Your customers are lying to you, and they’re lying to themselves too

Many of the world's biggest companies are unwittingly undermined by inaccurate consumer data, and they may not even realize it yet. With innovative tools such as synthetic audiences entering the fray, boardrooms are confronted with a new challenge. To begin with, businesses require accurate data to comprehend their consumers. To market effectively, brands need to grasp their audiences' thoughts, emotions, and actions.

For nearly two decades, I have guided brands on connecting with younger audiences through "culture." During my tenure at VICE at its peak, our mission was to scare senior marketing executives by uncovering the attitudes, behaviors, tastes, and desires of the young people they aimed to engage. This approach enabled VICE to thrive due to the scarcity of accurate data available to decision-makers.

Despite the ongoing prevalence of this issue, the fundamental factors remain unchanged. Brands are inundated with lines of consumer data they struggle to utilize, while their competitors rely on the same generic narratives. Traditional polling companies have historically informed brands about X percent of people who think Y about their product.

These surveys employ straightforward, generic questions and receive equally generic responses, leading to perfunctory outcomes. Although it may appear to be a minor or personal problem, brands frequently invest millions in projects based on the lies people tell about themselves. Significant funds are directed toward the wrong places; a study last year by research organization Esomar estimated the "insights industry" to be worth $150 billion, with $56 billion spent on market research alone.

How many of the individuals in charge of these budgets can confidently assert they receive full value for their money (and possess accurate data to substantiate this claim)? My co-founder, Alfred Malmros, faced this predicament. He oversaw a startup called Anyone, an application offering five-minute advice calls from anyone worldwide.

Like any prudent founder, he sought to determine whether people desired the concept and conducted a product-market-fit survey. The survey results bolstered his confidence that everyone desired it. However, the download statistics revealed a different story, skyrocketing to 25,000 downloads but dwindling to 1,000 active users over time.

The same individuals who claimed they would appreciate an on-call advice service never downloaded the app. This painful experience taught us the importance of asking the right questions to generate accurate answers. It became clear that flawed questions yield inaccurate results. To avoid this issue, businesses should consider asking what their friends think.

Academic research demonstrates that when people respond on behalf of their social circle instead of themselves, they effectively provide information about multiple individuals, offering valuable insights into societal trends beyond a single person's perspective. Additionally, it is crucial to assess not only what people think at a given moment but also what they experienced and anticipate.

By inquiring about a person's beliefs and behaviors five years ago, today, and three years in the future, businesses can obtain data on the trajectory of beliefs and actions rather than a mere snapshot. At our research consultancy, my business partner and I implemented these principles. Consumer data sets predominantly consist of individuals' projections and manifestations—how they wish to be perceived.

To uncover genuine truths, even hidden ones, researchers must address these issues. Furthermore, designing questions with a blend of human and artificial intelligence can help unlock new futures and opportunities, allowing those who ask the questions to gain a fresh perspective on their world. The C-suite leaders should be concerned about this matter because bad data is not a novel issue.

It has been exacerbated by the emergence of synthetic audiences: AI-generated simulations of actual consumers that businesses can interview and test ideas on before implementation. While this approach can be beneficial, it becomes detrimental if these audiences are trained on flawed consumer data. Although the results may appear promising, they are fundamentally flawed and may lead to catastrophic consequences.