Category Archives: Social Media

FACE’s ALS Ice Bucket Challenge

This week the inevitable happened. FACE became the next in a long line that has been nominated for ALS’ Ice Bucket Challenge. Yes, the social media phenomenon reached us in our London Office; we have Brightsource to thank for that!

ALS is a disease that many had never heard of or understood before the Ice Bucket Challenge; it has brought a huge amount of awareness to the charity and many similar non-profits who are trying to fight the cause.

The challenge represents more than just throwing iced cold water over your head. It shows us the incredible power of social media, and how quickly content can go viral.

We would like to thank Brightsource for the nomination. Our nominations go to Extendi, Sennep and Sensum. You have 24 hours – considering it’s a Friday we’ll give you until Monday.

Please donate what you can to the following links:

ALS in the US here:
https://secure2.convio.net/alsa/site/…

Or to the UK’s Motor Neurone Disease Association here:
http://www.mndassociation.org/news-and-events/Latest+News/the-mnd-ice-bucket-challenge

Find out more about ALS (known more commonly as Motor Neurone Disease in the UK or Lou Gehrig’s disease):

http://www.alsa.org/about-als/what-is…

10 tactics for rigour in social media market research

Last week I went to the MRS Connected World conference, a really excellent event gathering together an inspiring crowd to talk about new technologies and consumer behaviours. Not just to listen – though listening was great! I was also putting forward the FACE point of view on a panel with Tom Ewing of Brainjuicer and Paul Edwards of Working Plural & JKR.

Our topic: “cutting through the noise”. Digital media & technology has generated a dramatic shift – for the first time in history, there’s not a shortage of information but an excess. But how to make sense of it all? How to find the insight amid the flood?

Our session was kindly written up by Research Live, so I won’t go into the details here. Instead, I want to pick up on a really smart question from an audience member – How do you do social media research with real rigour?

Great question. How do you move beyond a set of observations made on a vast and potentially rather amorphous dataset, to get to something we might actually call research? On the spot I came up with 3 ways  - but on reflection, there are more.

Here’s my top 10 ways to make your social media research rock solid:

1. Capture the complete universe

If the dataset’s incomplete (and especially if you don’t know what’s missing), you can’t say anything about how your findings relate to the wider universe. Tweets found directly through Twitter search are really no more than anecdote until you can contextualise them within a meaningful totality of everything that’s going on in social.

figure13

Image source: Mapping The Global Twitter Heartbeat: The Geography of Twitter, by Kalev H. Leetaru et al., 2012

So make sure you’re using a social media research tool that’s built on top of Twitter Firehose (the 100% data API) and robust blog, forum & news data collection.

Of course there’s still a gap between “everything said in social” and “everything people think”. But that’s true for every research method – this is a risk we can only minimise, never remove entirely.

2. Your search strategy is critical

Great data sources aren’t enough on their own – you’ve got to set them up right. If you’re searching for a particular category (e.g. haircare), you need to be confident you’ve collected the whole category – every possible way people can talk about hair, from products to styles and stylists, and verbs & adjectives as well as nouns. Just searching for all mentions of “hair” won’t cut it – you’re not capturing a meaningful totality.

How to build good search syntax: Brainstorm. Then test it in Twitter & Google search, then iterate to add in new words & phrases that come up. Analyst experience is key here to build a search strategy that’s both comprehensive and focused.

3. Qualify your quant insights

Social data is qual data at a mass scale, says Francesco D’Orazio, our chief technologist.

Numbers on their own aren’t insights. Positive sentiment is 20% – so what? What are people saying? What are the needs and emotions driving that figure, and why is it higher for one brand than another? Read, synthesise, code. Quote the actual messages, show the verbatim. Keep the people visible in how you tell your insights.

4. Quantify your qual insights

Say you’re doing an innovation project, find out that fighting frizz is the most important consumer haircare need. Your immediate client might love the depth of qual insight you can build from beauty blogs and forums… But she’s also got to communicate that insight around a larger organisation & to lots of people who won’t ever read your full deck.

So quantify that qual insight and rank it against other needs. Savvy use of Boolean search strings – NEAR operators & smart exclusion terms – can give you sensible approximate volumes for almost any concept. You’ll not capture every nuance, to be sure – but it’ll help support that qual insight as a really solid finding.

puggit pug AND rabbit

(Ok, not really an example of quantifying qual insights – but a very cute example of Boolean syntax!)

