Category Archives: Blog

A Whole New World: our 2 new qual researchers on their experience of joining FACE

Aladin 1

Here at FACE we like to think we do things differently to other agencies – what agency doesn’t, right? But it’s been a few years since many of us worked anywhere else! To get a fresh point of view, we asked the two newest members of our qualitative research team to tell us about why they joined.

Beca has previously worked at a boutique quant agency in Ireland and a digital qual agency in London. Rich has worked as a parliamentary researcher for an MP and a commercial analyst at a premier league football club before specialising as a qualitative researcher.

Joining FACE brought with it many changes for both of us, and lots of learnings too, so we’d like to share with you what we’ve discovered in our time here so far.

Beca: So Rich, what was it about FACE that made you want to work here? Was there anything in particular that really excited you? 

Rich: I joined FACE to get experience on wider range of research methods. I remember in my last role, I was crying out for the use of a mobile app so I could record people’s behaviour in situ. Here using our mobile app, consumers can record their instinctive reactions almost immediately with minimal disruption to their lives. This has great benefit to a researcher as it negates a lot of factors that add doubt over the validity of responses. It adds precision and detail because it drastically shortens the time between experience and response.

This is key because a person’s memory naturally filters out detail and often leaves only summary. You may test drive a car and note many things you disliked. Interior too cluttered, steering unresponsive, poor satnav, weak handling/brakes etc. If asked straight after the test drive why you didn’t like it, you’d have no problem reeling off details. If asked a few weeks down the line, you will have forgotten many of the particulars. You will simply remember that your overall feelings towards the car were negative, but not necessarily all of the reasons why.

The Pulsar platform is another great tool that I wanted to be able to call upon when making strategic recommendations. By tracking the buzz about a brand – not just on social media, but anywhere mentioned on the web, we are able to grasp the feelings of the consumer like never before.

This kind of information is invaluable when it comes to making valuable recommendations. If I, as a researcher, don’t truly understand how your consumers feel about you today, then how can I possibly help you to be successful tomorrow? 

The online community has a great advantage also and is something I was very keen to learn and use. I knew the world of research was changing and that online was the way forward. The online communities are very useful especially as they are so cost effective compared to the alternative, leaving us with more budget for the stuff that matters – the research.

So the tools FACE uses were the flame to the moth for me, what about you?  Does FACE do things differently than what you’ve done before?

BecaBeca: Coming from a predominantly digital background I am fairly new to the world of face-to-face qual research. Since joining FACE I’ve had the pleasure of sitting in on some groups and I was so pleasantly surprised by how different they were from what I’d seen before. Rather than the moderator being an authoritative force within the group, the playing field was levelled and the participants were much more natural and relaxed because of it. But the thing that made FACE stand out from the crowd for me is co-creation!

Rich: I agree, ‘co-creation’ is a buzzword you hear a lot, and many people have copied the approach. To find out that FACE were among the first do adopt it in a market research capacity was a huge draw for me. I wanted to go beyond just probing for reactions and actually have a hand in sculpting creative outputs going forward. 

So the methodologies are quite new for both of us then, an exciting learning curve! Anything else different at FACE?

Beca: What was probably the most difficult thing to get my head around upon joining FACE is that I am no longer expected to be everything. Coming from an agency where researchers are responsible for everything, from the very seed of a project to the felling of the tree, this was difficult to get my head around initially. I’ve quickly realised the immense benefits of having teams dedicated to production and commercial as well as account managers and in-house technical support. Each team has different strengths, to handle different stages of a project. Having the time to dedicate to the research, which is after all why I chose this path, is a luxury I am still getting used to and one I appreciate more than I ever thought I could.

Analysis of qualitative data is central to what we do as researchers, and FACE really gets that! In previous agencies analysis was often a solo pursuit, but here at FACE analysis is a team effort, with people challenging each other’s conclusions and pushing them to the next level.

You came from automotive research, right? This must be a big change from that?

