Summer vacation time is a key time for many food and drink brands, particularly those for whom teens are a key audience.  It’s open season right, parents are off guard and teens have more freedom?

To test how this plays out in social media, we set out to see just how much difference there was between summer vacation and school time in terms of how some key brands were talked about by teens in social media.  Was there more of an emotional connection with these brands in vacation vs. school?  Is vacation time really a time for treating?

We decided to focus on how brands were talked about in the context of family purchasing.

We tracked mentions of 5 key brands in connection with purchases by family members (mom, dad, brother, sister) across 2 summer vacation weeks and 2 term time weeks

The first thing we noticed was the in the family purchasing context, these brands were talked about more in School Time than they were in Summer Vacation

Yes, that’s right; in a family context these brands were mentioned more during the school period, so when it comes to family purchasing for teens, we see that it was something to shout about more in term than out of term..

So they are talked about a little more in term, but are they talked about differently?

After delving into the data, one very important difference emerged between vacation and term time.  In social media, these ‘treat’ brands are something to shout about much more emotionally during school time

These brands may be more plentiful during the holidays, when teens have more time to enjoy, but they are actually more valued as a family purchase during the school term.

The summer time Tweets were factual. The treats were enjoyed, but the teens weren’t very emotive, particularly when compared to the second search.

Across these mentions of family lead purchases there is a continual pattern of enthusiasm around the treats during the school term. Though the words themselves might be similar, the punctuation and use of smileys is more in evidence in the school time Tweets.

When it comes to purchases by Mum and Dad, Treat brands can be even more treat-like during the school year.

They mean more, perhaps, because of their rarity.

Brands are always looking to leverage a seasonal and emotional connection with their target audiences, but shouldn’t lose sight of the fact that even out of the open summer season, there are still reasons – and perhaps even more so – for teens to shout about how much they enjoy them!

For researchers this is exciting because it means so much is up for grabs. With change comes opportunity; the opportunity to meet emerging client needs head on. One of these is how to ensure an idea has the best chance of success in the shared and created media space. It was a question that was at the heart of a recent project we did for a major ice cream brand. The brief was about launching the brand successfully in a social way in a new country with a discerning taste for ice cream. It allowed us to show how our new thinking delivers better results for brands craving success in the earned and created media space.

The 4Cs Proposition

There were four key stages to our approach that fed into each other namely, conversation, content, communities and conversion. Our model is powered by our philosophy of co-creation (doing things with not at) and technology (our social media insight tools). It is circular and iterative more of a loop or series of loops as we believe that the new marketing-cycle is no longer linear, planned over 3 years and populated with campaigns that have a beginning and an end. The role the consumer plays in each of these stages is crucial but I am just going to talk about the first two for now.

Conversation

This stage is all about identifying and understanding your key audiences within the context of the brand landscape in real time. By using Pulsar we have developed a more dynamic way to map audiences through the social web. This helped us to identify four key cohorts within the brand’s target audience. One of them we identified as the group most likely to embrace and propagate the social mission of the brand based on their passions, interests and behaviour. It was this cohort that we invited into the community and to co-create the creative platform that would best link the brand mission to content and conversations consumers were already engaged with. This stage highlights why brands need to stay on top of what’s truly important to audiences at any given time. It is less about isolated market research data and more about understanding your customers, in the moment. This requires a data processing and data analytics model that will allow a more real-time, agile and active approach to planning based on what people are doing and saying with each other as it happens.

Content

Co-creating with the right cohort of the target audience through an on-line community and face-to-face co-creation workshop allowed us to do two things very well. The first was the ability to generate a range of creative platforms rooted in genuine consumer insight that linked the brand mission to the target audience in a relevant and credible way. The second was the ability to generate hundreds of ideas within the umbrella of the creative platform that leveraged existing consumer content and enabled the brand to join current consumer conversations and activity in an engaging way. This stage showed that building platforms by co-creating with consumers is the best way to finding and sourcing potential areas of content that either already exist, could be created or added to that can inform a content strategy to support the given creative platform. Once this is in place consumers working together with the brand can populate the content areas with loads of ideas that have the potential to start lots of little “fires” some of which will take off and some of which will go out.  The involvement of consumers though means that brands will have worked out why they have permission to be in that consumer space as well as what role they can play there.

