As consumer shopping dollars continue to migrate from brick-and-mortar stores to online, so do advertising dollars. This shift has changed brands’ and agencies’ expectations for market research and has created blind spots when using traditional methodologies. Today, market researchers are expected to understand consumers’ online behavior, as well as how and why they travel the path from discovery to purchase.
Consumer shopping behavior has evolved dramatically in the digital age, but the market research industry has primarily relied on surveys to understand how people shop online. In other words, we will typically ask people how they behave online, rather than simply observing how they behave online. However, business decision-makers may be surprised to learn that this data is often inaccurate — not because research participants are untruthful — but because humans are not wired to remember minute details, like which retailers they browsed, what brands they considered, or how much they spent three weeks ago. This discrepancy between what people say they do and what they actually do is referred to as the recall gap. And, in short, it’s a problematic form of inaccurate data that can be remedied.
With behavioral data — consumer online activity that’s passively observed and collected — market researchers, brands, and agencies can measure customer behavior more effectively and more accurately. Let’s consider the key benefits of behavioral data, how to use it in tandem with attitudinal data, and the market factors that are preventing some organizations from leveraging this valuable tool.
Behavioral data’s intrinsic advantage
As mentioned, most people struggle to recall minor details about their day, especially when asked about events from a few days or weeks ago. But, if they are taking part in a market research study or survey, they rarely skip questions. Instead, they answer to the best of their ability — which, unfortunately, is often upward of 20% inaccurate when recalling shopping behavior. This data — inaccurate, although well-intentioned — gets processed into reports and circulated with decision-makers who go on to take consequential actions based on the insights. Bottom line, when brands and agencies rely on survey-based data alone, they risk making decisions based on inaccurate information, and market researchers fall short of our fundamental goal: to help clients understand their audience and make smart, informed choices.
Today, however, we have greater access to richer, more accurate consumer data. Passively collected, 100% opt-in consumer digital behavior removes much of the need to collect recall-based stated behavior. We now have a structured output that reveals how consumers navigate from site-to-site, how they search, what products and categories they consider, and what advertising they view. In today’s digital-first world, the ability to dissect and analyze how people shop online or interact with a brand is immensely valuable, and behavioral data allows organizations to do exactly that. Behavioral data has less risk of inaccuracies or research bias than self-reported data. That is part of why it is the most accurate tool for understanding people’s online behavior.
Layering behavioral and attitudinal data: 1+1=3
That said, behavioral data has its limitations. Although it allows you to make important observations and have access to rich consumer activity, you can’t confirm intent, motivations, or barriers — or collect long-form feedback. For example, a brand might learn that 30% of customers abandon their online cart; but the behavioral data doesn’t tell them why. This is where attitudinal data is best applied in tandem to engage the consumer with questions based on the behavior of interest. People may not remember how long they spent on a website three weeks ago, but they probably know why they bought a product and if they like it. There is certainly value in asking consumers about their feelings, opinions, and brand sentiments. Used correctly, surveys and focus groups allow researchers to dig into details, collect feedback and opinions, and gain valuable insights.
By using these datasets in concert, researchers and their clients glean the most robust and accurate understanding of their audience. With validated behavioral data, they can draw ironclad conclusions about customers’ paths to purchase and online behavior, then use attitudinal data to better understand the behavior they observed. And, vice versa, behavior data can be used to validate attitudinal data trends and dig deeper.
For instance, film studios are layering behavioral and attitudinal insights to create more effective movie trailers and optimize marketing spend. Behavioral profiling helps identify the ideal test audiences, and dynamically triggered surveys help determine if a given cut conveyed the desired message. Subsequently, a combination of surveys and behavioral measurement uncovers how ad exposure, expressed attitudes, and measurable online activities affect movie ticket purchase behaviors. As a result, the studios can create highly effective cuts of their trailers that drive better box office results.
So, what is the holdup?
One of the reasons some market researchers, and their customers, are yet to take full advantage of behavioral data is that collecting and analyzing this information requires precise skill sets and tools. They need to learn how to collect the data ethically, structure it efficiently, and analyze and process it in a way that is meaningful for decision-makers.