‘Digistalgia’: has social media shaped teen attitudes to brands?

I think, or at least imagine, it has always been the case that a shorter passage of time need pass before nostalgic tendencies develop in younger people.

To a teenage girl, a few years will clearly appear to be a far longer time span than to a woman twice her age.

However, the digital age appears to have ushered in a new era of early-onset nostalgia.

In much of the social media research I’ve done, younger teens are increasingly reflective when discussing brands that they used to eat, drink or play with only a couple of years ago.

It’s easy to envisage it has always been this way but I think social media, and the particular way younger people use these sites, is playing a big part in advancing this.

For most adults, Twitter is primarily a broadcasting tool and the intention is generally to reach as many people as possible.

As the widespread infatuation with tools like Klout highlights, people (and ‘professionals’ in particular) are increasingly obsessed with showcasing the impression of digital influence.

For this type of user, the number of people following you is the most obvious illustration of your social media success.

In contrast, teenagers are far more likely to use Twitter as a tool through which to engage in genuine conversations; hence the increased likelihood they will ‘lock’ their profile as private, often referencing a stranger following them as ‘creepy’.

For many teenagers, the number of times they’ve tweeted is regarded as more of a badge of honour than the number of followers they’ve acquired.

Younger Twitter users frequently reference milestone tweets, and indicating how long and often they’ve used the site appears to be more important than how many people are actually paying attention to them (“4000 tweets is a big accomplishment for me!”).

It’s hardly surprising that tweeting thousands of times a year, and being reminded of that tally on a daily basis, creates a greater sense of more life having been lived than is actually the case.

Perhaps in a period of uncertainty, encroaching student debt and widespread youth unemployment, there is an ever stronger desire to cling to the familiar and those brands which remind young people of their early teenage years take on a heightened and more immediate importance.

There’s a clear opportunity for ‘fun’ brands, like children’s cereals and soft drinks, which are encountered on a daily basis to leverage, or even create, excitement without the need for a specific social media campaign.

J2O’s Glitter Berry is an example of a launch that caused near breathless excitement amongst many teenage girls, keen to Instagram photos of the glittery drink and share them with friends on Twitter and Facebook.

Very simple and classic marketing tricks, like adding a toy to a cereal box, can often spark more online enthusiasm than a campaign aimed at generating social media ‘buzz’.

Nostalgic brands typically lack relevance but this newer form of ‘digistalgia’ appears to provide an opportunity for a much wider range of FMCG brands, in particular, to tap into.

(image courtesy of Spree2010)

It’s just another brick in the (research) wall

Two criticisms that we often face when discussing the effectiveness of social media research with clients are:

i)  People only post in very particular situations (so we’re effectively only capturing opinions expressed in front of a laptop at home).

ii) A person’s online persona isn’t a true reflection of who they are and how they behave in ‘real-life’ situations.

Whether it’s through a survey, in which consumers attempt to recall and rationalise past behaviour, or a focus group, with the problem of observer dependency and the setting itself, conducting any form of market research will always be open to some level of criticism.

I’d reiterate at this point, that we see social media research as just another stream of insight through which clients are able to augment other forms of research to make more informed decisions.

However, I think it is important that these particular criticisms are challenged as they’re based on a couple of false premises.

More than half of Britons now own a smartphone and research from Lightspeed indicates that 73% use their devices to access a social networking site on a daily basis.

This has led to a situation in which a very sizeable proportion of the UK population can immediately tweet or update their Facebook status with a comment on any brand they’ve just come into contact with.

As Relish Research’s Monique Drummond recently told the MRS Shopper Research Conference, to better understand consumers as shoppers, researchers need to stop relying so much on claimed behaviour and focus instead on actual behaviour.

In a very short space of time, we’re witnessing more and more people expressing their opinions about brands in real-time, not once they’ve returned to their PC later in the day.

Yes, we probably are capturing more polarised and virulent opinions as a result of their underlying motivation to share their views with a wider audience, but I don’t think that’s a valid reason for ignoring it; it’s just why it should be viewed as another methodology available in the market research mix. It’s also why the research always needs to be understood in context.

In any case, with an increasing number of people shopping online and always having a smartphone to hand, the argument that our online persona is different from our offline one is becoming increasingly redundant for the simple reason that it’s becoming more and more important for brands to understand how people behave online anyway. In fact, in time, I think we’ll start to see more traditional research methodologies facing the reverse criticism.

