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Post-Launch Performance Readout Builder

Turn your own launch's sales, trial and awareness data into a structured retrospective.

What is the Post-Launch Performance Readout Builder?

The Post-Launch Performance Readout Builder is a free AI skill that turns a food or beverage brand's own post-launch data into a structured launch retrospective. You give it the launch details, the sales, trial, repeat, or awareness data you've gathered since shipping, and the goals the launch was measured against; it returns a headline read on performance versus goal, a diagnosis of where in the funnel — awareness, trial, or repeat — the launch is over- or under-delivering, the likely drivers behind the pattern, and a recommendation on whether to invest further, hold, adjust, or exit. It is built for innovation and insights teams who ran a launch, now have real numbers, and need to turn them into a decision-ready readout before the next stage-gate review. Because it separates what the data shows from what it merely suggests, the readout survives a skeptical question in the room. Live food and beverage demand data helps confirm whether a launch's early numbers reflect a durable trend or a temporary spike.

Who it's for

  • Innovation managers preparing a stage-gate review after launch
  • Insights teams turning raw launch data into a leadership readout
  • Brand managers assessing whether to invest further in a new item
  • Category teams reporting launch performance to a retail partner

What you get

  • A headline read: performance versus the launch's original goal
  • A funnel diagnosis: awareness, trial, or repeat as the bottleneck
  • Ranked likely drivers behind the performance pattern
  • An invest, hold, adjust, or exit recommendation
  • The follow-up data or time needed before a final call
  • A one-page readout ready for a stage-gate or leadership meeting

How to use it

  1. 1Paste the prompt into ChatGPT, Claude or Gemini.
  2. 2Paste your actual post-launch data and fill in the original goals.
  3. 3Answer its clarifying questions about what's driving versus lagging.
  4. 4Ask it to draft the stage-gate recommendation as a standalone slide.

See it in action

Example input

Product: fictional 'Homestead' bone broth cups, launched 4 months ago at Meadow Market. Goal was 15% household penetration among broth buyers within 6 months and a 30% repeat rate by month 4. Actual: awareness tracking at 22% (ahead of plan), trial at 9% of aware shoppers (below the 15% we modeled), repeat rate currently at 34% among triers.

Example output

Post-launch performance readout — Homestead Bone Broth Cups, Month 4

Headline
Homestead is a trial problem, not an awareness or a quality problem. Awareness is running ahead of plan and repeat rate among triers is strong — the bottleneck is converting awareness into a first purchase.

Performance versus goal
Awareness: 22% actual vs. an implied pace toward your household penetration goal — running ahead, not the constraint. Trial: 9% of aware shoppers vs. the 15% modeled — this is the clear shortfall and the metric to focus the readout on. Repeat: 34% at month 4 vs. a 30% goal — already exceeding target, which is genuinely good news and should be stated plainly, not buried under the trial miss.

Funnel diagnosis
The pattern — high awareness, low trial, strong repeat — points specifically at a trial barrier, not a product barrier. If the product itself were the problem, you'd expect a weak repeat rate among the people who did try it; instead, triers are converting to repeat buyers faster than plan. Something between "I've heard of Homestead" and "I bought it" is the actual bottleneck.

Likely drivers, ranked by confidence
1. High confidence: price or shelf-location friction at first purchase — bone broth is a category where format is often unfamiliar (cups vs. cartons), and unfamiliar formats commonly see an awareness-to-trial gap larger than modeled. Recommend checking shelf placement and any format-education gap first.
2. Medium confidence: sampling or trial-driving promotion may be underweighted in the launch plan relative to what awareness-building media delivered — if the media plan built awareness faster than the trial plan built trial mechanisms, this gap is a natural result, not a demand problem.
3. Low confidence / assumption to validate: whether price is the specific barrier, since no price-elasticity or purchase-barrier data was included in what you provided — this needs a targeted check before being named as the cause in a leadership readout.

What the data does not yet explain
Whether the 9% trial rate is weak in absolute terms for this category and format, or only weak relative to your internal model — if 9% is actually a reasonable trial rate for a new bone-broth format industry-wide, the real issue may be that the original 15% goal was set too high, not that the launch underperformed. Pull a category-comparable trial-rate benchmark before concluding the launch itself is off track.

Recommendation: INVEST FURTHER, with a trial-specific intervention
The strong repeat rate is the most important number in this readout — it means the product delivers once tried. This is not a hold-or-exit situation; it's a trial-mechanism problem with a product that's already proving itself. Recommend a targeted sampling or format-education push in the next 60 days rather than a media-weight increase, since awareness is not the constraint.

Evidence still needed before the next stage-gate
A category-comparable trial-rate benchmark for new format launches, to confirm whether 15% was a realistic goal. A shelf-placement and format-visibility audit at underperforming doors. A quick purchase-barrier check (price, placement, or format confusion) among aware-but-not-tried shoppers.

One-page stage-gate summary
Homestead is ahead of plan on awareness (22%) and exceeding its repeat-rate goal (34% vs. 30%), with trial (9% vs. 15% modeled) as the single bottleneck. Because repeat performance confirms the product delivers once tried, the recommendation is to invest in a targeted trial-driving intervention rather than reconsider the launch itself.

Want me to draft the trial-intervention brief this recommendation points to?

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 innovation performance analyst for food & beverage brands who turns raw post-launch data into decision-ready stage-gate readouts. You diagnose exactly where in the funnel a launch is over- or under-delivering, and you never recommend a fix for the wrong stage.

# Context I'll provide
- Product and launch details: [PRODUCT / LAUNCH DATE / RETAILER OR MARKET]
- Original launch goals: [GOALS e.g. penetration, trial rate, repeat rate targets and timeline]
- Actual data collected so far: [DATA — awareness, trial, repeat, sales, whatever you have]
- Time since launch: [TIMEFRAME]
- The decision this readout feeds (optional): [DECISION e.g. stage-gate, retailer review]

# Your task
1. If the launch goals or actual data are missing or vague, ask up to 3 clarifying questions BEFORE writing anything.

Frequently asked questions

What is a post-launch performance readout?
A post-launch performance readout compares a product's actual post-launch results — awareness, trial, repeat, sales — against the goals it was launched to hit, and diagnoses exactly where in the purchase funnel the launch is over- or under-delivering. This skill builds that readout from your own data and turns it into an invest, hold, adjust, or exit recommendation for a stage-gate review.
How is this different from the Competitive Launch Brief skill?
The Competitive Launch Brief analyzes a competitor's new product launch — an outward-facing read on someone else's move. This skill is inward-facing: it takes your own product's real post-launch data and turns it into a retrospective on how your launch is actually performing against its own goals. Use the launch brief to react to a rival; use this to judge your own results.
Which AI models can run this prompt?
Any capable chat model — ChatGPT, Claude, or Google Gemini. The prompt is model-agnostic, so paste it into a chat, save it as a Custom GPT, or store it as a reusable skill so every stage-gate review follows the same funnel-diagnosis discipline.
Will it tell me a launch is succeeding even if the data is mixed?
No — it is built to separate genuinely strong metrics from weak ones rather than average them into a falsely reassuring or falsely alarming headline. If awareness is ahead of plan but trial is behind, it says both plainly and diagnoses which one is the real bottleneck, rather than blending them into a single vague verdict. It will not invent data to fill gaps; missing metrics are flagged as evidence still needed.

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