Solutions One Twin vs. One Survey What 100 Twins See That Panels Can't Watch the Demo Research About Us Request Demo

Test Every Decision Against
Real Consumer Behavior

Twin Persona builds digital twins from real purchase data. Predict what consumers will actually do — not what they say they'll do.

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Built on
Consent-based data collection
GDPR & CCPA compliant infrastructure
Real transaction history — not survey responses
Predictions in hours, not weeks
Longitudinal behavioral modeling
Category-level and SKU-level granularity
Consent-based data collection
GDPR & CCPA compliant infrastructure
Real transaction history — not survey responses
Predictions in hours, not weeks
Longitudinal behavioral modeling
Category-level and SKU-level granularity
Solutions

Real questions, answered by real purchases.

Price & Promotion Modeling
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.
Pricing Elasticity Promotion
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.
Loyalty Segmentation R&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.
Switching Basket 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 Loyalty Trade 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 Space Portfolio 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%
Grounded in: 18,200 twins with verified private-label switching events across 3 retail channels. 24-month purchase history.
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%
Grounded in: 24,400 twins. 36 months of verified purchase history across 6 retail channels.
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%
Grounded in: 11,600 twins with cross-brand basket overlap. Price-indexed transaction history. 18 months across 4 retail banners.
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%
Grounded in: 9,800 twins. 52 weeks of verified purchase history across promoted and full-price periods.
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
Grounded in: 6,400 top-decile twins. Full basket composition across 8 categories. 12-month longitudinal spend.
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

100 digital twins.
Ask them anything.

A walkthrough of the simulation sandbox. Every profile in the panel is built from a real person's verified Amazon purchase history, connected through consent-based data infrastructure. No surveys. No synthetic profiles. Just actual behavior.

01A twin built from 1,200+ verified purchases — actual SKUs, prices, brands, timing
02A category study run in minutes, answered with purchase context
03“Why This Answer” — every response links to the records behind it
04Segment comparison by purchase behavior, not self-reported demographics
05Ingredient preferences inferred from what people actually bought

The research behind
the twins.

Validation study
How well can a digital twin answer a survey?
Twins built from purchase history alone, scored against the humans who actually answered. 88.4% on factual behavior, 86–93% on opinions — and one place it stopped working.
Guide
Digital twins for consumer research: a data-first guide
The three data foundations in use today, why the say-do gap matters, and what transaction-grounded twins require.
Guide
Behavioral digital twins: the definitive guide
How consumer simulation works, how survey-based, pure synthetic, and purchase-data-grounded approaches compare, and how to evaluate a platform.
All research & insights →
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See Twin Persona
in action.

Request a Demo
We respond within 48 hours. No sales sequences. A direct conversation.