Twitter, Tweetdeck and what a purchase means for the symbiosis between developers and social networks...

The relationship between social networks and 3rd party developers is an interesting one. A good working relationship allows networks to expand development capabilities beyond in house resource, allowing their users to, in effect, do "R&D" for them. Looking at this end of the developer/network relationship, everything seems to be pretty advantageous for the company. This sentiment was most passionately stated by Microsoft's Steve Ballmer, who when asked what the most important asset to Microsoft's Operating System success was, responded (3rd Party) "Developers". Fittingly, Steve has since also shown love for 'Web Developers', showing the growing focus of web external developers in today's world. 

However, the cost of 3rd party developer work isn't free for a social network. Fostering real advances from developers requires stability to allow developer's products to succeed, resources (such as robust APIs and documentation) and most importantly, a company culture that is comfortable with the network expanding through non-internal channels. This permission sums up what is perhaps the largest cost for a network's involvement with external developers, as it pays to not only create a development framework to harness the power of the network, but must also cede control in how that network is used.

The risk/reward balance of the external developer relationship plays out all around the web in a variety of configurations. Facebook is perhaps the most robust example, with a variety of frameworks to allow developers to harness almost every part of site. Website developers can integrate Facebook features into their sites, as well as syndicate external content through Facebook via pages, like buttons & more. Application developers can build entire games, commerce solutions and more within the network, tapping into the 600 million user base to expand the reach of their creations. Every object on Facebook from pages, to users, to status updates is an clear object within the network's API or graph. This robust approach to a developer framework has led Facebook to rapidly expand their hold on a share of a user's online time, with gaming and expanding communication options making 3rd party development companies such as Zynga increasingly rich. Alternatively, while Facebook has made a robust framework, they have also clearly demarcated where developers fit within the network's structure. Through careful design, Facebook have made sure that developers enhance the network and collaborate with it, instead of being in a position to usurp any functionality. Through this clearly marked development ecosystem, Facebook have made possible competitors into collaborators.

Similarly, Twitter's relationship with developers has historically been a story of collaboration, with external developers driving network adoption and awareness from making mobile/desktop clients to improvising ways to drive analysis. Twitter's development framework has been clearly refined over time to handle the immense load developers place on the network, around 13 billion calls to the API per day. Subsequently, developers have leveraged this API to help Twitter become a household name, making everything from interesting data visualizations to raise perceived value of the network's communications, to integrating twitter conversation with sites, media outlets and broadcasts, and creating clients to increase network access across multiple platforms such as mobile.

However, Twitter's recent rumored acquisition of 3rd party client Tweetdeck may signal the biggest in a series of changes to this dynamic. In the run up to Twitter's acquisition of the client, the network tightened restrictions on data access and whitelisted accounts (accounts which are enabled to make more API calls an hour than regular accounts), as well as banning development of new 3rd party clients. The ban on third party clients may have seemed inevitable to some developers after last years acquisition of iPhone Twitter client 'Tweetie" by the network, re-branding it as the official iPhone client. This move, mirrored by the current acquisition of Tweetdeck, shows that Twitter is consolidating the ways in which user's can access the network, under the banner of unifying the social network experience for users. While this move makes sense from an advertising perspective, as Twitter can claim to advertisers and agencies that their increasingly robust paid offering (promoted tweets, trending topics & accounts) is unified across platforms, it comes at the cost of the current developer environment.

Tweetdeck's Trending Topic Functionality

Advertising and media opportunities for the network seem to be the current focus for the network as it aims to continue its transition from start-up to established social network to a network with a paid support offering. The initial success of products such as promoted tweets have shown that the market is there for advertisers to support brand presences through spend, but the previous variety of 3rd party clients represented a weak spot for the offering's success. Twitter couldn't gaurantee that paid support options were as effective as they could be across multiple 3rd part clients and additionally, 3rd party network tools represented an additional option for media placement outside of negotiating with Twitter.

While this reorientation makes sense from an advertising and control point of view, the network must hope that it doesn't disrupt the developent ecosystem from producing other useful offerings which expand the usefulness and reach of the network. Twitter finds itself in a unique position in striking this balance as the user experience is very straightforward compared to other networks such as Facebook. Going back to the factors for a successful developer environment, Twitter still possseses a robust API, but must drive a sense of stability for developers to balance out the reorientation of its network's access.

On a wider scale, the Twitter example may indicate how the relationship between social networks and developers will progress with a new generation of social networks. As startups, a network aims to draw in an established community, growing this into a robust developer network as it grows in popularity. Developer networks have previously driven interest in networks and expanded the list of the offering past its basic premise. However, as paid support within networks becomes a ubiquitous demand amongst larger social players, networks must reconcile keeping developers engaged vs. offering a verified commercial solution. If this balance isn't maintained, the competitor into collaborator dynamic that currently exists may change into a much more dangerous permutation for the future.

The Top 30 London Ad Agenices on Foursquare (for 16.07.2010)

Thanks to everyone who suggested new venues on Twitter, by email or in the comments last time. I've added quite a few new listings to the overall list. I'm hoping to upload the full list and simplified data in a few days, once I can plug it into some quick data visualization software for a post.

Also, I've fixed a bug in the aggregator that combined data from Proximity London & SapientNitro, so you should see these as distinct entries now.

As always, if you think that I've missed an agency or location to add to the search list, just email/DM or comment.

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What can the Means-End Chain teach us about digital content?

      So, its no big secret that two of my largest geek obsessions: psychology & digital marketing, come together rather often on the blog. However, where as I normally start with a digital marketing concept/story and throw in some consumer psychology, I thought it might be entertaining to do the opposite.

Aside from being as far from the actual World Cup as possible, what makes Nike's ad easy to share while others aren't?

      With this in mind, I was recently considering what tools might be useful to differentiate the mass of digital content posted by brands. With the World Cup coming to a close and people marveling over the number of views advertisements like the Nike 'Write Your Future' video garnered, its interesting to consider what separates this from other similar, less viewed World Cup content. Along these lines, I thought I would take a relatively basic consumer psychology concept, the means-end chain, and apply it rather liberally to the world of online viral marketing, to view content in new ways.

What is the Means-End Chain?

       The means-end chain is, at its most basic, a way to describe how a product interacts with the consumer. Whereas more complex explanations can be found elsewhere, for our purposes, I'll go with a basic definition I've used since school At its heart, a product on the means-end chain breaks down in three different areas:

Not every product touches the consumer in each of these areas (some basic products may only deliver a basic benefit and possess simple attributes), but by analyzing a product along these lines, we begin to take apart its essence. Within each of the three groupings, two subgroups exist, which begin to paint a progression for product analysis (shown below).

 These six sections allow marketers to analyze not only how a product interacts with the consumer, but where advantages may exist in relation to similar products. For example, on the surface, few differences exist between Coke & Diet Coke. If we put Coca-Cola Classic on the means-end chain, we see that it hits the boxes expected from a soft drink:

Purchasing, possessing & consuming a Coca-cola isn't a life changing decision, no matter how many people you buy a Coke for or teach to sing. Even its psycho-social benefits (those conferred from others) are tenuously weak, with a fleeting acceptance coming from only the most needy of consumer. However, when we compare it to Diet Coke, we can see that in some interpretations, a soft drink can become more than just a beverage.

By dipping into enabling values the consumer may have about fitness or getting into shape, Diet Coke has the ability to interact on a level traditional Coca-Cola cannot. While it may be tenuous to say that one Diet Coke could enable a consumer to achieve a step towards a long term goal, it does point out how we project possibilities onto products in noticeable ways.

What does this have to do with viral content?

Moving away from the straight forward example of two related products, its clear to see that the basic concept of the chain is useful to think of products in a different way. However, relating this product analysis to online content however, may be more difficult.

When someone mentions viral marketing & the internet, a variety of things can come up. From tweets to pictures, video & websites, users & brands both enjoy the benefits of the way content travels online. The reach generated by a user generated movement can multiply the effectiveness of a brand's campaign exponentially, but predicting what content will resonate with the online audience is difficult. Much like predicting product/consumer interaction, the way users consume online content is unpredictable, leading to uncertainty about communication performance.

A focus group discussing re-tweeting last year attempted to sum up how content is passed on to me as 'attributional cool'. This meaning that users only pass on what they deem cool & interesting, hoping to impart a bit of that feeling to themselves as they share with their friends. Predicting what achieves the level of 'attributionally cool' is a nearly impossible task involving analysis of social norms, reference groups & a host of other complex factors. However, the relationship between the user, content & viral transmission begins in the same way as with the user & product.

Instead of a purchase & consumption, online content hopes to motivate the user into interaction & distribution. Therefore, it may be possible to compare two similar pieces of content, in the same way Coke & Diet Coke were compared, and see if some level of interaction dictated greater depth of engagement. While the very nature of viral content & 'attributional cool' lives in the Psychosocial area of the means-end chain, I believe the other areas can still help to unlock basic insights about content.

Applying the Means-End Chain to Digital Content....

Applying the chain to viral content, it seems that the attributes and benefits may possess a more obvious presence than values, as the relationship between content and the user is rather shallow. That aside, each area seems to take on new characteristics when thought of in the digital space.

Ok Go's 'Here It Goes Again' Video helps to illustrate how concrete attributes, such as embedding functions for content, can heavily shape pass-on performance

     Attributes, as we think of them in traditional product analysis, entails the actual dimensions & capacities of the product. With our Coke & Diet Coke example, the concrete attributes described unchanging things about the packaging and the liquid itself, while functional attributes described interpretive aspects such as taste. In applying the means end chain to digital content, concrete attributes can become the aspects of the product that control viewing such as resolution (HD or non-HD), encoding, video delivery platform (i.e. flash based or HTML 5), sound or no sound, embeddable or non-embeddable, etc. Functional attributes becomes analysis about the content itself, (i.e. does it feature recognizable characters, what is it's tone, etc.). While functional attributes may seem to play a larger role in viral pass-on than concrete attributes of a product, remember the performance of OK Go's viral music videos. After their treadmill based 'Here it goes again' went viral, their follow-up 'This too shall pass' featured disabled youtube video embedding at the behest of their record company. While this was eventually overturned, views of the video were significantly lower than the embeddable original, due to a change in the concrete attributes of the digital content.

Content such as the Toyota Sienna viral 'Swagger Wagon' illustrate the challenges in balancing depth of benefits with a target group to widespread appeal.

      Benefits, in traditional product analysis, are the deliverables that the product can bring (i.e. Coke quenches thirst (functional benefit) and it may signal something to others about your choices (psychosocial benefit)). Alternatively, when analyzing digital content sharing, the perceived psychosocial benefits play a large role in deciding if content will be passed on. The functional benefits of digital content speak to the enjoyment the viewer gets from consuming the content, while the psychosocial benefits speak more to the possible 'attributional cool' that the user feels he may get from sharing such content. While the psychosocial benefits may seemingly outweigh the importance of the functional benefits in assessing if content will be shared, we shouldn't discount the importance of the intial viewer experience in assesing content. How the initial viewer enjoys the content is very important on how he/she feels others will react to it. The reference groups of an individual also heavily shape expectations of what would be considered 'attributionally cool'. For example, a 15 year old might not progress down the means-end chain to the same level as a 35 year old family man when presented with content such as the 'Swagger Wagon' music video, though both may find humor in it. Therefore, the means-end chain helps to illustrate the challenge content faces when it hopes for a large pass on rate, delivering both functional and perceived psychosocial benefits to the largest audience possible.




