Face MD Job Muscroft started a series of posts about the emerging roles changing the face of market research earlier this month. Now, we’re going to take a closer look at each of these roles in turn, starting with Face social media researcher Jess Owens.

How would you describe your role?

As a social media researcher you’re a customer barometer for your clients – you see far more of what customers are thinking than probably anyone in their business. And, unlike almost all other information sources, it’s all real-time.

In social media monitoring projects, you’re feeding that back to the business – often hundreds of people, including the board – weekly or fortnightly to help the different client teams understand the impact of what they’re doing, what’s working, and what’s not. In insight briefs, the scope is a lot wider – often there’s a big element of explaining trends, “internet culture”, emerging technologies and so on.

How did you become a social media researcher? What’s your background?

I got a job at Face in part through having a Twitter account (@hautepop) which demonstrated that (a) I understood social media and (b) I think analytically about it. I’d been talking to Fran (our Director of Innnovation and head of Face Labs, @abc3d) on Twitter for a while, so when I sent in a speculative CV it got read. As a friend of mine put it the other day, “Our social graph is our passport” now.

But it’s also pretty crucial to be at home with both qualitative and quantitative thinking. I’m a bit of an extreme example here (BA in social anthropology; A-levels in maths, further maths & physics), but being able to handle both analysing datasets and explaining “what it means” is central to the job.

Beyond this, though, I’ve been active in online communities since 1995 and I’m fascinated by online culture. At heart, perhaps every social media researcher is an ethnographer or a “native informant” mediating between online and client worlds.

Any tips for how to stand out from the competition when you’re trying to get a job in social media research?

It’s amazing how many people don’t even have an active Twitter account… It’s a new field, we don’t expect 10 years of experience in “social media research” per se – but demonstrating specific interest and experience in both fields is pretty essential. Don’t be a generic marketer.

Ideally I’d love to see someone who’s big on Tumblr, or makes influential comedy or beauty videos on YouTube – something that really demonstrates that not only do they “get” how a particular channel works, they’re passionate about online sociality in general. Participant-observers can reach deeper insights than voyeurs.

What are the top three rules you have to follow as a social media researcher?

(1) Don’t lose sight of the wood for the trees. When doing analysis, it’s incredibly easy to be distracted by interesting discussions and follow these off, losing sight of the research question you’re trying to answer.
(2) An anecdote is not data. If you’re seeing 2000 mentions/day, one interesting tweet or forum thread is not in itself meaningful. First you need to establish whether or not it’s part of a wider pool of comment or complaints.
(3) Social media research is not PR – if customers are pissed off, your job is to explain that objectively, not play it down or try to cast them as in the wrong.

What’s the biggest mistake you most often see in social media research? What’s so bad about it?

People breaking rule #2 above (mistaking anecdote for data) – a singular “cute story” doesn’t necessarily mean anything and may in fact be misleading. It risks making social media analysis look like PR fluff rather than one of the central sources of business intelligence for the next decade and beyond.

Where do you see your role going in the next five years? What’s the future for social media research?

Big! Social media – and its intersection with mobile – is a really booming field, and moving incredibly quickly. In five years, social media research won’t be a distinct field – in fact it’s not one now, it never has been. Instead it’ll be an umbrella-category for a wide range of research methods done by all sorts of people, from brand and customer analysis to data journalism, economic & financial forecasters, policing & security services, academics and digital humanities researchers. The raw material of analysis certainly won’t just be social media content but integrating social and network data of all kinds.

Obviously market research only owns a minute part of it. The social media analytics field is dominated by tech firms, from IBM to Radian 6, Sysomos etc. Some follow more of a strategy consulting business model, but most are essentially selling product licences and require companies to either accept only pretty basic top-line insights or have an in-house analytics team. Analytics platforms from the social networks hosting the content may also emerge (e.g. Google+ Analytics, Twitter Analytics).

The qualitative and strategic side – the “what it means” – will be a much smaller part of the whole, but doubtless still offering huge opportunities.

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Sounds like you too? If you’re interested in joining Face and helping shape the future of market research as a social media analyst, please submit a CV and cover letter to job@facegroup.com.

