It’s unquestionable that consumers become emotionally attached to certain devices and technologies. For instance, a global survey of 8,000 people found that one-third of respondents said they “couldn’t live without their phone” and if given a choice, more people would rather lose their wallet than their phone, reports Reuters.
But what determines whether something will be a long-lasting hit (e.g., the smartphone) or quickly fizzle (Pokémon Go)? In a new report, Forrester analysts Anjali Lai and Juan Salazar suggest that measuring specific emotions helps predict whether and to what extent consumers will engage with new technology.
Look behind the comments
Let’s face it: Consumers change their minds or they may say one thing and do something else. Previous behavior, demographics, and stated intentions are not reliable indicators of technology adoption rates.
Even positive and negative consumer reactions can lead to false conclusions. Consumers may be initially critical of a product or experience, but ultimately use the product. By the same token, positive reactions aren’t necessarily commensurate with usage. Fitbit devices, for example, garnered a lot of positive consumer sentiment, but Fitbit sales and consumer usage have been disappointing.
Why emotions matter
Researchers have found that how people feel about a product may do more to influence their buying decisions than what they know about the product. Neuro-imagery has shown that when evaluating brands, consumers rely more on emotions rather than information about a brand’s attributes, features, or facts, according to Psychology Today.
Forrester took this finding one step further by creating a model that captured and measured emotions that often correlate with certain behaviors. For example, the analysts found a statistical relationship between “happy surprise” and willingness to spend. Using this approach, the analysts included eight emotions in their model: anticipation, joy, surprise, trust, anger, disgust, fear, and sadness.
The emotions are captured through passive tracking mechanisms like facial analysis in order to capture the emotions as they’re unconsciously expressed. The analysts then use an algorithm to process the raw data.
Using consumers who have engaged with virtual reality products as an example, the algorithm revealed a number of insights about how consumer emotions evolve as people learn about the technology, purchase it, engage with the product, and for some, return to customer service for support.
Notably, while many people expressed high anticipation and surprise when learning about a VR product, the predominant emotions often turned into anger or disgust when they experienced motion sickness from using the device and/or had to contact the manufacturer for guidance on how to operate it.
“Our analysis suggests that despite enchanting consumers initially, virtual reality in its current form does not create the emotionally fulfilling consumer experience needed to change long-term behavior,” write Lai and Salazar. Such an observation could be useful to retailers as they submit orders for the holiday season and to developers making improvements to VR products. Indeed, sales figures of VR headsets for 2016 were “not exciting,” according to Fortune and although research firms (including Forrester) are optimistic that VR technology will soon gain wide adoption, the consumer experience is still lacking.
The takeaway is that there are myriad data points for predicting purchase decisions from demographic data to sentiment analysis. But if those data points don’t include human emotion and its influence on decision-making, companies should question the veracity of the data. One might say such data is missing the “heart” of the matter.