What is Repeat Rate?
Repeat rate measures the percentage of customers who return for additional visits within a defined time period. In QSR, this might track customers who return within 30, 60, or 90 days—typically measured through loyalty program data, payment patterns, or location-based analysis. Repeat rate indicates customer satisfaction and loyalty better than single-visit metrics because customers vote with their feet over time. Voice AI affects repeat rate through consistent order accuracy, pleasant interactions, and efficient service.
One good visit creates a customer. Consistent good visits create a regular.
Why Repeat Rate Matters
Customer Lifetime Value
Repeats drive value:
- Acquisition cost spread over more visits
- Higher lifetime revenue
- More opportunities for upselling
- Brand relationship building
Profitability Indicator
Repeat customers are more profitable:
- Lower marketing cost per visit
- Often higher check average
- Less price sensitive
- More likely to try new items
Experience Quality Signal
Repeat rate reflects experience:
- Good experiences → return visits
- Bad experiences → customers leave
- Consistent quality → loyal customers
- Metric tied to operations
Competitive Defense
Loyal customers are protected:
- Less likely to try competitors
- More forgiving of occasional issues
- Source of referrals
- Stable revenue base
Measuring Repeat Rate
Calculation Methods
Basic formula:
Repeat Rate = (Customers with 2+ visits in period / Total unique customers) × 100
Time-based variations:
- 30-day repeat rate
- 60-day repeat rate
- 90-day repeat rate
- Annual repeat rate
Data Sources
Loyalty programs:
- Linked visits to accounts
- Most accurate method
- Requires program participation
- Rich customer data
Payment analysis:
- Credit card patterns
- Digital payment tracking
- Not fully comprehensive
- Privacy considerations
Location analysis:
- Mobile location data (aggregated)
- Traffic patterns
- Less precise
- Privacy constraints
Measurement Challenges
Identification:
- Not all customers identifiable
- Cash transactions untracked
- Privacy limitations
- Partial visibility
Attribution:
- Why did they return or not?
- Many factors involved
- Isolating Voice AI impact
- Correlation vs. causation
Repeat Rate Benchmarks
Industry Ranges
| Performance | 90-Day Repeat Rate |
|---|---|
| Excellent | 40%+ |
| Good | 30-40% |
| Average | 20-30% |
| Below average | 15-20% |
| Poor | <15% |
Segment Variation
Different segments have different norms:
- Coffee/breakfast: Higher repeat (daily habit)
- Burger/chicken: Moderate repeat
- Occasional dining: Lower repeat
- Geographic variation
Factors Affecting Repeat Rate
Experience Quality
Positive drivers:
- Order accuracy
- Fast service
- Friendly interaction
- Consistent quality
Negative drivers:
- Order errors
- Long waits
- Poor interaction
- Inconsistency
Convenience
Location factors:
- Proximity to routine
- Accessibility
- Competition density
- Drive-thru availability
Value Perception
Economic factors:
- Price vs. quality balance
- Promotional activity
- Loyalty rewards
- Perceived value
Menu Appeal
Product factors:
- Item variety
- Menu freshness
- Diet/preference fit
- Quality consistency
Voice AI and Repeat Rate
Direct Impact
Voice AI affects experience through:
- Order accuracy (get it right)
- speed of service (respect time)
- Interaction quality (pleasant conversation)
- Consistency (same good experience every time)
Hypothesis
If Voice AI delivers:
- More accurate orders → fewer reasons to not return
- Faster service → better time experience
- Consistent quality → reliable expectation
- Then repeat rate should maintain or improve
Measurement Approach
Before/after analysis:
- Baseline repeat rate pre-deployment
- Post-deployment repeat rate
- Control vs. test locations
- Long-term trend monitoring
Expected Outcomes
Well-implemented Voice AI:
- Maintain existing repeat rate (minimum)
- Potentially improve through consistency
- No negative impact from AI interaction
- Positive customer perception
Improving Repeat Rate
Experience Excellence
Focus areas:
- Order accuracy priority
- Speed optimization
- Interaction quality
- Consistency across visits
Loyalty Programs
Engagement tools:
- Rewards for return visits
- Recognition and personalization
- Exclusive offers
- Progress mechanics
Recovery
When things go wrong:
- Effective complaint handling
- Appropriate compensation
- Follow-up and care
- Second chance opportunity
Value Delivery
Ongoing appeal:
- Menu innovation
- Quality maintenance
- Price/value balance
- Promotional relevance
Repeat Rate Analytics
Tracking Approaches
Cohort analysis:
- Track specific customer groups
- Follow over time
- Compare cohorts
- Identify patterns
Trend monitoring:
- Repeat rate over time
- Seasonal patterns
- Impact of changes
- Early warning signals
Segmentation
Analysis by:
- Customer demographics
- Visit frequency tier
- Acquisition channel
- Geographic location
Using Insights
Action from data:
- Identify at-risk customers
- Target retention efforts
- Understand drivers
- Optimize experience
Repeat Rate vs. Related Metrics
Customer Satisfaction
- Satisfaction: immediate perception
- Repeat rate: behavioral outcome
- Satisfaction predicts repeat
- But not perfectly correlated
Net Promoter Score
- NPS: likelihood to recommend
- Repeat: actual return behavior
- Both loyalty indicators
- Different perspectives
Visit Frequency
- Frequency: how often loyalists visit
- Repeat rate: percentage who return at all
- Related but distinct
- Both matter
Common Misconceptions About Repeat Rate
Misconception: “Voice AI will hurt repeat rate because customers prefer humans.”
Reality: Research shows customers care about outcome—getting their order right, quickly and pleasantly. Well-implemented Voice AI delivers on these outcomes consistently. Customer preference for human interaction often decreases when AI provides better results.
Misconception: “Repeat rate takes too long to measure Voice AI impact.”
Reality: While long-term repeat rate requires time, early indicators (return within 30 days) can show trends faster. Combined with satisfaction scores and order accuracy, you can assess trajectory before full repeat data is available.
Misconception: “Repeat rate is driven by food quality, not ordering experience.”
Reality: Food quality matters, but ordering experience creates or destroys the opportunity for food to matter. A customer who has a terrible ordering experience may not try the food—or may not return even if food was good. Both matter.