Marketers have long focused on the reach of their social media messages as a key measurement. After all, if your post, tweet, video or image is being shared, reach represents how big an audience that asset is potentially making an impression on. But marketers are realizing that reach, while valuable at the start of a campaign, is not enough to determine success and provides few meaningful insights into long-term customer engagement.
However, by monitoring two areas — message consistency over time and audience sentiment in response to the message — marketers can take better control of the impact of their social media presence.
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Social Buzz, All For a 'B'
The recent IHOP burger campaign is an example of how consistency and sentiment can be applied to create buzz. The restaurant chain teased its Twitter and Facebook followers with a faux name change to “iHOB” in an effort to build support for its new burger line. The company carried the name change ruse going by changing the letter "p" with the letter "b" in select social media posts.
"Looked great, and—big surprise—tasted great." We love your review, but you missbelled surbrise https://t.co/iVIcMAxUTO— IHOP (@IHOP) June 13, 2018
Consumer reaction on social media, particularly Twitter, varied from disbelief to encouragement to interest in the new burger line. Moreover competitors, such as Wendy’s and Burger King, jumped in with their own responses.
The IHOP campaign succeeded in part because it stayed consistent with its message. The effort generated positive sentiment around its brand, even at the risk of disappointment or other negative reactions from long-time customers.
Measuring Sentiment Analysis
The rise of social media brought with it a newfound interest in sentiment analysis. Up until now marketers could keep track of a hashtag to describe the sentiment of a potential message. They would do this by maintaining a dedicated channel in Hootsuite or Tweetdeck. But this relied on people using a hashtag reach does not capture audience reaction to a message.
The latest analytic solutions have some analysis complexity in gathering the data. However, they are bringing marketers closer to understanding the reaction to a message. Sentiment analysis, for example, can be applied to the words of a social media posts through R Programming, all while minimizing statistical biases. This is done by taking the words from a tweet, stripping out the punctuation and specialty terms (like a URL) and then comparing the words against a lexicon. Being able to estimate an audience feeling from the words can help marketers see how their messages are bringing an emotional impact. I demonstrated a sentiment analysis at the O’reilly OSCON conference this summer, showing that the reaction was indeed positive despite some negative and competitor commentary to the campaign.
Sentiment analysis is a signpost for marketers to use methodologies that go beyond reach to understand engaged audiences. Noting how far a message was reposted indicates volume, but it does not cover if people really appreciated the message. My demonstration shows that marketers must be ready to reach beyond reach metrics, to know the truth behind a marketing message.