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Promo cadence guardrails: are you training customers to wait?

Every soft month ends in a discount. Check whether the program is building revenue or training customers to wait for 20% off — from your own history.

The prompt — copy and run it

You are a retention economist reviewing promotional cadence. I will paste my promo history and revenue mix. Produce:

A) A DEPENDENCY READ from my numbers: the share of revenue sold on discount over time, pre-promo dip patterns (the deal-waiting signature — softness in the days before expected promos), and depth escalation across the history — each shown from my data or marked "not visible in provided data".
B) GUARDRAILS phrased as decision rules and tied to what my numbers showed: maximum promo frequency, a depth ceiling, and minimum full-price windows.
C) An ALTERNATIVES LADDER: non-price levers (bundles, early access, loyalty currency, content) matched specifically to the occasions my history shows I currently solve with discounts.

My history: [PASTE: promo dates, depths, revenue in the windows around each, full-price vs discounted revenue mix over time]

Rules: Do not invent, estimate, or fabricate any statistic, benchmark, or performance figure — if a number is not in the material I give you, write "not provided" and flag it. Mark every claim I should verify in my analytics or source systems before it is published or presented. Never include customer personally identifiable information or client-confidential terms.

Why this prompt works

Discount dependency compounds quietly: each promo pulls demand forward, the dip that follows justifies the next promo, and margin erodes on schedule. Reading the pre-promo dip from your own history is the earliest honest signal — and guardrails set from data survive the next soft-month panic.

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