Following on from my last post – 2012 Resolutions for the Market Research Agencies – I wanted to talk more about how we make these resolutions a reality by creating new agency roles with distinctive new skills sets.

1. Technologist

The MRX Technologist is primarily responsible for keeping up to date with new digital trends and is able to help the agency develop and pilot new research methodologies. This may take the form of designing new platforms from scratch or being the lead decision maker when it comes to buying 3rd party software. Alongside innovation, the Technologist plays an increasingly important role on project teams where the research briefs are UX or Service Design Orientated.
Skills: User Experience, Digital Project Management, Data Analytics

2. Community Manager

Communities are social places and need to be nurtured by people who are experts in digital communication. With the rise of MROC’s the fastest growing role in MRX agencies is that of the community manager. In fact, most of the problems associated with bad MROC research is when the agency does not have this person on the team. The Community Manager is responsible for setting the rules of the community, setting the tone of voice, making a personal connection with members and ultimately ensuring good quality engagement with the project. The Community Manager is also increasingly leading the way when it comes to applying game mechanics to research and is growing in influence when it comes to shaping research projects.
Skills: Copywriting, Video production, Project Management

3. Social Media Researcher

Real time social media monitoring is now commonplace but many companies are still struggling to interpret the data and use it to make strategic decisions. This knowledge gap is being filled by The Social Media Researcher who is responsible for developing strategic KPI frameworks for social media tracking programmes and harnesses social media data to help answer adhoc brand, product and comms briefs. The Social Media Researcher is quickly becoming a very important role, as they are both an objective and strategic voice advising clients about the ROI of their growing digital spend.
Skills: Quantitative Research, Qualitative Research, Social Media strategy

4. Co-creation Consultants

Companies are opening up and embracing more collaborative ways of working with third parties – including their consumers. Co-creation Consultants are responsible for the successful interaction between all parties on a project. Many of the touch points for this type of co-creation occur in workshop environments of one kind or another that require very skilled facilitation to get the best out of a wide variety of participants. Co-creation Consultants cover a wide range of disciplines, most often those from innovation, brand strategy and planning backgrounds.
Skills: Facilitation & improvisation, Planning, Qualitative Research

5. Big Data Scientists

We are living in the age of data, enabling companies to be more forward looking. Big Data Scientists are hot property in the research world as they are responsible for developing predictive data models & algrorithms using a wide range of data sources including dynamic social media data. Big Data Scientists primarily come from computer science, hard sciences, engineering and business backgrounds.
Skills: Mathmatics, Statistics, Computer programming

1. Learn how to tell better stories

We all know a good and engaging story when we hear it and our clients are no different! 2012 should be the year in which we take the art of MR storytelling seriously. Let’s ban the 100 slide reportage debrief and develop the skills of our teams to communicate findings in more engaging ways. Spend 10% more time on thinking about how we tell the story using imagery; video, graphics and customer voices will make a huge difference to the reputation of the MRX industry.

2. Ask less questions and listen more

As researchers we like asking questions. If we are totally honest, most of us think we know the answers before we run our surveys and are simply testing our hypotheses. Today, we live in the age of social media data – consumers globally are talking about every aspect of their lives 24/7. We no longer need to second guess and ask as many questions about what consumers think and feel with so much data available. We just need to develop the skills of our teams to listen and interpret more.

3. Stop using the word respondent

We have all done this. But is it not time to stop using this word to describe people who we work with in research projects. In 2012 we must encourage our teams to develop collaborative skills so that we can see consumers as people who we can co-create value with rather than as lab rats to carry out tests on.

4. Have more fun

The MRX industry has a pretty dull image and we need to ask ourselves why. A large part is because we need to try harder to be creative and have fun with our clients. We should be encouraging our teams to spend time experimenting, by piloting new ideas with clients. In a world where things are changing so fast, this is not only essential but fun.

5. Don’t just embrace change – drive change

Above all in 2012 I think there should be an acceptance amongst researchers that the pace of change we are seeing in technology is just going to speed up and that the old certainties of Quant and Qual research are over. It is only then that we can help shape the skills of our teams to adapt to the challenges of a world where so much data is available and where consumers expect to collaborate with brands.

