Spike in the Social Conversation? Is your Social Customer Trying to Tell You Something?

October 21, 2011
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Analyzing social media or enterprise text is not only about monitoring mentions but about understanding the ‘why’ behind a customer’s expressions of intent, outrage or delight. For example, simple monitoring may alert you to a spike in activity but the real value can only be realized by drilling down into the activity spike.

Analyzing social media or enterprise text is not only about monitoring mentions but about understanding the ‘why’ behind a customer’s expressions of intent, outrage or delight. For example, simple monitoring may alert you to a spike in activity but the real value can only be realized by drilling down into the activity spike.

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In this example, I’m going to step through the process of drilling down into an observed spike in the social conversations around a TV show. This CI:View dashboard has been configured to display details about:

  • Activity trending
  • VOC
  • Demographic details

Surfacing Social Insights from the Data

To get started you’ll want to to view the activity around a specific date range for a particular show; segmenting social or enterprise text data by date can surface more granular trending information allowing you on to pinpoint exactly the time frame in which the increase or decrease in activity occurs; this information can provide feedback on scheduled campaign efforts or season finale impact.

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The above chart displays dimensions, like viewing intent, affinity and favorite status for a selected show for a selected time frame.  I can also compare the level of engagement during the week of the spike to prior or subsequent weeks to observe the trending of these dimensions over time. You’ll notice changes in the dimensions of consumer ‘affinity’ and ‘favorite” that coincides with the week of the spike of July 10-16th.

Although, this dashboard has not been configured to display sentiment, applying a sentiment filter to a dimension, like Viewing Intent,  would add an additional layer of understanding to what is driving the conversation around a show.

Demographic details can provide audience information related specifically to the designated time frame. Some questions to keep in mind when looking at any of this data is:

  • Does the gender or generation balance change in relation to a spike in activity?
  • Is the targeted audience reacting positively or negatively to changes in plot, product placement, etc?
  • Is the show attracting and delighting the right audience for advertising purposes?

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Answering these types of questions can help develop advertising strategies or other campaign efforts.

Clicking View Data displays the actual conversation associated with this time frame to hone in on the possible ‘why’ in the customer’s own voice for the increase in social activity.

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Yes, monitoring and engaging with customers is important but it’s just as critical to drill down into anomalies to not only  better understand what prompted the changes in consumer online behavior but the demographic and psycho graphic details of those consumers, who are most engaged.  Having this level of knowledge can help your organization more effectively reach your social audience.