research

How we can get you closer to your customers to deliver robust insight quickly

research +

at workes

How we can open up your innovation process to deliver breakthrough ideas

innovation +

BRAND2 180111

How we create social brands to deliver social brand value and social brand equity

brand +

Archive for the ‘Insights’ Category

Insights, sxsw

Facebook Twitter

Brand Patterns

1

The Brand Paradox
One of the most interesting sessions I attended last week at SXSW was the panel “Brands as Patterns,” which we mentioned in our post “5 Panels for Researchers to See at SXSW“. The title for this session comes from a paradox. Traditionally brands have been definitive, singular and complete focused on the 3 year brand plan to deliver consistency using repetitive messaging while consumers interactions with a brand are more iterative, varied and changing in real time. Brands today though need to be both consistent and different, definitive and iterative. One way to help us make sense of this paradox is to see them as patterns – patterns create consistency through difference.

A person spinning clay

Photo by BLW Photography on Flickr

The shape of Brands is changing
We all know that the shape of brands is changing. At Face we see them much more as social entities because of the interactions, conversations and content consumers are sharing with each other, in and around the brand. This dynamic means that what the brand can be or mean to consumers is constantly shifting. Hence today brands are more about shared experiences defined as much by the user as by the brand manager.

Brands need to be coherent rather than consistent
Marc Shillum from Method who was one of the panellists agrees stating that brand value is defined by this two way experience and continued iteration. He goes further by saying the brand should be the interface of these experiences so “put the brand in the interface not on it”. Seeing brands as patterns and moving the debate from brand consistency to brand coherence is key. Shillum argues that “It is better to strive for coherency, where consistency in design is married with a system of meaning that people can believe in and choose to be a part of: the brand. This belief comes from the brand, and tying the two together - interaction and brand – in a coherent system will facilitate experiences that are far richer and longer lasting. So we must create the brand pattern. By understanding as much as possible what the brand means, how that meaning is constructed, and what elements make it unique, we can begin to explore and define patterns of behavior that help support the brand meaning in a way that is also valuable for people”.

Brands need to be Active and have a Rhythm
The theme of brands as patterns was continued by Greg Johnson the Global Creative Director of Hewlett Packard who talked about casting a set of principles and context to “pour the brand into”. This helps the brand to be coherent and distinctive by owning signature expressions that are varied but recongnizable, giving continuity to how the brand manifests itself but in a fluid and iterative way. In his view brands need to be active, built by what it does not what it says. Robin Lanahan, Brand Strategy Director from Microsoft talked about pattern language in brands being about the story – the story endures as the context changes so brands need to have a rhythm.

A violin on sheet music

Photo by M-Trudeau on Flickr

Brands need to have smart variation
Finally Walter Werzowa, Composer, compared composing to developing a brand. If music is too repetitive it is boring, too changeable then it is chaotic – both result in losing your audience. But if brands display smart variation like Beethoven then that’s different. I’m no musician but apparently in the first part of Beethovan’s famous 5th Symphony you hear the same motif 45 times yet he only repeats the motif in exactly the same way 4 times – the other 41 times there are variations to it yet we still recognise them to be  connected to the same motif. So we lose audiences with either too much chaos or too much repetition. He argued that patterns are a driving force in our brains so we are open to pattern recognition.

What this means for research
Of course, this is still just theory and observation, but it does pose some questions that researchers can explore, such as what does a successful brand pattern look like and what does a poor brand pattern look like? Researchers now have the challenge of creating a real time measurement model that can bring this to life with simple visualisations, and this is something we should be all be looking into now.

Augmented Research, Awards, Blog, Insights, Oscars, Pulsar, SMinR, Social Media

Facebook Twitter

Is a silent, black & white movie the strongest Oscars favourite ever? Social Media meets the Bookies.

  • Date February 25 2012
  • Posted by Francesco
  • Tagged with
7

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.

Blog, Co-Creation, Communities, Insights, SMinR, Trends

Facebook Twitter

2012 Resolutions for MR Agencies

  • Date January 12 2012
  • Posted by Job
  • Tagged with
1

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.

Blog, Co-Creation, Communities, Insights, Social Media

Facebook Twitter

2012 through the lens of client needs

  • Date January 11 2012
  • Posted by Andrew
  • Tagged with
1

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.

Understanding influence in social media
View more presentations from Face

Blog, Insights, Interesting Stuff

Facebook Twitter

Data driven insight, decisions and actions

  • Date December 08 2011
  • Posted by Andrew
  • Tagged with
0

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

  1. Pages:
  3. 1
  4. 2
  5. 3
  6. 4
  7. 5
  8. 6
  9. 7
  10. ...
  11. 26