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.

.jpg Version below (click to enlarge)

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.

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)

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.