Archive for the ‘Insights’ Category

As social media researchers we help clients make sense of people’s online behavior, which is of course complex. While they have their uses, KPIs such as volume and sentiment can only get you so far. One key limitation is that they only measure what people say – not what they do as well.

Social media research is moving beyond keyword tracking, something we’ve been innovating here at FACE with Pulsar’s content and audience tracking technologies. To really dig into online activity, we need to analyse metadata, the information spun off by social behavior, just as much as the messages people produce. And we need to embrace a wider range of methodologies drawn from the field of computational social statistics.

We can do this through social network analysis (SNA), a research framework giving us the tools and concepts to investigate questions such as how content is shared and how communities are formed.

We are going to dive into examples in more detail for our ‘Network Analysis for Market Research’ blog series, covering topics including:

  • Visualising networks to make sense of large & complex data sets
  • Methods for identifying influencers
  • Identifying communities
  • Tracking how networks evolve over time
  • Mapping how information spreads
  • Overlaying networks with other metrics

For great examples of how we can benefit from investigating social structures using network analysis you need look no further that FACE’s research carried out by Francesco D’Orazio and Jess Owens for Twitter on How Videos Go Viral, & How Stuff Spreads: Gangnam Style vs. Harlem Shake in partnership with Datasift.

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In our blog series we’re going to investigate methodologies used in these projects, provide additional examples of network analysis, dive into some theory & explain practically what this means for the process of social media research.

If you have any questions about what I’ve discussed in this blog or about our forthcoming blog series then please do get in touch.

Part 1 of the blog series has now been published. Read about Identifying Influencers with Social Network Analysis here

As Ray Poynter notes, mobile has finally arrived in market research!

 “people have been saying mobile is the next big thing for over 15 years, even in the days when that meant SMS, or WAP, or writing 100s of apps for different types of phones. At conferences and client sessions I keep being asked “So, when will mobile be the big thing?” The answer is that it is now a big thing, and it has been for probably 18 months or more.

What’s notable though is that industry discussion is still oriented around the ‘grand dames’ of the market research toolkit: surveys (now moving from online to mobile, albeit sometimes “accidental mobile”) and CATI (telephone interviewing). Here at FACE we’re wondering, what about qual?

Well, let’s start talking about mobile qual! We’re excited to have research director Sharmila Subramanian writing a series of articles for us sharing her vast experience of mobile research methods, something she’s built up over many years of research with Nokia in particular.

Mobile research

First, when do you need to use mobile research methods? Sharmila shares three case studies:

Why mobile is useful: 

Here at FACE, we are committed to trying to root consumer understanding and resultant insights within context as much as possible.  This requires us to be able to understand consumer moments and interactions when they happen – not just in the home, not just in the research environment. Out of any tool for capturing thoughts and behaviour, mobile presents the best means of doing so.

Beyond this, mobile provides a simple and intuitive interface for capturing consumer attitudes and behaviours for a number of obvious, but important reasons:

1.  It’s people’s primary communication device

2. It’s an extension of people’s bodies and selves: always with them, always on. This makes it invaluable in gathering in-situ understanding

3. It’s the most personal device that people own, so it’s a fantastic platform for capturing more  private or personal thoughts and behaviours

4.  People are used to engaging through apps, making a mobile research app a logical research interface

This is not to say that mobile should be utilised for any & every research activity. It is a one-way method of research, with little scope for researcher-participant interaction. As a result, it is not for briefs or lines of enquiry that require a great deal of laddering and researcher probing in real time.

Moreover, its very nature does not lend itself to long form, highly considered response. When was the last time you tried to write something akin to an essay on your mobile?  I bet it was pretty painful.  Don’t expect any different for a research participant!

Three use cases for qualitative mobile research

From our own experience on a range of projects, mobile research comes into its own on three types of briefs:

Mobile research FACE App

1. Understanding response to concepts:

Whilst we would not advocate a mobile-only methodology for concept testing and development, mobile can prove an invaluable supplement to F2F methodologies where we wish research participants to “live” with concepts beyond the confines of the focus group facility. Initial reads on concepts often give us an understanding of their initial impact and wow factor. However, getting participants to then live with the proposition, and document when they see roles for certain ideas and concepts via mobile, can go much further in identifying their potential usefulness, and ability to fulfil needs within the real world.

