Tuesday, 22 September 2009

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.

        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.

Monday, 21 September 2009

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.

Friday, 18 September 2009

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.

     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.
     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.

Thursday, 17 September 2009

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.

Wednesday, 16 September 2009

Habitat is back on Twitter and Case Study Writers Rejoyce!

Social Media and Marketing sites are abuzz about with stories about Habitat's return to Microblogging site (and my favorite repository of my innane thoughts and links) Twitter. For any that haven't heard about the retailer's first foray into creating a Twitter brand presence, copious amounts of case studies (1, 2) have detailed its marketing fauxpas (including a few presentations both I and others have created about utilizing the social network). The short version of Habitat's tale of transgression entails the use of spamming popular hashtags (such as the Iranian election) to spread the word about signing up for the chance to win a company giftcard.

 Iranians might have been fighting for freedom, but maybe they wanted a nice Ottoman to tie the room together as well?

According to Habitat, these tweets were sent out by an "overzealous intern" (the bogeyman of all image damaging gaffes) and once the digital outrage began, they were removed. The story recieved some press outside of the advertising/marketing community, but the lasting damage seemed to be limited to condemnation from those close to digital marketing, the overall industry and social networking.

It could be argued that Habitat just engaged in what firms have been doing since the advent of the internet (be it from IRC chat up to email and beyond), finding something popular/visible and blatantly attaching their name to it for promotion. What was different about this situation however, was a confluence of social norms (Twitter/Facebook/et. al have created digital subcultures with not only behavioral standards, but noticeable retribution) and a populist zeitgeist (the Iranian elections and Twitter were proving to be one of the most popular stories of the time) that created a relatively immediate call to arms amongst users to shame the brand.

In traditional crisis response fashion, Habitat attempted to sweep their error under the rug by not acknoweldging it and deleting the offending posts from their feed. What the brand failed to realize however, is that all tweets are cached for posterity and that the conversation about it had grown beyond their presence on the network. Social media marketing "theorists" will tout the conversation size relative to brand presence as an example of why engagement was key for the brand (both before the crisis,during and after) as they could have attempted to explain their actions or atleast stemmed backlash by apologizing. Looking at Habitat's behavior so far on Twitter, one wonders what lessons might have been learned by their initial attempts at Twitter marketing and what will be applied this time through.

Thinking from Habitat's point of view, the decision to come back was a necessary one. They've created a notoriety within the network and the overall consciousness of social media (be it rather infamous or hapless). This negative represents an opportunity to create a positive interaction with consumers. Without rehashing common points of use for brands on social media, I think it would be worthwhile to list 4 points that I think Habitat will keep in mind this time around:
  1. Twitter marketing is based on consumer consent for interaction (so we probably won't see hybrid Kanye West/Habitat tweets any time soon) and by invading network conversations (be they interpersonal or network wide trending topics) you draw the ire of users, regardless of their initial interest in the product. No one looked at a habitat spam tweet and said "My god, a giftcard! sign me up!"
  2. Twitter marketing is an ongoing conversation. Users that choose to interact with the brand need not only a reason to start conversing, but to continue doing so. Transparency, personality and content are all required to grow a brand relationship that can flourish both online and offline. If a brand isn't willing to own to something as small as spamming a social network, how can I trust their product?
  3. Brand interaction on Twitter is traceable and permenant. Messages sent from a brand are cached and (if not deleted) visible on the brand's home page, leaving a lasting testiment to brand behavior. If you overheard a sales associate say something horribly off brand image in a retail location, wouldn't that impact your perception of the company? Brand pages act to a less literal degree as brand embassies or retail locations. 
  4. Injecting your blog into the conversation successfully entails not just drawing users to your page, but building a network. The conversation on Twitter isn't just 1 to 1, it can be 1 to many when done successfully through WOM passon and content transfer. Spamming hashtags may have obtained some attention for Habitat if they hadn't been called out, but it wouldn't have fostered the valuable part of marketing on Twitter, the functional network. 
Overall, Habitat represents one of the first big "black sheep" on Twitter to strike out and try again. Much like Kanye West at next year's Grammy's, people are going to wonder what their next move is more than other brands. This notoriety can be leveraged for positive gains through best practice type behavior, or they can storm the metaphorical stage (probably not trending topics again though) and re-offend. I'd wager their strategy has been thought through with more than an intern this time around. 

Tuesday, 15 September 2009

Microsoft's Windows 7 Advertisement - A solution to Vista and an answer to Apple found in quirky simplicity

      I've always loved Microsoft advertising, its probably a personality quirk,though I can't say this translates to all of its software and hardware products. While Apple gets to be the cool kids of the "major players in the technology sector", Microsoft simultaneously fills the roles of the geeky iconoclast, too wrapped up in his work to care, and of the earnest traditionalist, too big/hard working/naive to compete efficiently on coolness. Its because of these deeply held brand images, that I was pleasantly surprised by the new Windows 7 ad from Microsoft & C+P+B (and not just for the ending musical selection). Check out the ad for yourself below and then my thoughts on why it stood out to me.

     Nowhere was this divide more typified than in the popular "I'm a Mac" campaign by TBWA/Media Arts Lab, which spawned over 65 ads in the US, UK & Japan on TV and the Web. The US version shown below, painted Microsoft Windows/PC (John Hodgeman) as a person embodying the traits of Microsoft as described above, while portraying Mac (Justin Long) as expectantly hipster-ish  and trendy.
My favorite advertisement of the campaign - Doesn't everyone want to learn C++ around the holidays (or VB)?

     As the ad above from the campaign shows (along with other brilliant tie ins and cameos), Apple really hits its mark on representing its brand image (something that has already been justifiably celebrated ad-nauseum) with all of this expansive campaign. At the same time however, it seemed to open the door for the PC and simultaneously, Microsoft to define its own defensible position. Just as everyone wants to identify with the Mac as the slightly scruffy/intrinsically self-defined type (off to do interesting things in trendy parts of the city with a camcorder and his record collection), we all also have a part of us (no matter how repressed or hidden in some) that identifies fully with the PC. If anything from the ads really worked in Microsoft's favor, it was Apple's reticence to make the caricature of the PC too hateful, lest alienating their audience. This allowed a subtle and relatable duality to form. 

     The PC represents not only obligation and responsibility, but earnestness, hard work and a naivete that in a sense transcends cool. While this duality might not come across well in the Apple produced commercials above, it strikes a cord with some and defines the differences between not only traditional Apple vs. Windows users (stereotypically a battle of functional style over functional substance) but also of analytical responsibility vs. carefree expression found in human personality (or atleast in my own).

Prelude to the current Microsoft Advertising Response
     Microsoft tried various responses to the success of the "I'm a Mac campaign" with varying degrees of success and surrealness. It's first attempt, a pairing of Jerry Seinfield and Bill Gates traveling the country, was one of the odder things I've seen. The presence of Bill Gates maintained the nerdy/earnest aspect of PC, while the interaction with Seinfield gave the advertisements a noticeably interesting appeal, but it lacked any strong distinction to Apple's product offering.
     Microsoft's next attempt, "I'm a PC", took the lack of distinction drawn from Apple in the previous advertisements and improved upon it. Showing various cuts of PC users claiming their identity as such, the advertisements attempted to distil the quirky/nerdy image used by Apple's PC portrayal into an everyman response. They attempt to claim that if Apple users are a niche subset of trendy computer users, PCs are instead the tool of everyone else doing a variety of activities. The current Windows 7 advertisement is an extension of this campaign, as the girl featured has been in a previous advertisement. 
Concurrently to these campaigns, Microsoft also tried two other notable pieces of communication: "The Mojave Experiement" and "Laptop Hunters".

      The Mojave Experiment attempted to tackle user rejection of the Vista operating system through the production of an ad featuring "hidden camera" interviews. Interviewees were shown an operating system, "Mojave", which was actually Vista and then told to give their impressions of it. Predictably, after trying Mojave, users found it much more useful than their view of what Vista was like. While the attempted message was to directly rebut stereotypes of Vista, it instead seemed to take the view that user perceptions are wrong and baseless, waiting to be refuted by a much more experienced and intelligent body (the producer). This view seems to counteract the good will built up by the earnest/nerdy figure of Microsoft shown earlier and the everyman put forth by "I'm a PC".

      "Laptop Hunters" was another directed campaign, put forth to attack Apple on a vulnerable point, price. Macs by default have had a hard time competing with other laptop makers on price and the campaign attempted to highlight this through giving everyday people various amounts of money to purchase a PC. Splicing this through multiple monetary amounts, the ads highlighted the hardware capabilities that could be purchased from a PC vs. possibly a Mac of the same price. In this subtle attack, Microsoft seemed to merge together their ideas of "I'm a PC" populism with value, once again eschewing the nerdy/quirky image for a directed message and effect.

Why I Love the Current Ad and How it Relates to These

    One of the reasons I love the Windows 7 ad mentioned in the beginning is that Microsoft and C+P+B blend together strands of all of the aforementioned campaigns. Through the opening of the advertisement, the distinction between Windows Vista and the upcoming Windows 7 is drawn clearly and quickly. It shows that while consumers hated/feared Vista (I'm still using XP at home & work), Windows 7 will be different. The use of 3rd party recommendations lends an element of truth to this and continues through the commercial; just as with the Mojave experiment, they attempt to say, "Don't take our word for it, listen to others."

    While the initial point is to sell Windows 7, the advertisement does it in such a way that it also promotes PCs and Microsoft as a whole. The use of the little girl draws out the "I'm a PC" campaign elements in such a way that people can find it endearing or engaging, while her demeanor makes it non-threatening and a soft sell.

     Perhaps my favorite point of the ad however, is the slideshow finale. Aside from what is, I believe, a step towards fixing the underuse of Europe's final countdown in all creative media, it borrows on the quirky/nerdy flavor of the original PC embodiment to entertain. The presentation clipart is just weird enough to not be childish, but instead promotes a sense of interest and bemusement. Its almost as if the ad borrows from various internet memes just enough to subtly say, "Here's a rabbit with a pancake on its head, buy a PC!" It creates a spectacle in the same way that a person driving a van with a spraypainted mural of Led Zepplin coverart on the side might, but trading the societal disdain for more third party reviews. Finally, it seems to dig at a previous "I'm a Mac" ad by showing that a young child can use a PC to make a slide show, something that was previously touted to be easier on a Mac.

Overall, the advertisement creates an entertaining and effective presentation of Microsoft's direct points:
  • That PCs work well and exhibit as small of a learning curve as Macs,
  • that the people using them are just as interesting or hip as Apple users in their own way
  • and that Windows 7 won't be rubbish (or atleast as rubbish as Vista).
True, it might err on the side of technically simplified (which is fine as most technical users are already sold on Windows 7 or using the RTM) and sacchrine (the kid's lisp is a tad disney movie-ish), but it proves to be an interesting synthesis of earnest/weird/engaging and funny.

What are your thoughts on it?