The AI Store Operator's Playbook — Complete Guide

Analyze the attached 90-day sales data [paste CSV].
For each SKU, calculate:
1. Average daily sell-through rate
2. Sell-through trend (accelerating, stable, or declining)
3. Recommended reorder point based on [X]-day supplier lead time
4. Suggested order quantity for 30 days of coverage
5. Flag any items where current stock < 14-day supply

Format as a table sorted by urgency (days until stockout).

Here's my monthly sales data for [product category] over the past 2 years [paste data].
Forecast demand for the next 3 months. Factor in:
- Seasonal patterns from the historical data
- We're entering [season/event — e.g., "back-to-school"]
- A new competitor opened 2 miles away last month
- We're planning a 20% off promotion in week 3

Give me weekly unit estimates with confidence ranges.
What's the worst-case scenario I should stock for?

From the attached sales and inventory data, identify:
1. Items with zero sales in the last 45 days (dead stock)
2. Items with declining velocity for 3+ consecutive months (dying stock)
3. Items where current inventory exceeds 120 days of supply at current velocity

For each item, recommend one of:
- Clearance (50%+ off to move immediately)
- Bundle (pair with a fast-seller at a discount)
- Return to vendor (if within return window)
- Donate (tax deduction play if margin is already lost)

Calculate total cash trapped in dead and dying stock.

I run a [store type] in [location/climate].
My top categories: [list them].

Create a 12-month buying calendar that shows:
- When to increase inventory for each category
- When to start clearancing seasonal items
- Lead time reminders (when to place orders for each season)
- Historical discount opportunities from suppliers (end-of-season buys)

Here are 30 of my top-selling products with my cost, current price, and competitor prices from [competitor 1, 2, 3]:

[Paste product table]

For each product, recommend pricing using these rules:
- Maintain minimum 40% gross margin
- Match competitors within 5% on commodity items
- Allow premium pricing on exclusive or hard-to-find items
- Flag where I'm leaving money on the table (priced too low)
- Flag where I'm losing sales (priced too high vs. all competitors)
- Suggest which items to use as loss leaders for foot traffic

Write product descriptions for the following 10 items.
Format for each:
- Headline (5-8 words, benefit-focused)
- Description (50-75 words for web, customer-focused — not spec-focused)
- 3 bullet points (practical benefits, not features)
- SEO meta description (155 characters max)

Tone: [your brand voice — e.g., "friendly, expert, not corporate"]
Audience: [your customer — e.g., "suburban parents, 30-45, value-conscious"]

Products:
1. [Product name, key specs, price point]
2. [Product name, key specs, price point]
...

[Upload store/display photo]
Analyze this retail display/store layout:
1. What's the visual focal point? Is it the right product to highlight?
2. Are there "dead zones" — areas where eye flow gets stuck?
3. How does the color story work? Does it draw attention to high-margin items?
4. Based on retail merchandising principles, what 3 changes would increase conversion?
5. How does signage hierarchy work — can customers orient themselves quickly?

I want to run a [type of promotion — e.g., "BOGO," "20% off category," "flash sale"] 
on [products/category] for [duration].

Based on my margin data [paste]:
1. Which items can sustain this discount and still be profitable?
2. What's the minimum sales volume increase needed to break even on the discount?
3. Suggest a promotional messaging angle for email and in-store signage
4. Create 3 subject line variations for the promotional email
5. What complementary items should I feature alongside the promoted items (cross-sell)?

Here's yesterday's sales data and the same day last week [paste both]:

Generate a morning briefing:
## Yesterday's Performance
- Revenue: [total] (vs. last week: [+/- %])
- Transactions: [count] (vs. last week: [+/- %])
- Average ticket: $[amount] (vs. last week)
- Top 5 sellers (units)
- Top 5 revenue generators (dollars)
- Returns/refunds: [count and reason patterns]

## Flags
- Any category down more than 20% week-over-week
- Items approaching stockout within 7 days
- Unusual transactions (unusually large, unusual product combinations)

## One Thing to Do Today
- Based on this data, what's the single highest-impact action?

Analyze these 6 months of transaction data [paste]:

Segment my customers into groups based on:
- Purchase frequency (how often they buy)
- Average transaction value (how much they spend)
- Product category preferences (what they buy)
- Recency (when they last purchased)

For each segment, tell me:
- How many customers and what % of revenue they represent
- Their defining behaviors
- One marketing action specifically for this segment
- Risk of losing this segment (low/medium/high)

I run a [store type] in [location].
My main competitors are: [list 3-5 competitors with brief descriptions].

Based on what's publicly available, analyze:
1. What's each competitor's apparent positioning? (Premium? Value? Specialty?)
2. Where are they likely stronger than me?
3. Where are they likely weaker?
4. What could I do that none of them are doing?
5. If a customer chooses them over me, what's the most likely reason?

Here's my [category] sales data for the past quarter [paste]:

Category Performance Review:
- Total category revenue and % of store total
- Top performers and underperformers (rank by margin contribution, not just sales)
- SKU count: Am I carrying too many options (paradox of choice) or too few?
- Price point distribution: Am I covering the right range?
- Velocity trends: Is this category growing, stable, or declining?
- Recommendation: expand, maintain, or contract this category? Why?

Here are our last 100 customer feedback responses [paste or upload]:

Analyze and report:
1. Overall sentiment score (1-10)
2. Top 5 themes (positive and negative)
3. Exact quotes that represent each theme
4. Which issues are getting WORSE over time vs. improving
5. Three actionable fixes ranked by impact-to-effort ratio
6. Compare feedback by customer segment if data available

Here's my loyalty program data: [paste enrollment numbers, redemption rates, 
point earn/burn rates, active vs. dormant members]

Audit my loyalty program:
1. What's the actual redemption rate? (Industry benchmark: 13-15%)
2. How many members are "dormant" (earned points but never redeemed)?
3. What's the breakage rate and is it too high (customers losing points)?
4. Does the earn rate feel rewarding enough to change behavior?
5. Suggest 3 modifications to increase engagement without destroying margin
6. What would it cost to run a "double points week" to reactivate dormant members?

Identify customers who purchased in [earlier period] but NOT in the last [X months].

For this "lapsed" group:
1. How many customers and what was their average annual value?
2. What categories were they buying?
3. Draft a win-back email sequence (3 emails):
   - Email 1: "We miss you" + personalized incentive
   - Email 2: "Here's what's new" + category highlights
   - Email 3: Final offer + urgency
4. What incentive level makes financial sense? (% off, free shipping, bonus points)

Based on customer [name/ID]'s purchase history:
- Purchase history: [list recent purchases]
- Average spend: $[amount]
- Preferred categories: [list]
- Last purchase: [date]

Generate a personalized outreach:
1. Product recommendations they'd likely want next (based on purchase patterns)
2. A personalized email subject line
3. Timing recommendation (when to send based on their purchase cadence)