Archive for the ‘Innovation’ Category

We’ve just come back from Insight Innovation Exchange Europe. And what an inspiring two days! From Mark Earls and John Willshire making the audience work with Artefact cards to identify innovations needed in market research, to inspiring presentations on neuromarketing, gamification and mobile, there’s a lot of exciting ideas to take away.

We hope to have contributed to this ourselves: Our CEO, Andrew Needham and our Research Manager Jess Owens shared their thoughts on Using social media research for agile, adaptive customer intelligence” in a joint presentation at 17:00 on the first day of the conference.

Following a classic Andrew introduction – getting the audience to stand up and be agile, by squatting up and down doing an agility exercise – they talked about:

  1. What does “agile” research really mean? It’s not just about quick thinking – it’s about empowering clients to take action.
  2. Lessons from agile software development: it’s all about the feedback loop
  3. Why agile social media research? Jess shared stories from two social media crises, showing how real-time social media listening can get research a seat at the table
  4. Partnership with clients to build an agile, actionable research programme – aka is the weekly report always the best way to share research insights? We talk about the “client as superuser”
  5. The true power of the brand tracker dataset - how the unprompted nature of social media mentions enables highly adaptive and flexible research, providing the ability to instantly answer questions brands didn’t even know they had

Here is their presentation, for those of you who couldn’t make it:

 

We’d also like to congratulate our colleagues from Pulsar for winning the first DIVA (Data Visualisation Award) for our How Video Spreads Twitter network visualisation:

We tracked the conference on Pulsar (of course!). Here’s how the 1,711 IIeX-related tweets performed over the two days of the conference:

Conversation volumes by hour:

IIeX Volume per hour

Most active Twitter users:

IIeX Influencers

 

Most shared links:

1. Pulsar’s winning entry to the DIVA awards 

2. DIVA Awards Panel announcement

3. IIeX Europe Homepage 

4. #IIeX Focus Series – Technology & Market Research (2 of 5): Social Media

5. #IIeX Focus Series – Technology & Market Research (3 of 5): Photo & Video

 

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Bad Andrew picture

Andrew Needham is a Founding Partner and CEO of FACE Research. A pioneer in the use of social data in qualitative and quantitative research to deliver a holistic view of the consumer, Andrew is leading the global expansion of FACE. Read more of Andrew’s thoughts here. Or reach out to him on LinkedIn or Twitter.

 

Jess Owens profile photo

Jess Owens is a social media researcher in FACE’s London office. As one of the first members of the Global Social Insight team, she has pioneered new research methods with social data, from audience mapping, channel effectiveness studies and studying social media virality and content diffusion. Get in touch with Jess via LinkedIn or Twitter – she tweets for us @FaceResearch as well as from her personal account, @hautepop

 

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.

Amazon aspires to be “Earth’s most customer-centric company” and CEO Jeff Bezos speaks passionately not about customer-driven, but customer obsession. An empty chair is often kept in meetings, where the customer is symbolically seated.

As we start 2014 more and more CEOs are recognising that to deliver sustainable competitive advantage in the digital age they have to be able to meet the demands and expectations of today’s empowered and connected consumers consistently and continuously in real time. To achieve this successfully their organisations are going to have to put the customer at the heart of everything they do and then apply scaleable social technologies across the entire company to help make this happen in a human way. They are going to have to become what is being coined a “human era” company, able to manage brands that are more “human”, people-powered entities.

Applying customer obsession in the digital age

Customer obsession in the digital age means understanding that consumers expect their interactions with your organisation and brands to be not just “always on” but “on demand”. They want to be able to do things immediately and interact anywhere at anytime (immediate, real time); they want to do truly new things that create value for them and that delight them (valuable). They expect all data stored about them to be targeted precisely to their needs and personalised to their experience (relevant & personal). Above all they expect their interactions to be simple (easy). And lastly the “on demand” customer desires a more human interaction with companies and brands (human).

Meaningful human connections can’t be formed in one direction — they require the company or brand to reciprocate, to level with consumers. When they do, the connections become a foundation for something we all intuitively understand and value highly: trust. However, for companies to be human in deeds as well as words, a fundamentally different mindset must prevail – that the role of the firm is no longer just to make and sell products, but also to engage deeply and openly with customers as collaborators in creating value together  (Social Capital).

Already, search technologies have made product information ubiquitous; social media encourages consumers to share, compare, and rate experiences; and mobile devices add a “wherever” dimension to the digital environment. Technology will only continue to empower consumer expectations in these five ways. Further developments in mobile connectivity, better designed online spaces created with the powerful new HTML5 web language, the activation of the Internet of Things in many devices through inexpensive communications tags and micro-transmitters, and advances in handling “big data” will just accelerate the appetite of the “on demand” customer. Soon they will be able to search by image, voice, and gesture; automatically participate with others by taking pictures or making transactions; and discover new opportunities with devices that augment reality in their field of vision (think Google Glass).

So to deliver against these 5 key expectations of Immediate, Valuable, Personal, Easy and Human in real time there needs to be a clear framework where the “on demand customer” is at the heart of everything a company does. A customer driven knowledge framework that sits at the centre of a company’s organisation like the hub of a bicycle wheel where all marketing and business disciplines feed in to and out from the “on demand” customer.

Putting the voice of today's consumer at the center

Applying social technology with a human touch

The application of social technology is essential to helping companies put the “on demand” customer at the heart of a company.  In many ways technology can also allow companies to be more human; to do things that we would naturally do in 1-2-1 and face-to-face situations. Technology can help us apply a human touch but on a mass scale. But achieving this can be a major effort for organizations that were not born digital.

What is most challenging for our clients is the ability to operate in a joined-up, end-to-end way. Many of the companies we work for are siloed around different functions or geographies. But “on demand” customers expect a fully consistent and joined-up experience. And that requires companies to think quite differently about the way they organize, their governance structures, and their standards for data and systems.

It’s also apparent that this is not just about the marketing function on its own. The company as a whole must mobilize to deliver high-quality experiences across multiple disciplines and across the entire value chain: sales, innovation and collaboration, service, product use, finance, logistics and marketing. Social technologies can help ensure “on demand customers” touch every part of the organisation in a human way and every part of the organisation is driven in real time by “on-demand” customer expectations.

Applying Socially Intelligent Research

McKinsey said recently: “We’re placing a bet that as customer behavior becomes more fluid and complex and where business models can be disrupted overnight, the client community will welcome the opportunity to have a more holistic, adaptive and responsive view of the customer”. 

To win over on-demand customers, companies will increasingly need to spend a lot of time getting to know them, what they expect, and what works with them, and then have the ability to reach them with the right kind of interaction and content at the right time. Unsurprisingly at FACE we believe that big social data integrated with other research methodologies lies at the heart of efforts to build that understanding—data to define and contextualize trends, data to measure the effectiveness of activities and investments at key points in the consumer decision journey, and data to understand how and why individuals move along those journeys.

Here are 3 important questions we want our clients to be asking themselves this year:

1. How does our customer experience compare with that of leaders in other sectors?

2. What will our customers expect in the future, and what will it take to delight them?

3. Do we have clear plans for how to meet or exceed their expectations?

We believe that socially intelligent research has a big role to play in helping companies and brands become more socially intelligent by informing their behaviour with a holistic view of the consumer so that they become more human, more social, people powered entities.

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Bad Andrew picture

Andrew Needham is a Founding Partner and CEO of FACE Research. A pioneer in the use of social data in qualitative and quantitative research to deliver a holistic view of the consumer, Andrew is leading the global expansion of FACE. 

 

 Read more of Andrew’s thoughts here. Or reach out to him on LinkedIn or 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: