Affect Performance Team

|AI|Nov 11, 2026

Toluna Introduces Bina and Dynamic Awareness for AI Consumer Research

Toluna Introduces Bina, an AI Research Platform for Synthetic Consumers

On October 1, 2026, research company Toluna introduced Toluna Bina, a new AI platform for marketing research that enables interviews with synthetic consumers and tests of advertising messages, product concepts, and marketing hypotheses. The company also announced Dynamic Awareness, a technology that updates synthetic personas' knowledge daily to reflect developments in the world around them.

Toluna Bina combines two research approaches: preliminary exploration with AI consumer models and subsequent validation with real people through Toluna Start.

The rollout of the new product was scheduled for mid-October 2026. Before the official announcement, approximately 50 clients across industries, including e-commerce, FMCG, financial services, and consumer healthcare, had tested the platform.

Toluna and Its Research Platform

Toluna is an international marketing research and consumer intelligence company. It helps businesses understand consumer preferences, test products and advertising materials, measure brand perception, and evaluate market opportunities.

The company operates in more than 75 markets and has a global research panel of over 80 million consumers.

Its main technology product is Toluna Start, a platform for quantitative and qualitative marketing research.

Toluna Start brings together survey design, respondent recruitment, data collection, analysis, and reporting tools. The platform supports self-service studies and projects involving Toluna research specialists.

Its main applications include:

Brand Research: Brand awareness, perception, preferences, and competitive positioning.
Advertising Research: Pretesting advertising assets, messages, and concepts.
Product Research: Research into new products, features, packaging, and value propositions.
Consumer Insights: Understanding consumer needs, motivations, and behavior.
Customer Experience: Customer experience research and evaluation of brand interactions.

For these studies, Toluna uses real consumer panels and its own data processing and analysis technologies.

In recent years, the company has expanded its platform with AI tools. One area is synthetic respondents capable of modeling consumer answers to research questions.

Toluna Bina does not replace Toluna Start. It is a new AI environment for preliminary hypothesis exploration, integrated with the company's existing platform for research with real audiences.

Toluna Bina: Interviews With Synthetic Consumers

Toluna Bina is a research environment with a conversational interface. Users can describe a marketing problem, formulate a hypothesis, or propose a product concept, and the system creates a corresponding synthetic audience.

Instead of preparing a full study in the first stage, marketers can interact directly with AI consumer models.

For example, a researcher can present several positioning options for a new product, ask follow-up questions, and explore potential reasons for positive or negative reactions.

Toluna describes several supported research scenarios.

Synthetic Persona Interviews. Users ask questions of modeled target audience members, probe their answers, and explore possible consumer preferences.
Marketing Concept Testing. Teams can compare advertising messages, product ideas, and positioning options before conducting research with real consumers.
Competitive Positioning Research. The platform can explore modeled brand perceptions relative to competitors.
Research Preparation. The conversational interface can help develop qualitative and quantitative surveys, research questions, and hypotheses for later validation.
Analytical Summaries. The system generates structured findings for marketing, research, and product teams.

A key distinction between Bina and a general-purpose AI chat is that its synthetic audiences are built on Toluna's research data, rather than solely on a demographic description entered by the user.

How Toluna Synthetic Personas Are Created

Bina is built on Toluna Synthetic Personas technology.

These AI consumer models are generated using the company's anonymized research data. They are intended to model individual responses to questions and reactions to advertising messages, product features, and other research materials.

Synthetic respondents are not a new area for Toluna. In February 2025, the company introduced HarmonAIze Personas for testing product claims. In October of that year, it reported creating more than one million unique synthetic personas.

Bina expands the use of this technology. Synthetic audiences can support interactive exploration of business hypotheses, not just predefined research tests.

According to Toluna, the models draw on 25 years of proprietary research, including billions of responses from real survey participants.

An important feature is the modeling of individual consumers rather than average demographic segments.

For example, two synthetic personas of the same age and from the same region may differ in interests, preferences, anticipated purchase motivations, and attitudes toward product categories.

Audiences are built for specific research tasks.

Toluna also reports conducting more than 3,000 comparative validations across more than 20 markets, comparing synthetic persona responses with research findings from corresponding groups of real consumers.

The company describes the agreement as high. However, the official announcement does not provide universal numerical accuracy metrics or detailed error statistics for individual research categories.

These vendor-reported results should not be treated as proof that synthetic responses are equally reliable across all products, markets, or consumer audiences.

Dynamic Awareness: Daily Updates to Information Context

The second announcement is Dynamic Awareness.

One challenge of synthetic research is that AI models may rely on knowledge assembled as of a particular date. Real consumers, however, encounter news, advertising, new products, and public events every day.

Those developments can influence awareness, interests, and attitudes toward offers.

Dynamic Awareness is designed to model these changes.

The technology provides synthetic personas with new information about world events every day. Updates are filtered according to each persona's individual characteristics.

For example, a synthetic consumer interested in technology may receive context about new AI products, while a persona with different interests considers an entirely different news agenda.

Different members of a synthetic audience therefore do not receive the same set of new information.

Toluna aims to model differences in information awareness that characterize real consumers.

For marketing research, this could be valuable in categories sensitive to rapidly changing information, including consumer electronics, financial products, technology, entertainment, and other markets.

For example, when testing an advertising message for a new AI service, it matters how familiar the prospective audience may be with current technologies and competing products.

Dynamic Awareness models probable awareness; it does not record what individual real people actually consume or know. It is not a standalone measurement tool for consumer behavior or a real-audience monitoring system.

From Synthetic Research to Validation With Real People

One of Bina's most important capabilities is its integration with Toluna Start.

After preliminary research with synthetic personas, users can move to testing the same hypothesis with real consumers.

Toluna says that this transition can be initiated directly from the Bina interface with one action.

The research process has two stages:

Synthetic Research. The marketing team formulates hypotheses, interviews synthetic consumers, compares options, and identifies questions requiring further validation.
Human Validation. Selected hypotheses are tested through Toluna Start with real respondents and appropriate research methodologies.

For example, a cosmetics manufacturer could preliminarily assess ten advertising claims, select the three most promising, and then test them with a real consumer sample.

Similarly, an online retailer could explore several concepts for a new product category before investing in a full consumer research project.

This approach can reduce the number of options sent for subsequent validation and focus research budgets on promising hypotheses.

However, synthetic research does not eliminate the need for validation with real people, particularly when decisions involve substantial investment, demand forecasts, or consumer willingness to pay.

Practical Marketing Applications

E-commerce and Consumer Goods

Online retailers and consumer goods manufacturers can use Bina to explore new product categories, test messages, and preliminarily assess product positioning.

For example, an e-commerce company could compare different descriptions of a product's benefits, examine potential buyer objections, and prepare hypotheses for later A/B testing.

Synthetic responses, however, cannot directly predict Conversion Rate, ROAS, or actual sales volume.

Advertising and Brand Marketing

For advertisers, a primary use case is preliminary comparison of advertising concepts.

A team can explore reactions to different value propositions, identify potentially confusing language, and determine which messages need additional testing.

Findings can support the design of advertising effectiveness studies or real experiments on advertising platforms.

Customer Services

Service businesses can use Bina to explore potential criteria for choosing a provider, perceptions of service benefits, and possible reasons for abandoning a purchase.

For example, a service company might compare offer structures, service conditions, and explanations of its product benefits.

Those findings should be checked through actual customer interviews, surveys, or marketing experiments.

Financial Services and B2B

Financial companies could use Bina to preliminarily examine perceptions of new products, communications, and customer offers.

In B2B, potential applications include positioning research, product concepts, and communications hypotheses.

However, the suitability of synthetic personas for narrow professional audiences must be evaluated separately. Toluna does not disclose universal accuracy metrics for modeling complex B2B purchasing processes.

Toluna Bina Availability and Limitations

At the time of the October 1, 2026 announcement, Toluna reported that approximately 50 clients had participated in pilots of the new product.

A phased rollout of Bina was scheduled for mid-October 2026. The company invited prospective customers to register for information about demonstrations and subsequent access.

Public subscription tiers and pricing for Bina were not disclosed in the announcement materials.

Three limitations are important when evaluating the platform:

Synthetic personas model probable consumer responses; they are not actual research participants.
Claims of high accuracy are based on comparisons reported by Toluna itself. Without a published detailed methodology, they should not be treated as a universal measure of reliability.
Preliminary research findings do not replace actual purchase, advertising conversion, or user behavior data.

What the Update Means for Marketing Research

Toluna is developing a research platform that brings traditional consumer research together with AI audience modeling.

Toluna Bina allows teams to begin with interactive hypothesis exploration among synthetic consumers. Dynamic Awareness adds continuously updated information context to those models. Toluna Start supports subsequent validation of selected hypotheses with real people.

For marketing, product, and research teams, the main practical value is the potential to shortlist ideas for further investigation more quickly and reduce the cost of early-stage research.

Decisions about product launches, advertising budget allocation, and changes to commercial offers still require confirmation with real consumer data.

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