James Lovejoy
For any social media intelligence stack, data lies at the foundation and dictates the capabilities and credibility of any insight extracted.
It seems painfully obvious that the quality of the data would determine the success of all ensuing efforts, but too often do community managers, social media researchers and marketers miss a significant portion of the conversation. That may be the result of poor web crawlers, incomplete data providers, a social intelligence platform with weak data management capabilities or a query that does not capture the relevant chatter.
The Missing Conversations
That truth became all too apparent in our experiment on the retail industry. By examining the Twitter activity of 67 leading retailers, we identified how likely they were to respond to various tweets.
The results: 64.6% of brands responded to direct questions, 28.8% of brands responded to neutral or casual mentions, and brands responded to untagged complaints a mere 1.9% of the time.
Most leading retailers, with highly active social media customer service teams, are missing untagged mentions. That is surprising, given the value that engaging and appropriately managing disgruntled customers provides businesses.
Finding the Untagged Mention
In Brandwatch Analytics, identifying untagged mentions is actually quite simple. It’s only a matter of identifying all brand related chatter on Twitter and removing directed or tagged mentions.
For example, if we want to examine basic untagged mentions of Best Buy, a basic query might look something like this:
By capturing all conversations surround the brand (“best buy” OR besbuy) but negating tagged mentions directed toward Best Buy’s Twitter account (NOT at_mentions:bestbuy), this query uncovers mentions that will often go unnoticed.
This query alone will identify broad conversations around Best Buy and will reveal the many tweets that users have not aimed directly at Best Buy’s Twitter account. However, the immense volume of untagged chatter may be difficult to manage effectively. By adding some context terms, we begin to find much more targeted tweets.
This query, specifically including negative context words, helps provide customer service teams with potential complaints that would otherwise go unnoticed. For Best Buy, that may mean solving more of their customer’s problems, alleviating negative buzz and promoting their reputation as a helpful and responsive company.
For more information on how to uncover targeted conversations, request a demo of our Analytics product.
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