Geo-location and Privacy: A Subtle Balance

This post is also featured on the recently launched "Typing On The Wall" Blog.....

Location Based Social Networking (LBSN) is prominent within the media zeitgeist at the moment, driven by the increasing growth of the category's current darling, Foursquare. As Foursquare recently added its 2,000,000th user and its 100,000,000th check-in, some can argue that the increasing growth rate (having added its 1,000,000th user only 3 months ago) and press coverage of the network are currently the most prominent stories.







CNN partnered with Foursquare during the World Cup to direct users to check-in at certain locations worldwide, unlocking badges and interacting with content


Given the company's growth rate and the utilization of the network by advertisers such as Domino's, the Huffington Post and CNN, this may be set to change. For media however, questions about Foursquare and other LBSNs go past the common questions of user growth and campaign concepts into "What insights can the network's data generate?"

Campaign metrics are key to creating attractiveness for advertisers, as greater data availability allows marketers to justify activity and modify comms efforts. Standing conversely against data availability however is user privacy.







User privacy concerns are inherent in social networking, but even more so when dealing with a user's location history. Sites such as PleaseRobMe.com popped up early on in the emergence of the current geo-location trend, highlighting a feed of users exposing their locations through auto posts onto Twitter. Concerns about user privacy have also been illustrated in recent studies, with a recent US/UK survey showing 55% of respondents worry about privacy relating to geo-location (Webroot).

Balancing privacy, key to growing the base of active users, and providing useful data & insights, key to growing advertiser investment, means that LBSNs must strike a careful balance as both network operators and data providers. Foursquare's solution to this issue has been two fold, providing basic data about venues & friends through the API (Application Programming Interface), while also providing business level analytics to venues claimed by the owners.

Given the open nature of the API, anyone can obtain a key and begin obtaining data and building applications, data is limited to what is available on the website. User specific data is limited to friends, while venues can provide the current amount of check-ins, who the mayor is and venue information. Data from the API is useful to monitor popular venues for a certain area or to create visualizations such as these, but it doesn't provide the specific level of data required to manage a large amount of CRM or loyalty plans.

Alternatively, the Foursquare analytics dashboard for a venue currently displays check-in data over time, different time periods and whether users clicked through to provided Facebook & Twitter links. In addition, it provides a stream of recent check-ins & top users, creating the ability to target individual users based on their behaviour for promotions. User check-ins not relevant to the specific venue are still protected.

Foursquare's approach to data visibility balances user privacy with metric creation in a way that provides venues an opportunity to customize promotions, while not risking user outrage by making data completely public to marketers. This strategy seems to be the way forward for LBSNs, as it encourages growth by unlocking the value of the network's data, without leveraging the privacy of the user base.

London's Agencies Squared: How London Advertising/Media Agencies Rank Up on Foursquare....

        As I mentioned in my last post about the Psychology of Foursquare, I've been looking into data aggregation and analysis of geo-located networks lately. While I wouldn't want the blog to become a 'one concept pony', I thought it would be interesting to see how agencies around London use Foursquare, since adoption around the industry is key to driving new networks and ideas.

 One aggregation engine & a list of IPA agencies later, I've managed to pull together a rough 'league table' of the top 25 advertising and media agencies in London by check-ins (as of 4/5/2010).

Agency	Id	Check-ins
1. McCann Erickson Advertising Ltd	172633	660
2. Wieden & Kennedy London	219558	529
3. Agency Republic	154743	460
4. Delaney Lund Knox Warren & Partners Ltd	154868	446
5. MindShare	161142	385
6. Dare	154565	297
7. Saatchi & Saatchi	244912	288
8. Profero Ltd	161146	259
9. Digitas	154598	227
10. PHD Media Ltd	173059	217
11. Proximity London	154900	187
12. Wunderman Ltd	605057	178
13. Bartle Bogle Hegarty Ltd	154630	178
14. Zed Media (Branch of ZenithOptimedia UK Limited)	154651	178
15. MediaCom	203677	178
16. Mediaedge:cia	537324	153
17. Abbott Mead Vickers BBDO Ltd	171053	148
18. LBi Limited	154854	138
19. Publicis Ltd	236312	138
20. MPG Media Contacts	1200388	133
21. Media Contacts (Branch of MPG Media Contacts)	1200388	133
22. Glue London Ltd	523581	130
23. Ogilvy Advertising Ltd	156590	128
24. Fallon London	183035	122
25. MediaVest	208064	116

       By taking the agency list and searching rather quickly through Foursquare, I've managed to compile the main Foursquare IDs for a variety of agencies (Currently 105 in total). I know many agencies have a variety of check-in locations within their offices (3rd floor desk with some interesting tips at a certain advertising agency...I'm looking at you), but I chose the most popular/obvious choice presented to me, much in the way a new visitor to an office might. I'm also aware, as many of you have probably thought, that there are many agencies left out by using only the IPA website list. If you have an agency you feel I've left off (there are many), and you have enough check-ins to place you in the top 25, message me on Twitter (@dubosecole), email me or leave a comment and I'll add your Foursquare ID on the list for next time.

Social Networks, Geolocation, Recommendations and Double Jeopardy....

      Warren Buffet was attributed to once saying "Your premium brand had better be delivering something special, or it’s not going to get the business”, but is this necessarily true? While logic would dictate that those brands which charge more must deliver a greater value in return, theories such as Ehrenberg's "Double Jeopardy" highlight the power larger brands have in the market place."Double Jeopardy" as described by William Mcphee and more famously, Andrew Ehrenberg, describes the concept that lower market share brands face a double challenge in competing with larger brands as they lack market share and brand loyalty from consumers. In such situations, the advantage large brands have from such jeopardy may encourage consumers to repeat purchasing behavior on factors beyond the value delivered by a specific purchase.

     To combat the advantage large market share brands have in the market place, smaller brands much differentiate. However, as large brands hold the advantage of possessing the promise of a standardized consumer experience, smaller brands must assure the consumer of quality, while still differentiating in tangible ways from larger competitors. Certain sectors allow for this more so than others, with clothing and food retailers serving as an example where double jeopardy and large brand advantage is chipped away by factors such as convenience, location, product differentiation and varied cost. In an example of such, Pizza Hut may provide a standardized dining experience over the Italian bistro down the corner, but consumer taste may eschew what is seen as pedestrian fare for a more personalized experience.

      Using the food retailing example further, it becomes clear how smaller market share (i.e. non-chain) restaurants face the dual challenge of differentiating in image while still assuring the consumer of quality vs. larger chain restaurants. Segments of consumers may always ignore larger brands out of principle (I for one irrationally loathe Arby's), but attracting the majority of consumers hinges on convincing them of not only an interesting and different product, but also of a level of performance and quality. The development of social media use within the last decade provides a unique opportunity for such retailers. Large brands have the challenge of convincing users to trust them in a way small brands don't. The "corporate stigma" of such means that smaller brands have the opportunity to gain consumer trust quicker. While these brands still have to convince consumers of their standard of quality, they have the ability to confer an earnestness to their image that goes well with the communication model found in social media.

The ability of recommendation sites to get the word out about smaller retailers works both ways....

        Sites such as Qype and Yelp operate as a network of consumer reviews and recommendations with an established and growing database of users and locations. Having been established since 2004-2005, these sites have become a hub of user generated information for consumers, providing third party recommendations on the standards of quality provided by smaller/medium brands. Where smaller restaurants lacked the ability to widely spread their message of differentiation and quality, these recommendation sites have taken on the job for them. These recommendations chip away at an already weakened double jeopardy concept (due to the sector's composition and nature), allowing small food retailers to speak with a verifiable quality larger than their size.
    
        With the benefits of sites like Qype, the next issues for smaller brands and reputation in social media becomes the level to which user recommendations are trusted by others. A recommendation from a trusted source or with demonstrable elements can hold more impact than a glut of others. With the advent of microblogging sites, reviews have become much more instant, allowing users to confer a level of immediacy to their thoughts on smaller brands and retailers. While sites like Twitter may trim the amount of detail that can be given about a business or consumer experience, it does allow for opinions to be disseminated while still in the retail experience. Furthering this, geolocated services such as Gowalla, Foursquare, Rummble, Loopt, Dopplr, etc. add perhaps the highest level of authenticity to consumer reviews, confirmation that the reviewer is currently there or was in the past. By demonstrating consumer action, these services extend the depth of consumer reviews, as well as opening new avenues for promotion through their network.

Just a few of the many networks that are driving the ability of small brands to establish big loyalty

      The opportunities afforded by social media channels may challenge the traditional idea of brand loyalty and scale, but they can be co opted in both offensive (small to medium brands) and defensive (large brands) ways. For small to medium companies attempting to use social media advocacy to build their brand, its important to remember a few concepts:
          1.) Make sure the product delivers on its claims....no amount of advocacy will help (or actually be present) if the consumer experience is negative
          2.) Be honest about how your product fits into the consumer's mind.....performance and reputation building will function differently for different products. People are more apt to recommend certain product types overothers.
          3.) If the product fit is right, take advantage of the enthusiasm of networks and network users to grow their community....Just monitoring what people are saying is fine, but to actually chip away at large market share brand dominance, offensive measures promoting consumer involvement are useful. Programs such as Foursquare's "Foursquare for Businesses" initiative are useful to go beyond user recommendations to user interaction.

Larger companies face a more defensive structure when dealing with social networks and their brand loyalty. Without the organizational agility of smaller to medium sized brands, loyalty has to be protected through more thoughtful measures:
           1.)  Make sure the product and the surrounding associated products....no amount of advocacy will help (or actually be present) if the consumer experience is negative or if brand perception is firmly entrenched. Larger brands with multiple locations are at more of a disadvantage when it comes to standardizing the consumer experience.
           2.) Use the scale of the brand to respond robustly to consumer comments. Unlike smaller brands, large brands aren't as likely to be able to build up earnest consumer recommendations as quickly without overcoming perceptions about corporations and size. The increased resources of big brands means that one can go beyond reacting to consumer reviews and opinions and actually respond, rectify and encourage advocacy.
            3.) Don't simply match what smaller brands are doing to increase advocacy, surpass it. If the resources are present to dwarf smaller brands interaction with the consumer, do it. Consider social network partnerships (as long as it fits the brand identity) or cross network promotions. The more users that can be gathered through the brand's scale effectively, the better.

        While the points made about double jeopardy and social media don't easily extend to all market categories, the original concept stays the same. Consumers may be more apt to review bars, pubs, restaurants and other retailers more than cereal brands, but that doesn't mean that something like smaller FMCG breakfast products can't leverage some consumer sentiment to affect distribution and retailer adoption. Within the above example, double jeopardy and brand loyalty is affected by inherent factors within the restaurant market, however the idea is clear that the way brands maintain and generate loyalty and adoption is changing rapidly due to the ongoing advancements in social media.

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.