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.
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.
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.
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.
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.
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.
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 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 →Agentic Simulation