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How To Detect Communities Using Social Network Analysis

Part 2 of our Network Analysis for Market Research series– read part 1 ‘Identifying Influencers with Social Network Analysis’ here.  

Introduction

Social media research isn’t just qual or quant market research translated on to a different dataset – it’s got its own methods. At FACE we’re big believers in using the unique properties of social media data to answer questions that other research methods can’t get at.

And what’s special about social data, particularly on Twitter, is that with sufficiently advanced analysis platforms (Pulsar!) you don’t just collect the message, but also metadata about that message and its author. This provides the information needed to analyse how that message is shared through social networks – or alternatively the network of who follows whom. The result: proper social research that starts from the premise that people are connected, not just atomized individuals.

In the first part of this blog series we introduced some of the possible applications for network analysis in market research, revealing how network data visualization can enable you to identify influencers that have real-world meaning in the context of the social groups in which they belong.

I also discussed how influence exists in different ‘sub networks’ or ‘social groups’, and if we are to truly grasp the structure of these relationships then it’s essential to take these into consideration.  It’s this point that the second blog in the series will expand upon. Here I’ll  look at communities: we all know what these are, but what do they mean in terms of social network analysis? And what can you learn from identifying them?

Why look for communities?

detecting communities

When investigating the role of influencers we highlighted previous research carried out at FACE by Francesco D’Orazio and Jess Owens: the How Stuff Spreads project. In this research we discovered how communities are vital in driving the spread of information. The more communities there are in the audience, the slower viral content spreads, as it takes time to spread between the different groups.

So that’s one reason to understand social media communities – if you’re trying to spread a brand campaign or a piece of content, you need to understand the audiences it travels through. Different groups may well benefit from different messaging specifically targeted to their needs and interests – not one size fits all.

Understanding communities is also important to ensure your influencer program is comprehensive: have you got influencers in all the social groups you want to target?

How are we defining communities?

A community is most often defined as a  group of individuals living in the same geographical location. It can also be used to describe a group of people with a shared characteristic or common interest: the research community, for example Within the social sciences, there is also the approach that views communities as something socially and symbolically constructed, resting on a shared understanding that “I am part of this community alongside these other people”. Political scientist Prof. Benedict Anderson defined the nation state as an “imagined community” (1983).

Using social networks analysis we define communities differently – by looking at how people are connected to each other, and clustering these into similar groups.

So it is a statistical measure of connectedness, and it’s not based directly on whether these people would recognize themselves as being part of the same community. However, what’s so fascinating about networked community detection is that the communities it identifies very often DO have significant real-world meaning, and can help us explore what it is that is defining communities.

How to identify communities? Using a social network analysis program such as Gephi, we can use a clustering algorithm called “modularity” to detect hidden patterns in the network. Modularity looks for groups of people who are more densely connected to each other than would be expected if they were connected by chance. . A network with high modularity has dense connections between nodes within clusters, but sparse connections between nodes in different clusters. As a result all individual nodes (people) in a network can be attributed to a specific cluster, as determined by the modularity algorithm.

A real-world example: my Facebook social graph

Let’s start by revisiting the ego network from my Facebook graph that we investigated in the previous blog. When identifying influencers in the graph I mentioned that it’s vital to pin-point people who the key connectors between different sub-networks on the graph. I was able to provide some real-world context to the data due to my personal knowledge of all the individuals in the network. But even on a small dataset such as this, modularity allows us to develop an even more granular understanding of the relationships.

detecting communities 2

Here nodes are portioned by modularity, with each node belonging to a separate cluster or community, and coloured accordingly. For many of the separate and very distinct clusters on the edges of the network, it shouldn’t come as a surprise that these people belong to their own community.

What is interesting is within the main component, where without the colour coding it’s hard to see any clearly divided partitions. But now we now have four different communities (blue, brown, purple & maroon-ish). So the question is, are these 4 different groups just statistical figments of the network structure? Or do they relate to anything real about the relationships between the people involved?

  • The blue community is made up of people I met at school, all around my age (17% of the network).
  • The brown community is people I went to school with, but also lived close to where I grew up (9% of the network).
  • The maroon community also went to school with me, but all at least a year older that me (7% of the network).
  • The purple community is people I attended college with directly after finishing school (also 17% of the network).

This is a great example of how we can segment individuals by very subtle differences, simply by analyzing the structure of the connections they share.

But how could a network “know” these things about my friends? Well, it’s all based on the connections they have with each other. People who were in the same yeargroup at school are more likely to know each other, and therefore be friends on Facebook – so that’s what connects the real world to the network relationship.

Large scale network analysis

Strictly speaking I could have analysed my Facebook social graph manually – I know who my friends are friends with, after all, so I could have drawn the network manually (though it’d have taken a long time).

But network analysis becomes even more powerful when the analysis is scaled up to a level at which manual analysis is impossible. Using Pulsar to gather our data means we can use network analysis to investigate the relationships in networks of thousands or even millions of people, where obtaining an understanding of the real-world relationships that make up the communities isn’t anywhere near as straightforward.

detecting communities 3

Reverting back to FACE’s previous research into How Videos Go Viral, you can see that modularity and partitioning has been applied on the audiences in the same way it was applied to my Facebook graph. We then applied statistical modelling of the demographics of each group to understand who was in each.

So for the Dove Real Beauty Sketches video (top right), we can see there’s one community averaging 32-year-old white women, in the USA/NYC, working in marketing – and another of teenage girls in Los Angeles who may be white or Hispanic, and who’re into pop music and reality TV. And indeed, it’s that appeal to a diverse audience that made the Dove advert so successful and the most-viewed on YouTube.

How can this work for you?

Think of communities as very similar to the segments identified in a brand’s customer segmentation model. (With demographics analysis layered on, you might even find that they’re the same.)

While direct marketing communications is often customized by segment, historically this hasn’t been something brands have done in social. But, using social network analysis and also Twitter & Facebook ad targeting, it’s possible to send specific messages to specific groups of people.

Powered by Pulsar TRAC these could be people engaging in a specific conversation, individuals sharing a piece of content online, or the followers of an account on Twitter. Any group of people, in essence, as long as we can define that audience through some property of its behaviour in social media – such as keyword, user bio, or location.

Community analysis allows brands to really understand the behavior of their audiences in a way they can’t replicate with offline, non-social data.

It enables brands to get maximum benefit from their influencer outreach and content seeding, by ensuring they’ve got contacts in each sub-community of their audience.

And once communities have been identified, there’s scope for deeper analysis of how each community interacts with brands, the language they use, and the topic . This can allow for truly customized marketing, allowing brands to understand each group’s social media behaviour, and how best to communicate with them.

Network analyses are also great communication tools – each time we put one on screen at a conference, the cameras come out and people start taking photos. We’d love to see more companies going public on their network analysis, and illustrating their audiences back to their followers. As we said earlier, community isn’t just about shared interests but a shared imaginary, a shared recognition that “We are part of the same group.” Sharing community visualisations could be one tool for a brand to create a real “customer community” – moving beyond individualized buyers towards positioning their brand as a source of meaning and identity.

Thanks to Jess Owens for contributing her ideas to this blog post. 

Meet us at… Marketing Week Live London (June 25-26)

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Marketing Week Live, one of the biggest events in the UK marketing world, takes place next Wednesday and Thursday and we’re really excited to be part of it.

If you haven’t already, you can register for free here.

Here’s what we’ll be up to:

Cats vs Dogs

Cats vs. Dogs: the experiment
Stand E372, Understand zone

Who’s better, cats or dogs? This question can start more intense debates than even those between rival football fans. And while everyone knows if they’re a cat person or a dog person in real life, things may be a little bit different when it comes to digital media. So when it comes to internet pets, who do people love more?

Since everything we do here at FACE is about understanding people, we thought we’d do a little experiment.* For this, we teamed up with our friends at Sensum who specialise in mobile solutions for capturing, visualising and reporting engagement. Using Sensum’s proven biometric technology (yes, we are taking this seriously) we will measure people’s emotional reactions to one cat and one dog YouTube video and solve this debate once and for all.

So if you’re at Marketing Week Live next week come by our booth (E372 in the Understand zone) and be part of the ultimate internet cats vs internet dogs experiment. You will not only get to see who wins YOUR heart but also who is the overall winner.

