A little over a year ago, our Francesco D’Orazio presented this slideshow at the WARC‘s “Online Research Now and Next” conference. Since then it has been one of our top presentations on Slideshare. Augmented Research is still relevant, which makes this presentation another installment of our Top Posts of the Past Series.
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!
With the arrival of networked consumers have come huge amounts of user-generated content, shared conversations and the explosion of Big Data. As a result we now live in a new marketing ecosystem where the shape of brands is changing. At Face we see them more as social entities where the coating of the brand core is shrinking and the layer of earned and created media space is growing (see diagrammes). Even though it is still just as important for brands to carve out distinctive, emotional, enduring spaces that people can rally around, we need a more adaptive, continuous and real time research and marketing model to make this happen.
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.
Increasingly, companies and organisations are using social media as a crystal ball to predict the future. Negative spikes in sentiment to predict a drop in stock prices, explosive volumes of mentions to predict the election of a candidate (or a hung parliament, as Tweetminister predicted at the last elections. Check out a couple more examples here and here)
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.
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.
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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:
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?
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:
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:
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.
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?
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.
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