Richard Addison 3

Rich: Definitely! One of the great things about FACE is the clients we have and the type of work we do. Not wanting to be typecast in one industry led to me wanting to move somewhere with such an enviable FMCG client list; brands like: Coca-Cola, Unilver and Reckitt Benckiser!

You’ve worked in FMCGs before though, haven’t you?

Beca: I have, although quick turnaround projects were much less common in my previous agency. And although watching a project grow and evolve can be very rewarding it can be more difficult to maintain the same level of interest and creativity and keep the momentum going. The short-term nature of most projects at FACE encourages excitement and enthusiasm from start to finish, and allows for continuous creativity throughout. As an added bonus it also opens you up to working on a whole range of projects in a variety of industries, keeping the nature of your work diverse and varied.

How’re you finding it? A new job can be quite daunting…

Rich: The attitude of everyone on the team is first class. From the intern to the directors, we all sit together like a happy family of beavers, all with different roles, but ultimately unified in our goal of making robust and long lasting dams! From a personal point of view, it’s been a touch being able to feel so comfortable so quickly at a new company. There is an eclectic mix of cultures and backgrounds making for a great dynamic both in and outside of work.

With unemployment at an all-time high, working somewhere that both challenges and stimulates is increasingly rare. In joining FACE we have both found a great opportunity to continue to learn and grow as researchers. The multi-faceted approach of online, social and traditional techniques will help us to develop skills we would not have gained elsewhere.


If you want to learn more about what it’s like to work at FACE, check out Oana’s post, ‘A Peek Through The Keyhole of the FACE Office’.

Or think you’ve got what it takes and want to join us? Let us know here.

Thoughtful, observant and collected: the value of introverts as research participants

Calin Chua from FACE’s Singapore office, on the role of personality in research:

This blog post is born out of the challenges that both clients and researchers face in recruiting and speaking to the right participants.

Being articulate is an attribute that recruiters and researchers seek in every research participant. We believe this verbal fluency indicates that we will be able to tease out deeper insights from them to help us build a strong and richer story.

Despite the rigour recruiters go through to find articulate research participants, we still get two common comments from clients: either some people aren’t participating enough, or others are overly fired up. If we had screened all the participants on their ability to articulate their thoughts, why do we still face these situations?



To unpack this question, there are a few factors to address as we unravel the heart of the issue:

  1. Research participants’ personalities: extrovert or introvert
  2. Nature of the research methodology: group or individual; offline or online
  3. Nature of the research objectives: insights generation or concept creation

Are extroverts always the best participants?

Let’s start off with the nature of research participants. Some recruiters seem to practice a common understanding that people who are extroverts are articulate. They believe that if someone is an extrovert, he/she is naturally outgoing, which means he/she is able to talk a lot in front of strangers, and is therefore articulate.

But that logic makes a big leap: being good at talking isn’t the same thing has having something to say. And we may end up with some fired-up consumers –and then the rest of the story is what all researchers and clients are familiar with: managing dominating voices to ensure there is a balanced conversation within the group.

While it is the moderator’s job to manage the group’s dynamic, it would still be a tough challenge to get constructive comments from someone who is only good at critiquing ideas. We need a thinker, not a speaker. We need ideas, not more issues.

What about introverts?

US writer Susan Cain’s TED talk has driven a lot of attention on social media towards introverts. Her presentation demystified the stereotype of introverts as “antisocial”, and unveiled the understated virtues this personality can display. Introverts are less quick to warm up to new faces but they can still be sociable people. Introverts may not be the first to contribute an idea because they are finessing a big thought. Introverts are great listeners and observers, which means they are processing their thoughts and not fighting to talk.

Susan Cain


Different personalities fit different research methods

This brings us to our next factor to address – research methodology. At FACE, we run co-creation workshops, online communities and in-depth interviews. These methodologies are extremely different in nature, which calls for different dynamics and types of participants.

Although introverts may take longer to warm up at a co-creation workshop, this is not to say that they should therefore be excluded from group setting research studies. Research participants should all be equally screened for their ability to work with others in a group setting.