On a recent project using FACE’s mobile research app, this approach proved invaluable in deepening understanding around a concept for a new service.  Whilst an online community and groups gave understanding of the initial comprehension and appeal of that concept, subsequent mobile research gave us a richer picture of where participants actually saw a role for the proposition – in terms of where, when, how they would utilise it and why.  We would not have been able to get that level of understanding by utilising other methods that rely on hindsight or recall.

2. Product trialing:

Mobile can come into its own in terms of understanding product usage and response – ultimately, it gives us the ability to understand those moments in-situ, as they happen.  And it makes it easier for the user to document those moments – no paper diary completion, no need for recalling of hazy memories on an online community or in a group.  Everything from first impressions of a new product, to first and repeat usage, to understanding how response to a product can change over time can be readily captured within mobile research. Moreover, it gives us the ability to understand all of those things across a variety of contexts, times of day, as well as the social dimension that may be at play.  As a result, we get closer to a more holistic understanding of product usage.

A recent example of the power of mobile for product trial can be seen in a project FACE conducted looking to understand response to a new product format.  FACE’s mobile app was used by a range of participants over a week to understand their first impressions of the product, how they used it, the triggers and barriers to use, and how their response changed over time.  This helped us to define the key benefits and use cases for the product prior to launch, as well as helping to provide starter thoughts for which elements of the product experience future communications should leverage.

However, the approach also proved powerful in providing a wealth of rich multimedia material that could be utilised by the client to provide more compelling evidence of the value of the product.

Mobile research FACE App

3. Shopper interaction:

The very mobile nature of the, well, mobile, clearly lends itself to helping to better understand the shopper experience. Whether in terms of gaining learnings on retail environment, in-store communications, or product placement, the discrete form, and bite-sized mode of interaction of the mobile makes it ideal for consumers to gather quick thoughts, images, and documentation of journeys within store.

FACE employed a mobile approach for understanding response to a new store layout format for a well known food and drink brand. This was invaluable in gaining firsthand accounts of what was a new concept in-store – accounts that were not influenced by researcher presence. The unmediated nature of this capture was essential in identifying exactly what the key hooks, and turn-offs of the new format were, and helped provide a compelling story for the client, through the use of raw, consumer generated content, to help our client sell the concept to retailers.

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So, that’s an initial overview of three times mobile research is one of the best methods we’ve got in our market research toolkit. Next up: getting the most out of a mobile approach – the do’s, the don’ts, and  best practice for making a mobile methodology a success.

If you’d like to discuss this further with Sharmila, contact her at Sharmila@FaceGroup.com, on LinkedIn or Twitter @SharmilaSub. To stay in touch with more of our qual thinking and methodology knowledge-sharing, join our mailing list.

While growing up in Singapore, I don’t recall visiting art galleries or events as a regular activity, either in school or in my own leisure time. Art, for me, was merely another subject in school, where you get some fun out of making colour imprints from cut-up tomatoes and potatoes. There were organized annual art & craft competitions where only the best pieces would be displayed – the less good pieces would be shelved at home for closed-door appreciation. In my memory, there was little about exploring art.

Things have changed dramatically in the recent years. There is an upbeat tempo in the art scene in this region. Unlike in the past, art has become more disposable and accessible – more informal pop-up locations than formal museums; more free and open events than having to pay for expensive entrance tickets; more recognition of local artists rather than only foreign artists whose names we can’t even enunciate; more close-to-heart topics than abstract art that we can’t connect with .

In this post, I will share some of my “art encounters in Asia” with you and conclude with some thoughts on how this changes the way we approach advertising and consumer insights.

The following 5 exhibitions are ones that have particularly captured my imagination. I feel it’s valuable for us to share these from us here in the Asia office back towards colleagues and readers in the West, so you can get a sense of what the new creativity looks like in this market and how it may differ from trends in London or New York galleries. Brands and marketers headquartered in the West need to pay attention to artistic expression coming from China, Singapore, Thailand, Malaysia etc  in order to understand the interests, questions, concerns and aesthetics at play in these markets. Brands’ visual languages can’t be imposed from the West on to “the rest” any more – they need to listen and learn.