Curating diffusion

The work we did with this ice cream brand was a brilliant example of how to tackle the challenge of creating ideas that have legs in the shared/created media space. The role the audience plays in making this work is key and understanding there are many community cohorts within a target audience you can potentially co-create with and getting the right one to do this with is important if you want to be successful. This helps the brand to understand and identify those content areas within the creative expressions of the “Big Idea” that are already in play in the lives of consumers. The next stages namely Community and Conversion are all about curating “diffusion and monitoring what we call return on engagement. But more of this another time.

So far the trick worked: high levels of social media mentions and engagement = social relevance. But this case is different. Nine films are nominated for Best Picture at Sunday’s Oscars. According to many sources, “The Artist” is the favorite to claim the big prize. But the Academy choses the winners, not the general public. Or does it?

Yes and no. The members of the Academy are members of the audience too, and as such they are influenced by the people who surround them, especially the ones that are most similar to them, and share similar tastes. However, there are many other factors that come into play in this case, and a simple prediction model based on social relevance (= high levels of mentions, sentiment, engagement) will probably not do the trick.

First of all, sheer volumes of mentions in this case are less relevant than they are in a political election or in any other public event shaped by the audience.

A few other studies on the Oscars have used volumes of tweets or likes on Facebook as indicators. One study is predicting The Help to be the absolute favourite. Another one predicts the Midnight in Paris to be the favourite. There seems to be a little confusion around.

Our data points elsewhere. First of all, we didn’t just measure volumes of mentions of the movie, we looked at volumes of mentions in relation to the award nomination. And not only at that: we looked at the sentiment of those mentions, their visibility and the engagement they generated.

Second, this can’t be just about social media as the final judgement will be expressed by a panel of experts/practitioners. We think social media data is most useful when mapped against other data streams, because social media doesn’t happen in a void.

This approach is part of what we call Augmented Research. In this study AR meant combining the following streams of data harvested for two weeks (Feb 7th – Feb 21st):

1) Volume of tweets, status updates, blog posts, forum posts, news articles, images and videos.
2) Odds for each movie nominated against each Award.
3) Box Office Data for each movie.
4) Experts ranking via Polls and online ratings.

So we have been looking at something like this for each movie:

We are not going to delve into the details of the graph above, but what is interesting is that there seems to be a correlation between the box office data and the social media data. Peaks at the box office anticipate peaks in social media in smaller and smaller increments. We haven’t seen any of the opposite: peaks in social media anticipating peaks in the box office data. Which could potentially indicate something interesting in terms of influence dynamics and the relationship between traditional media and social media, at least for now.

But let’s not digress. We wanted to see if any of the above could be of any use to predict which film is going to win the Oscar for Best Picture on Sunday.

We started looking at volumes of buzz around each of the nine nominated movies (Feb 7th – Feb 21st). The doughnut below shows days as circles and within each circle the proportion of buzz associated to each movie.

According to this model, The Help should be the Oscars favourite, but the ranking is rather balanced:

1) THE HELP
2) MONEYBALL
3) THE ARTIST
4) THE DESCENDANTS

We then introduced the Sentiment of those conversations in order to weight volumes. But the landscape got even more balanced. Unfortunately.

We decided to try something else. When it comes to the Oscars, social relevance doesn’t necessarily mean being Award-worthy. So we then looked at just the conversations that were related to the Oscar nomination for Best Picture (“movie title” + “Oscars” | “movie title” + “Best Picture” and so on for 15 stings per movie, Feb07th-Feb21st). We started seeing some clear movements in the chart.

The Artist got some serious traction and the new ranking looked like this:

1) THE ARTIST
2) THE HELP
3) HUGO
4) THE DESCENDANTS

Although The Artist looks solidly ahead (more than double the volumes than any of the contenders), there is still a good chance of a catch up, especially since all the top contenders are extremely close to each other.

We needed another opinion, and we asked it off the people who are actually closer to it all: the critics. We pulled some good data off Metacritic and layered the critics score on top of the social media scores. We used the Metascore, based on 40+ critics globally for each movie. And this is the result.

The Artist is now clearly running away and the competition lags behind in a rather compact front of four movies including The Descendants, Hugo, The Help and Moneyball.

In search for an even safer bet we then looked at the betting experts. We layered the daily data coming from the bookies for each movie on top of the social media data. And this is what happened…

Well, this kind of helps. I guess we will be placing our bets on The Artist as Best Picture at the 2012 Oscars.

