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Substitution Popularity Report Documentation

Menu Location: Reports > Products > Substitution Popularity

Access Level: Manager and above

Last Updated: 2026-03-01

Overview

The Substitution Popularity Report identifies which product substitutions are well-received by customers. This data helps refine automatic substitution rules, improve customer satisfaction, and optimize inventory planning by understanding which alternate products customers accept and appreciate.

Primary Functions:

  • Track successful substitution acceptance rates
  • Identify customer-approved substitute pairs
  • Optimize automatic substitution rules
  • Reduce customer complaints about substitutions
  • Improve inventory planning with proven alternatives
  • Build better fallback product strategies

Page Layout

Header Section

  • Date Range Selector: Filter by delivery date period
  • Product Filter: Search for specific original or substitute products
  • Minimum Substitutions: Filter to show only frequently occurring pairs
  • Export Button: Download report data for analysis

Main Content Area

Table displaying substitute product pairs sorted by popularity metrics, showing original product, substitute product, frequency, and customer feedback indicators.

Summary Dashboard

  • Total successful substitutions in period
  • Overall customer satisfaction rate
  • Most popular substitute products
  • Best-performing product categories for substitutions

Report Data & Columns

Column Description Calculation/Source
Original Product Product that was unavailable Ordered item
Substitute Product Product given instead Actual delivered item
Times Substituted How many times this pair occurred Count of substitutions
Acceptance Rate Percentage of customers who accepted substitute (Accepted / Total) × 100
Complaint Rate Percentage who complained or rejected (Complaints / Total) × 100
Reorder Rate Customers who later ordered the substitute Percentage who added to future orders
Avg Customer Rating Average satisfaction rating if collected Customer feedback scores
Price Difference Cost difference between original and substitute Substitute price - original price
Category Match Whether substitute is same category Product categories
Auto/Manual Whether substitution was automatic or manual Substitution method

Filters & Search Options

Date Filters

  • Delivery Date Range: Period when substituted orders were delivered
  • Substitution Date: When substitution was made
  • Quick Select: Last 30 days, Last 90 days, Last 6 months, This Year

Product Filters

  • Original Product: Search for specific unavailable product
  • Substitute Product: Search for specific substitute given
  • Product Category: Filter by category (Produce, Meat, Dairy, etc.)
  • Brand: Filter by product brand

Performance Filters

  • Minimum Times Substituted: Show only pairs that occurred X+ times
  • Acceptance Rate: Show only substitutions with X% or higher acceptance
  • Complaint Rate: Show only substitutions with low complaint rates
  • Price Relationship: Upgrade (more expensive), Downgrade (cheaper), Similar Price

Method Filters

  • Substitution Method: Automatic Rules, Manual Selection, Both
  • Customer Type: All customers, VIP only, First-time customers, etc.

Sorting & Display Options

Sort Options:

  • Times Substituted (most frequent first)
  • Acceptance Rate (highest first)
  • Complaint Rate (lowest first)
  • Reorder Rate (highest first)
  • Price Difference (biggest upgrade/downgrade first)

Display Options:

  • Show customer feedback comments
  • Show individual substitution instances
  • Compact view (more rows)
  • Detailed view with customer names
  • Group by original product
  • Group by substitute product

Export & Download Options

Export Formats:

  • Full Report CSV: All substitution pairs with complete data
  • Top Substitutes: Best performing pairs only
  • By Product: Grouped by original product
  • Recommendation List: Substitutes to add to automatic rules

Export Process:

  1. Apply filters to focus on relevant data
  2. Click "Export" button
  3. Select desired export format
  4. Open CSV in Excel
  5. Use for substitution rule updates or inventory planning

Actions & Operations

Identify Best Substitution Pairs

Purpose: Find proven successful substitutes to add to automatic rules

Steps:

  1. Set date range to last 90-180 days (good sample size)
  2. Set minimum substitutions to 5+ occurrences
  3. Sort by acceptance rate (highest first)
  4. Filter to acceptance rate ≥ 85%
  5. Review top 20-30 pairs
  6. Note pairs with high reorder rates (strongest signal)
  7. Export list for rule creation