Technology is changing faster than consumers. Consumers are changing faster than organizations. Therefore, organizations need to change faster if they are to keep up. Many are finding this difficult to achieve.

A recent IBM Global CEO Study that covers 1,130 CEOs across 45 countries and 32 industries showed that organizations not only felt bombarded by change but many are struggling to deal with it. 8 out of 10 CEOs saw significant change ahead and yet the gap between the expected level of change and the ability to manage it had almost tripled since the previous study in 2006.

There are many different manifestations of this change (too many to cover here) from faster product life cycles and globalization (the shift of budgets to emerging markets), to changing demographics and the challenge of ageing populations on Western economies. But one of the biggest is the impact of the social web on everything we do. EMarketer predicts that the tipping point will happen in 2012 when 60% of all marketing budgets will become social. Linked to this is the arrival of Big Data. In 2010 the human race created 800 exabytes of information. To put this into context between the dawn of civilisation and 2003 we only created 5 exabytes; now we’re creating that amount every two days. By 2020 that figure is predicted to sit at 53 zettabytes (53 trillion gigabytes) – an increase of 50 times. As Hal Varian, Google’s Chief Economist said “We used to be data poor, now the problem is data obesity”.

This presents us with a number of new challenges that I have set out below as hardening client needs. I have concentrated on just a few with some suggestions on what research companies need to do to make sure they’re in a position to meet them.

1. Moving from Big Data to Big Insight

Making sense of all the data out there and simplifying it so that we can derive valuable meaning and insight will be one of 2012′s client mantras. Social listening will give way to social media insight. Having researchers in your team that are also technologists e.g. digital anthropologists that can help to analyse real time social data will become a required skill. Being able to augment different data sets from the virtual and real worlds so that we can help to create one closer view of our customer will depend on our ability to mix different on-line and offline methodologies in a coherent and credible way.

2. Quality without speed is not enough

One of the greatest demands from clients is how to deliver fresh, robust and relevant insight more quickly and cost effectively than we have ever done (or needed to do) before. Qualitative research companies need to lead in the use of technology so that we can become quicker, faster and more responsive in the ways in which we gather insight about our clients’ consumers. We also need to develop research and planning tools that are less generic and more focused on the CMI client needs of today and tomorrow.  This does not mean replacing human analysis – to the contrary the role of the researcher has become even more important than before because of the need to find real quality from the huge quantities of data that is out there. It must also mean we can do better than relying on tools such as the TGI Index.

3. Logic needs to give way to more magic

We are going to see more emphasis on qualitative research as a robust exploratory tool to understand better consumers’ emotional drivers as well as to help improve the quality and shaping of social ideas and social content before things go too far and way before the quantitative testing stage. Too much blind reliance on testing things to death has seen some of the “magic” and “creativity” in marketing lose out to the “logic”. Creating magic today means creating social brand stories that are contagious and can be propagated effortlessly by key consumer cohorts. Co-creating with these consumers, involving them much earlier in the marketing process, leveraging their content and creativity as part of the marketing process will have an increasingly important role to play here. If what goes in is rubbish then testing what comes out will be rubbish. The Coca-Cola Company is leading the way and I am sure other FMCG clients will follow.

4. Creating content excellence

There is a new marketing ecosystem where content is more important than channel, where audience passions/interests are becoming more important than demographics and where the media model has changed – placing more emphasis on created and earned media as opposed to bought and owned. Understanding which “big ideas” have enough social currency  (it’s not what consumers are doing with your brand but what they are doing with each other that counts) and can work effectively across all platforms will attract much more focus. Understanding the different consumer cohorts within a brand audience and their influence will also be key to understanding what content areas will have the most impact when it comes to propagating ideas. Researchers need to come up with a new model here: one based on rational, emotional and social metrics that is continuous and adaptive.

5. New measurement models

With the increasing socialisation of brands and the importance “connected” brands are placing on new metrics such as social brand value and influence (see below), helping clients to understand, validate and measure what ideas work best in the earned and created media space as well as why it works will be increasingly important. Finding ways of proving that the more customers of a brand are interconnected the more they are willing to pay for the product and the more loyal they will be is vital. Working out a more real time model for measuring which big ideas have the best potential for success; are the most likely to be propagated and can work across all media is another area that needs close attention.

