Simply put: If you can’t trust your audience is truly who they say they are, then you can’t trust your results.
But as long as we offer rewards for taking part in research, the temptation for some to engage in fraud will exist. Estimates on the frequency of fraud in market research vary: from the low teens to as high as 30-40 percent.
No matter the frequency of fraud, its effect on data and insights can be disastrous, leading to flawed insights and poor decision-making.
Yet, there are steps market researchers can take to educate and protect themselves, leveraging practices and tools to ensure the integrity of the audience and their responses, and therefore, the insights gained.
Fraud is becoming more sophisticated
Digital fraud has increased across all industries over the past 18 months, in large part, due to the innovations in technology. As AIs prevalence continues to evolve, so will the opportunity for fraud.
If not properly addressed, its impact on quality – for data, research and insights – will continue to be felt. As fraudsters become more sophisticated in their practice, fraud prevention and management must embrace and outpace innovation – of tools, processes and solutions – to stay ahead of the threat.
However, it’s important to separate fraud from quality issues caused by inattention. While the effects seen in the data can be the same, the prevention methods in each case are very different.
The fight against fraud begins with targeting. Making sure a chosen audience is vetted and verified – both using industry-standard tools and proprietary processes and solutions – to identify, and remove, fraudsters before they can be included in any sample.
The best quality outcomes and outputs are going to come from panellists who are who they claim to be, are actively engaged in sharing their opinions and are engaged throughout the process.
In this way, the businesses need to know they are hearing from real, engaged and qualified people is inherently tied to the experience of survey respondents.
The panellist experience
The greatest risk of fraud is not the occasional scoundrel, but an organised attack on an individual panel, source or survey.
Straight lining, poor open ends, contradictory data or incredible answers are often caused by a disengaged panellist – one who is fatigued, confused, frustrated, distracted or has a hesitancy to share.
In addition to technical fraud prevention, ensuring data quality is preserved requires curating a more transparent and worthwhile experience for the consumers sharing their data. This includes relevant surveys, reward and loyalty offerings, and security and protection of information.
Fraud prevention and management is a two-part process
Fraud prevention and management starts with collecting clues about who an individual is, gathering the data both comprehensively and quickly. These clues include, but are not limited to, device and geolocation data, third-party matches against known lists, and profile and behavioural data.
Once the data has been collected, models determine whether a respondent is real, and unique, based on those available clues. This creates a positively reinforcing cycle as we collect more data, machine-learning based models make decisions more quickly and accurately.
It’s an innovation combining advanced technology – like AI and machine learning – to create norms for things like keystroke patterns, answer speed and other response characteristics, then using those benchmarks to examine outliers and take fast and early action.
Combined with manual review as well as the adoption and use of both general purpose and industry-specific third-party tools at every touchpoint, with every person.
Finally, we monitor performance and behaviour within the survey to detect any early-warning signs or activity that indicate suspicious behaviour leading to an attack, intervening and correcting before that action has a chance to be successful.
Ultimately, protecting the integrity of the data and insights they receive to help ensure decision-making isn’t compromised in any way.
The fight for the future
It is a hygienic practice, maintaining real and vetted panellists to therefore ensure the data and insights they offer are high quality. Doing so requires a focus on all aspects of the process, starting with prioritising participant retention and trust.
Data quality, and the sustainability of online research, requires this hygiene and vigilance.
The emphasis will always be to protect and extend the quality of data, research and insights. It’s a continuous arms race to exclude them requiring continuous, concerted, targeted effort.