For many brands, creating a 30 second YouTube movie that goes viral is the holy grail of marketing. But ensuring the success of a viral-produced movie is still largely hit-and-miss. Some of the more well-known ingredients known to increase the chances of viral success such as babies, pranks, and stunts seem to have great success on some occasions, but turn into catastrophic failures on others. There are little tricks that can help kick start a viral produced movie, but these tricks only work if the movie has the potential to spread on its own. Even if a formula for success is chanced upon, the issue of how to attach a brand to the movie remains. As soon as many people sense the movie is actually an advertisement, the virility of the movie typically gets cut short. In this blog post I summarize our research on viral brand marketing success. Our algorithm, though adolescent in development, is the first step towards identifying a production framework that ensures the success of viral movies.
Viral transfer of information through networks is essentially word-of-mouth buzz on steroids. Since the emergence of the internet as a social communications tool, word-of-mouth communications has taken center stage as traditional advertising slowly falls behind the wayside. In a study I reported in 2010, we found strong evidence suggesting the effects of traditional advertising are weakening. Increasingly, modern day consumers rely on the opinions of others online rather than what corporations are telling them. The key towards understanding how viral messages spread is understanding what motivates people to transfer information to others, and what makes others act on that information.
Richard Dawkins drew similarities between packets of information that spread down through generations, and Darwin’s theory of evolution. He named these packets of information ‘memes’, after the biological concept of genes. According to Dawkins, a packet of information needed to be ‘fit’ in order to transfer or replicate. In the same way genes need to be fit to survive extinction, so too do memes need to be fit in order to be transferred. The prediction of survival therefore rests on the degree of fitness. Figuring out what makes a packet of information ‘fit’ is the basis of developing an algorithm that can explain and predict messages spreading virally. There are four elements that need to be in place for a branded movie to become viral: congruency, emotive strength, network-involvement ratio, and paired meme synergy. These four are the basis of the branded viral movie predictor (BVMP) algorithm.
Congruency refers to the consistency of the BVMP theme with brand knowledge. To understand how this works, we first need to understand how consumers form judgments towards brands. Judgments towards brands are shaped to a large degree by past knowledge. Our minds are full of a series of associations that are attached to a brand. Our feelings towards a brand are shaped by these associations. For example, Harley Davidson for most people is associated with Freedom, Muscle, Tattoos, and Membership. Our attitude towards these feelings shapes our attitude towards the brand. If we place a high value on Freedom, Muscle, Tattoos, and Membership, then likely we will form high value towards the Harley Davidson brand. But as soon as we witness associations with the brand that are inconsistent with our brand knowledge, we feel tension. For example, a Harley Davidson scooter designed for inner city commuters is incongruent with the brand knowledge of many people. This incongruency negatively affects a person’s ability to use existing brand knowledge to elaborate on the BVMP theme. The BVMP therefore remains weak and exposed to extinction.
The next element is emotive strength. Each day humans process thousands of packets of information. The weaker ones (most) earn a single thought in short term memory, and then become extinct almost instantaneously. Our minds couldn’t cope with elaborate processing of all information in a day; if we did then likely we would go insane. A BVMP needs to evoke a stronger response than these other packets of information to survive. Stronger responses are tied to emotion; information that evokes a strong emotion sinks into long term memory, and benefits from multiple episodes of processing. Emotion may come in many forms, in different combinations, and in contrasting strengths. Disgust and fear are powerful, and are relatively immune to extinction. Sentiment can be equally as powerful, but is more dependent on the network-involvement ratio. Humor and happiness are weaker, and tend to turn reliance onto the other three elements for survival.
The network-involvement ratio is the next element. Another way of describing this element is in terms of how relevant the message is to the seeded network. Transferring packets of information is not enough if the receiver is not motivated to accept the information. In turn the receiver must then transmit. The internet has taught us that acceptance of a message is necessary, but can exist at any transmittal node without transmission. In other words, there only needs to be one point of transmission (e.g., YouTube), but every node (person) must process (view) the information. The BVMP needs to be relevant to most of the nodes in a network in order for it to spread, and the network needs to be large enough for competing network nodes to also process the information. For example, University students are a large network of nodes sharing similar sparks of involvement. Thus, a movie of a student spray painting a message on the whiteboard during class will most likely spread quite efficiently. The office-workers-who-have-a-degree network is fairly close on several dimensions to the University student network, so node transfer to this new network is also likely to be efficient. The degree of BVMP theme involvement within the networks is high in comparison to the network size.
Finally we consider paired meme element synergy. The three elements described up to this point are necessary but insufficient for BVMP success (see Paired Meme elements table below). In our analysis of successful BVMP we noted certain patterns of memes that only appeared effective when paired with certain other memes. For example, impromptu entertainment acts appeared to work when paired with ‘Eyes Surprise’. When paired with ‘bubblegum nostalgia’, the BVMP when pair doesn’t work. Anticipation works with Voyeur, but not on its own. And so forth. Of course it is entirely possible more than one pair could be combined; we have only begun to look at BVMP success when meme elements synergize in pairs. The Webreep algorithm for example comprises four elements, but is fine tuned to predict word-of-mouth only in the context of website experience. Modifying the algorithm to predict word-of-mouth more generally would become extremely complex.
These are the four elements of the BVMP algorithm. On its own, the BVMP algorithm needs the support of classic viral movie production strategies to work. Keeping the movie short, using a hook title, initial seeding, key word tag strategy, are just some of the outside factors that comprise the capsule that holds the BVMP algorithm. Most importantly, and not mentioned so far, is how to attach the brand. We need to ensure that the movie does not look like a brand sponsored message because “selling” is very rarely a positive association tied to a brand. The best way to ensure the movie does not get interpreted like this is to include the brand as self-discovery by letting the viewer discover who the brand is themselves. The Wackness viral movie showing the Buckingham palace being graffitied illustrates this strategy. The brand isn’t obvious until the sprayed message is Goggled. Mark Echo used a similar strategy when graffiti-ing the Airforce One. These examples are textbook in terms of meeting the BVMP criteria.