The Inherent Pitfalls in Social Media Data

social media sentiment

Many marketers are focusing their efforts on big data analytics. Big data entails the gathering and analysis of data from all possible sources, including social media data.

Unlike other sources, data from social media can be tricky. While the basic premise of big data analytics is to gather as much valuable data as possible, including all social media data, it may become counterproductive and distort the analysis.

Successfully navigating the minefield of social media data requires the following considerations:

  1. Do not ignore sentiment. Data from transaction logs are straightforward. The customer purchased “x” at a specific date and time. However, a Facebook post can have multiple meanings. Rather than taking the message literally, the marketer needs to identify the underlying sentiment in the posted message.
  2. Look beyond the company name or usual keywords. Many social mentions may not include the company name. Many customers, for instance, may type “me, too” in reply to someone else’s post, duplicating the sentiment. Automated keyword-based scanning would miss capturing this expression. Marketers need to pay attention to everything their prospects or customers say on social media.
  3. Social media is real-time. That said, it stands to reason that social media data has an expiration date. Posts made yesterday may not be relevant today. Marketers need to ensure social media data aggregated is relevant and not out-of-date.
  4. All social media is not equal. Privacy settings may make it impossible for marketers to see everything a prospect writes on Facebook. In contrast, something posted on Pinterest stays there in open display forever. On the other hand, tweets are time-barred. Marketers have to factor in these limitations before assigning value to data.
  5. Everything on social media isn’t always true, considering that it is a free-for-all landscape. It is common for reputation managers and people with various vested interests to express incredibly positive or negative sentiments. These posts, far removed from reality, can distort big data analytics.

As part of our lifecycle marketing automation solution, Right On Interactive incorporates social integration so brands can gauge sentiment and better manage social reputation.

What kind of tools do you have in place to gain visibility into what people are saying about your brand on social media?

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