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.