bid recommendations for the category segment?
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 retail media performance specialist who works one layer beneath the overall media plan — deep on search terms, match types, and bidding. You separate proven converters from exploration budget, and you defend brand terms as the cheapest, highest-priority spend. # Context I'll provide - Product: [PRODUCT] - Retail media network: [NETWORK — e.g. a major grocery or marketplace retail media platform] - Objective: [OBJECTIVE e.g. grow a hero SKU, defend brand terms, launch] - Budget: [BUDGET] - Margin (for breakeven ROAS): [MARGIN] - Known issues (optional): [e.g. poor-converting broad terms, competitor bidding on brand name] # Your task
Frequently asked questions
- What is retail media keyword strategy?
- Retail media keyword strategy is the search-term and bidding layer inside a retail media account — deciding which branded, category, competitor, and long-tail terms to bid on, at what match type, and how much, so a retail media budget converts rather than just spending. This skill builds that keyword-level tactic set: segmentation, bidding, negatives, and a harvesting workflow that turns discovery spend into proven, controlled campaigns.
- How is this different from the Retail Media Plan Builder skill?
- The Retail Media Plan Builder covers the whole retail media campaign — objectives, overall budget split across upper- and lower-funnel, ad types, and the full measurement framework. This skill goes one layer deeper into a single piece of that plan: the actual search and keyword tactics — segmentation, match types, bidding, and negative-keyword management. Use the plan builder to set the overall campaign strategy, then this skill to build the keyword execution underneath it.
- Which AI models does this prompt work with?
- Any capable chat model — ChatGPT, Claude, or Google Gemini. It's model-agnostic, so paste it directly into a chat, save it as a Custom GPT, or store it as a reusable skill so keyword strategy stays consistent across every retail media account your team manages.
- Do I need to know my margin before running this?
- Yes, ideally — the skill ties its recommended breakeven ROAS directly to your margin, and without it, that number stays a placeholder rather than a real target. If you don't have exact margin figures on hand, provide your best estimate; the skill will not invent a target ROAS on your behalf, but a rough number is still useful for directional bidding guidance.
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