I attended WARC’s Datacentric Conference last week where Fran D’Orazio was presenting with O2 on Mining Big Social Data In Real Time. The overriding central theme of the day was how to move from being data driven to becoming more data decision and data action orientated. Some of the key points are worth summarising here.

1. Measure people, not channels

Dan Hagan, Head of Planning at Carat, talked about the importance of getting closer to individuals and measuring people rather than channels to help “Manage data to gain insights into brand strategy”.  One of the new ways to achieve this was to use agent-based modeling that required the creation of fake digital personas with basic rules & behaviours. These digital robots, if used in large numbers, provide rich qualitative data on potential customer profiles in a social context. The model allows researchers to compare the fake ecosystem with real life.

2. Doing it right versus doing the right “it”

The real power of insight does not come from measuring every piece of data but understanding the most important pieces of information to drive action. Gavin Meggs, Sky IQ’s Strategic Insight Director talked about moving from Big Data to Big Insight. His advice was simple:

-          Understanding what’s possible is about understanding the customer attributes and behaviours; interactions at each touch point, attitudes and preferences, getting a single customer view and having a memory

-         Put the customer at the centre of your organization not just at the centre of your model

-         Optimization of Data sources and the importance of data matching

-         Connecting insight to business objectives so one can prioritize what’s important

-         Scope – the problem of size. The cost of doing too much can sometimes be more than doing too little

3. Social Attribution Modelling- combining social data, with on-line and off-line models

One of the Conference inspirations came from Louisa Middleton at Google in her presentation “On-line data analytics: From the Customer decision to the bigger picture”. Here was the opportunity to combine social data with click stream data and off-line methodologies to deliver a new attribution model – one that can help put the customer purchasing journey in a social and brand context.

4. Separate data planning from data execution

In terms of making data part of every conversation, Lee Feinberg from Nokia argued that it is important to separate data planning from the execution with his DRAW ON approach. This was essential to help companies move from being data driven to decision driven.

Planning Phase

·      Decisions you need to consider – make sure that you cover all of them at the outset

·      Results that drive the decision – write them down but also sketch them out as visualizations as this helps to get key points across

·      Analysis of achieving the results. Build a list of all of the questions that might be asked about the key measures so you can make sure you have all the data available to answer them

·      What else to complete the analysis. Bring information from outside into the conversation

Execution Phase

·      Make important information Obvious – otherwise can camouflage data

·      Neatness counts

The idea is simple: powering traditional qualitative and quantitative research with real-time data.

When we were invited to speak at Warc’s Datacentric Conference, we thought it would be interesting to discuss one of the latest research pilots we have been running in the area of augmented research.

The objective of the O2 Brand Graph pilot was to mine social media data in a way that would allow us to connect it to audience studies.

What follows is an initial exploration of how we can you use social media to augment a segmentation model with real-time data.

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Many companies are learning to listen to conversations related to their brands and competitors.

However, there’s more to social media intelligence than tracking conversations by keywords.

Current social media research focuses on opinion mining and declares itself unable to map audiences. But I think we are giving up too soon.

This inability appears to be born from an assumption in the research industry that you can’t use social media to map audiences because you don’t have access to demographics.

Far from being reality, this assumption is mostly due to three reasons:

1. The architects of social media mining platforms are often not led by a research agenda, but by a tech agenda – this leads to a tendency to productise and mass sell platforms, which can run in counterpoint to an openness to experimentation;
2. Researchers are often not makers or technologists – therefore, they are often lazily happy with what they are given in terms of tools;
3. Researchers do not always know what can be done with existing social media data streams, such as basic machine learning to figure out gender and age groups.

However, mapping audiences through social media IS possible. It’s just not in the way we used to research audiences before.

It’s all in the way you screen your audience and sample it, and in social media sampling via demographics doesn’t work. But there are many other ways of defining and screening an audience. In this study we explored one way.

Instead of tracking contents by keywords (“horizontal” tracking – any content mentioning specific keywords and keyphrases), we looked into mining social media contents and behaviours by audiences (“vertical” tracking – any content generated from a set of sources, regardless of the features of the content).

Whilst tracking social media by keywords allows us to get an understanding of how a specific topic is discussed online, tracking social media by users allows us to build a map of an audience, its hubs, its behaviours and its interests.

We called it the Brand Graph: the conjunction of the Social Graph (defined here as the network of people who are within 2 degrees of separation from the brand through social media channels) and the Interest Graph (the network of interests, topics, activities and behaviours associated with the nodes of the social graph).

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What can you do with it?

• Dynamically understand who your audience is and how is it changing, in real-time;
• Dynamically understand what your audience is about, what makes an interesting topic and how broader cultural conversations affect it;
• Segment your audience in clusters based on topics of interest, passions, life stages, professions, online behaviours etc.;
• Plan and fine tune the content of your social media strategy;
• Engage with your audience in the right way (channels, mechanics, times of the day, tone of voice etc.);
• Assess the impact of your strategies in real-time.

Going forward, we see the brand graph becoming one of the key tools to build a seamless connection between your brand and its audience, networking it with its passions and synching it with its behaviours to maximize relevance and impact.

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So, how did we go about building the O2 Brand Graph?

First of all we had to identify a specific pool of social media users and then analyse their public activity.

For the purpose of this pilot we limited the online audience to one channel – Twitter. We focussed on Twitter because of the granularity of the data publicly available around contents and behaviours.

Sample: We defined our sample as the entire audience of O2 on Twitter, i.e. 58.339+ Twitter users who were following @O2 (as of November 2011).

Methodologies: Statistical analysis, Semantic analysis, Network analysis, Netnography and Content analysis.

By looking at the profiles and the activity of this audience we were able to map the O2 Brand Graph on Twitter.

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We grouped the findings in three areas:

Mapping the Social Graph

We wanted to identify sub-communities within the O2 audience on Twitter.

Because Twitter is an interest graph, we assumed that following someone implied sharing the interest of the followed user.

Therefore, a subcommunity would be identified by a high concentration of horizontal connections within the graph.

To get this information we had to map:

1. 58,339 users following @O2;
2. Who was following each of the 58.339 users;
3. Who else in the graph any of the users was following other than O2 or the primary O2 follower.

For the sake of this exercise we looked at a sample of 1000 users. We then selected the top users with less than 2000 followers. We then mapped their connection to O2. And finally mapped who was following them.

Finally we mapped how the primary and secondary followers were connected to each other user in the graph.

We ended up plotting a graph of 1 million nodes, 1 million primary connections and 574,278 horizontal connections within the graph.

The blue links represent how primary and secondary followers are connected to each other within the graph.

By looking at the density of the connections we could identify hubs within the audience and points of high concentration of similar interests.

Once we knew where the hubs were we than isolated then and looked into the clusters.

We spotted 10 clusters and profiled them, identifying sub communities around topics such as fashion, music, rugby, technology and marketing.

Mining the interest graph / profiles and behaviours

We then analysed the static data of 58,339 profiles on Twitter gathering insights around 10 key dimensions:

-       Who are they (life stage, profession, passions that define them etc.)?

-       When did they join Twitter?

-       Where are they based?

-       Where do they tweet from?

-       How often do they Tweet?

-       When do they Tweet during the day?

-       How many people are following them?

-       How many people are they following?

-       How often are they engaging in conversation with fellow users?

-       How influential are they?

Mining the interest graph / interests and passions.

Finally, we analysed 3,120,371 public tweets, 122,220 tweets/day (avg), generated by the @O2 followers over one month (November 2011).

Based on this corpus we were able to gather real-time insights around a series of questions such as:

-       What does the audience talk about?

-       How and why do the topics change over time?

-       Which contents are the most engaging (i.e. generate the highest number of reactions)?

-       Which contents get shared the most?

-       Which social media channels are the most popular amongst the audience?

-       Which news sites are referred to more often?

-       Which brands and products do they talk about?

-       Which adverts do they mention?

-       What movies are they into?

-       Where does the brand fit in this landscape?

-       How do they talk about the brand’s main competitors?

All this information is constantly updated to the second and can be sliced according to any timeframe, audience segment, audience location and basically any dimension of the audience profile or of the audience social graph.

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The deck above outlines some of the initial data gathered and the insights uncovered. But as you can imagine this is only a glimpse of what we could learn with this kind of study. An example? Slice the topics of conversation of your audience by time of the day and you will know who would you be talking to and what you should be talking about at what time of the day.

As the last image in the deck – “The Measurers” – alludes to, with social media data we are at the very beginning of a new era of audience understanding powered by a new science of measurement.

Pilots like the Brand Graph are initial attempts at defining the boundaries of what can be measured, what could and SHOULD be measured and what we can learn from it to do a better job.

Feedback and questions welcome, belligerent challenges even more so.

Happy Thanksgiving! This is quite a memorable Thanksgiving, too – our first! That’s right. Face US is celebrating its first Thanksgiving today!

Now, I may be American, but this is Philip’s first ever Thanksgiving. So when the office (which includes our sister companies here in New York) decided to put on a Turkey Day potluck, he got a full introduction to what exactly goes into a Thanksgiving spread.

It all started with an office conversation, as so many great things do. Someone suggested that we celebrate Thanksgiving the Friday before with a potluck. Then someone else wondered if it wouldn’t be nice if we all brought traditional Thanksgiving dishes. And finally a third person insisted that we all make, not buy, these dishes.

So last Friday the whole office sat around our big conference room table, which was expertly decorated by our fantastic Office Manager, Jen. We piled our goodies on the table and prepared to dig in!

The most impressive part is that most of the dishes on the table really were homemade. I had discovered a recipe for candied sweet potatoes from allrecipes.com and brought that in. There was homemade string bean casserole, homemade stuffing, and plenty of other delights. As you can see, most of that table was covered with food! About the only things store bought were the turkey and some corn bread.

Even though this wasn’t Thanksgiving Day, that was a traditional Turkey Day dinner. For lunch! We had all agreed on a two hour lunch, and a good thing, too! As per Turkey Day tradition, we all stuffed ourselves silly. Well, at least I did. I think at one point I had not one, not two, but three plates full of food in front of me.

And of course, at the end, a lot of the food was gone. But not all!

We had all cooked so much food that we just couldn’t eat it all. One of us had made somewhere along the lines of three pounds of baked macaroni and cheese! I’m sure many of you reading this will agree that after any holiday dinner, the best part is the leftovers. This was definitely true here, but for different reasons.

Rather than greedily hoard our leftovers, or divvy them up so we could each take some home, we decided to get into the giving spirit of the season. We donated the leftovers to a nearby soup kitchen. Most don’t accept opened food or leftovers – and for good reason – but some soup kitchens do. After all, why let good food go to waste? One such kitchen is close by our office, and that’s where the remainder of that huge amount of macaroni and cheese, as well as my sweet potato casserole and everything else, went.

I hope someone enjoyed that dinner as much as I did. And today, both Philip and I get to enjoy it again! Even our home office in the UK got into the Thanksgiving spirit with some pumpkin pie in the office.

Happy Thanksgiving, wherever in the world you are!

An underrated skill in social media research is simply knowing what to search for.

Really? You’re interested in a particular brand, so surely you search for that word or phrase, right?

For brands such as Three, Apple, or the AA? Go ahead, give it a go!  Just don’t be surprised when you get back a lot of content about “Three ways to boost your Twitter profile”, apple crumble recipes, and AA rated sovereign debt.

Searching the internet

We often describe setting up a social media research search for a brand as like doing a Google search.  This is loosely accurate – like Google, a social media research tool ‘crawls’ news, blogs and forums for instances of your keywords. The research tool will also filter social media APIs (e.g. Twitter, Facebook) for instances of these words or phrases.

The problem is that the internet contains a lot more content than you think it does. Normally you never see it, and much of it isn’t even designed for human readers.  Here are a few forms this takes:

Challenge 1: SEO spam

Being at the top of Google’s search results is a very valuable place to be if you want to get visitors to your website or online shop. This has made gaming Google results an industry in itself, called SEO: search engine optimisation.

SEO aims to guess Google’s search algorithms to manipulate clients‘ websites to the top of search listings. Google basically rates sites more highly the more in-bound links they have – i.e. the more popular they appear to be. So gaming Google results entails generating a lot of false links and content, with methods including:

• Fake news sites reprinting press releases and “content farms”, e.g. Demand Media or Articlesbase
• Promo blogs with high numbers of links to the client’s site, with random or copy-pasted text to make Google believe they are legitimate blogs rather than spam
• Legions of Twitter bots (automated accounts with an algorithm rather than a person generating their content) posting links to websites
• Using bots or real people (incentivized by micropayments) to post high volumes of blog comments with links to the client’s site

This gives rise to a lot of misleading digital data, all of it only designed to be “read” by Google’s algorithm rather than human eyes. To a reader – or anyone tracking a brand – it is useless.  The problem is that it’s got your brand name in it, so any generic brand-name social media search will bring back this “noise” too.

Challenge 2: unexpected content

Do a Google search these days and the internet seems an ordered and relevant place. Even when you search for an ambiguous word with several meanings – let’s say “Orange”, the results are sensible – Orange the mobile company and the Wikipedia page on the fruit.

This is because Google have spent years refining their algorithms to ensure it brings back the most relevant content possible.  This doesn’t just mean putting the most popular links at the top of results. Instead Google uses everything it knows about you – your previous searches, your Google profile and Gmail, your stored cookies and more – to deliver personally tailored results.

Social media research tools don’t however work this way. The APIs and scrapers collecting content return all keyword mentions, relevant or not. In searches we’ve run, some of the most unexpected things we’ve found have been:

1. Searching for banks will bring back posts on “carder forums” – the sites where credit card fraudsters sell the card details they have stolen from databases.

2. Almost everything is a word in Indonesian. You thought you were searching a specific and unambiguous acronym? No, it also means something in Indonesian – and volumes can be enormous because Indonesians are one of the most active populations in the world on Twitter.

3. Pharmaceutical searches are near-impossible. Dubious medication sellers will include hundreds or thousands of drugs as keywords on their pages, whether or not they’re selling those products.

Challenge 3: not all relevant content is indexed

So far we’ve described some ways that irrelevant content or “noise” can get into your social media search. There’s also the opposite problem, however – not being able to ‘see’ certain types of social media content, particularly forums:

Message boards are part of the Internet known as the ‘Invisible Web’ and pose many problems to traditional search engine spiders. The dynamic content is usually very deep and hard to search. In addition, many of these sites change their locations, servers, or URLs almost daily presenting special searching challenges [Boardreader]

This makes it essential to use a social media research tool that allows you to check which forums are tracked, and customize the panel of sources as needed.

Impact on social media research

What this means for social media research is that if you’re using an off-the-shelf monitoring package, you’re probably getting a lot of junk in your results. Brands are often keen on easy usability – type your brand name into the search, and get a volume figure and sentiment stats out. But without tailormade search syntax, those figures are almost certainly meaningless.

So how do you make your social media research search relevant?

1. Specific is better

Using broad search terms and then excluding keywords you’re not interested in doesn’t usually work very well. You’ll never be able to filter out all irrelevant content – language is too varied and dynamic. E.g. if you’re after the mobile brand, search for Orange AND mobile, not just “Orange”.

2. But you can filter irrelevant websites

Not that many big content farms exist – so we exclude everything from them by URL.

3. Also filter particularly spammy keywords

e.g. “Viagra” for anything medical.

4. Boolean search syntax

This is the logic that enables you to search for content including A and B, A or B, or including A but not B.  It’s essential not only for designing your social media search strings, but for also searching within the dataset.

5. Test your search terms on Twitter

Enter your search phrases into Twitter Search to see whether you’ve got them right, or if they’re bring back unexpected or irrelevant content instead. Twitter search also helps you understand the volumes of content that’ll come back (is it multiple posts a second, or a couple per day?)

6. Get personal

If you’re specifically interested in what consumers are saying, searching  with personal pronouns – e.g.“my iPhone” – will bring back a much more relevant dataset than “iPhone” on its own.

Which is to say, designing a relevant and accurate social media search can involve a surprising amount of time, thinking and ongoing refinement. Few brands have the time or expertise to do this in-house using an off-the-shelf monitoring tool.  This is why our clients have come to us instead for our expertise in locating what matters in social media – the signal amongst the noise.

1. Hubs

The value of data is only fully leveraged when multiple data sets are connected. Connecting the data allows us to understand the context of the dataset and turn figures into stories and insights. MROCs will evolve to become Hubs for consumer understanding by enabling clients to overlay other data streams, such as sales

2. Co-creation

The more experience clients have with MROC, the more they will understand that the power of these communities goes beyond gamification of online research tasks. By segmenting consumers by their ability and skill to co-create we will see more consumers being invited to work closely with brands to crack strategic brand challenges.

3. Real-time

MROCs will increasingly be connecting to the social media profiles of their members, thereby giving clients access to selected areas of their real-time social data. Such data might include their status updates, their musical preferences, their Likes, the people they follow on Twitter. This will mean as researchers we will use MROC to ask fewer questions and concentrate more on actual behaviours.

4. Mobile

As smart phone penetration increases, MROC members will be able to use apps to post pictures, videos, soundbytes, status updates, respond to polls, engage in discussions and generally participate in tasks on the go. This mobile interface will enable a richer contribution from members and a deeper and more seamless connection between what they do in their daily life and what they do in MROC.

5. Smarter

Automated analytics tools will enable researchers to gain faster and deeper understanding of MROC data. This will include natural language processing software to run semantic analysis of the contents and cluster consumer feedback by topics. Machine learning will also start to be overlaid to enable more effective categorization of textual, visual and audio content. Real-time interactive visualizations via dashboard will also be adopted to spot patterns quickly and guide in-depth analysis of content.

Infographics are everywhere. As a method of presenting information clearly and quickly and avoiding the need for vast tracts of data, they’ve been in use since prehistory, when our primitive ancestors would use cave paintings to illustrate their journeys and keep track of livestock and grain. More recently, Florence Nightingale used the Coxcomb Chart above, which was made in 1857, to illustrate her point to Queen Victoria. The blue areas represent deaths by preventible or treatable disease during the Crimean conflict, compared to wounds (in red) and other causes (black). To take a closer look, click the image.

Just four years later, the erstwhile Inspector General of Bridges and Roads, Charles Joseph Minard, created the infographic below to depict the disastrous Napoleonic invasion of Russia. The red line represents the march to Moscow whilst the black line shows the retreat. This infographic fulfils its function beautifully, showing simply and powerfully how an army of 422,000 men came back as just 10,000 as bad weather (the line graph below the main body of the graphic) and stiff resistance took its toll. Minard was renowned for the advances he made in the presentation of data, having already created this map of the cattle sent from around France for consumption in Paris in 1858. Scientist and photographer Etienne-Jules Marey expressed the influence of Minard’s depiction of the Russian campaign perfectly: “it defies the pen of the historian in its brutal eloquence”.

The 20th century saw further, dramatic developments in the field of infographics, as one of the most iconic infographics ever emerged in London. The London underground map, designed by Harry Beck, communicates the necessary information clearly and concisely in comparison with the geographically accurate maps of the late 19th and early 20th centuries.

So has the infographic retained its power and “brutal eloquence” into the 21st century? Certainly, they remain popular and ubiquitous. In the last 12 months Google have seen a significant upward trend in people searching for the word itself, and marketing companies are using infographics as a way of pushing their message viral. There are even dedicated websites for the curation of the best infographics.

However, there’s also a growing issue of people creating infographics which are unclear, illegible and, frankly, downright ugly. As we’ve established, the best infographics are powerful enough to validate that old cliche that a picture is worth a thousand words. But the worst do more to confuse and bewilder than even the least carefully chosen words. The above is a prime example – a whirl of text and colour that serves only to obfuscate whatever point the creator was trying to make. Almost equally heinous is this example, below, from Ikea, usually purveyors of simple, attractive design – made with honourable intent, perhaps, but as attractive as a school science project and ultimately a waste of their designers’ time.

The moral of the story is this: if you’re making an infographic or illustrating a report, be certain that it is simple, strong and worth the time it will take to make. A poor infographic, or one which could have served purpose as a chart or table, is more often than not a waste of yours and your audience’s time.