Real price response patterns in historical purchase data. Elasticity grounded in actual switching behavior — Twin Persona gives revenue management teams behavioral evidence for multi-million dollar decisions.
PricingElasticityPromotion
Product Evolution & Loyalty Risk
Purchase history separates truly brand-loyal buyers from price-opportunistic ones. Before changing a formula, know exactly who stays and who switches — grounded in what they actually bought.
LoyaltySegmentationR&D
Competitive Switching Analysis
Cross-brand basket analysis from real transaction data reveals competitive overlap that consumers don't consciously report. Directly informs trade marketing and shelf strategy.
SwitchingBasket Analysis
Category Management
Promotional response cadence and private-label tolerance are visible in transaction history. See which segments are truly brand-loyal versus buying on promotion.
Brand LoyaltyTrade Marketing
Innovation & White Space
Basket composition shows adjacent categories where customers spend with competitors — not yet captured in your portfolio. Find the white space in your customer's basket.
White SpacePortfolio Strategy
Live Scenario Output
Brand Query
"If we cut our flagship cereal SKU by 12%, how many private-label switchers come back — and do they stay?"
Twin Population Response n = 18,200
Return to brand permanently
34%
Return but revert within 90 days
22%
Stay with private label
44%
Live Scenario Output
Brand Query
"If we reformulate our hero SKU to remove artificial colors, how does our customer base respond?"
Twin Population Response n = 24,400
Remain loyal to brand
61%
Switch to competitor
24%
Exit category entirely
15%
Live Scenario Output
Brand Query
"At what price gap does our customer base start switching to Brand X's organic line?"
Twin Population Response n = 11,600
No switching (gap < $1.50)
72%
Trial Brand X ($1.50–$2.80)
19%
Full defection (gap > $2.80)
9%
Live Scenario Output
Brand Query
"What share of our yogurt volume is genuinely brand-loyal versus buying on promotion?"
Twin Population Response n = 9,800
True brand loyal (full price)
28%
Promotion-dependent
47%
Channel-loyal, brand-agnostic
25%
Live Scenario Output
Brand Query
"What adjacent snack categories are our top-decile buyers already spending in that we don't serve?"
Top-Decile Basket Leakage n = 6,400
Protein & energy bars
$42/mo
Premium nuts & trail mix
$31/mo
Functional beverages
$27/mo
Why Twin Persona
We predict how consumers spend because we have where they already did.
Traditional Research
Survey panels and focus groups
○Weeks to months for fielding
○Stated preference — what people say they'd do
○Strong for exploratory and attitudinal research
○Limited by sample size, recall bias, and social desirability
Synthetic Research
AI agents prompted with demographic profiles
○Hours to days for generation
○Simulated cognition — what a persona might think
○Strong for brand perception and concept testing
○Limited by absence of real behavioral data
Twin Persona
Digital twins built from real transaction data
✓Hours for scenario results
✓Revealed preference — what people actually did
✓Strong for pricing, switching, loyalty, and commercial prediction
✓Grounded in consent-based, longitudinal AI analysis
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