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Archive for the ‘Conferences’ Category

Data Visualisation is a key tool in a any researcher’s toolbox nowadays. But since graphic methods were first designed and then revisited with the introduction of computers, we kind of stopped questioning data visualisation in terms of the real value that’s adding to our research and our ability to produce new knowledge.

Now with Big Data and the Real-Time web we are entering a whole new phase in the history of data Visualisation. New challenges lie ahead and new methods are being devised, so we felt compelled to look into it again to try and focus on how exactly data visualisation really helps us make sense of complexity.

Fresh from our presentation at BigDataWeek London last night, here’s a quick intro to the 10 reasons why we like visualising data.

In 2012 we are reaching a tipping point where marketing strategies are finally moving from traditional broadcast to content-led social media engagement. So the question I pose is, What role can researchers play in helping brands succeed in this brave new world?

Here are 3 areas where as an industry we can add real value to the new social marketing process:

Return on Engagement Specialists

The rules for this new model of marketing are still being written and this has led to a position where many digital agencies are still marking their own homework. With the larger investments being made in this space by brands, research agencies are well positioned to play the role of objective analytics partner. As researchers we should be offering clients advice on developing KPIs for their social media activity, helping them to design the right measurement framework, and making sure they select the right tools for social data collection. Beyond simple measurement, researchers also have the opportunity to help clients develop return-on-engagement models that demonstrate the link between behavioural data and the impact on the things that clients really want to measure, e.g. consumption.

Fanbase Analysts

Many companies are learning to listen to the conversations related to their brands and competitors. However, there’s more to social media research than tracking conversations by keywords. Brands are social entities. People establish connections with them (cognitive, emotional, functional) and these connections foster further connections to other people. As brands build audiences online, it is increasingly important to understand and map audiences and the content and passions that connect them. When brands understand their social audience they can design content and strategies to engage them more effectively. Research agencies will have an increasingly important role in helping brands segment their social media audiences and give strategic advice on strategies to engage them.

Content Co-creators

Generating content that people want to share is a difficult business for brands as the traditional advertising creative process is disconnected from the communities they want to engage with.

To create social ideas that have the potential to be loved and shared by people in communities it is important to involve them in the creative process. This is why co-creation as a methodology of developing and refining content will become increasingly common over the next few years. Involving consumers in the production and creative development of content via MROC and co-creation sessions is a process that plays to the strengths of community researchers and those planners with great facilitation and social media expertise.

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.

Augmented Research
View more presentations from Face, the Co-Creation Agency

As someone who has been working on the idea of making brands human by plugging them into the fabric of society, today I definitely couldn’t miss a session called “Brand As API” hosted by Peg Faimon and Glen Platt from the Armstrong Institute for Interactive Media Studies, Miami University Oxford, Ohio.

The premise is clear and simple, and extremely agreeable:

“As brands finally begin to deliver on the promise of a 1-to-1 relationship with their customers (through social media, mobile, and data-driven tools), it is critical to develop a new foundation for that relationship. This requires brands to leave the “broadcast relationship” and, instead, build a relationship sharing communication, innovation, and the very product/service itself. Insight into this relationship can be found in the structure, language, and use of APIs (Application Programming Interface). APIs provide a set of rules – a language for connecting to data and services. To remix. To build. To leverage. To extend. Many API calls provide explicit metaphors for the ways brands can connect to customers. Generally, the API relationship provides insights into the role of brands in the customers’ life. This conversation will explore these metaphors, share case studies, and work to build a language for better connecting consumers with their brands.”

You can look at the full presentation below and get the details on how they think a brand as API might work.

The main idea behind the concept of the Brand as API is that it would allow to open up the Brand, its assets and its services and allow people (consumers, businesses, developers) to do things with that Brand, from playing with the contents and the identity of the brand all the way down to designing products and services.

Peg and Glen went on discussing the key elements of an API and how they relate and map against new ways of building meaningful relationships between brand and consumers.

While this is completely agreeable and sensible, the idea of the Brand as API as crafted in this presentation still seems to rely on two assumptions:

1) The assumption that people want to do stuff with that Brand, pulling information and data assets off a Brand in order to create something custom. And while we know this is true, we also know this only applies to a very small percentage of the user base of the Brand.

2) And the mother of all assumptions: the belief that the relationships consumers have with brands are primary while we know that consumers’ most valuable relationships are with other consumers, and what brand CAN try and do is fit in those relationships in a meaningful and/or useful way, i.e. as social currency or enablers/problem solvers.

It seems that while the analogy between brands and APIs has got incredibly long legs, we are still looking at it from the wrong perspective: the brand perspective.

What if, instead of focussing on what the API allows the user to Pull we start focussing on what the API allows the user to PUSH, meaning allowing the user to ingest a controlled and owned selection of brand-relevant personal data into the brand API such as user context, passions, interests and behaviours?

What if I could feed for example my location data to the API of my mobile network operator (plugging in my mobile gps, Foursquare or Sonar data) and get the most customised international plan based on my travel habits?

And what if consumers could ‘sell’ this personal data to brands? Consumers used to pay brands for products. We are now heading towards a future where digital data abundance means brands are going to pay consumers for their personal data. Users get customised offerings while remaining in control of their personal data, brands increase their relevance by investing on live audience intelligence rather than push strategies.

This is why I believe the biggest added value of a Brand API lies not so much in the ability to provide a Brand-to-User stream of data rather in its ability to manage a bi-drectional stream of data, where the user can shape the brand around itself using the vast amounts of personal data he is in control of.

And this is why i believe the biggest and most important asset of a brand API is not the Brand Essence, rather the User Profile.

Such an API would not be shaped around the brand but around the user and his needs. And effectively it would be an Audience API rather than a Brand API. Something that could sit at the centre of the business and power any decision the business has to take, from innovation to marketing to CRM.

But the thing is, in order to be plugged into the fabric of society brands probably need both, or even more than two APIs. Like any other social product/service out there.

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:

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

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

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

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

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

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

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What can you do with it?

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

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

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

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

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

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

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

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

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We grouped the findings in three areas:

Mapping the Social Graph


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

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

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

To get this information we had to map:

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

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

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

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

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

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

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

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

Mining the interest graph / profiles and behaviours

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

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

-       When did they join Twitter?

-       Where are they based?

-       Where do they tweet from?

-       How often do they Tweet?

-       When do they Tweet during the day?

-       How many people are following them?

-       How many people are they following?

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

-       How influential are they?

Mining the interest graph / interests and passions.

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

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

-       What does the audience talk about?

-       How and why do the topics change over time?

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

-       Which contents get shared the most?

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

-       Which news sites are referred to more often?

-       Which brands and products do they talk about?

-       Which adverts do they mention?

-       What movies are they into?

-       Where does the brand fit in this landscape?

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

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

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

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

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

Feedback and questions welcome, belligerent challenges even more so.