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Methodology

How Twin Persona Works.

A different approach to consumer simulation — one grounded in what people actually buy, not what they say they would.

The Problem

The say-do gap is real.

Everyone wants to understand actual consumer behavior — will they switch brands? What flavors do they truly prefer? What drives their real buying decisions?

Most players in this space build profiles based on "personas." They create digital and AI versions of consumers using surveys or census data, which speaks to the general demographics of a population. However, what those people will actually do when it comes to a buying decision is merely inferred.

The say-do gap is real: what people say they'll buy and what they actually buy diverge sharply at the point of purchase. Humans are notoriously hard to predict. Survey respondents project intentions, not behavior. Census data describes populations, not individuals. And the gap between stated preference and revealed preference is where billions of dollars in product decisions go wrong.

0%
of new CPG products fail within the first year
0%
of consumers say one thing in surveys, then buy something else
$0B+
spent annually on market research still relying on stated preference
Our Approach

Built on what people do.

Twin Persona takes a fundamentally different approach. Instead of building twins from what people say, we build them from what people do — specifically, what they spend their hard-earned dollars on.

Even a slice of real purchase data is enough to anchor a persona in actual behavior. This is revealed preference, not stated preference. Every transaction reflects a real decision made with real money — the brand chosen, the one passed over, the price point accepted, the substitution made when the first choice was out of stock.

The result is a behavioral digital twin grounded in how a consumer actually shops — not how they say they would.

Every dollar spent is a preference expressed. Every brand chosen over another is a decision made — not hypothesized.

The Twin Persona principle
The Pipeline

From real transactions to simulation.

Four steps from real purchase data to actionable consumer insight.

Step 01
Consumer connects through Ario
Consent-based data sharing. Consumers opt in through Ario's infrastructure, connecting their retailer accounts. GDPR/CCPA compliant, consumer-initiated.
Step 02
Real purchase data flows in
Actual transaction history — what was bought, what was skipped, at what price, and how often. Even a slice of real spending is enough to anchor a twin in observed behavior.
Step 03
Behavioral twin is constructed
A digital twin built on actual purchase patterns, brand preferences, price sensitivity, and substitution behavior — all derived from real spending, not surveys.
Step 04
Simulation and scenario testing
Run scenarios against the twin: pricing changes, reformulations, new product introductions, competitive threats. Get answers grounded in observed behavior.
Why This Is Better

Three reasons purchase-grounded twins outperform.

01 — Grounded in Actual Spending
The most honest signal of consumer behavior.
Not surveys. Not demographic proxies. What people have actually bought with their own money is the strongest available indicator of what they will buy next. Every dollar spent is a preference expressed.
02 — No Inference Gap
Eliminating compounding error at the source.
Survey-based and census-based approaches lose fidelity at every step of inference. By starting from observed transactions, we avoid the compounding error that comes with predicting behavior from stated intent.
03 — Proven Results
Better inputs produce better simulation outcomes.
Our purchase-grounded approach produces fundamentally better simulation outcomes. When twins are built on what consumers actually do, the simulations reflect reality, not hypothesis.
Landscape

How approaches differ.

Not all digital twins are built the same way. The data source determines the ceiling of what a simulation can tell you.

Approach Data Source Strength Limitation
Interview-grounded 2-hour structured interviews Deep individual psychology Expensive, hard to scale, still stated preference
Pure synthetic Census, public data, social media Massive scale, instant No individual grounding, purely inferred
Survey / workflow Existing surveys, CRM, NPS data Integrates what you already have Ceiling limited by source data quality
Purchase-grounded Real purchase transactions Revealed preference, actual behavior Requires consent-based data infrastructure

See how it works
with your data.

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

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