 Possibly one of the most well known pieces of digital content in the last few years, 'Yes We Can' illustrates how the rare phenomenon of tapping into the values part of the means-end chain can benefit a piece of content's consumption & pass-on

        As mentioned before, Values are the hardest level for both products and digital content to interact with the consumer. While few products can reach the consumer in a way that leads to delivering instrumental or terminal value, even fewer pieces of digital content can, due to fleeting engagement. However, given the scarcity and power of value based digital content, those that can latch onto some larger life goal gain a powerful chance to encourage pass-on. Content linked to political campaigns, such as the 'Yes We Can' video for Barack Obama's campaign illustrates how content may tap into larger goals due to its affiliations. While many politically related videos may touch on a similar topic in both attributes and benefits, the ability of the video to deliver the perception of instrumental value delivery gave it an appeal over others.

Conclusions

        So after attempting to apply the means-end chain to digital content, what have we learned? I think breaking down each section has shown that the 'content as a product' model works for analyzing how content is consumed. I think limitations exist in the power of the model based on the impact of reference groups and social norms, but that two similar pieces of content, with similar target groups and interests, can feasibly be compared for differences along the means-end chain. Overall, this entire exercise shows the value in applying new points of view to existing ideas, mining insight from the synthesis of different, but related, fields in marketing.

The Top London Ad Agenices on Foursquare (for 29.06.2010) [Infographic]

 As always, if you think that I've missed an agency or location to add to the search list, just email/DM or comment.

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London Advertising Agencies on Foursquare (14.06.2010) [Infographic]

 As always, if you think that I've missed an agency or location to add to the search list, just email/DM or comment.

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Why Online Buzz Shouldn't Equate to TV Ratings....

   An article in last weekend's 'New York Times' posed the opinion that 'Online Buzz Doesn't equate to Ratings', which is something I completely agree with, but for different reasons. The NYT's piece compares an index of social media conversation volumes to Nielsen viewer data to illustrate that of the Top 10 most discussed shows, only 4 are in the top 10 by US viewership. Though the comparison starts to illustrate the nuances of online conversation analysis, I think that it also touches on a relatively global & basic idea about networking & human conversation.

   As shown previously in the analysis of Twitter conversation about the Superbowl & the FA Cup Final, events are normally a safe bet to drive conversation volumes online. I use 'normally' because not every large event targets the majority of the heavy online demographic or cohesively calls to action users in a way that generates a noticeable conversation mass. However, generally we prefer to share our thoughts and experiences around a singular event with others and this leads to a relatively easy to track message/activity volume (Eurovision final, I'm shamefully looking at you).

   Extrapolating online tracking into television, I can see where the NYT article gets its premise and its contradictory tone. The Top 10 US television series are highly anticipated and, in their own way, weekly broadcasting events. Logic would dictate that such shows drive conversation for an activate audience which would generate a cohesive and noticeable mass of conversational activity through sharing the viewing event. The contradictory discrepancy in top online and viewership shows illustrates that not every top viewership show activates an on-line audience.

The lack of support for my #bumbum hashtag is listed as the primary reason that the show was canceled....

    Though the promotional purpose of combining TV viewership with online activity is to share the viewing experience and promote content, after the fact analysis is different. While large events like the Lost finale or an American Idol episode generate something to rapidly discuss (all the while interacting with a viewership that is relatively digitally savvy), I imagine less marquee events such as CSI, NCIS or a really good Law & Order (possibly a personal bias) won't galvanize action in the same volume or time frame, skewing the relationship between ratings and conversation volume.

    Alternatively, the nature of online networks means that conversation volume can spike for TV events which lack overall popularity. Due to the propagation of online viewing 'sub-cultures', its feasible to say that activity spikes for the season finale or prominent episode of a low/mid rated show could mimic the online activity of larger mainstays. Online 'sub-cultures' can drive their specific viewing tastes away from the mainstream, supporting viewing events with such interaction that they mimic the volume of conversation for large, mainstream events.

   So between the activity and content biases towards discussing certain shows online, is there a place for networks such as Twitter to predict viewership? I say there is still a large role for online messaging analysis in predicting/monitoring show performance, but not from overall message volumes. If we, as marketers & analysts, move more towards analyzing the volume of conversation in segments or over time, as opposed to relative to other shows or in general, we can begin to gather insight into content effectiveness.Even with a specific focus though, frequency analysis seems better suited for online PR analysis or advertising effectiveness, than viewership analysis.

Safe....for now...

   More effectively, the real worth of online TV content analysis comes from 'what' users are saying, not the frequency of it being said. While message frequency can begin to illustrate quick reactions to events, it doesn't posess the power to predict larger events that would equate to rating a viewing session. If we analyze what users are saying, we can begin to form a general opinion on how a user reacts to content (not just if and when) and whether they would return to share such an event again. While analysis of such content isn't currently quick or easy, it represents the true value of online conversation analysis.

London Advertising Agencies on Foursquare (28.05.2010) [Infographic]

    So given that the last London Foursquare Agency graphic was a bit more graphic than info, I decided to add a little bit on the current version. I've posted the current infographic in both .pdf and .png formats. Also, thanks to Sinead Doyle for publicising the last Foursquare ranking post on her blog.  

Click for Larger Version....  As always, if you think that I've missed an agency or location to add to the search list, just email/DM or comment.