Requirements:

  • Sufficient historical data (at least 30 days)
  • Multiple instances of substitution to be statistically meaningful

Update Automatic Substitution Rules

Purpose: Implement proven substitutes into automatic system

Steps:

  1. Generate list of best substitution pairs (above)
  2. For each high-performing pair:
    • Navigate to original product settings
    • Add substitute product to substitution rules
    • Set priority based on acceptance rate
    • Enable automatic substitution
  3. Test rules with upcoming orders
  4. Monitor future reports for continued success

Identify Problem Substitutions

Purpose: Find substitutes with high complaint rates

Steps:

  1. Sort by complaint rate (highest first)
  2. Filter to show pairs with 20%+ complaint rate
  3. Review why substitutions failed:
    • Price too different?
    • Quality mismatch?
    • Category mismatch?
    • Customer preference ignored?
  4. Remove these from automatic substitution rules
  5. Add notes to product: "Do not substitute with [X]"

Analyze Price Upgrade Success

Purpose: Understand if customers accept higher-priced substitutes

Steps:

  1. Filter to "Upgrade" price relationship
  2. Sort by acceptance rate
  3. Compare acceptance rates at different price points:
    • $1-2 upgrade
    • $3-5 upgrade
    • $5+ upgrade
  4. Identify maximum acceptable upgrade amount
  5. Set substitution rules to respect price thresholds
  6. Consider offering credit for large upgrades

Common Use Cases

Use Case 1: Building Substitution Rules for New Product

Goal: Set up good automatic substitutes for newly added product

Steps:

  1. Identify similar existing products in catalog
  2. Search report for those similar products as "Original Product"
  3. Review what substitutes worked well for them
  4. Select top 3-5 successful substitutes
  5. Add these as substitution rules for new product
  6. Set priority order based on acceptance rates from report
  7. Monitor new product's substitutions in future reports

Example: Adding "Organic Honeycrisp Apples" to catalog. Search report for "Organic Fuji Apples" substitutions. Top substitutes: Pink Lady (92% acceptance), Gala (88% acceptance), Granny Smith (78% acceptance). Set these as automatic substitutes in that priority order for new Honeycrisp product.

Tips:

  • Choose substitutes from same category when possible
  • Match price points closely
  • Consider seasonal availability of substitutes

Use Case 2: Seasonal Product Planning

Goal: Prepare substitution strategy for seasonal products

Steps:

  1. Set date range to same season last year
  2. Filter to seasonal product category (e.g., "Berries - Summer")
  3. Review which substitutes worked during season
  4. Note which products had high reorder rates
  5. Before this season starts:
    • Update substitution rules based on last year's data
    • Increase inventory of popular substitutes
    • Train staff on customer-approved alternatives
  6. Monitor daily during season

Example: Last summer, "Organic Strawberries" were frequently unavailable. Report shows "Organic Raspberries" had 94% acceptance as substitute, "Organic Blueberries" had 89%. This year: order extra raspberries and blueberries June-August, set as automatic substitutes, stock up before strawberry season gaps.

Use Case 3: Customer Satisfaction Improvement

Goal: Reduce complaints about substitutions

Steps:

  1. Set date range to last 30 days
  2. Sort by complaint rate (highest first)
  3. Identify substitution pairs with 15%+ complaints
  4. For each problem pair:
    • Read customer feedback comments
    • Identify common complaint themes
    • Determine if: wrong category, quality mismatch, price issue
  5. Take corrective action:
    • Remove poor substitutes from automatic rules
    • Require manual approval for these substitutions
    • Find better alternative substitutes
  6. Follow up in 30 days to measure improvement

Example: "Grass-fed Ground Beef" substituted with "Conventional Ground Beef" has 32% complaint rate. Customers complain about quality downgrade. Solution: Remove this automatic rule, instead substitute with "Grass-fed Sirloin" (ground at packing) or manually contact customer for approval.

Use Case 4: Inventory Optimization

Goal: Stock more of products that work well as substitutes

Steps:

  1. Set date range to last quarter (90 days)
  2. Group by "Substitute Product"
  3. Sort by "Times Substituted" (highest first)
  4. Identify products that frequently serve as good substitutes
  5. Calculate: total times used as substitute + regular orders = total demand
  6. Increase standing inventory orders for these versatile products
  7. Ensure adequate stock of "hero substitute" products

Example: "Organic Baby Spinach" was used as substitute 47 times in 90 days (for kale, arugula, mixed greens) with 91% acceptance rate. Plus 212 regular orders = 259 total demand. Increase weekly order from 180 units to 280 units to cover both regular and substitute demand.

Use Case 5: Staff Training on Best Practices

Goal: Train team on customer-approved substitutions

Steps:

  1. Export top 50 most successful substitution pairs
  2. Filter to pairs with 5+ occurrences and 85%+ acceptance
  3. Create printed reference guide organized by category:
    • "If customer ordered [X], good substitutes are [A, B, C]"
  4. Include acceptance rates and any special notes
  5. Distribute to packing and customer service teams
  6. Update guide quarterly based on fresh report data

Example: Training guide entry: "Organic Cherry Tomatoes (if unavailable):

  1. Organic Grape Tomatoes (96% acceptance) - same taste, slightly different shape
  2. Organic Roma Tomatoes (88% acceptance) - good for cooking
  3. Organic Heirloom Tomatoes (85% acceptance) - premium upgrade, offer $1 credit"

Troubleshooting

Report Shows No Data

Symptoms: Report generates but displays no substitution pairs

Solutions:

  1. Expand date range (may need 30-90 days for enough data)
  2. Remove minimum substitutions filter (may be set too high)
  3. Clear all product filters
  4. Check that substitutions are being tracked in system
  5. Verify date range includes period when substitutions occurred

Common Causes:

  • Business recently started using substitution tracking
  • Date range too narrow (only few days)
  • No substitutions occurred during period (excellent inventory management!)
  • Filters too restrictive

Acceptance Rate Seems Wrong

Symptoms: Acceptance rate shows 100% or 0% when reality likely different

Check:

  1. Verify sample size - less than 5 substitutions not statistically meaningful
  2. Check if customer feedback is being properly collected
  3. May lack complaint data if customers don't proactively report issues
  4. Some customers accept substitution but don't reorder (silent rejection)

Understanding Metrics:

  • High acceptance + low reorder = Tolerated but not loved
  • High acceptance + high reorder = Truly successful substitute
  • Low complaints ≠ High satisfaction = May indicate customers not complaining

Can't Find Specific Product Pair

Symptoms: Know substitution happened but can't find it in report

Solutions:

  1. Check both "Original Product" and "Substitute Product" searches
  2. Expand date range to include when substitution occurred
  3. Check for spelling variations or product name changes
  4. Verify substitution wasn't cancelled or reversed
  5. Check if manual substitution was logged properly

Common Causes:

  • Substitution made but not properly recorded in system
  • Product names changed and using old name in search
  • Substitution was reversed before delivery

Numbers Don't Match Other Reports

Symptoms: Total substitutions differ from No Substitution Found Report

Explanation: This report shows successful substitutions that were delivered. No Substitution Found Report shows failed automatic substitutions requiring manual intervention. Numbers should be different.

To Reconcile:

  • Successful auto substitutions = This report
  • Failed auto substitutions = No Sub Found Report
  • Manual substitutions = May appear in both depending on configuration
  • Total substitution attempts = Sum of both reports
  • No Substitution Found Report - Failed automatic substitutions
  • Substitution Unpopularity Report - Poorly received substitutes
  • Product Detail Reports - Individual product performance
  • Customer Feedback - Direct customer comments about substitutions
  • Inventory Management - Stock planning for substitute products

Typical Workflow:

  1. Review Substitution Popularity Report monthly
  2. Identify best-performing substitute pairs
  3. Update product substitution rules in Product Settings
  4. Monitor No Substitution Found Report for fewer failures
  5. Track Substitution Unpopularity Report to catch new problems

