When consumers buy a product or a service, to form their decision they consider an abundance of factors, impressions, perspectives, and prejudices that they have accumulated over time. One of those factors is the risk associated with buying the product or service, which I refer to as the Perceived Risk. Until COVID-19, Perceived Risk was generally not considered by the majority (but not all) of purchases; now it is a big factor, and business owners need to be sensitive to this newly elevated aspect.  If they are fortunate enough to have an offering that benefits from this shift in consumer consideration, they should promote that advantage heavily (e.g., ProBox https://uvprobox.com/). If not, they should look for ways to mitigate concerns by re-engineering or focusing on the positive aspects of their product or service.

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EXAMPLE: As you can imagine, online dating has changed since the onset of the pandemic, especially since the World Health Organization considers kissing as the most efficient way to spread a disease. Dating apps have worked to “redefine” dating, communicating that social distancing does not mean disconnecting. Prior to COVID-19, the apps put a lot of pressure on individuals to meet in person, whereas today the emphasis is on getting to know someone online. They have adjusted the apps and pricing to encourage the online interactions. For many, this is not only safer, but a relief as the other pressures of a first in-person date are no longer factors.

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Identifying and Marketing to Levels of Perceived Risk

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Perceived Risk is also ever-changing, as new information becomes available from a plethora of sources at our fingertips all the time. So how do we measure, manage, and target Perceived Risk?  We start with consumer research to understand attitudes, and then develop models to understand and predict consumer behaviors and demographics that describe the varying attitudes—in this case the levels of Perceived Risk.

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Linking Data Sources with Statistical Modeling

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There are three main categories of data that help us fully understand consumers:

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• Surveys (market/consumer research data) are used to identify consumer attitudes, needs, wants, preferences, etc. This data is forward-looking as it allows us to better understand what a person is thinking before they act.

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• Behavioral data is used to understand what consumers actually did. The data is, traditionally sourced from a company’s transactional system which is backward-looking. Today we have other behavioral data generated as consumers research and shop online that are predictive indicators of a transaction. We also have geo-location data captured via mobile devices (aka POI or Point of Interest) that tell us where and how consumers are interacting with brands over time. This data is incredibly dynamic, predictive and real-time which is what we need to understand how consumers’ interactions with brands change as they receive a constant feed of new news.

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• Demographic data helps us understand what consumers look like. This data is typically not as predictive in linking attitudes to behaviors but is very easy to comprehend and leverage to inform creative and marketing.

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Combining and analyzing these sources of data provides a much more complete consumer view—linking what people say to what they do, where and when they do it and what they look like. Statistical models may be developed to link the behaviors to the attitudes so that we can understand how the varying levels of Perceived Risk impact a company’s relationship with their customers. The models will also help us understand those products and services that are potentially risk-averse and those that are less desirable given consumer needs have shifted post-pandemic. This information may then be used to tailor creative and messaging appropriately to customers based on their level of Perceived Risk. Most of this data is also updated real or near-real time which allows marketers to make targeting adjustments more quickly as consumer views change frequently with the constant feed of new information.

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USE CASE: Fitness clubs have taken a hit as many people are nervous about exercising indoors, regardless of the newly implemented safety measures. Exercise routines have changed for those who rely on fitness centers given hours of operations are shorter and the restriction on the equipment open for use which is to help enforce the required social distancing. This can be frustrating for some who show up to work out, but then cannot because there is no available equipment. This frustration could push the member to terminate. A solution may be for clubs to leverage the geo-location traffic data to monitor and report attendance patterns. The information could be updated on their website so members could monitor when attendance is higher or lower and adjust their workout time accordingly. This could be taken a step further, where members could be texted when attendance is lower, encouraging a workout in an environment that is safe.

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In Summary

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We still don’t know how long it will be before Actual and Perceived Risk converge. In the meantime, we need to be aware of the Perceived Risk and communicate accordingly to maintain our customer relationships. If you are interested in understanding how to identify, measure and predict Perceived Risk so that you can maintain and potentially improve your relationships with your customers, Zapfel Group can help.