5. Can another analyst find the same insights?

Classic research methods such as data coding still can have a key role to play in turning social media data into insight. It provides a structured template for content analysis that helps iron out bias from the analyst’s own preconceptions. Instead you’ve got a random sample of 200 messages and a structured grid, and it’s easy to review across team to help standardise what you mean by particular categories and concepts.

6. Benchmark

Is this finding real? How much does it actually matter? Display your research findings contextualised against other brands, other categories, or as share of voice – so your reader can get a sense of proportion.

7. State what you don’t know, or can’t prove

  • e.g. “This visualisation is based on Twitter data, a channel used by 26% of the UK population.”
  • “Social media messages almost never identify a store by its exact street address, and only 1.6% of tweets have geolocation. Consequently we cannot locate the se complaints to specific store, only town or region level.”
  • “Social media data includes only information that is publicly available on the web, and not private email or text message data” (yes we get this one!)

Make the gaps explicit. It shows you know what you’re talking about – and helps ensure your insights are interpreted accurately. Overclaim isn’t rigorous!

8. Test hypotheses. Test a null hypothesis.

Having hypotheses makes your data useful – instead of just drawing a picture of the landscape, you’re trying to find out something specific. But in the spirit of scientific enquiry, proving a hypothesis isn’t just going out looking for data that supports it. It’s also about looking for data that supports the null hypothesis – the counter-possibility that nothing is happening, or the opposite. Look for both – and if all the evidence really falls on one side, then you can be confident that your finding is really robust.

Null hypothesis cartoon aliens socks

Testing the null hypothesis or counter-factuals  is also a great way to find interesting things you weren’t expecting (see point 10!)

9. Triangulate against other data sources

Extract everything you can from your client, from sales figures to  qual research to semiotics decks.  Turn these into hypotheses. Is your research supporting these? Building on them? Taking them a new direction? Or disagreeing entirely? All are legitimate outcomes – and putting your insights in this context makes them much easier for your client to use.

10. Don’t do social media research if it’s not the right way to answer your question

A contrarian point for closing – but here at FACE we’re honest about the fact that social media data can’t answer all research questions. Its genius is that the data we’re analysing is largely spontaneous and unprompted, making it a great way to find “unknown unknowns’ – the things you didn’t even know you wanted to know, or needed to ask.

Unknown-Knowns-invert-657x600

But sometimes you’ve got really specific questions to answer – how far are consumers prepared to trade off price vs. quality, perhaps, or whether a different shade of blue would make a better bottle top. And I’m afraid people just aren’t talking about bottle cap colours in social media… So you’d need to ask them directly: time for a focus group! Not social.

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So that’s 10 ways to make your social media research really robust. Any more to add? Get in touch with us on Twitter – we’re @FaceResearch – and tell us your top tips! I (Jess) do a bunch of tweeting for FACE, so let’s keep the conversation going.

Or if you’ve got a really thorny research problem and you’re looking for a rigorous solution, get in touch with my colleague James on James.Hirst@Facegroup.com – we’d love to talk it through with you.

 

Meet us at… the MRS Connected World conference

On Thursday 10th July, Jess Owens, one of our Social Media Managers here at Face, will be speaking on a panel at the Market Research Society’s Connected World conference in London.

Connected World is an exciting new conference for the market research industry which aims to “help the insight and marketing world capitalise on the new technologies, behaviours and beliefs that are driving relationships between individuals, brands and consumers.”

Screen Shot 2014-07-03 at 23.00.09

It’s a privilege to be one of the only research agencies speaking at a conference drawing on an excitingly wide range of speakers and expertise. Connected World aims to inject new ideas into the market research debate, drawing on everything from experts in consumer creativity (Hazel Robinson on tapping into the power of fans) to technologists visioning the future through pervasive computing (Adrian David Cheok, City University) and the Internet of Things (Moeen Khawaja, Umbrellium).

Jess will be on a panel at 11.40am called Cutting Through The Noise, alongside Tom Ewing (Brainjuicer) and Paul Edwards (Working Plural and JKR), with discussion chaired by journalist Richard Young.

The pitch:

“An ever-growing amount of interaction between consumers, brands and beyond means only one thing for research professionals – an ever-growing challenge. How can the analysis keep up with the flow of information? How can research adapt to the new technologies and practices? In this case study-free debate, we discover the scale and nature of the task ahead of us.”

For more information, full programme details and registration, please have a look on the official site of the conference.

Or catch up with Jess at the conference by saying hello on Twitter (@hautepop) or email jessica@facegroup.com.

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. 

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