As an industry, we seem to have moved on from the point where results from surveys or focus groups are always met with scepticism and attacked for their drawbacks.

Perhaps once the uninformed evangelists stop claiming social media research will replace all forms of market research, we can move to a point in which it’s also viewed as just another tool at the disposal of researchers, with its own distinct advantages and unique shortcomings?

(image courtesy of Jude Doyland)

The man behind the machine: the (un)changing role of the researcher

The increasing abundance of consumer-driven data has led to the development and adoption of tools that have the ability to mine, aggregate and make sense of (a large proportion) of this information.  There is no doubting the opportunities this presents, but what does it mean for the role of the researcher?

You might think that the development of technologies such as machine-based learning or text analytics to deal with large, pre-existing data sets would ensure greater impartiality and accuracy by removing researcher bias – by removing the researcher altogether. It would appear these developments eliminate the need for researchers to form questions, recruit respondents, and ask them about events and feelings which occurred in the past.

The data trail we inadvertently leave behind us gives companies access to wide-ranging information about what we do online and offline, meaning that research findings should be based on more robust data than when generated via traditional research methods. But is this true, and does this mean that the role of the researcher will soon be defunct?

In reality, the technology-driven analysis of Big Data sets still only provides us with a set of generalisations that are formed by judgements and are open to interpretation.

Before these data sets can be analysed by one or more of the various tools available, there are still some questions to be answered. We need to define which data sets to look at, how to access them, and which tools to use to analyse them.

We are a long way from having a tool which has the ability to look at all available data sets from all angles and provide us with an in-depth understanding of the content. Each tool currently available has varying capabilities, and we often need to use a variety of different ones when looking at any one data set.

Furthermore, the format and context behind each data set varies by platform, meaning that each set of data has to be accessed in different ways and analysed on different terms.

These are the problems we face each time we conduct social media research at Precise. We use a range of different tools, such as Crimson Hexagon and Radian6, to tap into social data to provide insights for our clients. But in order to get to these insights, we first have to decide which data sets we will look at, and which tools we will use to access and analyse them.

These decisions always involve a level of judgement and we can only begin once we have formed a hypothesis or question, such as ‘What are people saying about X within forums?’ or ‘How many people are talking about X on Twitter?’. These questions give us a starting point, shaping which data sets we will look at and which tools we will use to do so.

The influence of the researcher continues as the search terms and parameters of analysis are defined.

When conducting the analysis, the tools effectively perpetuate and reinforce the judgement and decisions made by the researcher during the set-up process – therefore a sophisticated understanding of the various social platforms and the types of content generated on them is essential to conducting sound analysis, as is knowledge of and access to the tools available for use.

Turning the data reads and analyses returned into meaningful insights requires yet another layer of interpretation, knowledge and skill.

The increasing abundance of data and growing range of tools with which to access and analyse it means that, in theory, decisions should be more informed and based on more robust evidence.  But it does not necessarily eliminate the role of the researcher or eradicate their bias.

Instead, the knowledge, skills and influence of the researcher is becoming increasingly important in the quest to draw meaningful insights from the growing sea of Big Data.

(image courtesy of  Kheel Center, Cornell University) 

Could a ‘social media poll’ prove to be dangerous?

With the London Mayoral election just around the corner, and, on the other side of the Atlantic, the race for the Presidency fast approaching, I have no doubt we’ll see even more agencies releasing ‘opinion polls’ derived from social media data.

With more people using social media, there is, as Tweetminster have shown at the national level at least, a growing validity for using this data in a predictive capacity.

However, as I’ve discussed on this blog before, I don’t believe social media analysis is in a position to replace more traditional political polling and, unless we quickly see a far more representative sample of the wider population using social networks (and also willing to share their political opinions), I’m unconvinced it will be anytime soon.

However, with an increased willingness amongst the media to use this data, I think it’s important to understand what effect this could be having on elections.

Building on German political scientist Elisabeth Noelle-Neumann’s “spiral of silence” theory, in 1984, Lang and Lang suggested that poll results can set the agenda by reinforcing majority opinion.

Four years earlier, Public Opinion Quarterly made the link to agenda-setting explicit by stating that “over time, people’s political beliefs and behavior have been affected by evidence of polls presented by the press—a special case of the larger claim of the mass media’s agenda-setting functions”.

If the reporting of social media polls can have an impact on voting (either through a bandwagon or ‘backing the underdog’ effect), I think we need to better educate the media as to how we’re arriving at these results and be transparent about some of the current limitations of using this data.

As we’ve seen in recent predictions in the GOP Primaries, fringe candidates, such as Ron Paul, can be vastly overrepresented in social media conversations and their predicted vote consequently becomes overinflated.

As individuals begin to game tools like Klout and exert influence by merely creating an illusion of influence, there’s a very real danger that extremist parties will see social media polling as an opportunity to manipulate polls and attract more media coverage.

In 2006, the misreporting of a JRRT report, which stated that a quarter of Londoners would consider voting BNP (wrongly, and widely, reported as revealing that 25% of the UK population were considering voting BNP), was followed by a YouGov poll placing the party on 7%.

Despite the fact Populus had the party at less than 1%, the YouGov poll resulted in another bout of media coverage for the BNP reinforcing their poll boost.

The coverage in the press helped legitimise the false idea that the BNP was an ordinary political party and they went on to increase their local election votes from just 3,000 in 2000 to 230,000 in 2006.

It’s not too much of stretch to envisage a situation in which a rushed, ‘real-time’ poll, based merely on a volume of mentions for a candidate or party within social media, could be manipulated by a small number of individuals (or even misrepresented by researchers not taking the time to understand why they’re being mentioned in the first place) and lead to an extremist political party being reported as leading in the polls.

If it’s for no reason other than this, I think it’s important that we all start being more open about, not only the limitations of social media polls but, in particular, the methodology behind how we’ve arrived at the results.

Using a social media monitoring tool to quickly grab a share of mentions without understanding what’s being said is not a poll and it’s not necessarily without consequences either.

(Image courtesy of Martin Bamford)

The single hero brand: a high-risk strategy?

Samsung was recently crowned the king of the smartphone market by the media after it announced profits had surpassed expectations and doubled in size.

Samsung’s results no doubt signify a blow to rivals, including Apple, but the conversations within social media appear at first glance to tell a different story.

Among conversations about Apple and Samsung, Apple generated more than double the share of voice compared to Samsung.

In fact, Apple outperforms all of the handset brands we looked at in terms of both the volume and favourability of posts.

It appears that, despite lagging behind in the sales race, Apple dominates social media conversations about mobile devices with its iPhone brand. Our hypothesis is that, in addition to the many well-known attributes of Apple, its long-standing architecture model of having only very few ‘heroic’ product brands, such as iPhone, helps to create less diffuse conversations.

Samsung is now deploying Galaxy as a hero product brand for its mobile devices, and it will be interesting to see if this pays dividends in more conversations that mention Galaxy.

However, there are several dangers in a ‘less is more’ brand architecture model.

One danger is that rather than being too diffuse, conversations actually become too focused around a brand, amplifying its perceived presence.

For a mainstream brand, this is no great issue, but for a brand like Apple, for whom exclusivity is an important component of desire, perceived ubiquity – when the reality is that iPhone is not the most sizeable player in the market – is no great advantage.

Apple’s volume advantage over Samsung in social media conversations may therefore be a decidedly mixed blessing.

Another risk is that, the fewer strong brands a device manufacturer has, the greater the chance of contagion across the franchise if something goes badly wrong.

As an extreme example, the service outage which hit BlackBerry users in October last year led to the condemnation of BlackBerry handsets by many – evident in the overwhelmingly unfavourable conversations within social media (“Why is my blackberry not working? Oh yueeeeah.. Blackberries are s*** /:”).

Ultimately, it may not have mitigated against the damage done to the Blackberry brand, but the lack of equity in Blackberry’s product brands, such as Bold, did mean that there were very few ways in which consumers were able to make a separation between the service failure and any affinity they might feel for their specific handset.

Unlike Blackberry, Apple does have a heroic handset brand, but it only has one, and the risk of contagion should something go wrong is therefore arguably still present.

A third risk of a ‘less is more’ model is that, should a manufacturer brand or hero device become unfashionable, a speedy decline is a real risk.

As Motorola, Nokia and Blackberry have all found, the brand lifecycle in mobile devices appears to be accelerated beyond that experienced by brands in many other categories – perhaps a number of heroic product brands, whilst undeniably expensive to create and build, might provide the best insurance policy against the vagaries of mobile device desire.

(image courtesy of JD Hancock)

People don’t really know why they do what they do

According to Nielsen’s latest ‘Global Trust in Advertising’ report word-of-mouth recommendations and reviews, either from someone they know or a stranger’s opinions online, are the most trusted sources of information for buying decisions. In contrast, trust in paid advertising is reported to remain on the decline.

I guess, as a social media researcher, the finding that online word-of-mouth recommendations and reviews are so highly regarded should be the most interesting aspect of this study because it adds weight to what I do. I guess it would equally be easy to jump on the power of ‘online word-of-mouth’ bandwagon too. However, the thing that really struck me reading this study, was how dated it made the traditional survey look.

Of course, people like to believe they’re savvy and rational beings who can see through any cynical attempts to persuade them to buy products. An “advertising doesn’t work on me” attitude prevails.

And yet the same study finds that global ad spend increased 7% from 2010 to 2011, driven by a 10% rise in that written-off and decried medium of TV advertising.

Why are companies spending more on a tool of persuasion that is increasingly viewed by the general public as unpersuasive?

As Duncan Watts states in his book ‘Everything is Obvious (Once You Know the Answer)’ not only are we poor predictors of our own actions, we’re also poor at explaining our own situation.

A study by Joel Cohen and Marvin Goldberg highlighted more than 30 years ago that post-purchase rationalisation is a cognitive bias whereby someone who purchases a product or service overlooks any faults or defects in order to justify their purchase.

In an age where information is so readily accessible online, we’re arguably more likely than ever before to believe that any decisions we make are entirely rational and informed.

A survey essentially captures our subsequent rationalisation of our past behaviour and, as these studies have shown, we’re pretty poor at reflecting on the reasons behind the decisions we’ve made.

One of the advantages of using social media to research opinions is that we’re able to capture opinions expressed in the moment which, not being conveyed in a research environment, are less likely to suffer from any post-rationalisation.

An analysis of social content can reveal what statements made during more structured research don’t always reveal.

Qualitative analysis has always relied heavily on the researcher’s interpretation of the unspoken, but the advantage of using social media as a source of insight is that it enables us to take a more evidence-based approach because the spectrum of willingness to reveal appears greater.

As I’ve stated on this blog before, I don’t believe social media research is in a position to replace surveys and I don’t believe it ever will be. However, I do believe that using it in conjunction with more traditional forms of research puts a researcher in a position where he or she is able to provide more informed and, ultimately, more illuminating insight.

(image courtesy of Millionaire Mindset Secrets)

If the only tool you have is a hammer, you tend to see every problem as a nail…

Social media research plays a crucial role in the research mix – uncovering insights that traditional methods cannot.

As we talk with more people about how and why it is important to draw consumer insight from social content, we are often confronted with the thorny issue of ‘ownership’:

 ‘our PR team deal with all social media analysis and we get a weekly email report’

‘our brand team have an ongoing Facebook brand engagement tracker’

 etc, etc

Aside from the fact that the argument about who ‘owns’ social media is utterly futile (it belongs to and informs EVERYONE) it feels like this debate is obstructing us from the ultimate goal – to use social media to better understand and empathise with people; to use social media for insight.

I have spoken with many insight teams with varying degrees of sophistication when it comes to social media research. For instance, we recently helped one client understand the nuances of how people construct their own understanding of sustainability.  A topic, as far as we’re concerned, that is a perfect fit for social media research.

On the other hand, I spoke with a brand recently who never use social media for insight because they felt it was too ‘rose-tinted’ and would always rather conduct a focus group.

In both cases we offered our perspective on why the social web is a crucial environment for research, which we’ve already covered on this blog, and also where it fits in the overall research mix, which I wanted to outline here:

 Social media research has unique benefits and advantages:

• Provides a snapshot of real time opinions and social conversations
• Retrospective (c. 2-4 years of data) research on opinions as they were expressed (not recollections of opinions).
• Is quick and efficient to do (no set-up costs, travel and moderation time etc.), meaning we can focus our time on search and analysis.
• Can validate qualitative findings, with the benefit of greater numbers, and can also reveal previously unrevealed insights

 However, there are some important considerations:

• Social media research only works when the brand / issue / theme for exploration is discussed and defined
  (as we are not asking questions).
• It is difficult to replicate demographics
  (as we are not as such recruiting consumers).
• It is useful in one or a number of ways within an integrated, multi-sourced research approach

Social media research can be used as an alternative to quant or (in particular) qual methods, depending on the brief, but is best used in a complementary way, to augment other research methods: