---
name: customer-trend-insight-summary
description: "Synthesizes scattered in-store customer signals — repeated product requests, complaints, questions, and behavioral patterns — into a structured trend summary with quantified frequency, category tagging, and prioritized escalation recommendations for a Store Director and HQ product/experience partners. Use when preparing a weekly or monthly customer insight report, flagging emerging demand or friction, or escalating recurring store-floor observations up the chain."
argument-hint: "[time-period] [store-observations]"
allowed-tools: ["Read"]
---

# Customer Trend Insight Summary

## Purpose
Turn a messy pile of in-store observations — associate anecdotes, repeat requests, return reasons, and floor behaviors — into a clean, prioritized trend summary that a Store Director can read in five minutes and forward to HQ product and experience partners with confidence. Solves the problem of valuable frontline signal dying at the register instead of reaching the people who set assortment, layout, and experience decisions.

## When to Use
Invoke this skill when the user asks to:
- Prepare a weekly or monthly customer trend report for their Store Director
- Summarize recurring customer requests, complaints, or questions from the sales floor
- Flag an emerging product demand or a growing friction point for HQ
- Turn associate notes and shift observations into an escalation-ready document
- Compare this period's customer signals against a prior period
- Build a case for a merchandising, staffing, or assortment change based on floor evidence

## Inputs Required
Before starting, collect:
- **Time period**: The window being reported (e.g., "week of March 3–9" or "February")
- **Store observations**: Raw notes — associate anecdotes, repeated asks, complaints, return reasons, out-of-stock frustrations, behavioral patterns
- **Store context**: Store format, location type, and primary customer profile
- **Traffic/volume note**: Rough sense of foot traffic this period (up, down, flat, or a number)
- **Known drivers**: Any promotions, campaigns, seasonal shifts, or local events active this period
- **Prior-period signals**: What was flagged last time, if available, for trend comparison
- **Escalation audience**: Who receives this — Store Director only, or also named HQ product/experience partners

If traffic volume or prior-period data is missing, note the limitation and proceed with qualitative frequency framing.

## Instructions

### Step 1: Cluster Raw Observations Into Themes
Group the raw input into distinct trend themes. A theme is a pattern mentioned more than once or observed across multiple shifts. Merge duplicates and near-duplicates. Discard one-off comments that lack a repeat signal — but keep a single "notable one-off" watchlist for anything strategically interesting even if it happened once.

### Step 2: Categorize Each Theme
Tag every theme with one primary category:
- **Product/Assortment**: Requests for items not carried, sizes/shades out of stock, discontinued favorites
- **Experience/Service**: Fitting room, checkout, wait time, staffing, sampling, consultation friction
- **Merchandising/Layout**: Findability, signage, display, adjacency confusion
- **Pricing/Promotion**: Price sensitivity, promo confusion, competitor comparisons
- **Brand/Sentiment**: Loyalty, values, packaging, social/online-driven expectations

### Step 3: Quantify Frequency and Assign a Signal Strength
For each theme, estimate frequency using whatever evidence exists:
- **High signal**: Mentioned daily / across most shifts / multiple associates independently reporting it
- **Medium signal**: Recurring several times over the period, one or two associates reporting
- **Low signal / Watch**: Emerging, 2–3 mentions, worth monitoring next period

Where a count exists (e.g., "asked for the discontinued lip oil ~12 times"), state it. Where it doesn't, use the signal-strength framing rather than inventing numbers.

### Step 4: Assess Direction and Business Impact
For each theme, note:
- **Direction**: New this period / Growing / Stable / Fading (compare to prior-period signals if provided)
- **Impact hypothesis**: What this trend likely means for the business — lost sales, satisfaction risk, an opportunity, a competitive gap

### Step 5: Prioritize for Escalation
Rank themes into three tiers:
- **Escalate now**: High signal + clear business impact + actionable at HQ or Director level
- **Monitor**: Medium signal, or high signal without a clear action yet
- **Note for record**: Low signal, watchlist, or already-known items included for completeness

### Step 6: Write Recommendations Per Escalated Theme
For each "Escalate now" theme, write one concrete recommendation aimed at the right owner — Store Director for anything the store can control (layout, staffing, sampling), HQ product/experience partners for anything requiring assortment, pricing, or system change. Make each recommendation specific enough to act on.

## Output Format
- **Structure**: A short executive summary followed by a prioritized trend table and per-theme escalation write-ups
- **Length**: 500–800 words
- **Format**: Executive summary in prose (3–4 sentences), main trends in a table, escalations as short labeled blocks, watchlist as a bulleted list
- **Sections**:
  1. **Executive Summary** — period, traffic context, and the single most important takeaway
  2. **Trend Table** — columns: Theme | Category | Signal | Direction | Impact
  3. **Escalate Now** — per-theme recommendation with named owner
  4. **Monitor** — themes to watch, one line each
  5. **Notable One-Offs / Watchlist** — bulleted
- **Tone**: Frontline manager to leadership — concise, evidence-led, no fluff, confident enough to forward without rewriting

## Quality Checklist
Before delivering output, verify:
- [ ] Every theme in the table has all five columns filled (Theme, Category, Signal, Direction, Impact)
- [ ] Each theme is tagged with exactly one primary category from the five defined
- [ ] Frequency claims use real counts where available and signal-strength framing where not — no invented numbers
- [ ] Every "Escalate now" item has a named owner (Store Director or HQ) and a specific, actionable recommendation
- [ ] Themes are sorted into the three priority tiers — nothing left uncategorized
- [ ] The executive summary states one clear top takeaway, not a list

## Notes
- The most escalation-worthy signals are usually repeated product requests for items that are out of stock or discontinued — these are direct lost-sales evidence and land well with HQ product partners.
- Resist inflating a single loud complaint into a "trend." One angry customer is a note; a pattern across shifts is a trend. The credibility of the whole report depends on this discipline.
- When comparing to prior periods, a fading trend is as valuable to report as a growing one — it tells HQ a prior fix worked or a fad passed.
- If observations are thin for the period, say so plainly and recommend a structured capture method (a shared shift-note log) so the next report is stronger.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
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Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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