Define Measure Visualize: Using Network Analysis in Place Branding


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  • This is Efe, from American University – SIS. I will talk about the issue of measurement in place branding, and its relation to the identity of place branding as a distinct field of study. I started this on the blogosphere.
  • We all answer questions about measurement and assessment. Marketing studies have been doing this for ages. Same goes for communication, PR, and etc. So, what usually happens is, we take our hammer and try to use it on place branding. Maslow's hammer , popularly phrased as "if all you have is a hammer , everything looks like a nail” We are not doing this because we are lazy. This is how we think about ‘stuff’. Yet, there are scholars (as I will talk about) who take a step back and talk about place branding. When we see it as an extension of marketing, extension of propaganda and political communication. There is something about the place’s identity. When a place’s name is mentioned, people start thinking about ‘stuff’. This is what (I argue) we should be measuring.
  • Talk about the ideal here A motto, and how I propose to use social/semantic network analysis. This is also how I looked at the other indices. What is measured? How is it measured? And their end products were their visualizations 1) Define what a place brand is properly. Capture the entire social phenomenon to ensure validity. 2) Measure – what you set out to measure. 3) Summarize – report
  • exports, tourism, people, culture and heritage, investment and immigration, and governance. Sampling issues in survey There are people out there who don’ t care about your country/city/region. They don ’ t know about it. But also, there are people out there that you don ’ t care whether they care or not. , his interpretation of NBI findings include descriptive adjectives such as strong and weak , NBI defines brands as unique characteristics of places in different areas, yet produces quantitative results. Therefore, it is difficult to understand what ‘ranks’ and ‘scores’ mean in terms of social phenomenon.
  • a country’s brand is “an asset that represents the sum total of the associations that influence preference” The focus on ‘associations’ in country brand concept, enables CBI to better capture this social phenomenon than NBI. Besides, the combination of qualitative data with quantitative protects the distinct characteristics of brands. Apart from associations, CBI also measures awareness, familiarity, preference, consideration, visitation, and advocacy. Despite the fact that overall rankings tend to disregard associations, CBI and its measuring system seems to have higher validity than its counterparts. Robust data-gathering and analysis procedures also ensure reliability of the measurement.
  • I am not saying these are not good. They do give you some kind of measurement. But it is not necessarily what we mean when we say place brands, place branding. They are metrics imported from other stuff.
  • Bold – all three (US, France, Australia) Underlined – 2 (UK, Japan, Italy, Switzerland, Sweden) Cbi bold – cbi & nbi XX Poor Germany.
  • So, when faced with such a difficult to answer question in a multi-disciplinary environment. There are three things you can do. One simply import the tools you use. Another option is more plug and play. Last one is taking a step back. Complexity and diversity is my point. Yet, let’s take that and make sure they are still comparable.
  • Huge problem with ranking. Pretty much all commercial products try to rank and score place brands. But we also say place brands are about ‘distinct’ characteristics. So is #1 more distinct? If so camembert has a very distinct smell, is that good? 3) So, how do we –on one hand- describe how different places are from each other, and then rank them? Because we talk about competition here. 4) Brand value, ranking, competitive identity A model to comprehend social phenomenon measure place brands report findings
  • When I am measuring, I want to measure the last two steps. Place messages – how they are formed, not really interested for the time being. So, I want to see how audience is shaped. I mean, how they are related to each other. And what are their perceptions? A place brand is something out there I can objectively analyze. But its roots are not objective. It is based on the audience. A country might be seen as an awesome travel destination by one target population and not so good for the other. When you look at NBI, you can get country-by-country results right? But combining them does not make sense. This is not how you combine them.
  • Define: so, a place’s brand ‘objectively’ exists out there (ontological dualism here) Yet, it is constructed subjectively by people. One definition I always refer to is Braun & Zenker. I just take the beginning and underline the importance of audience, and the existence of multiple audiences. Measure: Important concepts and people. Visualize: Here something about comparison. I still need to compare. So, when talking about a brand, I want to talk about lower level and higher level of abstractions. It is very much like categorization in one sense. Talk about a higher level of abstraction. i.e. Tourism > Sports tourism > Sports team > Baseball > Red Sox I can try to compare them in terms of tourism and say one is more interested than the other. Or I can say in the same network, Red Sox is more prominent, liked, preferred, than Yankees. But I need to say something. Or actually medium level abstractions here can show that the brands of two places are practically incomparable.
  • I am not finding the brand. I just wanted to show what we can say! A total of 12,484 tweets for New York and 18,744 tweets for Boston are collected between December 3 rd and December 24 th , 2011. Tweets are scrapped with a python-based scrapper code, and are based on three keywords (Boston, New York, and NYC). The content of tweets are used to find out the ‘associations’, while a tweet-retweet network is created to find more about the social network of communications. The type of twitter user (i.e. individual, business, sports team) is used as a node attributes. Density and distance are calculated to discuss the level of connectedness in the network. degree, closeness, and betweenness centrality
  • Boston Vacation Market Occupy_Boston Deals Celtics Twitter As medium level abstractions High level Sports Vacation Politics
  • NickFrost Platinum Job Video Building Guys NYC ---- Higher Celebrity Shows
  • Only on December 4 th – Tweet / Retweet map Just to show that the map is dispersed and there are subnetworks.
  • The reason for that is, I did something similar over the summer. When Derek Jeter was about to reach his 3000 th hit. Then sports was something we could compare. Now, for my given audience (twitter users using the keywords), boston and nyc are not comparable.
  • We are still trying to learn who is talking about what? Do it with quantitative surveys Interviews My keywords were really bad. Try Boston – brand. Boston and tourism. Boston and sales etc. Sub-networks are important MTML understanding. Because we are trying to understand how messages are formed and how they travel. Measuring – understanding what a brand is Evaluating – whether the narrative you want to push catches or not. Lastly, MTML saves us in academia.
  • Sports and celebrity.
  • Define Measure Visualize: Using Network Analysis in Place Branding

    1. 1. Define-Measure-Visualize Using Network Analysis in Place Branding H. Efe SEVIN American University, School of International Service International Place Branding Conference – Special Edition January 20 th , 2012 Utrecht, NL
    2. 2. The “Curse” <ul><li>If all I have is a hammer…. </li></ul><ul><li>‘ Disciplined’ scholars </li></ul><ul><li>Talking about roots of place branding </li></ul>
    3. 3. DMV in a nutshell
    4. 4. Anholt-GfK Nation Brands Index <ul><li>NBI Hexagon </li></ul><ul><li>Sum of “people’s perceptions of a country and its people across six areas of national assets, characteristics and competence” </li></ul><ul><ul><li>Partial definition </li></ul></ul><ul><ul><li>Communicative aspects </li></ul></ul><ul><li>Promoting distinct characteristics </li></ul><ul><li>Online survey </li></ul><ul><li>Ranking </li></ul>
    5. 5. FutureBrand Country Brand Index <ul><li>A country’s narrative and assets for internal and external audiences </li></ul><ul><li>Five areas: </li></ul><ul><ul><li>Value system </li></ul></ul><ul><ul><li>Quality of life </li></ul></ul><ul><ul><li>Good for business </li></ul></ul><ul><ul><li>Heritage </li></ul></ul><ul><ul><li>Culture and tourism </li></ul></ul><ul><li>Survey, focus group, crowdsourcing </li></ul><ul><li>Market-driven reporting </li></ul>
    6. 6. East-West Communications Nation Brands Perception Index <ul><li>Media-coverage </li></ul><ul><li>Volume / sentiment </li></ul><ul><li>Mass media – public opinion link </li></ul><ul><li>Categorization </li></ul>
    7. 7. 2011 ‘Top’ 10 NBI CBI NBPI 1 USA Canada Malaysia 2 Germany Switzerland South Korea 3 UK New Zealand France 4 France Japan Canada 5 Japan Australia Australia 6 Canada United States Thailand 7 Italy Sweden New Zealand 8 Australia Finland Brazil 9 Switzerland France UK 10 Sweden Italy Ireland
    8. 8. Literature <ul><li>Import (Kaplan et al, 2010) </li></ul><ul><ul><li>Brand personality </li></ul></ul><ul><li>Adapt (Insch and Florek, 2008) </li></ul><ul><ul><li>Customer satisfaction </li></ul></ul><ul><li>Devise (Hankinson, 2004b; Zenker et al, 2010) </li></ul><ul><ul><li>Relational network model </li></ul></ul><ul><ul><li>Complexity and diversity </li></ul></ul>
    9. 9. Story of measurement <ul><li>Issues with ‘ranking’ </li></ul><ul><li>‘ Incompatible’ requirements </li></ul><ul><li>Ambiguous terms </li></ul><ul><li>So, what do we need? </li></ul>
    10. 10. A Place Brand
    11. 11. DMV <ul><li>Define: Subjective network of associations </li></ul><ul><li>Measure: Who is talking to whom? What are they saying? </li></ul><ul><li>Visualize: What are the main concepts? Who are the main nodes? </li></ul>
    12. 12. Two examples <ul><li>Boston and New York </li></ul><ul><li>Tweets collected between December 3rd and December 24th, 2011 </li></ul><ul><ul><li>Content </li></ul></ul><ul><ul><li>Retweet networks </li></ul></ul><ul><li>Node attributes and network structure </li></ul>
    13. 13. Boston
    14. 14. NYC – High Level
    15. 15. NYC – Low Level
    16. 16. Boston - SNA
    17. 17. Findings <ul><li>No comparison between Boston and NYC (High Level) </li></ul><ul><ul><li>JB vs. Sports </li></ul></ul><ul><li>Ranking and comparison </li></ul><ul><ul><li>Boston>Vacation>Sports>Tickets </li></ul></ul><ul><ul><li>NYC>Video>JB </li></ul></ul><ul><li>Short term impacts of events </li></ul><ul><li>Low density </li></ul><ul><li>Significant ‘celebrity’ nodes </li></ul>
    18. 18. An Ideal Case <ul><li>Data triangulation </li></ul><ul><li>Data cleaning </li></ul><ul><li>Sub-networks </li></ul><ul><li>Measurement vs. Evaluation </li></ul><ul><li>MTML </li></ul>
    19. 19. Thank you! H. Efe SEVIN American University, School of International Service [email_address]
    20. 20. Boston - SNA