And while you’re there, we’d also love to chat about your marketing challenges and see if we can’t share a few ideas about how we can help out. But if you just want talk lolcat videos, we’re up for that too. We are, after all, keen afficionados of viral video.

*Warning: The experiment may involve butterflies in your stomach. Chills down your spine. Hysterical laughter. And even tears. 

 

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How Stuff Spreads presentation
June 25, 1pm, Insights Forum, Understand zone

Jess Owens, our Social Media Research Manager and one of the most experienced members of the Social Insights team will join the main stage to present breakthrough insights from our How Stuff Spreads research which looks at how content goes viral on Twitter.

Why do some videos go viral while others collect just a bunch of clicks? Most studies on the subject focus on virality as a feature of the content. But what if virality was (also) a feature of the audience? Can the demographics and the structure of the audience of a video explain how it goes viral? And how can you predict virality?

Jess will share what we learned about virality using content tracking technology to look at four videos that recently went viral on Twitter: a music video, an advertising campaign, a citizen journalism video and a Vine series. All videos went viral in different ways and whilst there is no simple answer such as a virality formula, the talk reveals the common traits of viral phenomena and how marketers can engineer them in their creative and planning process in order to achieve virality and develop a data-driven content strategy.

AURA one-to-ones

June 25, afternoon

Our Head of Research, Matt Arnold is very much looking forward to meeting the AURA members in the 1 to 1 sessions organised on Day 1 of Marketing Week Live.

As you can see, we’ve got lots of very exciting things going on. We can’t wait till next week and hope to see you there.

If you’d like to arrange a meeting with us during Marketing Week Live, please contact us at info@facegroup.com. 

Innovation, China Style: How Xiaomi is stepping up to challenge Samsung & Apple

When Xiaomi first launched its smartphone in 2011 in China, it was received by many as just another local “Apple wannabe” – the handset bears a strong resemblance to Apple’s iPhone and Xiaomi’s CEO launched the new handset with Jobsian flair, dressing almost identically to Apple’s late CEO.

However, in the past 3 years, Xiaomi has proved that its resemblance to Apple stops at its appearance. Instead of simply following Apple’s innovation, Xiaomi has adopted a unique innovation strategy that stems from the China market context and will likely shape and influence innovation in China going forward.

In this article, we will use Xiaomi as an example to understand how innovation in China takes a different shape compared to other markets and how Chinese brands make use of co-creation in a unique way.

CEO of Xiaomi, Lei Jun

CEO of Xiaomi, Lei Jun stands behind a background proclaiming Xiaomi’s motto “Just for Fans” (source: Huxiu.com)

Innovation through the customer journey

The rapid pace of technology development these days leaves many brands struggling to innovate truly differentiated products. Xiaomi recognizes this issue responding in a disruptive way. Unlike the most recent generations of Apple & Samsung handsets which offer only marginally superior appearance or specifications, Xiaomi has decided to sell smartphones with comparable specifications to these Western brands at very low prices. For example, its low-end Redmi handset features a quad-core 1.5GHz processor, a 4.7-inch display with a pixel density of 312 pixels-per-inch, an 8-megapixel rear-facing camera and a 2,000 mAh battery – and it’s sold at only US$130.

But don’t be fooled: as Xiaomi’s CEO Lei Jun said in an interview with The New York Times, Xiaomi is “not just some cheap Chinese company making a cheap phone” – it has the ambition “to be a Fortune 500 company.”

How are they achieving this? By moving away from product-focused innovation and turning their attention to innovating around the customer experience.

Unlike Apple & Samsung, which make their margin by selling hardware, Xiaomi’s margin is primarily derived from its after-sales services and content offered to customers. To Xiaomi, the bigger objective is to ensure a unique customer experience that keeps customers coming back:

  • Making customers heard at every touch-point. Without a physical retail store, Xiaomi leverages social media as its primary channel to interact with customers, from announcing new product releases, purchasing and customization of the smartphone, to capturing customer feedback. It has forums across all the key social media platforms in China to ensure that it builds an open and equal relationship with its customers, and keep customers following their latest news
  • Turning customers into fans – by offering consultation and after-sales service at the “House of Mi”, and holding “Mi Fan Festival” annually to inject excitement among Xiaomi fans
  • Opening up Xiaomi’s apps and content – by making its operating system MIUI open for download on other Android phones, it has made Xiaomi’s apps and content more easily accessible, widening the potential to provide services to more users

Families taking photos at Xiaomi's House of Mi

Families taking photos at Xiaomi’s House of Mi (source: Weixin.QQ.com – WeChat China)

Perhaps this doesn’t sound like breakthrough innovation, but in fact it’s a paradigm shift – a move from technology-centric product development differentiated primarily by low prices, towards a much more to a customer-centric innovation showing deep understanding of Chinese consumers’ digital behavior.

Innovation through commercialization

In its report “How China is innovating”, McKinsey argue that Chinese brands adopt an approach of “innovation through commercialization”. Instead of spending time on internal R&D to make the product “perfect”, Chinese brands tend to launch their ideas into the market quickly and improve them through a few rounds of commercial realization and testing.

Xiaomi embraces the competitive context of the Chinese market. In response to Chinese companies launching products that are not perfect, Xiaomi go one step further and essentially say to customers, “The product launched is not going to be perfect, but please get involved and help us make it perfect with you.”

At the heart of Xiaomi’s innovation strategy is the company’s process of quickly turning consumer feedback to their advantage. Unlike other smartphone brands that launch a new phone every 6 months or so, Xiaomi releases a new batch of smartphones every week. With their process “Designed as you build”, Xiaomi’s product managers spend a dedicated part of their time collecting user feedback from an online customer forum – and once they pick up a suggestion, it can be translated into an action appearing on an engineer’s desk within just a few hours. Features can then turn from customer feedback to an improved hardware or software in as fast as one week.

For instance, when Xiaomi launched the Xiaomi MI-3, it included a new wifi password-sharing function allowing people to automatically connect to wifi in a public area and share this information with other users. But consumer response was negative, with many people complaining that the function violates privacy and encourages ‘wifi squatters’. Within a day, Xiaomi responded to the feedback by announcing they had suspended the function with immediate effect and erased all 320,000 wifi passwords they had collected from public venues. A new interface was released within a week.

This process does not only make users extra-tolerant of imperfections in the smartphone’s functionality, it has essentially turned Xiaomi users into collaborators, keen to work and co-create with the Xiaomi brand.

Xiami employee's Weibo account reposts the wifi feature suspension message

 A Xiaomi’s employee re-posts Xiaomi’s announcement about suspending wifi sharing on their personal Weibo account, leading to it being picked up by news sites (source: CNbeta.com)

Conclusion

As a research agency founded around co-creation methods, we have always believed that we need to work collaboratively with consumers, not market at them. Therefore it’s very encouraging to see the rise of Chinese brands like Xiaomi bringing to life the spirit of co-creation by making consumers’ preference and feedback a central part of their innovation strategy.

It’s something for us and the wider research community to bear in mind as market research grows in China and other Asian markets – that is, co-creation isn’t a wholly ‘new’ or ‘outside’ idea here.  However, whilst a lot of Chinese business do apply “co-creation”, this tends to be haphazard and with little structure. We can help businesses optimise their co-creative efforts, harnessing their customer insights to drive business growth.

With Xiaomi leading the way, we believe there is going to be an innovation revolution in China as brands look to their customers for innovative solutions and inspiration. And as an agency, we are excited to be actively involved in this new wave of innovation in China.

Want to find out how we can help your brand develop locally relevant innovation in China through the power of co-creation? Get in touch with us at info@facegroup.com.

The Samsung vs. Apple court case shows the value of social media research

An excellent case study demonstrating the value of social media research has just emerged from an unlikely source: the Apple vs. Samsung patent dispute.

Apple-Samsung-Trial

Documents shared as part of the court case reveal some fascinating information about how the two companies were thinking about social data in 2013.

It shouldn’t still bear saying in 2014, but the messages seems slow in getting though: social media data isn’t just about “looking back” at campaigns or the last quarter’s KPIs. Samsung recognised the power of social data for “thinking forward”, for understanding customer needs strategically to feed into product innovation and early-stage comms planning. Here at FACE, we think this is an incredibly valuable and under-used use-case.

Here’s how it works:

1. Samsung used social data strategically: to attack Apple

From Neal Ungerleider in FastCo: Networked Insights Reveals How Samsung Used Social Media to Hack the iPhone:

“Samsung took on a company with the arguably most successful consumer product ever created,” Networked Insights CEO Dan Neely told Fast Company. “Samsung asked us how to use analytics to attack Apple.”

[...] Using aggregated online posts and machine learning techniques, Samsung found several specific weak spots where they could outperform Apple. Customers specifically complained about the iPhone’s comparatively poor battery life, the inefficiencies of Apple Maps, how small the screen was, unhappiness with the Lightning cable, the lack of customization, Siri, and the iPhone’s fragility. Samsung felt that it could compete with Apple on most of these points–and, importantly, that they hard data to back up these consumer preferences.

When working with Networked Insights, a big part of Samsung’s strategy was to vacuum up any information on the iPhone 5 that was posted to social media. This meant using the dashboard they licensed to obtain every iPhone-related post on Tumblr, Twitter, Disqus (a popular commenting platform), WordPress, and YouTube, as well as new hits on Google. This information was then classified, as Neely put it, “15,000 different ways.” A big part of the problem for Samsung and others, Neely said, was the difference in extracting relevant information when they needed it versus finding erroneous information on other aspects of individual customers that were irrelevant to the task at hand. That meant a lot of data processing and fine-tuned analytics.

Importantly, Samsung used the dashboard to find what people were posting online about the iPhone–rather than just looking for posts about Samsung’s own products. They then identified specific complaints about the iPhone where their own products outperformed Apple’s products, and tweaked marketing campaigns to emphasize these Samsung strong points.

So: social media research isn’t just about tracking your own brand activity.

It’s incredibly powerful when you search for unmet needs and pain points – what are the gaps where consumer desires aren’t being fulfilled? Do this across a category (e.g. smartphones) or a competitive set (Apple, Samsung, HTC, Sony Xperia, Nexus, Motorola) to identify the “whitespace” opportunities that  aren’t currently being met.

As such, social media has just as much of a forward-looking role to play in innovation and NPD as it does “looking back” at campaign performance and the past quarter’s KPIs. Use it to shape campaigns and communications, not just to measure their impact.

2. Apple thought it was “nuts” to pay for social media monitoring tools. Their loss

Business Insider’s Jay Yarrow spotted something else interesting in the court documents:

Jay Yarow quote

Apple famously don’t do research, you say? No, Apple do do research – but they don’t necessarily do it well, as Tom Ewing recently illustrated.

You’d see the occasional interesting message if you just look at mentions of “iPhone 5″ through Twitter search… But also an awful lot of noise, at a million mentions per day kind of scale. It’d only be through luck that you might stumble across a message that’d spark any strategic consideration.

You want to understand the relative dissatisfaction with battery life, screen size, and poor signal reception? You need a social data research platform. Social media monitoring tools make this data analysable as a whole  in a way that free online tools simply can’t. For example our platform Pulsar (pulsarplatform.com) collects over 1MB metadata around each tweet, making big datasets like this powerfully segmentable by sentiment, channel, hour, influence level, profile bio and other demographics – allowing for a really fine-grained analysis of not just what people are saying, but who and why.

Technology and data augmentations enable the unmet needs to be identified, quantified and ranked. Use a tree graph to visualise the most common words and phrases that follow “I love…” and “I hate…”. Use semantic analysis to aggregate topics, and compare the top topics across the range of positive, negative and neutral sentiment scores. Start coding tweets into clusters, and use machine learning to extend this across the whole dataset.

Through structured analysis, the depth of insight that can be gained from social data is vast – Samsung realised this, Apple didn’t.

3. What we’ve done

This story was met by us at FACE with a nod of recognition – we have been using social data beyond reputation management for many years now.

Here’s a couple of examples of previous work:

i) Mapping the 4G mobile launch

EE Launch Event..Mandatory Credit Tom Oldham/Tom Dymond

Like Network Insights with Samsung, we also dug into what people were saying around 4G to identify complaints and pain points. What topics were driving discussion – signal, pricing, contracts/tariffs, or the iPhone? For each we identified the specific customer pain points our client needed to address in both comms and their product offer.

“WHAT EVEN IS 4G THOUGH I DON’T UNDERSTAND” – tweet, Sept 2013

But it turned out the biggest unmet need was understanding – a high share of discussion came from people expressing their total bewilderment at the new, high-speed mobile spectrum band.  We used social data to identify and categorise people’s questions, helping our client (a mobile operator) recognise and simplify the messages they needed to communicate to help people understand the new proposition.

ii) “Designing Relevance” for Nokia

Here at FACE we’ve been using social data for strategic insight for years. Back in 2010, Francesco D’Orazio and Esther Garland presented at ESOMAR alongside Nokia’s Tom Crawford on how social media research can be used alongside co-creation to produce a better innovation process:

Innovation should not be so much about ‘creation’, but more about ‘emergence’. Defining the boundaries of possible futures means creating the conditions for fostering the emergence of ideas that are already taking shape in the social space, but have not filtered up to the top or are not formed enough to bubble up yet. In a connected real-time ecosystem where the consumer can be as creative as the designer, the new model of innovation should be listening, reducing complexity, decoding the signal from the noise, collaborating with consumers and only then defining the boundaries of possible futures.

The project started with a “download” from social media to gather the widest possible range of themes and scenarios for this project:

The project kicked off with a two week Social Media Monitoring and Trends Analysis programme using netnography, semantic and network analysis across forums, social networks, blogs, news sites, microblogs, video and photo sharing sites from the United States. Using Face’s social media analysis platform Pulsar we tracked more than 100, 000 ‘sources’ (where Twitter counts as one source) and harvested almost 1.5 million items of content. These were analysed to gather insight into how key consumer segments in North America talk about smart-phones and which key themes, topics and angles were most resonant with them. 

Analysing conversations amongst users talking to each other rather than responding to researchers yielded a huge amount of richness. Furthermore, this helped develop clear learnings on language, tone of voice and attitudes to the brand and the category. It allowed for a different kind of research landscape, one which subverts the traditional question and answer format and replaces it with something far more natural and intuitive. By working in a more natural communication mode we also ended up expanding our research agenda to challenges we didn’t even know existed or that we wanted to investigate.

For the full story, read the full whitepaper up on Slideshare here, or check out the presentation:

Or get in touch if you’d like to talk forward-looking social research – I’m at Jessica@Facegroup.com

Meet us at… Marketing Week Live, Social Media Cafe, SMWF and Big Boulder

May and June are looking busy for the Face teams across the world. Apart from some really interesting projects we’ve recently kicked off, we are getting ready for several conferences and events. Here’s what we’re up to in the next few weeks:

 

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We’re really excited to be part of one of the top marketing events of the year. It will be our first year at Marketing Week Live and we can’t wait! Our Business Development Team is putting the finishing  touches to the booth concept, while the Research teams are busy finalising the analysis of our presentation. Want to find out how online buzz influences sales? Then join our presentation in the Understand zone on the first day of the conference. Check out Marketing Week Live website for more info and registration. Hope to see you there.

 

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Also in London, we have partnered with Social Media Café for the next edition of their networking event on May 23. Click here to register, it’s free!

 

SMWF

Social Media Forum (New York)

After a successful Social Media Forum in London, we decided to join the New York leg of the conference, on May 28-29. Face’s Chief Innovation Officer, Francesco D’Orazio, will join the main stage to present breakthrough insights from our How Stuff Spreads research which looks at how content goes viral. Whilst there is no simple answer such as a virality formula, the talk will reveal the common traits of viral phenomena and how marketers can engineer them in their creative and planning process in order to achieve virality and develop a data-driven content strategy.

We are also looking forward to moderating the Brand Reputation breakout session. If you’re around, do join our sessions and come to our booth to say hello.

 

Screen Shot 2014-05-20 at 14.52.27

Francesco will also be speaking at the Big Boulder conference which takes place on June 5 and 6 in Boulder, Colorado. He will present on the topic of data visualisation and analysis of visual social media.

Hope to meet you at one of these events.

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