analyzing online conversations
March 14, 2017
One of the most common forms of data visualization among social listening softwares is the tag cloud. The graphic representation of which topics are the most common organized by word, size and color is easy for the layman to decipher so it is dangled at the end of the software company’s string like top sirloin.
But it’s just a chicken nugget. Or, more aptly, just the breading around the chicken nugget.
Topic wheels are a bit more informative methods of data visualization. They enable you to see subtopics easily. But the data visualization is still just a superficial layer around the insights your data contains. They help you see one layer down.
But insights are seldom found one layer down. Understanding of the conversation — the why behind the emerging topics and themes — means drilling down deeper.
Take our study of Dirt Devil, for instance. We may notice looking at Tag Clouds and Topic Wheels of data visualization that durability is an issue that surfaces in the negative conversations around the brand. But why does Dirt Devil have durability issues? To know that, you have to drill down into the negative, then into the durability topic, then analyze and understand the various issues there.
The level of detail that can provide a product manager with actual insights to improve the product is not found using a tag cloud or a topic wheel. It’s found by diving in and analyzing and understanding the full context of the conversation. With this information — certainly represented visually for ease of understanding — we can tell the product manager that there are structural issues in quality of construction, weakness in the unit handles and motor issues, particularly when used for pet hair. These insights give the product team direction so they can either A) Ask deeper questions in further research or B) Focus on the opportunities to improve the product.
The overarching point is that if you’re relying on visualizations of your data rather than analysis of it, you’re missing a lot. In fact, we would surmise you’re missing everything.
We would love to help understand your data. Want to know more about what customers say about your brand? Your products? What you can do better? Drop us a line. We can help.
February 16, 2017
The fun for me in analyzing online conversations is the proof points the data provides. No longer do product, experience or marketing communications decisions have to be left to assumptions. The data allows you to turn them into assertions.
In our recent report on senior living, we analyzed online conversations of people discussing the major types of senior care facilities. We found hundreds of conversations mentioning nursing homes, assisted living facilities, independent living facilities and long-term care options. We broke each of those conversations down by facility, sentiment and topic.
When you do this, you get a glimpse into what consumers truly think. Not only are we not prompting them for answers, which in and of itself biases the information, but we’re simply recording when they talk about the topic in question voluntarily and freely.
What does this type of analysis tell us? Take for instance this visualization:
This is a breakdown of the conversation topics within the posts we categorized as focusing on assisted living facilities where the main topic was the experience of the family of the patient (which is important since the primary buyer is the adult children of the patient), and those experiences were scored as having a negative sentiment. So 30% of all negative conversations about assisted living facilities (represented in the circle to the left) were determined to be about the family experience. The right hand circle breaks those down by specific topic.
What this tell us is that 32% of the negative family experience conversations were about shopping for the facility overall. What is it that is so bad about it? We’d need to move a layer farther in analysis to discover that, but since we have the data, we can! Another 32% mentions they prefer an alternative to an assisted living facility. Further analysis shows that they don’t prefer independent living or nursing homes, but rather staying home and not needing a care facility at all.
While this may seem a logical conclusion if you understand the consumer, that has not been statistically proven before, to our knowledge. Now it has. But that insight can also give assisted living marketers more pointed insights to develop better copy, sales materials or even sales strategies, enhancing conversions and driving more customers.
Emotions while enrolling and family in-fighting are significant portions of the negative family experience, too. What can that tell an assisted living marketer hoping to land more clients? Those conversations can be further vetted to see if common threads run throughout.
The more you peel back the layers on analyzing online conversations, the more interesting nuggets you discover to fuel decisions for marketing, user experience or even product development. And those can build a better, more profitable brand.
The only question left to answer is why haven’t you started?
For more analysis of online conversations around the senior living industry, including a mapping of the buyer journey for senior care, see our Conversation Report. For more about how CRI can help you in analyzing online conversations around your brand or market, drop us a line.