The Carling Cup "Digital Final" on Twitter: A Study in the Challenge of an Event's Scale

Introduction

      The 2010 Carling Cup Final has come and gone and while Man. U fans are probably a little more pleased with the outcome than Villa supporters, the game itself stands as a strong lesson on the ‘ins’ and ‘outs’ of driving digital engagement for an event. Carling touted the game as the world’s “first digital final” which included: putting fans names on digital displays during the trophy ceremony, voting on the winning songs, placing fan created banners on pitch side advertising and linking this to charitable donations.  In addition to this, the hash-tag “#CCF10” was publicized in the run-up to the game. In theory, the brand wanted to utilize Twitter (in concert with other digital channels) to unify viewers in conversation, extending the experience of the final to those viewing it outside of the stadium. Harnessing Twitter activity as its own point of interest, Carling also created pages tracking Twitter activity around the event, showing activity by geographic region and time.

Source  Add this to the list of things I probably won't win (this year)

      The logic behind Carling’s intentions seems sound, especially when looking back on other events which unify Twitter users in conversation. As I’m writing this on Sunday night, preparations are under way for tonight’s Academy Awards in the US, which will drive network conversation from the Red Carpet, through the event, to post analysis with the winners and losers on Monday morning. Previous events like the Super Bowl, Ashes, the BCS National Championship Game and the Winter Olympics have all also shown that Twitter activity is rather reactive to large scale viewing events. However, in the case of the Carling Cup, harnessing network interest in the event is harder, due to the lower engagement level of the game. If you supported the two teams involved, then the game was probably pretty big for you, but Carling faced a challenge in not only interesting those without a stake in the final, but also organizing the attention of the already interested fans for engagement. In declaring the game to be the “first digital final”, it seems they hoped to generate a meta-story around the match which could possibly multiply interest. While many aspects of the campaign are hard to independently look back and analyze, Twitter activity can stand as an interesting proxy for campaign interest. With this aim in mind, looking back and judging the effectiveness of the Twitter campaign requires answering a few core questions:

1.)    Did the brand manage to engage with those already conversing about the final, as well as those who were neutral?
2.)    Did those who conversed about the game do so more frequently because of the brand’s efforts?

Twitter Analysis

      To gain some insight on the brand’s Twitter efforts, data collection was set to gather figures on both the specific hashtag for the game (“#ccf10”) and overall mentions of the event (i.e. “Carling Cup”, “Carling”, etc.). Analyzing the data was focused specifically on the time directly before, during and after the Carling Cup to see if the brand was able to foster reactive user activity.

 

     Top line figures indicate that the volume of messages was overwhelmingly towards “Carling” or “Carling Cup” related tweets instead of specifically tagged #CCF10 messages. In fact, between 14:00 and 18:59 on gameday, only 8% of messages (388 out of the total 4,798) utilized the promoted hashtag. Conversation about the match can be assumed to have progressed as it normally would have, with non-specific instances of “Carling” or “Carling Cup” being the dominant form of expression (4,410 messages).

    The activity itself seems to show a rather continuous increase during the game, peaking after its conclusion. However, when the data is broken down by minute and charted against game events, as shown below, it becomes clear that the prolonged reactive growth shown in other events such as the Super Bowl wasn’t clearly found in the Carling Cup Final. The continuous growth implied in the above chart, is actually a rather varied mix of quick reactions in traffic.

     Minute by minute activity for the game builds quickly before kickoff and reacts to scoring from both teams, though the activity shows little carry over and dies off quickly.  Anticipatory conversation about the game during the pre-game and half-time periods wanes in a similar manner, with the conversation spike after the conclusion of the match dwarfing any pre-game or game activity. Such spikes usually indicate a boost in relevant network traffic from blogs/news sites syndicating links to articles through Twitter (in this case about Manchester United winning the Carling Cup). However, discounting syndicated tweets, it becomes clear that even post-match commentary didn’t utilize the specific “#ccf10” hashtag, with tagged activity dying off to nearly nothing after 30 minutes post match.

      Considering the first question about the Twitter campaign’s performance, the data seems to show that the “ccf10” hashtag failed to catch the attention of those already conversing about the game (through general brand terms). The difference between post game and during game activity also seems to indicate that users who reacted to the final’s outcome didn’t engage on Twitter during the game, implying that neutral or uninterested users weren’t encouraged to interact with the brand’s efforts effectively.

 

      Within users that did converse about the game, the majority did so sparingly. Analyzing user message frequency during the match afternoon shows that 82% of the 3,054 unique users talking about the game only did so once.  Of the 18% of repeat mentioning users, 2% could be considered heavy conversers, with more than 5 mentions. The heavy conversing users represented the best opportunity for the brand, as they were the most inclined towards the efforts.

     Within the ‘heavy mentions’ segment  19 users mentioned relevant phrases 10 or more times, while 2 did so more than 30 times and 1 did so in 61 tweets. Although adoption of the “#ccf10” hashtag was low overall, 100% of the messages from the two heaviest users (92 tweets in total) used the tag. Overall 38.5% of game related tweets from heavy users utilized the “ccf10” hashtag , versus 3.91% of messages from the rest of the analyzed users.

      Though the heavy mentioning segment may small relative to the overall sample of users who mentioned the Carling Cup, they did exhibit the most consistent conversational behaviour over time. As shown below, heavy mentioning users maintain a steady level of activity throughout the match, surprisingly peaking in activity after its conclusion and during the activity decline of other segments.

    Heavy Carling Cup mentioning users represent a relative bright spot for the brand within its Twitter efforts. Overall, they showed a consistent conversational behaviour throughout the match and adopted the hashtag at a rate well above other Twitter users.  The relatively small size of this segment means that the brand’s efforts didn’t shift the overall frequency of user conversation much, but insights about the depth of engagement for heavy users may indicate points of opportunity to grow this segment in future efforts.

Implications

    While the Carling Cup’s “#ccf10” hashtag and related Twitter efforts may have underperformed relative to the assertion of the world’s first “digital final”, several key learnings emerge from analyzing the effort.  With respect to the initial questions to rate the effort’s performance, it seems that Twitter users neither increased their frequency or depth of interaction with the brand due to Carling’s promotion.

     Carling faced a challenge in expanding the audience interested in the match beyond the fan bases of two teams. The Carling Cup Twitter data seems to show that the existing prominence of an event has a strong impact on how it influences network activity. Events such as the Super bowl are large enough that Twitter users discuss them simply because of their scale. The multifaceted draw of such events (Ads, Halftime show and the game for the Super Bowl or the Red Carpet, Comedy and Winners of the Academy Awards) mean that topics of related conversation are broad and open to multiple audiences. Small to mid level events such as the Carling Cup face the challenge of being singularly focused and small in scale. It seems that while Twitter can help to connect communities such as Man. U or Aston Villa fans, if such interaction doesn’t offer any new real depth of engagement, then overcoming a lack of scale is difficult. Without these smaller fan bases converting their close ties into high frequencies of interaction, related network activity remains low and no increased network prominence is gained for branded promotions.

     The difficulty of smaller events to gain interest on Twitter doesn’t mean that attractive small to medium scale brand events are impossible to create. The singular focus of such events may serve to limit initial interest beyond existing communities, but it also means that less clutter is present around branded communications. Those who discussed Carling on Twitter during the game tended to stay focused on discussing the teams involved, the game itself and Carling. If wider audiences had discussed the game, the clarity of the brand’s message stood a good chance of remaining evident.

     While the overall campaign seemed to feature various promotional draws, the Twitter campaign seems to stand rather independent of other parts. While the Carling website offered various Twitter based content (from analytics to page backgrounds supporting the teams), it doesn’t seem like any direct call to action existed. Using the hashtag allowed Carling to generate dynamic crowd sourced game commentary from the network, but this doesn’t seem like it was enough to drive user behaviour. Tighter integration with other aspects (such as the digital signage) is hard to do and rather risky, but could have created a more dynamic and salient feel to Twitter interaction with the event. The conversation about the game and the conversation about the game’s digital efforts never seemed to mesh together in the activity data, smothering any chance for the brand to foster network activity.

     One should note that overall, I really liked the idea of this campaign and that Carling was innovative to attempt to add digital depth to their final. Talking about underwhelming activity in the Twitter parts of the campaign in no way denotes that the overall efforts weren’t impressive. While the Twitter campaign activity may be considered a proxy for consumer interest, I imagine that those who interacted with the efforts in the stadium or through other digital channels had the depth of their experience enhanced. Overall, I think the Twitter aspects of the effort highlight the challenges faced by brands in growing interest for smaller scale events on the micro-blogging service, rather than indicating an error by the brand. Finally, I think it shows that the unpredictable attitudinal nature of users is fickle and even those who do engage do so in unpredictable ways, as shown below. Carling may have put on the first “digital final”, but hopefully it won’t be their last, as they can apply what they’ve learned this time to future games.

Super Bowl XLIV and Twitter [Infographic]

       So this time a week ago, as I sat down to watch two teams I had little interest in (thanks Packers and subsequently Vikings for not livening up the day) fight for the NFL championship, I also decided to track what activity Twitter manifested for the game. The results, shown below in my first attempt at an infographic, are interesting, though after examination, I doubt they are complete. My sample was over 300,000 messages from the hours before till after the game, but I think due to the speed at which they were sent, I didn't get everyone. I still thought the data was interesting enough to be represented below, but keep in mind this is more of a thorough global sample than an exhaustive collection.

Comments and Suggestions about the methodology, the infographic design and the results are welcome.

 

Click for larger version..... 

Mirror for Download (RapidShare)

Mindshare Twitter Research

    I've been waiting to put this up for a while as we've been conducting some network and user analysis for the course of this year. As you can probably tell from the previous (4 part epic tome) on brand analysis on Twitter, I and 3 other great people at Mindshare (@acotterill,@JezP76 & @picolim) conducted qualitative research on Twitter  users and quantitative research (through a bespoke analytics engine I'm still very proud of) on Twitter user behavior and WOM pass-on rates. Our analysis yielded some interesting facts on how users utilize Twitter, through what platform they do and how messages travel through networks.

     I plan on writing a bit more about the research and its implications later on; but for now, check out below and let me know what you think.

Mindshare Ispy Twitter Research

View more presentations from Mindshare.

Tweet it your way? Twitter's Capacity for Consumer Sentiment Measurement - Part IV

Research Conclusions
   In the previous two parts of our Twitter Consumer Sentiment analysis series (II & III), we aggregated and analyzed data relating to mentions of Burger King on the social network/microblogging site. When we consider these parts as a whole, insights are produced in one of three areas.

     Overall Twitter Performance:
           Without comparing Burger King with other companies within the sector (which would generate our share of activity for the UK), the company's Twitter activity is shown to be less than purely reactive to media or campaign events. As can be assumed with others within the sector, while some consumer opinions and experiences are stated, most messages are posted mentioning BK as a destination or location. Exceptions to this trend include certain rumors or news items which reasonate with the younger target demographic of the firm. Assuming the rest of the sector performs in the same manner on Twitter, opportunities for general performance increases exist through simple Twitter based campaigns. An audience sporadically tweets about the company and therefore the opportunity does stand to transition these sporadic 'experiential' conversations into a longer, more robust one through promotions ranging from simple (hashtag based contests or promotions) to complex (multi-step campaigns tied into a brand page).

