Consumer Behavior: How to Test Hypotheses Before Turning Them Into Campaigns
In brief: Consumer behavior is the study of how people choose, buy, use, and stop using products, and of the forces behind those choices. At a large consumer brand, that knowledge becomes a campaign through hypotheses: “This audience buys for convenience” or “This claim will reduce an objection.” Too often, those beliefs receive budget before anyone tests them. Turn each one into a question that could be disproved, then test it before execution. At Galaxies, Nexus does this before spending with synthetic personas built from the client’s own real research.
Every campaign starts with a sentence nobody writes down: “Our audience is tired of promotions.” “They want to see real people, not models.” “Free shipping matters more than a discount.” These are claims about consumer behavior, stated with confidence in briefing meetings and used to steer substantial budgets without being checked.
Working with Galaxies clients and partners has shown me that the problem is rarely a lack of consumer knowledge. Teams know their audiences well. What they need is a way to separate what they know from what they assume, and test those assumptions before they become creative assets, media plans, and sales targets.
What is consumer behavior?
Consumer behavior encompasses the decisions and actions people take as they search for, choose, buy, use, and dispose of products and services, along with the psychological, social, cultural, and situational factors shaping those choices.
For marketers, the practical point matters more than the academic definition: behavior is the context into which a marketing decision lands. The same message can succeed or fail depending on how people research a category, how much they trust it, what objections they already hold, and the situation in which they encounter it.
Why has consumer behavior become harder to keep up with?
Consumer habits can shift faster than the methods used to observe them. In January 2026, Clutch reported that 65% of consumers used AI to research products before buying [1]. PYMNTS reported that 53% of Brazilians used AI in their most recent online purchase [2]. In the United States, SparkToro reported that 68% of Google searches ended without a click, based on Similarweb data from January to April 2026 [3].
The purchasing figures change how we think about assumptions. If more than half of Brazilian shoppers have consulted AI in a recent online purchase, part of the decision may now happen in a conversation the brand cannot see. Meanwhile, a long research and campaign-approval cycle leaves time for preferences, category language, and objections to shift.
In a talk called “The Consumer Your Data Lost,” our CEO Daniel Victorino described the difference this way: marketing still draws journeys, but consumers move in loops. They discover, understand, consume, learn, and return, sometimes with different decision criteria on the next pass. That is the Signal-to-Decision Gap in practice: an assumption supported by the last study may be stale by the next campaign. Our decision-intelligence guide explores that gap.
How do you turn behavior into a testable hypothesis?
A statement about behavior becomes a hypothesis only when evidence could prove it wrong. “Our audience values sustainability” is too broad to test as written. “Given two packages at the same price, audience X will choose the recycled-material option and cite that material as the reason” is testable.
We use four questions to make that shift. Who exactly is the audience or segment, rather than “consumers” in general? What concrete stimulus will they see: a message, benefit, offer, or creative asset? What will it be compared with? And what result would support or overturn the hypothesis?
The last question is the most important. When a team defines in advance what would disprove its belief, it becomes harder to treat every outcome as confirmation.
Which consumer-behavior hypotheses should you test before a campaign?
The most expensive assumptions support the main message, the benefit being highlighted, the objection the campaign aims to resolve, and the value proposition. The following are illustrative examples, not claims about specific client projects.
Message: humor or a rational argument?
Hypothesis: younger audiences respond better to humor than to rational copy. Compare two versions of the same asset, one humorous and one rational. The hypothesis gains support if the humorous version leads and respondents cite identification; it loses support if the rational version wins or the humor causes rejection.
Benefit: light texture or higher protection?
Hypothesis: for skincare, light texture matters more than a high protection factor. Present the same offer with each benefit highlighted. A preference for texture, with that reason cited, supports the idea. A stronger response to protection, or no meaningful difference in reasons, challenges it.
Objection: is trust the real barrier?
Hypothesis: people avoid switching pain-relief brands because they doubt a newcomer’s effectiveness. Compare compliant assets with and without appropriate quality evidence. If that evidence reduces the objection, the hypothesis is supported; if price or another concern dominates, it needs revisiting.
Value proposition: convenience or price?
Hypothesis: the audience chooses for convenience, not price. Compare propositions led by each benefit. Convenience must win among the target segments to support the claim; a price-led result or a split audience would argue for a more nuanced strategy.
Channel and context: does AI inform the purchase?
Hypothesis: the audience consults AI before buying. Ask about information sources and trust in recommendations. Relevant AI use supports the hypothesis; traditional channels dominating responses would challenge it. These tests do not replace what a team has learned about its audience. They put that knowledge to work at the moment it matters, before execution.
How can you anticipate consumer reactions without guessing?
The short answer is real data, competing options, and stated limits. At Galaxies, synthetic personas for these tests come from research with real respondents conducted by clients or partners, not generic web data. Each persona represents a group present in the research. Present a hypothesis and it can surface the group’s likely preferences, reasons, and objections.
We assess reliability by comparing persona answers with a control group of real people from the same segment, using questions that were not used to construct the profiles. Conversations are continuously evaluated with the open DeepEval framework for relevance, bias, hallucination, profile fidelity, and toxicity.
Simulation indicates direction and reasons, not the statistical proof some decisions require. In those cases, it filters options before fieldwork. A persona can only represent behavior present in its source data, so a new audience requires new research. Questions about future events that have not happened call for more caution than questions about current preferences and objections. Small differences between runs are normal; the order of alternatives and consistency of reasons matter more than decimal points.
What does this look like from brief to campaign?
A practical workflow has four moments. At the brief, list the three or four behavioral assumptions behind the campaign and mark which are supported by evidence and which are still guesses. Before creative development, test the costliest assumptions, especially the main message and benefit, with personas reflecting the target audiences.
Before buying media, test the finished assets to check whether execution preserved what the earlier test supported. After launch, compare actual outcomes with the test’s indications and update the list of supported or disproved hypotheses. Over time, the team builds a living record about its own audience, more valuable than any study read only once.
This can fit into a campaign calendar: a Nexus study runs in minutes, and a structured verdict with human review is ready within 48 hours. At Mahta Bio, validating creative before a campaign reduced customer acquisition cost by 35%.
Know your consumers, and verify what you think you know
No marketing team starts from zero with its audience. The risk lies in treating knowledge and assumptions as if they were the same, or carrying a two-year-old belief into a market that changes daily. Testing the most consequential hypotheses before a campaign is a less costly way to learn which still hold.
This article is part of our decision-intelligence series. For another perspective on how audience reactions vary by group, explore our article on AI audience segmentation.
Test your assumptions about consumers
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Frequently asked questions about consumer behavior
What is consumer behavior?
It covers the decisions and actions people take when searching for, choosing, buying, using, and disposing of products, and the psychological, social, cultural, and situational factors shaping those decisions.
How can you predict consumer behavior?
Turn each assumption into a testable hypothesis with a defined audience, stimulus, alternative, and criterion for support or rejection. Then test it against real audience data, through research or synthetic personas built from that research, before investing in a campaign.
What factors influence consumer behavior?
Psychological factors such as motivation and perception; social factors such as family and reference groups; cultural factors such as values and habits; and situational factors such as the moment and channel of purchase. Product research through AI is also an increasingly relevant part of the context.
Do synthetic personas replace consumer-behavior research?
No. They are built from studies with real people and help test hypotheses quickly. Research remains essential for building and refreshing that foundation and for decisions that require formal evidence.
About the author
Pietro Lancieri is co-founder and head of channels and partnerships at Galaxies, a consumer decision layer for marketing leaders at consumer businesses. Galaxies develops Nexus: Galaxies Lab, a platform for exploring and supporting decisions before spending, using synthetic personas built from real research with Brazilian consumers.
Sources
[1] Clutch, “65% of Consumers Use AI to Research Products Before Making a Purchase,” January 22, 2026: https://www.businesswire.com/news/home/20260122526477/en/Clutch-Report-65-of-Consumers-Use-AI-to-Research-Products-Before-Making-a-Purchase
[2] PYMNTS Intelligence, Global Digital Shopping Index: Brazil Playbook, 2,273 respondents: https://www.pymnts.com/study/global-digital-shopping-index-new-brazilian-shopper/
[3] SparkToro, “When Google Stops Sending Clicks, What Still Works?,” June 9, 2026: https://sparktoro.com/blog/zero-click-search-what-still-works/
[4] Galaxies, client cases: https://www.galaxies.com.br/pt/clientes