Multivariate Testing

What is Multivariate Testing?

Multivariate testing (MVT) is an optimization method that evaluates multiple variables simultaneously to find the best-performing combination. Unlike A/B testing which compares two versions of a single variable, MVT tests combinations of multiple elements at once. In drive-thru Voice AI, this might mean testing different upsell items, phrasings, and timing together to find the optimal combination for conversion.

MVT answers not just “which is better?” but “which combination is best?”

Why Multivariate Testing Matters for QSR

Beyond Single Variable Testing

A/B testing limitations:

• Tests one thing at a time
• Slow to optimize multiple elements
• Misses interaction effects

Example:

• A/B test 1: Upsell item (fries vs. shake)
• A/B test 2: Phrasing (question vs. suggestion)
• A/B test 3: Timing (during vs. after order)

Sequential testing takes months. MVT tests all combinations simultaneously.

Interaction Effects

Variables interact:

• Fries + question phrasing might work best
• Shake + suggestion phrasing might work best
• You only discover this by testing combinations

MVT reveals these interactions that sequential A/B testing misses.

How Multivariate Testing Works

Test Design

Identify variables and variations:

Variable 1: Upsell item

• A: Fries
• B: Shake

Variable 2: Phrasing

• A: “Would you like to add…?”
• B: “I recommend adding…”

Variable 3: Timing

• A: During order
• B: After order complete

Combination Matrix

All possible combinations:

Combo	Item	Phrasing	Timing
1	Fries	Question	During
2	Fries	Question	After
3	Fries	Suggestion	During
4	Fries	Suggestion	After
5	Shake	Question	During
6	Shake	Question	After
7	Shake	Suggestion	During
8	Shake	Suggestion	After

8 combinations from 3 variables with 2 variations each.

Traffic Distribution

Orders randomly assigned to combinations:

• Each combination gets equal traffic
• Sufficient sample for statistical significance
• Run until results are conclusive

Analysis

Measure outcomes for each combination:

• Conversion rate
• Revenue impact
• Guest satisfaction indicators

Identify winner and interaction effects.

MVT vs. A/B Testing

Aspect	A/B Testing	Multivariate Testing
Variables	One at a time	Multiple simultaneously
Combinations	2	Many (2^n for n variables)
Sample size needed	Lower	Higher
Time to result	Faster per test	Faster for multiple variables
Interaction discovery	No	Yes
Complexity	Simple	More complex

When to Use Each

A/B testing better for:

• Single variable optimization
• Lower traffic volumes
• Quick directional decisions
• Early optimization stages

MVT better for:

• Multiple variables to optimize
• Sufficient traffic volume
• Finding optimal combinations
• Mature optimization programs

MVT in Voice AI Applications

Upselling Optimization

Variables to test:

• Which item to offer
• How to phrase the offer
• When in conversation to offer
• How many items to suggest
• Voice characteristics during offer

Greeting Optimization

Variables to test:

• Greeting length
• Energy level
• Promotional mention
• Question structure

Confirmation Flow

Variables to test:

• Detail level of read-back
• Speed of delivery
• Total announcement timing
• Next-step instruction

Sample Size Requirements

MVT requires more data than A/B:

Formula:

Minimum sample = Base sample × Number of combinations

Example:

• 1,000 orders per variation for significance
• 8 combinations
• Need 8,000 orders minimum
• At 500 orders/day = 16 days

High-volume locations enable faster MVT.

MVT Best Practices

Limit Variables

More variables = exponentially more combinations:

• 2 variables × 2 variations = 4 combinations
• 3 variables × 2 variations = 8 combinations
• 4 variables × 2 variations = 16 combinations
• 5 variables × 3 variations = 243 combinations

Keep tests manageable: 3-4 variables maximum.

Meaningful Variations

Each variation should represent real choice:

• Genuinely different approaches
• Not subtle tweaks
• Distinct enough to measure

Run to Completion

Don’t end early:

• Need statistical significance
• Interaction effects take time to emerge
• Premature conclusions are dangerous

Document and Learn

Track all tests:

• What was tested
• Results by combination
• Winner identification
• Interactions discovered

Build organizational knowledge.

Interpreting MVT Results

Main Effects

Impact of each variable averaged across combinations:

• “Question phrasing averages 5% higher conversion”

Interaction Effects

How variables affect each other:

• “Question phrasing + shake = 8% higher”
• “Question phrasing + fries = only 2% higher”

Optimal Combination

The best overall performer:

• May not be “best” of each individual variable
• Combination effect matters

Common Misconceptions About Multivariate Testing

Misconception: “MVT is just running multiple A/B tests.”

Reality: MVT tests combinations simultaneously and reveals interaction effects. Sequential A/B tests miss how variables affect each other and take much longer.

Misconception: “More variables means better optimization.”

Reality: More variables means exponentially more combinations, requiring much more data. Focused MVT with 3-4 meaningful variables is more practical than testing everything.

Misconception: “The winning combination is always the best of each variable.”

Reality: Interaction effects often mean the optimal combination includes variations that don’t individually perform best. That’s the value of MVT.