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Love/Hate Product Report Documentation

Menu Location: Reports > Products > Love/Hate Report

Access Level: Manager and above

Last Updated: 2026-03-01

Overview

The Love/Hate Report analyzes customer product preferences by tracking which products customers actively choose to add (love) versus remove or avoid (hate). This powerful insight helps optimize product offerings, reduce waste, and improve customer satisfaction.

Primary Functions:

  • Identify most-loved products (frequently added)
  • Identify most-hated products (frequently removed)
  • Understand customer preferences by demographics
  • Guide product selection and featured items
  • Reduce waste from unwanted products
  • Personalize default box contents

Page Layout

Header Section

  • Report Title: "Love/Hate Product Analysis"
  • Date Range Selector: Choose analysis period
  • Filter Controls: Route, box type, customer segment
  • Export Button: Download report data

Summary Dashboard

  • Most Loved Product: #1 frequently added item
  • Most Hated Product: #1 frequently removed item
  • Net Satisfaction: Overall love vs. hate balance
  • Customer Action Rate: % of customers making changes

Love/Hate Tables

Most Loved Products:

Rank Product Times Added % of Customers Trend
1 Organic Strawberries 342 68% ↑
2 Ground Beef 298 59% →
3 Blueberries 276 55% ↑

Most Hated Products:

Rank Product Times Removed % of Customers Trend
1 Brussels Sprouts 189 38% ↓
2 Beets 156 31% →
3 Turnips 142 28% →

Neutral Products

  • Products rarely added or removed
  • Indicates general acceptance
  • Sorted by inclusion frequency

Understanding Love/Hate Metrics

Metrics Explained

Times Added (Love Score):

  • Count of customers who added this product to their order
  • Higher number = more popular
  • Indicates strong customer desire

Times Removed (Hate Score):

  • Count of customers who removed this product
  • Higher number = less popular
  • Indicates customer aversion

Net Score:

  • Love minus Hate
  • Positive = overall loved
  • Negative = overall disliked
  • Formula: Times Added - Times Removed

% of Customers:

  • Percentage who took action on this product
  • Shows reach/impact
  • Higher % = broader sentiment

Action Rate:

  • Overall % of customers modifying their orders
  • High rate = default box needs improvement
  • Low rate = box well-optimized

Filter and Analysis Options

Date Range Selection

Preset Ranges:

  • Last 30 Days: Recent preferences
  • Last 90 Days: Quarterly trends (recommended)
  • Last 6 Months: Seasonal patterns
  • Last Year: Annual preference analysis

Filter Options

By Route:

  • Compare preferences across delivery routes
  • Urban vs. rural preferences
  • Regional taste differences

By Box Type:

  • Small, Medium, Large box preferences
  • Produce vs. Meat box preferences
  • Helps optimize box-specific contents

By Customer Segment:

  • New customers (< 3 months)
  • Long-term customers (1+ years)
  • Wholesale vs. retail

By Season:

  • Spring, Summer, Fall, Winter
  • Identify seasonal preference shifts

Common Use Cases

Use Case 1: Optimize Default Box Contents

Goal: Reduce customer modifications by improving default items

Steps:

  1. Run report for Last 90 Days
  2. Identify top 10 most hated products
  3. Review if these are default box items
  4. For highly-hated defaults:
    • Consider making optional instead of required
    • Replace with loved alternatives
    • Or remove from box entirely
  5. Identify top 10 loved products
  6. Ensure these are in default box or prominently featured
  7. Update box configurations
  8. Monitor action rate (should decrease)

Example:

  • Brussels Sprouts: 38% removal rate
  • Currently a default item in Medium Box
  • Action: Make optional or replace with popular broccoli
  • Result: Customer satisfaction improves, fewer modifications

Use Case 2: Plan Seasonal Product Additions

Goal: Determine which seasonal products to feature

Steps:

  1. Filter to same season last year
  2. Review loved products from that period
  3. Note which are seasonal (berries in summer, squash in fall)
  4. For upcoming season:
    • Ensure loved seasonal items are available
    • Order adequate inventory
    • Feature in marketing
  5. Remove or reduce hated seasonal items
  6. Monitor this season's results, adjust next year

Example:

  • Last summer: Strawberries 68% love, Peaches 62% love
  • This summer: Ensure both prominently available
  • Last summer: Eggplant 35% hate
  • This summer: Make eggplant optional only

Use Case 3: Reduce Food Waste

Goal: Minimize unwanted products that become waste

Steps:

  1. Run report for Last 90 Days
  2. Identify consistently hated products
  3. Calculate waste:
    • If in default box: hate % × total orders × product quantity
    • Example: 30% hate × 1000 orders × 1 lb = 300 lbs waste
  4. Make hated products optional instead of default
  5. Only include for customers who want them
  6. Monitor waste reduction
  7. Cost savings = reduced purchasing + reduced waste disposal

Result: Significant waste reduction and cost savings

Use Case 4: Personalize Customer Experience

Goal: Use love/hate data for individual customer personalization

Steps:

  1. For specific customer, review their personal love/hate history
  2. Note products they consistently add (love)
  3. Note products they consistently remove (hate)
  4. Create customer-specific default box:
    • Include loved products
    • Exclude hated products
  5. Customer gets personalized box automatically
  6. Less work for customer, higher satisfaction

Technology Note: Some systems support automatic personalization based on historical preferences

Use Case 5: Regional Preference Analysis

Goal: Understand if preferences differ by geography

Steps:

  1. Run report filtered to Route 1 (urban area)
  2. Note top loved and hated products
  3. Run report for Route 5 (rural area)
  4. Compare results
  5. Identify regional differences
  6. Customize default boxes by route/region
  7. Feature region-appropriate products

Example Findings:

  • Urban routes: High love for exotic produce, specialty items
  • Rural routes: High love for traditional meats, potatoes
  • Action: Customize default boxes by route type

Strategic Applications

Product Development

New Product Decisions:

  1. Loved products in one category → Expand that category
  2. Hated products in one category → Maybe category doesn't fit audience
  3. Gap analysis: What similar products might customers love?

Example:

  • Strawberries highly loved → Try other berries
  • Beets highly hated → Maybe root vegetables generally disliked by your audience

Marketing Strategy

Feature Loved Products:

  • Use in marketing materials
  • Show in emails and website banners
  • Guarantee availability
  • Build brand around customer favorites

Handle Hated Products:

  • Don't promote heavily
  • If unique/specialty, target niche audience
  • Consider discontinuation if consistently hated
  • Or reposition as optional specialty item

Supplier Relationships

Loved Products:

  • Ensure consistent supply
  • Negotiate better prices (higher volume)
  • Request quality assurances
  • Develop backup suppliers

Hated Products:

  • Reduce order quantities
  • Make contingent orders (as-needed)
  • Negotiate flexible terms
  • Consider alternative suppliers with better quality

Troubleshooting

All Products Show Low Love/Hate Scores

Possible Causes:

  1. Date range too narrow (not enough data)
  2. Low customer engagement (customers not modifying boxes)
  3. Box already well-optimized (minimal changes needed)

Solutions:

  1. Expand date range to 90+ days
  2. Review if system makes modifications easy
  3. Consider this positive if action rate low

Conflicting Data (Product Loved and Hated)

Explanation:

  • Different customer segments have different preferences
  • Some love it, some hate it = divisive product

Action:

  • Make it optional (not default)
  • Let customers self-select
  • Market to the segment that loves it

Seasonal Products Show Unexpected Hate

Check:

  1. Is product currently in season?
  2. Quality issues with specific batch?
  3. Price too high for value?

Solutions:

  1. Only offer during peak season
  2. Review with supplier, ensure quality
  3. Adjust pricing if needed
  • Bestselling Results - Aggregate product performance
  • Product Quantities Over Time - Volume trends
  • Customer Detail - Individual customer preferences
  • Products - Edit product defaults and settings
  • Create New Box - Optimize box configurations

Best Practices

Analysis Frequency

  1. Monthly review - track preference shifts
  2. Seasonal analysis - before each season
  3. New product eval - 30 days after introduction
  4. Quarterly deep dive - comprehensive optimization

Actionable Insights

  1. Top 3 hated in default box → Make optional
  2. Top 5 loved not in box → Add to defaults
  3. Divisive products (high love AND hate) → Make optional
  4. Neutral products → Good defaults, leave unchanged

Customer Satisfaction

  1. Reduce action rate - fewer modifications needed
  2. Feature loved products - give customers what they want
  3. Minimize hated defaults - avoid forcing unwanted items
  4. Enable easy changes - respect preferences

Quick Reference Card

Task Action/Location
Find most popular product View top of "Most Loved" table
Find least popular product View top of "Most Hated" table
Check specific product Search product name, view score
Compare routes Run report with Route filter, compare
Identify waste sources Top hated products in default box
Optimize box defaults Remove top hated, add top loved
Track seasonal preferences Filter by season, compare year-over-year
Export for analysis Export button > CSV

FAQs

What's a good action rate?

Healthy range: 20-40%. Too low (<10%) might mean customers not engaged. Too high (>60%) suggests defaults poorly matched to preferences.

Should I remove all hated products?

No. Some hated products may be: 1) Healthy options you want to offer, 2) Loved by a niche segment, 3) Seasonal items some enjoy. Make them optional rather than default.

Why would a product be both loved and hated?

Indicates polarizing product. Some customers really want it, others really don't. Perfect candidate for "optional" status.

How quickly do preferences change?

Core preferences stable, but seasonal shifts occur. Review quarterly to catch trends.

Can I see individual customer preferences?

Yes, most systems allow viewing customer-specific love/hate history in Customer Detail page.

What if loved products are expensive?

Balance customer preference with profitability. Offer as optional add-on at appropriate price, or include in premium box tiers.

Should new customers get optimized boxes immediately?

Consider offering "popular items" box to new customers based on aggregate love data, then personalize after 2-3 orders based on their preferences.

How do I handle products that were loved but now hated?

Investigate: quality decline, price increase, oversaturation, or seasonal shift. Address root cause.

Can preferences differ dramatically by route?

Yes, geography/demographics significantly affect preferences. Urban vs. rural, regional cuisines, cultural differences all play a role.

What's the difference between this and bestselling report?

Bestselling shows what sold most (quantity). Love/Hate shows what customers actively chose vs. avoided (preference). A bestseller might be hated if it's a forced default with high removal rates.

Change Log

2026-03-01

  • Initial documentation created
  • All sections completed following template structure

End of Documentation

For additional help, contact your system administrator or Kiva Logic support.