      Geographic and Chronological Performance
            Analyzing mentions of the firm by geographic UK region yielded similar results to the overall distribution for network usage. London reigns large in most geographic analysis of the UK and requires a much more granular analysis to get insights for comparably smaller areas. For Twitter based communications and promotions, this signals that the current trend of London based campaigns should continue specifically for the firm. The prominence of the catchment area in our results (users mentioning the brand outside of a specific radius of a metro. area) could signal the possibility of future possibilities outside of London, but a large amount of activity can be described as commuters or non-specific location coding.
              Chronologically, our hourly data and user analysis of dining mentions (i.e. Breakfast/Lunch/Dinner) showed that lunchtime activity was highest for the brand, both in content and volume. This, by itself, doesn't indicate much, but it might begin to hint at the brand's image as a lunchtime destination for network users.

        User Behaviors
             User and platform data yielded perhaps the most concrete insights of our analysis. Platform data highlighted the fragmented usage context for Twitter, something that is matched by overall network data. Burger King was shown to be mentioned on the go, at a desktop and everywhere inbetween. Data also demonstrated that users weren't likely to mention the brand frequently, another consequence of brand mentions being a product of experiential tweeting. User mention frequency was demonstrated to have little or no effect on when or what a user tweeted about when talking about Burger King, but an overall patten of traction was found for product launches or advertising campaigns.

Implications
        As we can see from the example analysis, a majority of the insights gathered from Twitter search are more topline than detailed. For getting a quick feel for the performance or promenance of a brand on Twitter, such an analysis may prove rather useful, however, further analysis or supporting data is required to produce detailed observations. Network analysis of user segments or a brand page could serve to deepen the insights produced from Twitter.

      Perhaps the most important thing missing from the current analysis is the examination of consumer opinions for sentiment. While we manually did this in our user analysis section, available online automated solutions for such are still in the rudementary phases. By scanning for key words or terms, various websites and programs attempt to classify messages as "Happy/Sad", "Good/Bad". While there is an inherent value in knowing the amounts of good vs. bad messages about a brand, the intricacies of why these messages were classified as such, as well as errors that can stem from semantic differences in wording, are still necessary considerations when thinking about automated analysis. Overall, without utilizing automated sentiment analysis (or doing a lengthy manual analysis), data should be examined from the top down, establishing points of interest or behaviors that warrant more attention. These can serve as starting points to segment users for analysis, cutting the work load involved.

        On the whole, the usefulness of utilizing Twitter search to measure customer sentiment is highly dependent on the company, the sector and the product. Search analysis shouldn't be viewed as the end point of generating consumer insight, but the beginning of seeing where your brand sits within user's minds and the network. From a completed analysis, a company can consider promotions, brand page(s) or adjusting online activities to raise prominence or conversation levels.

Tweet it your way? Twitter's Capacity for Consumer Sentiment Measurement - Part III

     Carrying on from our general analysis of Burger King's UK twitter messaging in Part II, we can move on to specifically examining detailed user data and behavior. General messaging volumes indicated that certain events spiked Twitter activity, but this effect was enhanced by events that resonated with the target market for the brand. In order to fully understand this interaction, we can examine general geographic and behavioral patterns before moving onto specific user behaviors.

     Moving from our general analysis measures, Twitter activity data can be cut by geographic or chronological layers. Analyzing Twitter data by time (as shown below), creates a pattern of usage similar to other social networks or general internet usage. Usage data does diverge from existing patterns around 11am to 1 pm, as usage peaks that would generally increase, peak earlier in the day than with overall UK internet usage. Analyzing messages between 11 and 1, there is a distinct trend of experiential messages involving going to Burger King for lunch or returning from Burger King after lunch.

      Geographically, mention data is limited by the methodology of the search. Geographic searches can be conducted two differing ways: manually through the interface (which allows for searching by mentioning of towns or other locations) or through the API (which limits searching to by geocode and radius). Being that our data was taken by geocode, each area analyzed within the UK was gathered by determining the coordinates for the center of a metropolitan area and then the radius of that body. In order to determine the entirety of the UK, a catchment area was set up encompassing the entire UK, with duplicate messages stripped out later on between all the areas.

     Analyzing the data for Burger King by geography (shown below) we see that the data mirrors the overall distribution of UK Twitter activity pretty closely. London, named the metropolitan hotbed of Twitter activity worldwide, dominates other specific geographic areas. The catchment area proves to be the largest area of activity, due to ambiguous location entries or commuter users being counted in this category. Geographic data doesn't yield as many useful insights in this example as it might in more geographically sensitive examples such as monitoring of political bodies within voter districts or global monitoring of a term by country.

     Analyzing data by platform can help to generate insight on variety of usage (i.e. mobile vs. static), preferred client (i.e. Tweetdeck vs. Twitterrific) or context for messaging (i.e. about something going on simultaneously or later). Previous research has shown that, as a whole, more than half of UK twitter messages are sent from either mobile or hybrid third party clients (meaning less than half of Twitter messages are posted through Twitter.com). Twitter users mentioning Burger King mirror increase on the trend of non-Twitter.com based Twitter usage, as only 32% of mentions came from the "web" platform (which represents site usage). The following four platforms (2 mobile platforms and 2 hybrid (desktop/mobile) options) account for more usage than Twitter.com. The overall fragmentation of usage (170 different platforms register at least one Burger King mention) means that users are talking about the brand through a variety of avenues, both on the go (leading to the possibility of in-store tweeting) and at home. Furthermore, future marketing on Twitter for Burger King, including possible sponsorships, should take into account not only Twitter itself, but this variety of 3rd party clients and platforms.

