The History Of #selfie
#selfie: A photograph that one has taken of oneself, typically with a smartphone or webcam and shared via social media.
Looking at the evolution of the #selfie, and of human nature, it’s easy to see why it has become such a popular trend. Essentially the modern-day selfie, as we know it, derived from the self-portrait. Historically, the representation of status was the motivating factor behind self-portraits, as well as control over how you were presented to society. The idea of preserving this desired depiction of oneself was also a consideration.
Today, self-portraits are no longer created by master painters as such. Instead, we ‘paint’ our own image instantly via smartphones and social apps. But the practice of documenting our very existence through the #selfie is generally motivated by the same sentiments. And considering that once something is uploaded onto the internet, it’s there forever, a sense of immortality can be linked to the way we currently record our lives online.
Despite the first mention of the term ‘selfie’ being from a Flickr post in 2004, the use of it has only really surged since 2012. The escalation in use of the term directly relates to the ever-increasing practice of taking selfies. According to Samsung, 30% of photos taken by people in the 18-24 years age bracket (otherwise known as millennials) are in fact selfies. It’s estimated that within this age group, 40% of people take a least one #selfie per week.
So we take selfies. A lot.
Havas Worldwide tells us that as of July 2014, 145 million pictures were posted on Instagram using the #selfie hashtag. It’s obviously a massive phenomenon. But why should your business care?
According to an infographic released by Sony last year, images that feature a face are:
- 38% more likely to receive likes
- 32% more likely to attract comments
And if the person in that photo is holding their favourite drink? Or wearing a branded sweatshirt? Intentionally or not, that brand is getting serious promotion. Everyone seeing that photo, sees the brand.
Of course, there are selfies where the focus is on the product – a changing room #selfie to show off a new dress for example. The brand may get a textual mention or hashtag here. But what of the selfies that feature a product that doesn’t get a textual mention. How do you find them? Why would you want to?
Well, for starters it helps identify how your customers naturally experience your brand offline. What situations do they link it to? Do they associate it with the consumption of other products? Is it a product they enjoy on their own or with other people?
This is where the broad classification of selfies into solitary and group comes into play. Do you know what category your product falls into?
For instance, let’s say your product is a moderate to premium priced bottle of wine. You’re currently aiming it at 35-44 year old couples, with children, who take turns hosting dinner parties once a month with similar couples. This market performs quite well for you right now. But what if there’s a market you’re missing? What if 26 year old single females indulge in a glass of your finest Cabernet whilst watching the latest episode of Girls? Imagine they’re documenting this event, as many millennials do, with a solitary selfie featuring your Cabernet? They’re sharing their idea of a treat and suggesting others should do the same. They’re recommending your product to their peers. They could be your biggest brand advocates and if you’re not investing in visual analytics, you’ll never know. You’ll be completely in the dark as to the opportunities available in that demographic. And you’ll never be able to leverage the influence of your brand ambassadors.
On the other hand, what if your target demographic isn’t evolving but the environment in which your product is consumed is?
For example, imagine your product is a facemask sold in single use sachets? Perhaps aimed at cash-strapped 24 year olds who can’t afford a facial with a beautician. They’re using your product alone in their own homes – it isn’t a social event. You’re doing well in this market and your current advertising approach, and spend, is allocated with this in mind.
But when you incorporate computer vision technology, you make a discovery. Cash strapped 24 year olds are using your product. And they are using it at home. But they’re not alone. They’re sharing the experience. First through solitary selfies encouraging their peers to follow suit. As these evolve, they inspire group pampering parties – friends get together to spend an evening applying your facemask and painting their nails and drinking green tea (complimentary brands or products you may not have associated with your brand had you not utilized visual analytics). Your customers are establishing a new environment in which to enjoy your product. They’re documenting it online with group selfies and hashtags such as #pyjamaparty #pamperparty, yet mentioning your product at all. But your product is there – front and centre in the photos your customers are sharing. And you might never have known. Thanks to computer vision technology, you do. Now you just have to leverage this knowledge.
Perhaps you could run a competition? ‘Share your #pamperparty #selfie with us to win a hamper full of pampering products for your next girly get together?’ Campaigns such as this encourage your customers to share their offline brand experience with their online world. Every photo is a recommendation for your product.
Effectively, this is known as peer-to-peer marketing. Using the influence of one person to inspire others. As mentioned in our previous post, 49% of people share on social media in the hope of changing opinion or encouraging action. According to Nielsen, 92% of customers trust peer recommendations. Only 36% trust ads on social networks. So with very little effort on your part, your customers could be acting as brand ambassadors and persuading their peers to purchase in your product.
As you’re probably coming to realize, peer-to-peer marketing can be extremely valuable. But it will not succeed without the right textual and visual data to support it.
Article co-edited with Sarah Heffernan.