In a recent experience at a co-creation workshop in China, all the participants were screened for their ability to articulate themselves and to be extrovert by nature. However, when they all came together, some were quieter and less participative during the group discussion. To ensure that every participant spoke up during the discussion, instead of mixing the articulate and quieter together, I decided to group all the quieter ones together. Interestingly, people in the “quiet” group started speaking up and contributing pretty good ideas. (Well, someone will have to start speaking somehow!)

This highlights another interesting finding that someone might be an extrovert under a setting but an introvert in another. This further strengthens the need to go beyond hunting for obvious personality traits, but to measure the desired behaviour that best fit the research methodology.

When dealing with online community research where people are more anonymous, it is less about finding research participants who are sociable and interactive, but more about their familiarity interacting through the medium. Online communities and self-ethnographies are designed to give people time and personal space to think and respond. Hence, these approaches would be excellent setting for introverts to contemplate over the questions and form their thoughts, without the pressure to socialise with a larger group.



The research objective should determine the participants we pick too

As brands face stronger challenge with engaging consumers, researchers are faced with more complex questions to answer, which means research participants are being asked some pretty challenging questions too. Also at FACE, we believe strongly in collaborating with consumers to create ideas. For this to happen, we need thinkers not mindless talkers, creators not solely critics.

In another communication development study, a client lamented that consumers tend to comment negatively on the creative work but are less able to provide ideas on how to improve it. Client wants to know what is wrong with the idea but also what can be done right. Without collaborators, it is challenging to build wilder, bigger and better ideas. Introverts are known to be more contemplative, thinking and observant. We believe there is a lot of value tapping into these characteristics.

Closing thoughts

At the end of the day, brands are marketing to consumers who can be extroverts or introverts. Eliminating or focusing only on either type during research stage may result in a bias in the final idea direction and tonality.

This open doors to think beyond traditional screening approaches – perhaps it is less about hunting for articulate consumers through a series of standard attitudinal questions to tease out for an extrovert, but more about assessing people’s ability to think, process and create idea (with others).

At FACE, we introduce additional rigour into the screening process by asking interesting and challenging questions to understand their personality and capabilities better. Given that every project brief is different, methodology is prescribed to address the project objectives, so should recruitment of the type of research participant. Finding a research participant is easy but finding a great one takes extra thought and effort.


Read more of our Asia team’s thoughts on research recruitment, with Nicole Li’s essay on “Going beyond ‘creative consumers’ for co-creation

Or if you’d like to talk to the team about recruiting for a project of your own, contact us at

Pulsar update: Visibility 2.0

Today we are introducing a new updated version of the Visibility algorithm that’s powering the Pulsar platform: Visibility 2.0.

The main reason why Pulsar is called Pulsar is that the whole platform is built around the idea of making it easier for anyone to sift through vast amounts of social data by making “important” social media content more “visible”.

One of the key ways Pulsar does this is through its proprietary Visibility algorithm. The algorithm defines “importance” as the ability of a piece of content to reach a larger then average audience and engage a larger than average crowd. The algorithm weights every content on the platform and applies a Visibility score to each post which is then available amongst the metadata used to index and filter the data.

Since we launched Pulsar the Visibility Algorithm has been one of the pillars of the platform allowing you to slice any data view (e.g. trends, influencers, topics) by Volume of data or by the Visibility of the content analysed. Below a series of comparative screens that show how different the same social data looks like when analysed by Volumes vs Visibility:

Posts per Day VS Visibility per Day


Sentiment Volume per Day VS Sentiment Visibility per Day

Top Posts by Volume vs Top Posts by Visibility
But the web is an ever-changing ecosystem: new channels are born, new behaviours are introduced, old behaviours evolve to a new scale or disappear and new ways of measuring them are introduced on a weekly basis. In an effort to keep up with the evolution of the web and continue to deliver effective measures of reach and engagement, over the last three months we have been working hard updating the Visibility algorithm.

