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.

***

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).

***

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.

***

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.

***

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.

***

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.

When did you get your first computer? I think I got mine when I was in middle school, and that was considered early. Most of my friends started getting one in their rooms in high school. They were not laptops, either, but old-fashioned desktops with towers. Compare that with the current trends. Computers are getting purchased for younger and younger children. According to this MSNBC article proclaiming mobile tech is in for kids going back to school, you may need to get your elementary school grader a new Droid Sonic!

In honor of the back-to-school season, we set up a Pulsar search for mentions of technologybrands looking for how people talk about tech in terms of kids and school. We wanted to see how this trend of youngsters with cool tech is perceived. Turns out there are two answers to this question: the physical technology that people think is weird in the hands of kids and the applications and program technology that can help us raise and teach our children better. And these two answers are dominated by two different brands: Apple and Android.

Children with Technology Is Ridiculous!

One of the first things we found was that people like to joke about children and technology, usually at the parents’ expense. Below you can see a graph of Retweets over time. The two spikes were caused by the jokes in quotes above them. These comments were passed along 50 times each, a pretty good shelf-life for a joke. People find the idea of children and this technology, particularly Apple technology, humorous.

Part of this may be the assumption that children with technology are rich. Several comments referred to how expensive these products are. There was even a small viral protest on Facebook about how spoilt modern children are. The protest called for people to repost varieties of this message to their own Facebook walls if they agreed with it: “When I was a kid, I didn’t have a laptop, iPod, Blackberry, PS3 or iPad. I played outside with friends, bruised my knees, made up adventurous fantasies and played hide and seek. I ate what my mom made and Jollibee was a treat. I would think twice before I said “no” to my parents. Life wasn’t hard, it was great and I survived. Kids these days are spoiled. Kids these days lost something – Appreciation. Re-post this if you appreciate the way you were raised. I think we were happier kids ”

Society just thinks that these high tech gizmos should not be juxtaposed with children. When people view a child with an Apple iPad, for example, the tendency is to either make a joke about it or deride it. It’s just that ridiculous.

But Applications Where It’s At

Phones were only mentioned a little over 5,000 times in this search and laptops and tablets were only mentioned a little over 8,000 times. Meanwhile, applications were mentioned over 9,000 times.

Applications sure have got people talking.

And they are talking in a good way. Many of the applications mentioned in the context of this search either factored in to children’s education or security. Examples include applications to help parents track their children or that allow children to mimic their parents’ driving. Of course school-focused applications are also available in abundance for the tech-savy student. I like this interactive periodic table for the iPad.

The difference between the discussion about applications and the general discussion covered in this study is where the discussion is occurring: news. Blogs and news were the leading channels in our general search, but only for applications did news surpass blogs.

And these mentions were on tech news sites, more so than in the general search which was dominated by local news. Technology news sites cover the up and coming, which includes apps. A quick search on Mashable.com showed 12,900 hits for “phones” while there were 17,700 hits for “applications.” Most people don’t go to their local news station for the latest technology happenings. They do turn to Mashable. Could the market be moving away from the technology itself and more towards what we can accomplish with it?

Apple Dances Center Stage with Android in the Wings

I’ve already hinted at this in this blog post, but yes, Apple reigns supreme. Apple has been involved with the education industry for a long time now. I will date myself by remembering when my school got the large and colorful iMac computers for our computer lab. This link with education has remained.

Or could it possibly be that Apple products are really cool and everyone seems to want one? Apple and Apple products were mentioned over 16,000 times in the search results, indicating how strong the brand image is. But we knew that.

What’s more interesting is that not only are Apple products fun to talk about, they are also considered necessary. Almost 400 mentions, out of a total of 600, of “necessary” and “must” and purchasing words mentioned Apple products. It is apparent that Apple products are more than just fun. They carry more weight than that. No other brand in our search had such a high level of need associated with it. Even generic items, such as “laptops” and “phones” only received around half the mentions of the Apple brand products.

