November 21, 2016

An Example of Why Social Listening Needs Conversation Analysis

A key value proposition for Conversation Research Institute is that we offer conversation analysis to social listening data that finds the insights that can help drive your business decisions. But that’s not just a fancy set of words, there’s real reason behind it.

First, know that what we mean by offering analysis is that social listening tools themselves aren’t enough. Pretty charts and graphs and word clouds don’t do your business any good if you can’t explain what they mean, how the data was discovered and what insights surfaced that can help you.

Conversation analysis

No social listening software does that for you. You have to have conversation analysis – from a human being – to understand the data and surface that information manually.

Case in point, while working on a research project for an upcoming Conversation Report, we found this entry in a sea of data about the elderly care space:

“The social worker at the nursing home ~ when mom first went there ~ had to go to bat for mom and went to court to get a guardian (not my brother) for mom.”

The software in question gave this entry a neutral sentiment and picked out no sub-topics or themes for the entry. The software surfaced “social worker” “nursing home” and “guardian” as word cloud entries, but again, did not attach any sentiment or context to them.

Because we are manually scoring and analyzing this data, and our perspective is to look at the voice of the consumer as it relates to the elderly care providers (nursing homes, assisted living facilities, independent living communities and other long-term care providers), we add lots more context to the analysis:

  • The sentiment is negative toward the nursing home because the patient needed an advocate
  • The sentiment is positive toward the social worker who served as advocate
  • The source is a family member
  • The theme is patient advocacy
  • A sub-theme is non-family guardianship

And that’s before we went to the original post (which has other excerpts appearing in the research) to clarify even more:

  • The brother in question filed for guardianship after lying for years about having the mother’s power of attorney
  • The social worker was advocating for the patient, but also the rest of the family
  • The author (a daughter of the patient) was considering hiring a lawyer to fight the brother’s claim for guardianship.

So family in-fighting over the burden of care, cost and decision was another important theme.

When you let a computer spit out analysis of tens of thousands, or even millions, of conversations you get roughly one tenth of the context and actual insight possible from truly understanding what is being said. Certainly, on scale there’s no way to be as thorough.

But relying on automatic charts and graphs is keeping you away from the one thing you’re looking for: True consumer insight.

That’s what we surface. If you’re interested in finding it for your brand, let us know.

 

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