On to the art:

Pop-up art exhibition in Georgetown (Penang, Malaysia) in a very dilapidated shop house 

13: Rebirth – a group exhibition in continuation of 12

A collection of installation and art pieces on the idea of Rebirth

Reflecting on the meaning of life, death, and rebirth

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 Art space in K11 shopping mall in Shanghai, China

This is the world’s first art shopping mall

 A photography artwork inspired by the life-threatening air pollution in China

Artwork portray commoners in masks, each mask telling a different story

“I am afraid this will be a silence call for help”

 Reflects the relationship between environment and people and the vulnerability of life

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Bangkok Art & Culture Centre (Bangkok, Thailand)

I am Fat / Mannequins – an interpretation of beauty and perfection

Reflects the hard truths of acceptance against social expectations on beauty

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Singapore Art Museum (Singapore)

Actual-size installation on a Primary School in Yangon

Brings fresh perspective on living standards and education

Set in a developed country, this installation is a thought-starter to get people thinking about their own (comfortable) environment and dwellings

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 Noise Singapore 2013[J1] 

An annual open-public event dedicated to recognize the creative works of youths – work ranges from art and design, music and photography

There is one of the photography pieces that were curated for 2013

This event encourages youths to showcase their creativity and infuse fresh perspectives

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Art is clearly another language to communicate meaning and emotions without language barriers. It opens up a different window to interpret the world, cultures, environment, politics, society and emotions. This brings about new values and perspectives, which may change our attitudes and behaviour.

The increasing vibrancy of the art scene in Singapore – and across a region becoming more affluent and more middle class – means potentially new public sphere for ideas. It allows more space for expression and creativity. By creative, I am not implying that everyone will become an artist, but that the audiences are able to realize themselves as capable and inspiring thinkers. There’s more of a public forum for people to discuss representation, and meaning, and creative ideas – and for these thoughts to feed back up and be heard by artists, brands and cultural institutions.

This changes how brands should approach consumers with their communication strategy. While a single-minded message is the rule-of-thumb, this doesn’t mean the delivery must be linear. With a changing consumer profile, consumers are capable interpreting meanings with broader perspectives, and brands that recognize this – who don’t feel they need to talk down to their audiences, but who trust them to be able to interpet complex imagery and narratives – have a chance to win a greater share of attention, personal recognition, and loyalty.

Many brands have succeeded in this – Coca Cola is a classic example that doesn’t need further introduction. Absolut Vodka continues to build buzz with its limited edition bottles with iconic designs. In 2013, Absolut Vodka put up its first Absolut Canvas exhibition dedicated to the art and creativity of the brand at the Singapore History Museum. Other brands advertised using creative art installations to connect with people in their daily lives.

We also need to move away from the traditional marketing hierarchy. Consumers are no longer ‘external’ stakeholders but ‘internal’ stakeholders, who can play a fundamental role in directing the brand strategies.  At FACE, we believe every consumer has a voice – hence it is essential for brands to engage with consumers at an early stage to shape the idea. Through co-creation workshops, brands can start conversations with consumers, build ideas and create stories together with them.

After all, it is an art to paint a full picture.

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Calin Chua is a Research Manager for Face Asia. She has worked on a diverse range of brands, including Starbucks and Nokia. 

Connect with her on LinkedIn or follow her on Twitter.

 

 

In the first part of this blog series (How Stuff Spreads | How Videos Go Viral part 1: Models of Virality) we identified 7  dimensions that describe and quantify virality. Although none of the variables alone proved able to define a viral phenomenon on their own, they correlated into two models of viral diffusion. One model we called “spike” – the sudden ‘explosion’ of sharing activity – and the other we called “growth”, where popularity is a slower and steadier grower.

Spikers vs Growers

In this blog post, our Chief Innovation Officer and  VP Product Francesco D’Orazio using social network analysis looks at how the audience composition and structure influence the way video spreads.

What makes a viral video spread in one or other of these ways? Most studies on the subject  focus on virality as a feature of the content. But what if virality is (also) a feature of the audience? Can the demographics and the structure of the audience for a video explain how it goes viral?

To recap, we were studying 4 videos:

In this blog post we will show how we analysed the demographics and the social network properties of each video’s audience to understand better how they spread.  Read on for some of our best network graphs yet and some fascinating findings…

Metric #1: Amplification and influence

The first thing we looked at is Amplification. Amplification is a measure of the average “visibility” of the tweets carrying the meme. Tweets with higher visibility imply a more influential status of the author who posted them. Can the influence level of the audience of a meme explain its slower or faster diffusion?

