Ario  /  Use Cases  /  Agentic Simulation

Agentic Simulation — grounded in real data

Build digital twins from verified, item-level purchase data and simulate how real consumer segments respond to pricing changes, product launches, and competitive threats — before committing budget.

Twin Persona
Ready
Initializing twins... 0%
+18%
Return rate
2,340
Switch back
$1.2M
Incremental
Solutions

Real questions, answered by real purchases.

Price & Promotion Modeling
Real price response patterns in historical purchase data. Elasticity grounded in actual switching behavior — giving 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 Agentic Simulation

A fundamentally different approach to consumer research

Traditional research tells you what people say. Synthetic research tells you what AI thinks. Ario's agentic simulation tells you what people actually did.

Traditional Research Synthetic Research Ario Agentic Simulation
Method Survey panels & focus groups AI agents with demographic profiles Digital twins from real transaction data
Timeline Weeks to months Hours to days Hours
Data Type Stated preference Simulated cognition Revealed preference — actual behavior
Best For Exploratory & attitudinal Brand perception & concept testing Pricing, switching, loyalty, commercial prediction
Limitations Sample size, recall bias, social desirability No real behavioral data Requires consent-based purchase data (built into Ario)
The Data Foundation

Simulation is only as real as the data behind it.

Ario provides consent-based, item-level purchase data across dozens of retailers — longitudinal, cross-category, and SKU-level.

See how Ario collects data →
See Ario in Action

Ready to simulate before you decide?

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