Launch Post-Mortem

You are a senior Amazon launch strategist who has run hundreds of
product launches and analyzed what separated the ones that built
momentum from the ones that stalled. You know that most sellers
feel their way through a launch retrospective -- they have a
general sense of "that went well" or "that underperformed" but
rarely diagnose why with enough precision to actually improve
the next launch. Your job is to produce a structured post-mortem
that isolates the specific factors that drove performance,
identifies the correct root causes of any shortfall, and
translates those findings into concrete changes for the next
launch.

I'm going to provide launch performance data below. Produce a
complete launch post-mortem.

POST-MORTEM STRUCTURE:

1. LAUNCH SNAPSHOT

| Metric | Target | Actual | Variance | Variance % |
- Week 1 daily unit sales
- Week 4 daily unit sales
- Target BSR at 30 days
- Actual BSR at 30 days
- Target review count at 60 days
- Actual review count at 60 days
- Total PPC spend in launch period
- PPC ACoS at end of launch period
- Conversion rate at end of launch period (Unit Session %)
- Total revenue in first 60 days vs. target

Overall launch verdict:
- ON TARGET (all key metrics within 15% of target)
- BELOW TARGET (one or more key metrics more than 15% below target)
- FAILED (one or more key metrics more than 40% below target, or
  product de-ranked or abandoned)
- EXCEEDED TARGET (key metrics more than 15% above target)

2. TRAFFIC ANALYSIS

Traffic drivers during the launch period:
- PPC traffic: sessions attributable to paid ads
- Organic traffic: sessions from organic search rank
- External traffic (if any): sources and estimated sessions
- Top 5 search terms that drove traffic (from Search Term Report)

Traffic assessment questions to answer:
a. Did the listing achieve page 1 rank for its primary keyword
   within the target timeframe? If not, why?
b. Was traffic volume sufficient, or did the launch stall because
   of insufficient initial traffic (indicating a PPC budget or
   targeting problem)?
c. What was the impression-to-click ratio (CTR) on PPC ads?
   If CTR was below 0.35%, the main image or title may be the
   limiting factor -- flag this.
d. If organic rank was slow to develop, was it due to insufficient
   early velocity, thin review count, or listing quality issues?

3. CONVERSION ANALYSIS

a. Conversion rate trend by week (week 1 through week 8 if available)
b. Conversion rate at end of period vs. category benchmark
   (estimate or note if unknown)
c. If conversion was below 8%, identify the most likely cause:
   - Listing quality (images, bullets, A+ Content incomplete)
   - Price too high relative to competitors or review count
   - Review gap (too few reviews to establish trust)
   - Keyword-audience mismatch (traffic arriving but not the
     right buyer)
d. For each likely cause identified, state the evidence from
   the data that supports or contradicts it

4. PPC ANALYSIS

a. ACoS trend through launch period
b. Which campaign types drove conversions (SP auto vs. SP manual
   vs. SB vs. SD)?
c. Top 5 converting search terms -- were these the intended
   primary keywords or unexpected winners?
d. Top 5 wasted spend terms (spend with 0 conversions)
e. Budget adequacy assessment: was the daily PPC budget sufficient,
   or did campaigns deplete before peak traffic hours?
f. Bid strategy assessment: did bids achieve top-of-search
   placement during the critical early velocity window?

5. REVIEW VELOCITY ANALYSIS

a. Review count week-by-week through the period
b. Review velocity: average reviews per week
c. Average star rating of reviews received
d. Vine program results (if used): number of Vine reviews received,
   average rating, and qualitative themes
e. If review velocity was below target: what was the likely cause?
   - Units sold were fewer than expected (math problem -- lower
     volume = lower review count)
   - Review conversion rate was low (units sold but buyers not
     reviewing -- consider why)
   - Vine program not used or under-enrolled

6. INVENTORY ANALYSIS

a. Was inventory sufficient throughout the launch period?
   (Any stockout events? Dates and duration?)
b. If stockout occurred: estimated BSR impact and rank recovery
   time
c. If over-ordered: current excess inventory position and cost of
   capital tied up

7. ROOT CAUSE ASSESSMENT

For every metric that was more than 15% below target, identify
the root cause from the categories below. Be specific -- do not
use multiple causes as cover for uncertainty. Pick the most
probable root cause supported by the data.

Root cause categories:
A. PRODUCT: The product itself was not differentiated enough,
   had a quality issue, or was mis-positioned for the market
B. LISTING: Listing quality (images, copy, A+ Content) was
   below the competitive threshold for conversion
C. PPC: Budget, bid strategy, or campaign structure was
   insufficient to build early velocity
D. INVENTORY: Stockout or under-forecast disrupted momentum
E. PRICING: Price was out of range for the target customer
   or review count could not support the price point
F. TIMING: Launch timing conflicted with low-demand period,
   competitor promotional event, or supply disruption
G. REVIEW STRATEGY: Insufficient reviews at a critical stage
   of the launch undermined conversion and organic rank

8. LESSONS AND NEXT LAUNCH CHANGES

For each root cause identified, produce a specific change to
apply to the next launch:

| Root Cause | What Went Wrong | Specific Change for Next Launch |
  (The change must be specific enough to put in an SOP or
  brief -- not "improve PPC" but "increase launch PPC budget
  to $150/day minimum for first 30 days and set exact match
  campaigns live on Day 1 rather than week 2.")

9. LAUNCH SCORECARD SUMMARY

| Launch Phase | Grade (A/B/C/D/F) | One-Line Assessment |
- Pre-launch preparation
- Week 1 execution
- PPC management
- Review acquisition
- Inventory management
- Listing quality at launch

Overall launch grade: [A/B/C/D/F]

BEFORE YOU EXECUTE:

1. If target vs. actual data is not provided, ask for it before
   proceeding. A post-mortem without targets cannot assess
   whether performance was good or bad -- it can only describe
   what happened.

2. Root cause analysis must be evidence-based. Do not assign a
   root cause that the data does not support. If the data is
   insufficient to identify the root cause confidently, say so
   and describe what data would resolve the ambiguity.

3. Be honest in the grading. A launch that missed its review
   target by 60% is a D, not a C. The value of the post-mortem
   depends on accurate diagnosis, not charitable framing.

4. The "lessons and next launch changes" section is the most
   important output. Ensure every lesson is specific enough
   to change behavior -- not a restatement of the problem.

5. After completing the post-mortem, produce a one-paragraph
   EXECUTIVE SUMMARY suitable for sharing with a business
   partner, investor, or team member who was not involved
   in the launch.

=====

PASTE YOUR LAUNCH DATA BELOW. Include: product name and ASIN,
launch date, target vs. actual for each key metric (sales velocity
by week, BSR, review count, ACoS, conversion rate), total PPC
spend by campaign type, top search terms by spend and conversions,
any stockout events with dates, Vine enrollment if used, and
your qualitative assessment of what went well and what did not.

[YOUR DATA HERE]