Permissions & Access

Required Access Level: Manager or higher

Access Level Capabilities:

  • Manager: View report, export data, analyze patterns
  • Administrator: All Manager capabilities + update substitution rules
  • Kiva Admin: All features + access to raw customer feedback and system configuration

Restricted Features:

  • Modify Substitution Rules: Requires Administrator
  • Access Individual Customer Feedback: May require Administrator for privacy
  • Export Customer Names: May require Administrator approval

Best Practices

Monthly Review Cycle

  1. Generate report on 1st of each month for previous month
  2. Identify top 10 new successful substitution pairs
  3. Update automatic substitution rules
  4. Remove any pairs with declining acceptance rates
  5. Share insights with inventory and purchasing teams

Statistical Validity

  1. Don't base decisions on single substitution instance
  2. Require minimum 5 occurrences before considering "proven"
  3. Prefer 80%+ acceptance rate for automatic rules
  4. Weight recent data more heavily (customer preferences change)
  5. Consider seasonal variations

Continuous Improvement

  1. Test new substitution pairs manually first
  2. Monitor for 4-8 weeks before making automatic
  3. Track trends over time (acceptance improving or declining?)
  4. Solicit direct customer feedback on substitutions
  5. A/B test different substitutes for same original product

Category Matching

  1. Prioritize same-category substitutes (produce for produce)
  2. Same-use-case important (salad greens for salad greens)
  3. Similar price points generate higher acceptance
  4. Quality upgrades better received than downgrades
  5. Consider dietary restrictions (organic for organic, etc.)

Things to Avoid

  • Don't implement automatic rules based on gut feeling (use data)
  • Don't ignore complaint rates (low complaints don't guarantee satisfaction)
  • Don't set up substitutes across very different categories
  • Don't forget to update rules as seasons change
  • Don't assume what works for one customer segment works for all

Quick Reference Card

Task Action/Location
Find best substitutes for product Filter to original product, sort by acceptance rate
See most versatile substitute products Group by substitute product, sort by times used
Identify problem substitutions Sort by complaint rate (highest first)
Plan substitution rules Min 5 occurrences, 85%+ acceptance, export list
Monthly performance review Last 30 days, export top substitutes
Find category-matching substitutes Filter by category, sort by acceptance
Analyze price sensitivity Filter by price relationship (upgrade/downgrade)
Get staff training data Export top 50 pairs, 85%+ acceptance

FAQs

What's a "good" acceptance rate?

Generally: 85%+ is excellent, 70-85% is acceptable, below 70% needs investigation. However, context matters - difficult-to-substitute items may have lower rates that are still acceptable.

How many substitutions needed for reliable data?

Minimum 5 occurrences for basic patterns, 10+ for confident decisions, 20+ for statistical reliability. Consider percentage too (5 out of 5 = 100% but small sample).

What if substitute is more expensive?

Data shows customers generally accept upgrades well IF: (1) quality is noticeably better, (2) price difference is under $3-5, (3) they're offered small credit. Best practice: offer $1-2 credit for upgrades over $3.

Can I see individual customer reactions?

Detailed view shows customer names and feedback comments (with proper access level). This helps understand context behind aggregate metrics.

How does reorder rate work?

Tracks if customers who received a substituted product later ordered that product on purpose. High reorder rate (30%+) indicates they discovered a new favorite. Low rate means they tolerated it but didn't love it.

Automatic substitution works best for proven pairs (high acceptance, multiple occurrences). Keep uncommon or new substitutions manual until proven. Hybrid approach: automatic for top substitutes, manual for edge cases.

How often should I update substitution rules?

Review monthly, update quarterly, or whenever introducing new products or changing suppliers. Seasonal businesses should update before each season based on previous year's data.

What if acceptance rate is declining over time?

Investigate: product quality changes? Supplier changes? Price increased? Customer demographics shifted? May need to find new substitute or address underlying quality issue.

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.