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Amplificiation is similar across all audiences. It’s fractionally lower for the Turkish protest video and for Ryan Gosling, the first primarily shared in Turkey, the second appealing to a slightly newer (though still very active) Twitter audience. And it’s slightly higher for Dove Real Beauty Sketches and Commander Hadfield. In both cases the variation doesn’t correlate with the virality model of the meme.

Metric #2: How international were the audience?

So the next hypothesis to explain the velocity of the memes was the geographic distribution of the audience. We quantified this as Globality: the percentage of meme shares coming from countries other than the main country. So does the “internationalness” of a video affect its virality? Does a more global or a more local meme spread faster?

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The answer is again, no. The Turkish protest video was “local” but so was Ryan Gosling – and one spread instantly but the other peaked on day 18. Since both Amplification and Globality seemed not to correlate with one or the other model of virality, we then looked at the demographics engaged with each video.

Metric #3: Demographics.

Does the demographics of the audience affect the way content goes viral? Do young, techie male students from global cities push a meme faster than, say, middle aged housewives from rural Germany?

We used Bayesian statistical inference to analyse the demographics of the audience. This method uses the available information on Twitter and matches it to a sample audience interviewed in real life to get known demographics, across the various countries involved in the study. Below is a summary of the most prominent demographics traits of the four audiences:

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Although students and global cities feature heavily in all audiences, there doesn’t seem to be any direct correlation between virality models and demographic traits. Instead the demographics are completely different for each meme. Not to mention that students represent 33% of the Ryan Gosling audience, the slowest meme of all – so it seems that youth demographic probably isn’t necessarily a critical cause for a video to go viral quick.

And now some Social Network Analysis…

The audience gets more interesting when you start to look at its social structure. As we couldn’t find any correlation between demographic traits and virality models, we turned to the structure of the audience by mapping the social graph of the people who shared the video.

Your ‘social graph’ is the network of the people you know, and how they’re connected to each other. Because we were studying Twitter sharing of videos, we had easy access to this data through two variables: who each video sharer was following on Twitter,  and who they’re followed by. In technical terms, this gives us a ‘directional’ network with two possible ways for nodes to be connected.  Analysing these connections highlighted some really interesting differences.

Metric #4: Degree, or ‘social connectedness’

First of all we looked at the Average Degree of each audience network. Each person in a network can be assigned a ‘degree’ value: that’s a count of the number of connections they have to other people in the network. We were studying how videos spread in Twitter, so those connections are easy to identify: it’s who they’re following and who they’re followed by.

Interestingly enough, the audiences of the Spiker memes (Commander Hadfield and Turkish protests) are showing the highest levels of interconnectedness – while the audiences of the Grower memes (Dove Real Beauty and Ryan Gosling) show the lowest.

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Metric #5: Modularity, or “fragmentedness”

The memes that spread faster could do so because the audiences that engaged with them were highly interconnected. But how are this connections organised? To do this we used another social network analysis metric called Modularity. This describes how fragmented the network is and how many sub-communities can be detected based on the density of mutual social connections within clusters of users.

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The lower the modularity, the less fragmented the audience is into sub-communities, the more cohesive it is and the easier to reach it is. Not surprisingly, the audiences of the Spiker memes are the most cohesive ones, while the audiences of the Grower memes are the most fragmented ones. Cohesiveness and fragmentation becomes much easier to understand when looking at the total number of communities identified within each audience.

Metric #6: the number of Communities

Social network analysis tools allow you to measure the number of ‘communities’ in a social network. Tools such as Gephi provide access to algorithms, such as the Modularity one, that can quantify how people’ s connections tend to gather together into definable ‘clusters’ of closely-connected groups.

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Whereas the audience of Commander Hadfield is split into 130 communities and the audience of the Turkish protests is split into 51, the audience of Ryan Gosling is split into 382 communities and the audience of Dove Real Beauty Sketches into 1356.

This has a strong impact on the ability of memes to spread through the audience network. Whereas reaching out to just 2 communities is enough to reach 50% of the audience of the Turkish protest, spreading the news to 50% of the audience of Dove Real Beauty Sketches requires reaching out to 8 communities. It follows, then, that where a meme has to travel through more communities to reach people, it moves a little slower – in a ‘grower’ model. By contrast, memes ‘spike’ where they take off in a small number of communities very quickly.