     Analyzing rate of user mentions, we find that 12.3% mentioned Burger King more than once. The distribution (shown below) indicates that while the overwhelming majority mentions Burger King once (showing that most users don't mention every time they interact with the brand), there are users who have exhibited an ongoing conversation. While all brands want to extend consumer awareness, its essential to mention that some brands won't be successful in generating positive commentary from consumer on Twitter, regardless of their efforts. While people may sporadically mention their detergent on Twitter in passing, spawning widespread and frequent mentions of such may prove nearly impossible, due to the nature of the product. 

      In order to discern what actually drove such high mentions for the brand from certain users, we can specifically analyze the tweet's contents and properties from those users. Comparing users who tweeted more than once and the overall tweet distribution shows that no obvious difference between frequently mentioning users and the overall user base exists.

    While the time series hasn't explained why some users have mentioned the brand more than others, specific analysis of tweet content sheds more light on the situation. First, examining the users who mentioned the brand more than 6 times, showed that the group comprises of both normal users (either conversing about Burger King or joking about it frequently) and functional/brand pages (mentioning specials about surrounding businesses or hosting quizzes for users that may mention the brand). One example of functional users mentioning BK is @Manairport (The Manchester Airport), which tweeted about "2-4-1 Burger King Angus Burgers with a VAT booklet" at the airport. Looking at the high frequency normal users, we can search for product mentions (Chicken Royale comes up a few times) or discern opinions (One user stated that in Worchester, he would travel to Burger King for the burger and then go to McDonald's for the fries - something I might try).

     As we move down the frequency distribution to 2-5 mentions, our analyzed sample size grows greatly and shows an increasing trend towards experiential tweets (43% are estimated to contain terms relating to going to, being at or leaving a Burger King). Analyzing the tweets by word frequency, it becomes evident that mildly moderate mentioning users infrequently compare Burger King with McDonald's (only 7% of this segments messages mention the competitor and 4% mention KFC), preferring instead to mention products (an estimated 46% mention the product either indirectly ("food") or directly ("Whopper")). Scanning the messages manually shows that users have commented on campaigns and products such as the "Angry Whopper" favorably.
 
      When we compare the tweet content from our moderate mentions segment with that of the overall sample,  37% of messages are estimated to contain an experiential term, down from our moderate sample. Product mentions also maintain a low frequency, as overall McDonald's is mentioned in 5.4% of messages and KFC in 4%. Messages mentioning "breakfast" (1.76% overall), "lunch" (4.2%) or "dinner" (1.55%) showed a progression in frequency similar to the hourly activity distribution, peaking midday.

     From this point  in an actual analysis, it would be possible to drill down the data to individual users based on terms used and then continue through their network identifying individual behaviors or opinions. Furthermore, user segment data could be contrasted against activities, such as we did above, to indicate how users with certain predispositions viewed campaign activity or stories. These activities can lead to possible outreach of individual users for advocacy or more detailed information, as well as identifying possible "influence leaders" for further analysis or activities.

     Tomorrow, we'll finish the consumer sentiment series by drawing some conclusions from our aggregated data and insights, as well as identify strong points and short comings of the process as it currently exists and in the future.

Tweet it your way? Twitter's Capacity for Consumer Sentiment Measurement - Part II

     Perhaps the best way to illustrate the possible applications of consumer sentiment measurement on Twitter is to illustrate it with actual data. As an example, I've chosen to use Burger King within the UK, based on its relevance to the UK market and its great examples of news coverage spurring activity. The analysis below is for example purposes and not as an exhaustive analytical case study, so the data will be indexed and used to illustrate points. While it is actual data, the point of this post is to illustrate capacities, not to provide an exhaustive how-to or actual market insight.

What are YOU saying about me on Twitter!?

    As we have already decided on a target for our analysis, the next step is to define what time frame we want to analyze. Twitter limits quick analysis as available search results are limited to 15000 responses or around 7 days. Caching services do exist to obtain data from a historical period older than this, but options such as geographic specification and amount of API calls are limited. In this sense, the best option possible is to start aggregating Tweets at a certain point and continue the process until a desired timeframe or amount is achieved. Aggregation can be manual (literally copying and pasting messages from the search engine results or xml) or automated (recommended - using some simple code to request data from the API, parsing it and storing it). Twitter's search API documentation is the best place to start in your development of software to cache messages. If all you want to see is message volume, various websites exist to tell you how many times a term has been mentioned on the network, these are limited however in the amount of data, terms and timeframe that are available to you. For our example analysis,  I'm using a cache of messages pulled on Burger King over the last few months as my starting point (involving slightly more than 3,500 messages).

     If we take the entirety of our BK data, we can compare peaks in activity to company activity and news coverage. Below, the example shows daily volume of Burger King messages for the UK. If we compare the peaks to amounts of news mentions from somewhere like Google Search insights, we can begin to paint a picture of what issues BK customers talk about on Twitter and where the brand lies in consumer's minds. News or web searches don't always correlate to activity on Twitter, so considering the product, brand and company image is necessary when interpreting this data.

Click for a Larger Version of the Chart

     Taking the graph above for analysis, we can see that the interaction between indexed worldwide Google web searches (the red line), news items(the purple line) and the index of UK Burger King Twitter messages leads to various points of interaction. Points A-E show various days in the time frame in which news and search index volume, Twitter message volume or both increased. By finding explanations for these increases, we can infer insight about the brand, both on Twitter and off.

• Point A: 9/7 and 10/7 - during the stories about Burger King's Spainish ad involving the Hindu god Lakshmi
• Point B: 20/07 - only involved a Twitter traffic increase. Looking at the messages, most are experiential (i.e. I'm going to Burger King), but some involved a web quiz mentioning jobs at Burger King and others involved various conversations about the brand (including some about the previous events around point A)
  
• Point C: 6/8 - involved the largest search and news spike in the period, as well as a smaller Tweet volume increase. Stories involving Burger King during this time were the downgrading of BK stock by JP Morgan and a mother being kicked out of a Missouri Burger King due to her baby not wearing shoes, both of which sparingly show up in Tweet content.
  
• Point D: 25/8 - involved the increased performance of BK profits for the quarter. Upticks in news content and twitter messages involved this story in various reposts, while simultaneously not driving any new search activity.
  
• Point E: Increasingly through the 4/5/6 of September and involved the discussion of rumors about "New Moon" (the Twilight sequel which I had to look up) promotional materials being distributed at Burger King. Investigating rumors drove search and tweet volume, but not actual news items.

     Taking analysis of these points and others together, we can begin to see what makes Twitter users talk about Burger King and what doesn't. Generally, we can see that re-tweeted information about stocks and other events will create a loose increasing relationship between news and tweet volume, but that the gains from this can be overshadowed simply by a large day of activity involving experiential tweets.

    More interestingly however, we can see that content more relevant to the target age grouping of BK (such as New moon promotional materials or Twitter based web quizes) can greatly increase message volume. This may muddle our insights in a way, as their exists no direct causality between certain events and message volume, but so far, a loose portrait of where the brand stands on Twitter can be generated (jokes about passing out Vampire movie crowns while creating controversal Hindu ads aside).

     In our next part, we'll utilize data specifically from September and attempt to create deeper user and geographic insights about Burger King's Twitter presence, as well comparing overall user statistics with that of the sector.

Tweet it your way? Twitter's Capacity for Consumer Sentiment Measurement - Part I

     I know two Twitter posts in a row (4 if you count parts coming tomorrow and over the weekend) may seem a little excessive, but with all the news surrounding the service right now, I'm writing my ongoing fixation off to the influence of news coverage and marketing zeitgeist. With today's valuation of Twitter at the $1 billion mark (paltry compared to the estimated $8 billion value of Facebook, but impressive none the less), attempting to quantify the various uses for the service seems rather envogue.

     From a marketing perspective, Twitter's capacity to deliver a breadth of consumer thoughts is already well understood. Typing a company's name into search.twitter.com or the homepage link is a rudementary proof of the torrent of thoughts available about a product or business. While some product sectors produce more insight than others (luxury/aspirational products, global brands, recreational products and the like lend themselves to more conversation than the more mundane or basic such as household FMCGs and financial services), the ability to look into the amorphous "crowd" and pull out buzz is invaluable. This utility has spawned not only business interest in the website itself, but has also impacted 3rd party services. Twitter specific services such as Hootsuite harness the reasonably accessible Search and REST APIs within the network (ease of use being something I can attest to having written my own Twitter analytics software),in addition wider buzz tracking offerings have added functionality beyond blogs and forums to include Tweet aggreagation.

But what do customers really think about the Baconator(TM) Hal?

     Anyone who's familiar with the advanced search functionality of Twitter knows that added value stems from the ability to segment messages gathered from the service. Searching by user, user mentions, network position from a fixed point (if you're willing to code a large amount of secondary aggregation data) and geographic location mean that user's messages can gain a relevance. However, with this wealth of possible data, what actionable insights can be generated?

     At the most basic level, number of mentions and time series data on mentions are the easiest data to procure. By simply collecting a period of data on a term, one can compare activities such as news coverage with Twitter activity.Combining message amounts with other competitors in a sector can generate topline statistics on its performance and loosely gauge twitter activity possibilities for sector ("market tweet size" if you will) and company share ("share of tweet" if you won't).

     At a slightly more advanced level, user mentions and user profiling are possible, as well as multiple searches to determine activity by various geographic areas. Combining this with analyzing Twitter profiles, a brand can gain insights on what type of users are talking about the brand (though the value of such topical profiling is limited). Geographic searching allows not only for regional activity comparison (great for larger brands), but also for specializing a search area (great for smaller brands and singular locations such as a local chain of restaurants).

Looking at the UK BK Tweets for the last few months makes me want to have a "Chicken Fries Whopper" for some reason

     Finally, data such as message content, message platform and links can generate insight on user behavior when mentioning the brand or associated content (through seperate link spidering - something that is easier said than done with bit.ly and tinyurl's api limits). Perhaps most evidently, the time consuming process of actually reading every user message can generate a specific picture of trends emerging around a brand. Depending on the volume of messages, this can be unfeasible, confusing or flatly impossible; in situations such as this, I've found that porting messages into a word cloud generator such as Wordle can provide top line figures on atleast the most basic of consumer sentiment.

     For the future, I see the nature of "real-time" search analysis becoming even more reliable and specific. Services such as Foursquare (Hurry up and get to London!) are ushering in consistent geolocation of messages in a system that encourages more detail than simple experiential tweets (i.e. "I'm off to get lunch at Business X" vs. "I just had horrible service at Business X").  Furthermore advances in automated semantic analysis are making the process of analyzing user messages for trends and sentiment quicker and more accurate, something that will be highly appreciated by those attempting to read the breadth of aggreagated content.

    In considering consumer sentiment measurement, perhaps the best way to illustrate current and future possibilities is to provide an example. Tomorrow in Part II, we'll take a real-world example and generate some topline data on Burger King within the UK utilizing a variety of web tools.