A few people have been campaigning in support of The Artist. We mapped them out and found out that one of them is Bret Easton Ellis.

We will be watching the Awards Ceremony tomorrow night and check whether our prediction was any good. Not that we are going to make any money though, looks like this is the safest bet ever.

]]> 7 http://www.facegroup.com/mapping-the-brand-graph-a-study-of-the-o2-audience-on-twitter-face-and-o2-warc-datacentric-2011-london.html http://www.facegroup.com/mapping-the-brand-graph-a-study-of-the-o2-audience-on-twitter-face-and-o2-warc-datacentric-2011-london.html#comments Tue, 06 Dec 2011 12:49:54 +0000 Francesco http://www.facegroup.com/?p=8506 Back in March we presented at WARC’s Online Research Now & Next Conference, introducing what we then called Augmented Research.

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.

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.

Three weeks on from Face’s return visit to Said Business School at Oxford University (this time Sharmila was accompanied by me rather than Fran) we have some of the results from the social media research exercise the students carried out on a variety of topic areas using Face’s proprietary social media monitoring tool, Pulsar.

To varying degrees there were a range of interesting observations and insights around behaviour, events, sentiment and social context that the students were able to draw from their analysis of Energy Drinks, Milk, Wine, Diet Soda and Absinthe. There were a lot of comments about the importance of being able to customize sources so that you can hone your searches; adapt key words (its an iterative process after all); the ability to dig deeper where necessary was also highlighted, as well as the need to be very clear at the outset in terms of what you want to achieve. What the students discovered is something we have learnt over the last 18 months and that is social media monitoring in itself is just the beginning; the quality of the data, what it means for brands, why it is important/interesting and most significantly how this builds into an iterative and adaptive insight and innovation approach is key. The thinking, interpretation and analysis that Face’s researchers bring to the data is just as important as the data itself, where it comes from as well as the way it is collected.

This is why one of the most insightful observations for me related to the presentation on Absinthe because the students here applied some thinking to a possible route for a brand in this space. I didn’t know this but Absinthe was banned in most of the EU until its revival in the 1990s, so to get hold of a bottle was seen as quite a coup. Being around 70% proof the correct way to drink it was with water (which, when added I believe gives it the famous luminous green colouring), and I think you’re even supposed to pour it into the water over a spoonful of sugar.

One key theme (potential platform) to be explored from a marketing perspective was the opportunity to build on its association with the “mysterious”. Absinthe can be ‘earned’ in the online game of Mafia Wars, distributed in a Mystery Bag. The ‘free gift’ results in boosted attack skill (+25%). Another example the students highlighted came from a movie awards event that captured the following “Minutes after accepting his second Movie Award for Best Villain, MTV News was chatting up “Harry Potter” star Tom Felton about the win; where he’ll keep his award and so on when Foo Fighters front man Grohl suddenly jumped into the frame and asked what they were talking about.

He went on to congratulate Felton on his win with a friendly hug. Grohl ended the brief encounter by extending an invite to a visibly star-struck Felton to join him for some post-show Absinthe….

Overall the results demonstrated why there is a real need for new, evolving research methodologies, and the significant role that social media research can play in the marketing and research arena.

The second part of the morning moved onto co-creation where we gave a quick presentation on “Co-creation: what’s it all about” including what makes good co-creative practice as well as some live case studies.

In the spirit of one of our key co-creation principles we turned the students from passive respondents (just listening to what we were saying) into active participants where they could become co-creators in the “Wallet Exercise”. This interactive exercise is designed to give participants an understanding of the customer point of view, experience of identifying opportunities, rapid prototyping, and giving a pitch.

Participants start by interviewing one another about what they like/hate about their wallets and based upon what they learn, they then build new wallets for their customer using a range of individual and group thinking techniques. Here is a video of the winner!

Since then we have had great feed back from students some of whom are coming in to see us for a potential job and we are looking to build our association with SBS on future potential projects.

Yellow dots = influencers / Purple dots = users retweeting, mentioning, replying. For the sake of legibility the number of reactions visualized in the chart (the purple dots) has been capped, however the figures on the right hand side are the actual reactions triggered by the user in the given time frame (July the 6th / July the 8th).

Below are three visualizations that show how the network of influence evolved over time.