Public Opinion Towards Competing Brands
Nike and Adidas are claimed as one of the most intense business rivalries. Both brands saturate the market in athletic apparel and equipment and have made themselves a household name among sports enthusiasts alike. With similar pricing and promotional strategies, the ultimate determinant in these brands success is consumer satisfaction and loyalty. This purpose of this analysis is to gather data on consumer preferences to determine the ways in which one brand exceeds the others in the eyes of the consumer.
Which Brand Holds the Most Popularity?
While favorites on a tweet do not directly translate to a sale, it can help brands to determine consumer loyalty. Consumers will tend to follow and favorite posts by their favorite brands either because they show interest in a product advertisement or to support an upcoming/current campaign. In this particular analysis, the number of total favorites by consumers for each company among the most recent 2000 tweets is visualized to determine which brand has higher consumer loyalty.
The data table displayed shows that on average, Adidas receive a larger number of favorites on their tweets compared to Nike. In order to retrieve such information a simply count function was performed to count the number of favorites on Nike tweet versus Adidas tweets. This informed was obtained through creating a timeline of Nike and Adidas’ tweets across 2000 data entries.
Are These Tweets by Real People?
Taking the same group of 2000 tweets, this analysis looks to see if the tweets recorded happen to be by consumers or robots. It is hard to determine consumer preferences and interests among a large group of tweets if these are in fact not by a real consumer. Using source data, we are able to observe the server in which tweets are being distributed from. It can be assumed that the majority of consumer tweets will be sourced from iPhone, Android, and web browsers. This visualization shows the number of tweet from distributed from a variety of sources.
The Visualization above shows that the majority of tweets written about Nike or Adidas are sourced from iPhone, Android or a web browser. With this information, a company like Nike or Adidas can actually use tweets in this instance to form an idea on consumer opinions. If the majority of tweets put out were written by bots, the brand can not accurately determine consumer reviews and interests. This visualization was put together through creating variables for each of the sources 1 being sources from Android, Twitter, and Web and 0 being other sources.
When are People Retweeting?
A good way for a brand to discover the best times to post is based on when people are most active on their phones and the times in which they are most likely to retweet. This visualization would be more accurate if it could be completed on an hourly scale, but given the current limitations on this platform, I chose to analyze based off a minutes scale. This partnered which average number of retweets on both an hourly and minute scale would be ideal for pinpointing the exact time in which a brand should post to maximize the number of retweets.
The data visualization shows that the most ideal minute to post during for the most amount of retweets would be around the 25 minute mark during the hour. There are additional factors to take into consideration, one being the popularity of the post. Ideally, a brand could use information on the popularity of their posts (based on favorites and interaction), coupled with hours and minutes with the most amount of retweets to pinpoint exactly when to post big news to their platform in order to maximize views and interactions. This visualization is a subset of the data in which time and whether the post is a retweet or not is extracted in order to determine when most retweets take place during a minute scale.