And yet, when it comes to applications, Android is more prevalent, particularly in news discussions. Android was the only other brand besides Apple mentioned in any considerable volume, though considerably less than Apple with only around 2,500 mentions. Android was mentioned most frequently in the news, unlike Apple for which blogs were the leading channel. Considering that applications are apparently the current direction everything is going towards, Android seems to be well positioned.

Could attention be shifting from Apple products to Android applications? We’ll have to wait and see. Meanwhile, it is clear that Apple is benefiting from the back-to-school season.

Do Your Children Have iPhones?

What do you think? Do children need technology in order to benefit from their educations, or is it just something for rich parents to buy for their kids? Is it useful or hazardous? General opinion seems to lean towards it being unnecessary, though news and bloggers tend to think it useful. People certainly like talking about it.

Over the past five years there has been a great deal of Social Media experimentation going on in big companies – usually within the marketing team and generally at quite a junior level with the help of advertising, digital, PR agencies and self proclaimed gurus. It has been a period of trial and error and not a lot of strategy!

According to a recent survey of blue-chip companies*, a legacy of this experimentation is that the average company now has 178 official social media accounts! With very little co-ordination or consistency between different regions, product lines or business units there is huge potential for confusion for the customer.

In fact, the result in the growth of social media and experimentation is the sheer amount of data it has produced and the white noise it creates. In a nutshell, companies have been struggling to determine what social interactions are useful and profitable because there is so much data.

Hal Varian, Google’s Chief Economist
“We used to be data poor, now the problem is data obesity, between the dawn of civilisation and 2003, we only created five exabytes of data now we’re creating that amount every two days”

Companies are drowning in a sea of consumer status updates, videos and blogs that has left many brand owners feeling cast adrift from the certainties of the broadcast age. Essentially, many companies fear that they have lost control of communication with consumers.

But this is changing in 2011…it is clear the BIG companies have understood that social media is too important to be left to the marketing department. Over the past 12 months they have been busy creating lots of new senior social media roles** with cross functional roles and significant new budgets.

Jeremiah Owyang defines these new roles as the rise of the Social Corporate Strategist:

“The business decision maker for social media programs – who provides leadership, roadmap definition, and governance; and directly influences the spending on technology vendors and service agencies, While this position doesn’t exist officially by title in every corporation today, this role will become pervasive in the coming years!”

So why is this important for the research industry?

When you look at the priorities of the new Corporate Social Strategist it becomes clear that there is a large role for Research Companies to play as objective and strategic partners.

Altimeter asked 140 corporate social strategists to give their top 3 social strategy objectives

*

The 3 areas where we can add value as research companies is clear:

1. Start to create robust ROI models for their social activities
2. Gain insight from social media to help develop better products and communications
3. Launching ongoing listening and social media research programmes to stay close to consumers needs

Of course experimentation will continue but we are entering a new era where decisions will be made within the framework of an overarching Social Media Strategy and will be driven by the analysis of data and not just on gut instinct. This provides Research companies who understand social media data with a clear opportunity to become strategic partners and help shape how companies can become more adaptive to consumers real-time needs.

* Altimeter Report: 2011 Internal Goals In Corporate Social Strategy

** @marshallk/social-strategists.

These days, it seems that all brands are trying to cultivate their “fans.” They want these “fans” to Like them on Facebook, follow their Tweets, write blogs about them, and talk about them to their friends. Businesses want people to truly love their brands, to the point where they will purchase their products over a competitor’s, even at a greater price, or travel further to visit their store instead of another. Businesses want devotion, but is that all that a fan is? Are fans merely slavish followers?

Let’s start off with a definition of the word “fan.” According to the Merriam-Webster Dictionary online, a “fan” in this context is:

1. An enthusiastic devotee (as of a sport or a performing art) usually as a spectator
2. An ardent admirer or enthusiast (as of a celebrity or a pursuit) ,science-fiction fans>

Neither of these definitions mention products, but this gives us an idea. Enthusiasm and devotion are certainly part of the definition of “fan,” but so are “spectator” and “admirer.” Fans do not only love something, they watch it. And if you watch something, you probably talk about it. Spectators are the proverbial peanut gallery. Brands want their fans to sell products and services to their friends and family so the brand doesn’t have to. But is that really what fans do?

To answer this let’s take a look at some example activities that some fans of non-brand things do, for instance the science-fiction fans mentioned in the Merriam-Webster Dictionary definition. This will let us go more to the root of what a “fan” is. This is by no means an exhaustive list, but it should give us a good starting point.
• Go to see the same movie multiple times in theater and then purchase it on VHS, DVD, and finally BluRay.
• Reread the same book multiple times until it falls apart.
• Write blog posts about science-fiction
• Write fanfiction (fan-written stories co-opting characters and worlds from pre-established publications)
• Purchase related merchandise featuring franchise characters
• Join physical and virtual clubs to meet and talk to other science fiction fans
• Engage on message boards related to science-fiction
• Attend science-fiction conventions where they might dress up as favorite characters
• Have near encyclopedic knowledge of specific segments of the science-fiction genre

To be sure, not all fans do all of these things. Yes, all of these activities indicate a love for something, in this case science-fiction. But being a fan goes further than that. Writing blogs and fanfiction, joining clubs to meet people who enjoy the same genres or stories, attending conventions, and dressing up all indicate that these fans identify with the category. Something about science fiction and fantasy represents them personally. And they’ve taken ownership of it. By writing blogs, their own stories, and dressing up themselves, they’ve made someone else’s creation their own.

Now let’s compare it to what a brand fan might do:
• Create content online about the brand and its products.
• Modify the products to make them their own, like this website IkeaHackers.net.
• Talk the brand up to friends, recommending it without being prompted to do so.
• Purchase every item in a product line
• Engage in communities, such as message boards, that discuss the products, like this Apple fan site
• Stay up to date on brand news
• Have near encyclopedic knowledge of the brand’s products

They’re pretty similar lists, aren’t they? Brand fans also take an item and make it their own, either by hacking it or writing about it.

Let’s take a look at some word clouds, made by Wordle. The one above was made from a blog post on the author’s favorite Star Trek captain. The one below was from a fan blog about Ikea. Looking at them, I’m sure you can tell which one is which, but they are pretty similar in many respects. Both use passionate words like “great” and “favorite,” which is no surprise. But they also use words that tell us about the authors, not just about the subjects. For instance, the Ikea author is discussing a bathroom remodel, and so the words emphasize what the author accomplished, like “transformed” and “found” and “skill.” Meanwhile the science fiction blogger is discussing a favorite character, but the words hint at the traits the author admires, shaping opinion of that character, like “original” and “skills” and “relationships.” These authors are using these brands to describe their own points of view as much as describing the brands. In doing so, they are redefining these brands to suit themselves, not the corporate owners.

Of course, once a fan makes a brand their own, it doesn’t entirely belong to the company anymore. This isn’t a new concept. You may have heard the phrase “your brand and isn’t what you say it is, it’s what your customers say it is.” Most of the time the focus is on the complainers and the dissatisfied. But fans can also shape opinions and activities, and companies aren’t always pleased with the outcomes.

Just take iPhones. Most of the people jailbreaking their iPhones are consumers. People who love their iPhones and want to do more with them, or keep them when they switch service providers. Yet the company does not want people jailbreaking their products. It makes sense. Apple is known for its walled-garden approach, and jailbreaking knocks down that wall. Unfortunately, this locks the company into a fight with the very people who love its products the most. In their new iOS 5, Apple is making jailbreaking even harder.

You can look to the music industry, as well. Who are the people who are illegally downloading large numbers of mp3s? Music buffs. They love the music. The talk about it. They go to gigs and large concerts. And they are reshaping the music industry in ways the industry isn’t too thrilled about.

Brands only want the consumers to purchase their products and encourage their friends to do the same. This is not being a fan. This is merely liking a product more than a competitor. Fans are more valuable. They not only represent increased revenue by themselves, since they purchase many products from the same brand, but they also engage in communities, egging each other on to make more purchases and evangelizing on behalf of the brand. However, brands must pay for this value. Fans take ownership of the brand away from the company, redefining it on their own terms.

If a brand desires love and devotion, it has to be willing to accept that one simple fact: fans take ownership of the brand away from them. Otherwise, they just want people who prefer their products over alternatives, not fans.

One of the most interesting meetings we had in New York a couple of weeks ago when we launched our office there was with someone about social media analytics. She described her role as “all about change and things are constantly changing” so social media research or “qualitative research on steroids” as she described it, is attracting a lot of her attention. Having spoken to a wide range of companies and potential suppliers she divided social intelligence into three broad areas: social listening, social media insights and social media measurement. Interestingly she placed Radian6 and Sysomos in the pure listening camp, while putting us in the social media insights arena – exactly where we want to be. The reason for this is our proposition unites proprietary social intelligence tools together with a team of anthropologists and social media analysts. It is this combination of a passion for and understanding of technology together with a passion for research and analysis that differentiates our social media research offering from just social listening platforms and is helping us to win an increasing number of blue chip clients alongside O2. We’ve learnt that our approach provides a number of unique benefits across four broad areas:

How we collect the data

We believe that it is vital for clients to customize their panel of sources so the search terms are refined to their specific category, competitive set and consumer target. Without doing this the data is too U.S focused and important niche sources specific to key topic areas and core influencers will be missed.

How we process the data

Because social media is made up of many different types of applications, influence spreads in different ways depending on the platform you are in. This is why we have developed specific algorithms for each different channel we track. This helps us to measure what we call the “visibility” of the data alongside the volume. Mapping visibility and key influence is calculated using different indicators depending on the channel we are looking at but it always takes into consideration three key parameters that we have identified as part of this process. Alongside these two key indicators is the way we combine human coding with software analysis so that we can increase the accuracy of our outputs. We crowd source the sentiment analysis with a global panel of “analysts” who are rewarded with micro payments in return for micro tasks.

How we interpret the data

Good, accurate interpretation and analysis comes down to the brilliant team of Face researchers and social intelligence analysts. Our team is growing fast to meet the demand brands are placing on the importance of social media insights. Social media listening as provided by Radian6 and Sysomos was just the beginning – as the market becomes more sophisticated clients will be looking for best in class solutions.

How we present the data

Real time research and planning means you have to craft and shape the insights in a different way. Making data beautiful through well designed visualisations and info-graphics so it’s easy to digest is a key part of this. But we are also learning there are many other important ways to deliver insight through this process.

Finally, many were interested in the next stage of our development: to combine real-time research with other F2F methodologies and on-line qualitative techniques to produce different layers of data some thing we are calling Augmented Research but more on this, another time.

For the past few weeks we’ve been asking if it is possible to use social media to predict how people will behave, and how to go about doing it. Our testing ground has been fashion at Glastonbury Festival 2011 – but while knowing what colour wellies to bring could be fun, knowing how to predict behaviour can help businesses grow.

Now that Glastonbury Festival has come and gone, it’s time to see how our predictions did? All in all, we think we got it pretty spot on…

From this exercise, we’ve learned a thing or two about how to make predictions. Here’s a few things that we learned.

1. Know your limits – detail

Predictions are more useful when they’re specific – more detail provides more information for retailers and brands to act upon. However, in any predictive method the level of detail you can achieve is restricted by the data you have, and people don’t always share everything you might want about their activities and thoughts.

Through social media monitoring for a wide range of brands, we have found different communities online have different norms around talking about products. Some are very specific – e.g. tech fans often talk about equipment model numbers. But others are less so, as turned out to be the case for Glastonbury fashion. For every detailed description of clothing (“my new shiny black Hunter Carnaby wellies), several people mentioned clothing categories (wellies, shorts) without any details of brand, colour or style. Consequently extrapolation is required from the detailed mentions to the overall population, requiring a careful balance between predicting specifically enough to be useful, without going too far and risking accuracy.

2. Look for steady not spiking

When we looked at our most accurate predictions we noticed a common pattern. All had maintained a steady volume, rather than being characterized by spikes or increases in volume.

To take our Hunter wellies example, volumes stayed relatively steady until people switched to discussing their packing rather than their plans. What we can begin to take from this is that the bigger trends tend to stay pretty static over time, so look for solid underlying volume rather than dynamic spikes of conversation to identify them

3. Who’s watching?

Social media is about more than people talking: it’s also about people listening. One of the things we have to understand in social media is whether a piece of content is likely to be read or seen by other people – a measure we refer to as visibility. By weighting the data to take account of this we can see whether one person’s opinion or expression will be read and seen by a wider audience.

At first you might question why this is relevant to prediction, as this is about individuals’ intentions. But choices are often social, through choosing what others choose or just experiencing anxiety around what others think of choices. By weighting data we can take account of not just what people say, but their likely impact on the social environment.

4. Take it beyond social media data

It’s easy when working in social media to forget that a piece of content is surrounded by a whole other world. When looking at the predictions we made that were less accurate, there is a clear impact from external factors; coats caught up with ponchos because of weather conditions, and the two items which needed the most effort (full fancy dress items and customized t shirts) were disregarded in favour of easier options.

This reinforces the need to incorporate data outside of social media into the equation. This could include other data sources such as weather data, or it could simply mean conducting a short piece of qualitative research to establish which clothing options were actually perceived as worth the effort.

So can you use social media data for prediction?

The short answer is yes, certainly most of our predictions did ring true and were accurate. But there is always room for improvement and we’re already looking at how we can draw social media together with other methodologies to form what we call Augmented Research. There’s an exciting future ahead for social media – part of which may well be social media helping us to predict that future.

The Glasto Goes Social series was written by Facers Riki Neill, Jessica Owens & Kate Davids. Click on their names to say hello on Twitter.

In 1980 Alvin Toffler wrote of the “Third Wave“. Following on from the Neolithic Revolution, when agriculture developed to replace hunter-gatherer societies, and the much more recent Industrial Revolution – the second wave – the Third Wave was at that time in the process of obliterating the second wave society. At the time it didn’t have a name, but now it’s known as the Information Revolution or Age of Information ( a name which has seeped into popular culture), the period in which the world is no longer led by economies based around manufacture but by information economies which specialise in innovation, finance and services.

A by-product of this information flooded world is the rise of the new amateur, or the ProAm. The ProAm blurs the distinctions between the professional & the amateur by feeding off the highly accessible information online to pursue amateur interests to a professional standard. The ability to self-publish means that these amateurs often feed off information from other amateurs. The rise of the food blogging community and, by extension, the supper club trend is a great example of the freedom of knowledge sharing from one enthusiastic amateur to the next. No longer are cookbooks from qualified, experienced chefs the only source of information for culinary information – instead you can Google any recipe and be confronted by blogs and opinions on the first page.

I should declare an interest beyond my work as community manager here at Face. I am one of these New Amateurs in the food space, as I pursue my interest in cookery and restaurants through my blog. My experience has been an encouraging one, as the feedback from the wider “foodie” community through Twitter and comments has been both positive and constructive. In a matter of months I feel I’ve learned more about both the technical and cultural aspects of food than I could by reading any number of “expert” publications.

It is the rise of these New Amateurs, these ProAms, which has enabled us to work in the way we do. Co-creation depends upon people who aren’t experts in the conventional sense, but who have a better inherent understanding of the brand in question due to living in the world where the brand has relevance. As community manager I have been taken aback again and again by how savvy the consumers we work with are and how deep their understanding of branding, marketing and innovation runs.

The good news is that this trend is only just beginning as the “digital native” generation comes into its own. As those of us who have grown up around these enabling technologies become the new impetus for business, it’s going to be more important than ever to include the demographic in the development of new ideas and insights, particularly as another side effect of more readily available information is a cynicism about any perceived preaching or one-sided selling from brands. This is a new audience which expects to be talked to rather than at and listened to in turn. Simply shouting about your brand won’t work any more. In 2011, you need a conversation, not a sermon.