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So what have we learnt so far?

Yes, the audience’s social structure – the way that people are connected within in – shapes the way something goes viral.

Audiences with a low Average Degree, low connectedness or low density, are more fragmented. The more an audience is fragmented into sub-communities (high modularity of the audience network), the slower a video or piece of content spreads through it . But what causes a higher or lower fragmentation within a specific audience?

Understanding the communities within an audience

To answer this question we tried to measure the demographic diversity of the audiences. The assumption being that an audience showing a higher demographic diversity will also be more fragmented and therefore slower to transmit viral videos.

So we ran the demographics analysis again on the four audiences: this time running it separately on each of the top 5 community clusters identified within each audience. You can see below the results for the top 2 clusters of each audience:

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The audiences of Ryan Gosling and Dove Real Beauty Sketches show higher demographic diversity, while the audiences of Commander Hadfield and Turkish protests show lower demographic diversity. So high demographic diversity correlates with high modularity and slower meme velocity after all. How is this useful?

How the audience affects How Stuff Spreads

To start with, this means that a meme which is appealing to a broad demographic is probably going to spread slower than a meme that is appealing to a narrow demographic.

This also means that a meme with a broad demographic appeal is going to be more expensive to make go viral. Expensive because it will require more intense paid for seeding/advertising in order to reach out to a higher number of disconnected communities (Dove Real Beauty Sketches is a good example). It may also need persistent replication of the meme to break through the attention of multiple audiences who might not take notice the first time (Ryan Gosling won’t eat his cereal is a good example).

Finally, the organisation of the audience in sub-communities means that influencers lists by subject are pretty useless when trying to reach out to an audience. For example, your top 100 influencers for beauty might well all be part of the same two communities out of the 1356 total communities that make the Dove audience. So identifying gatekeepers and influencers is useful only once the audience you want to reach has been mapped and its communities identified.

The social dynamics of virality

In our previous post we identified and defined two models of virality: Spike vs. Growth.

From this audience and community analysis, we can now augment that with a 3-part model of how content is seeded through groups of people:

1) TRIGGER: A higher than average emotional response to the content triggers an impulse to share

2) VALIDATION: The impulse to share gets then validated against the community the user is part of. This validation happens both in terms of topicality (is this of interest to my audience?) and timing (has anyone else already shared this within my circles?). See this paper for more research on this aspect

3) ESCALATION: The gatekeepers (e.g. media channels, celebrities etc) share the meme helping it reach the tipping point within a specific community. The tipping point is when every member of the community is likely to receive the meme from another member of the community.

Once everyone’s seen the meme and starts to share it on themselves… That’s when you’ve got virality on your hands!

 So what does this mean for you?

Content that generates an emotional reaction is more likely to go viral. People share to say something about themselves. Emotional content helps them figure out easily what it is they are saying about themselves by sharing it.

Picture the audience your content is going to be appealing to, and find them in social media. Learn who they are and what makes them tick.

Your online audience is not a monolith. Online audiences are organised in sub-communities and congregate around key demographics variables such as age, profession, passions and interests.

Map your audience and identify the key communities that are going to help you reach out to at least 50% of your audience.

Once the communities are mapped,  identify the key gatekeepers by community and the connectors between key communities. This will help you reduce the outreach effort.

If your content is going to appeal to a broad demographic expect a longer run and make sure you have the right resources in place for seeding and advertising to a fragmented and  harder to reach audience.

Good luck.

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Previous posts in this series:

Found this interesting? Got viral content of your own that you want to understand? Check out the tool we used for this study, Pulsar (PulsarPlatform.com) and contact us to arrange a demo – send an email to James.Cuthbertson@Facegroup.com and we’ll be in touch in no time.

Or get in touch with the study authors, Francesco D’Orazio (@abc3d /LinkedIn) and  Jess Owens (@hautepop / LinkedIn).

In an article recently published in Research World Magazine and on his Tumblr blog Abc3d, our Chief Innovation Officer, Francesco D’Orazio outlines the challenges facing the social media monitoring industry – and 10 ways to tackle them.

Following the article Francesco has been invited to present at the MRS Social Media Research Summit  in London and at the Researching Social Media Conference in Sheffield, you can find the full presentation here: