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 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.