The new algorithm takes into account:

  • New sources of engagement data, which are now factored in the calculation of reach;
  • New sources of online viewership data which are now factored in the calculation of reach;
  • New sharing and engagement metrics introduced by the new channels we have integrated, such as Tumblr;
  • Raising levels of engagement across all channels resulting in a need for new engagement and reach benchmarks;
  • New behaviours introduced by new channels like Tumblr, where for example the “weight” of a reaction (a re-blog) is completely different from the weight of a reaction on Twitter or Facebook.

Overall, the new algorithm introduces three key improvements:

  1. More accurate audience size estimates for all channels, particularly for News, Blogs, Forums and Review sites;
  2. More accurate engagement figures across all channels;
  3. A more balanced cross-channel view of reach, to enable effective comparisons between the reach of top down and bottom up media (eg. news vs. tweets).

The new visibility weighting applies from April 10 onwards. However, should you want to re-analyse historical data you can extend the reach of the algorithm to historical data from the Data Management interface in the Results View.

We think the new Visibility algorithm is going to help you run better analysis and make more effective decisions and we look forward to hearing your feedback as you start seeing the new data coming through on the Pulsar platform.

If you are not yet using Pulsar and want to know more about Visibility and Pulsar get in touch here.

Identifying Influencers with Social Network Analysis

Part 1 of our Network Analysis for Market Research series by Rob Parkin – read the introduction here.


In our work as social media researchers we are regularly answering clients’ questions about online influence and influencers. They know that they’re not the only force influencing perceptions of their brands, and they want to reach out to the other people who are. This could mean identifying the right bloggers to bring on board to increase the likelihood of a successful social campaign, or tracking who is most shaping a discussion about a brand or topic.

Pinning down who is influential isn’t straightforward. The data hardly ever exists to connect a social media message with the actions it may have inspired, such as products purchased or businesses boycotted. Instead what we can really assess is ‘potential to influence’: who’s reaching a big audience, who’s engaging that audience the most and getting a lot of interaction, and who’s demonstrating consistent expertise on a topic. So influence is complex, an outcome of a combination of properties about people, contexts and relationships.

That’s why here at FACE we developed our own proprietary metric to analyse which messages were reaching the biggest audience. Our visibility algorithm assigns each piece of content a visibility score, taking into account the properties of the channel it’s on (e.g. blog content lasts longer than Twitter), the size of the author or website’s audience, and the virality of the post – how many times it’s been shared.

Influencers ranked by Engagement & RTs generated (Pulsar visualisation)

Alongside visibility, we also use Social Network Analysis to understand influence through analyzing the dynamics of online behaviours and relationships. It provides the theory, the algorithms and the software to capture, visualize and explore the data gathered using Pulsar. This can enable us to take influencer analysis to the next level – and it’s what we’re going to discuss in today’s blog.

The role of influencers 

Previous research carried out here at FACE by Francesco D’Orazio and Jess Owens highlighted the role of influencers in how information spreads through social media. It discovered that while influencers may only represent a small percentage of an overall conversation, their role does ultimately shape how information spreads. Tapping into close communities makes content shareable, but top-down influence is essential for content to achieve truly viral speed and scale.

We’ll cover communities in more detail in our next blog, but for the moment let’s understand that influencers play a vital role in shaping conversations, and insight into how their influence is structured can also prove important.

Pulsar_Twitter_Hadfield_Visibility crop for website

Network visualisation of how the Commander Hadfield video was shared on Twitter, with nodes sized by Visibility

Identifying influencers

In essence Network Analysis views relationships as connections. Some people in the network might have only one or two connections (e.g. they only have 1 or 2 Twitter followers), and others might have hundreds or thousands.

So hubs or influencers in networks can be identified by looking for people who are highly connected in comparison to the remainder of the network. Because they’re better connected, these are the people who you may wish to bring on board with an online campaign, to help maximize its chance of successfully reaching the greatest number of people.

So let’s look at an example that demonstrates how networks can help us investigate relationships between nodes and identify influencers.

Investigating my ego network

I’m going to use a very self-centered approach and investigate my Facebook network! I used an application called netvizz to capture the data, and Gephi to perform the analysis.

When compiling a list of influencers you may start with a very basic measure, the number of friends/followers. Using Network Analysis and my social graph, we’ll explore the limitations of this metric, and how we might do a better job.

Introducing my friends & family…..

Rob Identifying influencers 1

In this visualisation the nodes are people who are my friends on Facebook, and the edges are the friend relationships between them. It’s important to note that I’m not on the chart – so the connections aren’t their relationships with me. Instead, the connections shown are the friendships that they have with each other e.g. I’m friends with Amy and Bob, and if Amy and Bob are also friends, there’d be a connection between them. If they’re not friends, no connection.

We can rank nodes by a number of measures; in this instance I’ve chosen degree centrality, which is the number of connections each person has. I’ve used this to determine the size of each node: the larger the node the greater the number of connections. This makes the highly-connected people easier to spot.

We’ve also used what’s called a “force directed layout algorithm” to visualize the graph. This means that linked nodes attract each other and non-linked nodes are pushed apart. So the most-connected people tend to end up towards the middle of the chart.

The first analysis that can be taken from the graph is that a lot of nodes share connections. This why why there is one large giant component in the centre of the graph with lots of highly-connected people all clustered together. This is to be expected as the sample of individuals is taken from my Facebook account, the majority of whom do share common acquaintances.

The thing is, we can also see that the biggest nodes are basically the same size, meaning that they’ve got the same number of connections. This isn’t really telling us the story we need – but using network analysis we can go further.

Identifying Influencers 2

Here we’ve taken the same graph and ranked nodes by betweeness centrality. A betweeness centrality algorithm starts by finding all the shortest paths between any two individuals in the network. It then counts the number of these shortest paths that go through each node. Nodes with high betweeness centrality can be considered information brokers that can connect disparate parts of the network.

The result is a smaller list of potential influencers, pin-pointing the people who are vital in connecting the different sub-networks (i.e. the different social groups) in the wider graph. We have identified four people who are now shown to hold a position of influence on the graph. And the layout of the graph begins to tell us how their spheres of influence are structured.

The person over on the right for example is crucial in connecting two small clusters of individuals to the rest of the graph. I know network analysis has correctly identified this node as an influencer – because she happens to be my girlfriend! So she’s the key person connecting both our families to the larger network of my friends.

How can this work for you?

Admittedly there’s a very short list of people who are interested in the finer details of the network structure of my Facebook graph! Nonetheless it’s an interesting example to demonstrate some of the principles of Social Network Analysis.

What can we take from this example? Using network analysis it is possible to study social groups in-depth, not just as homogenous wholes but understanding them as comprised of dynamic relationships between different individuals. And using data visualization and data exploration it is possible to infer a level of understanding which would be otherwise difficult to get hold of without real-world personal knowledge of the individuals involved.

Using Pulsar TRAC it’s possible to scale this analysis up significantly, sampling mentions by keyword, content or user, and applying network analysis we can powerfully:

  • Identify individual messages driving engagement
  • Explore who is influential in shaping a discussion
  • Map a network of individuals following a brand online
  • Better inform future outreach strategy

Exactly the same methods would apply if we were studying, for example, the community of people talking online about beauty & make-up, or audiophile hi-fi equipment, or photography. We could first find the best-connected people, who a brand might want to target to promote their product to the largest number of people. But we could also find the connectors, the people that allow discussions to travel into new communities and ultimately travel further.

In the next blog in our series we’re going to dive into this further, explore how we can identify communities in network structures and get stuck into some more network analysis previously carried out here at FACE.

LinkedIn’s privacy policy change is really about context, not data

Privacy is currently a hot topic in the market research industry. Some of our colleagues are worried that consumers will stop sharing their data, as Greg Heist discussed in Greenbook earlier this month.

Often, the focus tends to be on researchers’ ability to access data – either data is entirely public, or it is entirely private. But I think privacy is much more than whether or not data is public. It is also about the context of that data. Who is accessing the data and why can be just as important as what data they are accessing. So that’s what I want to discuss with you today.


Image by Flickr user James Cridland

Do ‘oversharing’ teenagers care about privacy?

Teen social media use provides a fantastic illustration of the importance of context as part of privacy. Teens put a lot of their data online. According to a PewResearch survey in 2013:

  • 92% post their real name to the profile they use the most
  • 91% post a photo of themselves on social media
  • 71% post a city or town where they live
  • 53% post an e-mail address.

Being public doesn’t seem to bother them that much. Indeed, 64% of teens on Twitter have public profiles, and a good percentage, 12%, don’t even know if their tweets are public or private.

danah boyd its complicated

But, as any parent of a teen will tell you, privacy is very important to them. But for teens privacy isn’t a simple binary of putting personal information online or not – instead it’s about managing the context of who sees what and engages with what on their profiles. danah boyd, a Principle Researcher at Microsoft Research and Assistant Professor at NYU, has worked extensively with teens around privacy.

As she points out, “While adults are often anxious about shared data that might be used by government agencies, advertisers, or evil older men, teens are much more attentive to those who hold immediate power over them – parents, teachers, college admissions officers, army recruiters, etc.”

Teens try to manage the context of their discussions. For example, according to the Pew survey 58% of teen social media users use inside jokes or other ways to hide the meaning of their messages. They hide the meaning and the context of the messages, not the access to the messages themselves.

I don’t think this emphasis on context is isolated to children. The internet allows us to network, share our thoughts and see the thoughts of others. In many ways, the very public nature of it is the draw. Yet people are still concerned about privacy. To see this dichotomy in action, let’s take the new LinkedIn member blocking feature.

The need to be public, the need to retain privacy

The story of this new blocking feature actually began in April of last year when a petition was posted on asking LinkedIn to implement better privacy features.

linkedin petition on stalkers and privacy

A woman named Anne R. was being stalked by a man who had sexually assaulted her at work. After she left the job, he continued to harass her online. The problem was that, unlike just about every other social media website out there, LinkedIn did not have a block feature. She wanted a professional experience on LinkedIn, but her stalker had other ideas. She couldn’t control her situation, and that was the problem.

Her options at the time were to either change her name on the site or remove herself from the site entirely. But that, she argued in her petition, was in essence sacrificing her networking opportunities in the face of something she was powerless to prevent. In other words, she wanted her information to be public. Just not available to the man who was stalking her.

Anne’s problem wasn’t that her data was public. Yes, that was part of the situation, but what she was concerned about was the social context she found herself in. She wanted to carry on her life, specifically engaging in professional networking on a service that promised just that. What she got was a nightmare. She wanted her data available to other people who would respect the professional context, not to people who would victimize her.

And she wasn’t alone. Taking a quick look at the stories people shared on the petition website, we can see a variety of situations and people. These stories came from both men and women and covered a variety of topics, from digital stalking by an abusive former boss to fears of a harasser finding their phone numbers or where they work.

Taking care with context

Protecting our data from exploitation by companies or spying by our governments is important. I’m certainly not trying to belittle the importance of data management. But I think that treating it as just data removes the human element that is really at the root of these concerns.

People want to control what situations they find themselves in. They don’t want things taken out of context and used that way, or even just have an organization jump in when they were having a nice chat with a friend – regardless that it is on a public site.

As market researchers, particularly social media researchers, we have to understand what the privacy debate is really about. Many are worried that people will choose to stop communicating on open channels where we can access their data. While some behavior may change, I don’t think people will stop sharing on public sites entirely. People like talking and sharing with people they may not know, as well as just with their friends. But they don’t want these public engagements to be taken out of context, or for that context to suddenly change out from underneath them.

As social media marketers, we have to widen the privacy debate beyond the black and white of data access. We have to respect the context of the data, perhaps even try to protect it by helping our clients understand what types of discussions are okay to join and what aren’t, or what types of insights are good to use in ad copy or communications overtly, and which are probably best left as subtext. We need to help our clients respect consumers’ control over context online, not just the access to data.