What We Do

At Brand Dummy, we go beyond data analytics by pairing rigorous statistical methods with behavioural science to uncover the motivations, biases, and decision patterns that shape real‑world behaviour. Our approach turns complex data into meaningful insight—helping organizations understand how people think and choose, anticipate responses, and make decisions with greater clarity and confidence.

  • Pattern Recognition

    Spotting recurring behaviours that signal trust, skepticism, or values alignment.

  • Behavioural Explanation

    Using psychology to explain why audiences respond to certain claims or narratives.

  • Predictive Modelling

    Anticipating how consumers will react under new scenarios, crises, or ESG disclosures.

  • Values Mapping

    Aligning brand communication with the identities and priorities of diverse stakeholder groups.

  • Resonance Testing

    Experimentally validating which messages connect most authentically across segments.

  • Emotional Triggers

    Identifying which emotions (pride, fear, empathy, humour) drive engagement and long‑term recall.

  • Identity Anchoring

    Connecting brand narratives to consumer self‑concepts, cultural values, and generational identities.

  • Decision Pathways

    Mapping the cognitive shortcuts and heuristics consumers use when evaluating claims or offers

  • Narrative Alignment

    Ensuring brand stories are consistent with consumer expectations and broader societal values.

  • Trust Calibration

    Measuring how credibility shifts depending on message framing, evidence, or source authority.

Data analytics capabilities

We believe data analytics on its own isn’t enough—but that doesn’t mean the data doesn’t matter. At Brand Dummy, we’re experts in rigorous statistical methods such as regression, time-series analysis, and structural equation modelling. We simply pair this statistical depth with behavioural insight to reveal the motivations and decision patterns behind the numbers, turning technical outputs into meaningful understanding.

  • Regression Analyses

    Identify drivers of trust, loyalty, and purchase intent.

  • ANOVAs

    Test differences across demographic or ideological groups.

  • Cluster Analysis

    Segment audiences by values, trust signals, and communication preferences.

  • Bootstrapping

    Provide robust confidence intervals for messaging outcomes.

  • Best-Worst Scaling

    Prioritize which claims resonate most strongly with stakeholders.

  • Structural Equation Modeling (SEM)

    Examine complex relationships between trust, credibility, and behavioural outcomes.

  • Latent Class Analysis

    Segment audiences into hidden groups based on shared attitudes or messaging responses.

  • Time‑Series Analysis

    Tracks how sentiment or credibility shifts over time, especially useful for crisis monitoring.

  • Factor Analysis

    Identify underlying dimensions (e.g., trust drivers, value clusters) that explain consumer perceptions.

  • Conjoint Analysis

    Test how audiences make trade‑offs between competing claims, benefits, or attributes.

  • Monte Carlo Simulation

    Test messaging resonance by modeling thousands of possible audience reactions under uncertainty.

How can we help you out?

  • Most organizations collect data without a clear framework for interpretation. We help you move beyond dashboards and KPIs to uncover the behavioural patterns—motivations, biases, and decision drivers—hidden inside the numbers.

  • Traditional analytics stops at description. We apply behavioural science and experimental methods to explain the underlying causes, giving you insight into how people think and choose.

  • Many teams track metrics that feel intuitive but don’t predict real behaviour. We audit your measurement strategy, identify gaps, and design research that captures the signals that actually matter.

  • Conflicting data often reflects poor question design or misinterpreted responses. We diagnose methodological issues, redesign instruments, and extract the behavioural meaning behind the noise.

  • We design A/B tests, controlled experiments, and behavioural simulations that reveal how customers will respond—before you invest in a campaign, product, or strategy.

  • We translate complex sustainability, trust, and reputation data into behavioural insight, helping you anticipate how different audiences will interpret claims, risks, and commitments.

  • We provide independent, evidence‑based interpretation grounded in statistical rigour and behavioural science—clarifying what the data supports, what it doesn’t, and what decisions it can reliably inform.

  • We help you design stronger surveys, experiments, and longitudinal studies so you’re not just gathering information—you’re generating robust, decision‑ready data.

  • Using segmentation, cluster analysis, and behavioural profiling, we reveal how audiences differ in their motivations, trust signals, and decision patterns.

  • Our diagnostic frameworks identify behavioural vulnerabilities—places where assumptions, claims, or strategies may trigger skepticism, confusion, or backlash.