Nearly every digital marketer has a goal of creating a viral campaign. Getting mass exposure for high-quality content provides huge value to clients, but it’s not always easy to pull off; it takes an understanding of the complexity of human emotion and how it plays into consuming and sharing content online.
To gain better insight into what makes people share content online, Fractl studied the emotions associated with viral marketing campaigns, plotting the ones that are most commonly associated with viral content on Robert Plutchik’s comprehensive Wheel of Emotions:
Then, we looked more closely to see how certain demographics respond to different types of content.
To get a better understanding of how people of different genders and ages react to content, we surveyed 485 people online and asked them to indicate which emotions they felt when viewing 23 viral Imgur images we chose from over a three-month period. They could select feelings related to joy, sadness, fear, disgust, or surprise by choosing an adjective related to that emotion. We also conducted a similar survey featuring non-viral images instead of viral ones for comparison purposes. The subtle differences we discovered could have big implications regarding the nature of virality and content marketing.
One of the more interesting insights in our study comes from the 18-24 age group. This age range reported feeling fewer positive emotions while looking at the overall group of images compared to the participants in the other age groups. Specifically, they reported fewer emotions related to joy, trust, or surprise (the latter we considered to be an “other” emotion, as it can be both negative and positive). This lack of positive response can mean that this age group is more difficult to target.
This group’s reports of surprise are noteworthy, as well, because this emotion has an important role in the probability of going viral. When we compared reactions to viral and nonviral images, we found that viral images were more likely to trigger reactions of surprise than non-viral images were, while there was no significant difference among the other emotional groups. This indicates that surprise may be a central component to what makes an image go viral.
Given that the 18-24 age group had fewer reported positive and surprise-based reactions to images, this demographic may be more difficult to engage with and could require additional targeting. It’s possible that this age group is simply inundated with images online (like the 11% of those aged 18-29 who use Reddit.com) and thus they are more discerning and harder to emotionally activate. Since their threshold is higher, you’re more likely to engage them with particularly new and highly intriguing content.
The 25-34 demographic has something in common with the 18-24 set, which is that both groups reported feeling fewer interest/anticipation emotions compared to the older two age groups. It’s possible that this is because younger online users are more captivated by dynamic forms of media rather than the common static images that were used in our study. However, this group also reported more emotional complexity than any other age group, making them the most likely to share content that incorporates a range of emotions — once their initial views are earned.
In contrast, Gen X and Baby Boomers reported more positive and interest/anticipation emotions when viewing static images. Therefore, when creating campaigns targeted at these two age groups, you’ll want to focus on those that incorporate emotions such as joy, trust, interest, and anticipation. This is a key insight for marketers planning campaigns on LinkedIn, which is the social network home of the largest percentage of older audiences.
The differences in gender are less dramatic than the differences in age groups. Men and women generally reported the same number of instances of feeling each emotion when viewing the viral images overall. There was one emotion type that approached a statistically significant difference, though: joyful emotions. Men reported feeling more joyful emotions than women did, which could mean that women might be slightly less likely to respond with positive emotions, possibly becoming a factor in whether or not they’ll view content. However, women did feel one positive emotion — trust — statistically more than men, which may be key for earning their initial views.
When we compared the responses to viral and non-viral images, we found that women experienced a greater variety of emotions. While both men and women reported more fear, surprise, sadness, and anger emotions when viewing viral images compared to non-viral images, only women statistically experienced more emotions related to trust, in addition to more negative emotions and total emotions overall.
If you’re trying to target women, it might be worth aiming to generate trust in order to promote sharability.
The fact that women experienced a greater variety of emotions is also significant, because we found that viral images activated more emotions overall than non-viral images did. In a nutshell, if an image evokes complex emotions, it has a greater chance of getting shared. The female propensity toward experiencing a range of emotions may make women more likely to share, but their fewer positive feelings may pose a challenge in garnering crucial initial views.
Conversely, men may be more likely to view content, based on their reports of greater positive emotions, but less likely to share due to less emotionally complex responses. Marketers may want to target emotions in men that can help increase the variety of emotions they feel; for example, since we previously mentioned men reporting more joy-based reactions to viral content, contrasting emotions such as sadness or fear could be prioritized to create a more complex emotional field and balance out the potential for joy.
As you create content, keep this in mind: Consideration of positive feelings and emotional complexity, and the challenges of achieving each with various demographics, will give your content an edge in its chances of going viral.Go to Source