NetBase: Insight Delivered

This weekend, it’s all about burgers and beer, hotdogs, watermelon, and where you buy them. Just in time for Memorial Day, we’re introducing our Brand Passion Index. The Index uses our ConsumerBase tool to analyze consumer passion for brands in various categories. Each month, we’ll come up with something fun and publish the results on the NetBase blog.

Since Memorial Day is the official start to the barbequing and picnic season, the Index looked at the source of all that fun — grocery stores. According to the Index, the most loved store with the highest emotional involvement is Costco, closely followed by Whole Foods. Walmart is the most hated of all the brands analyzed. Consumers are dispassionate about Safeway and Kroger, they merely like them.

In this graphic, the amount of sentiment and chatter about a brand is indicated by the size of the bubble, while the placement of the bubble shows the intensity of passion.

Here are some sample verbatim surfaced in ConsumerBase:

To generate the Index, ConsumerBase reads online conversations and extracts the meaning from those conversations, capturing the intensity of emotions that consumers express about a brand. The combination of passion level and amount of chatter determines the involvement level. For more about ConsumerBase, click here.

Next up: June and Father’s Day fun.

Comments (4)

Even though today’s weather forecast is for the low 60’s, it’s heating up over here at NetBase. Lead411 has just named us a winner of its “Hottest Silicon Valley Companies” award.

NetBase was selected as one of 69 Hottest Companies, along with Facebook, AdMob, and Playdom.  Important factors that lead to this recognition: securing $9 million funding and expanding our business by 300% in 2009.

As you probably know, our ConsumerBase product is now in Limited Release. Seems the NetBase FanBase is growing (also see Jason Falls’ product review ).

Comments (0)

I met last week with Jason Falls, author of the Social Media Explorer blog. He’s a smart guy and he asked some great questions about ConsumerBase. In his review, Jason wrote “I’ve been in search of something that A) Produces more meaningful information and B) Is priced somewhere other than the “are you effin’ kidding me” range.”

Jason really hit the nail on the head. He keyed in to one of our major differentiators. There is a lot of noise these days about social media monitoring. With our ConsumerBase tool, NetBase is really trying to up the ante. There’s definitely a need for monitoring tools, and especially ones that help companies communicate directly with their customers. But there is a huge hole in the market to serve market research with a way to use social media  to understand how what is being said impacts your brand. And, to make those important discoveries quickly enough that you can act on them and without spending your whole research budget. Check out Jason’s review, and let us know what you think.

Comments (0)

“Do I need a tool to do netnography? Why can’t I simply read all the relevant blogs and forums on the question I’m researching?” If you’re a researcher doing netnography for the first time, that might seem like a sensible approach.

Well, you could do it yourself—if you had unlimited time, which most of us don’t. The fact is, there’s too much content out there. Social media is such a highly fragmented realm that it’s unrealistic to expect that you can find all possible content relevant to your question on Facebook, Twitter, and the thousands of topic-specific forums, millions of individual blogs, and so on. While some brands have a single user community or fan page, a commodity product like mouthwash doesn’t have a 1-stop shop for insights. Discussion about most brands is fragmented—it’s everywhere, including in casual remarks made about other subjects. You can even find mentions of Listerine on gardening blogs! And finding it is the easiest part—then you have to read, categorize, and distill the meaning of terabytes of information.

The reality is that there’s no way you can go to all those different places on the Web, and no shortcut in the form of a single place you can go to get all the insights. Even if there were such a compendium of relevant content, the amount of material you’d have to read would still be completely overwhelming.

That’s why we developed ConsumerBase. It’s a source-agnostic tool, seeking out and extracting actual consumer preferences from millions of social media postings. It reads and understands full sentences and organizes search results in the context of the questions you need to answer. When you don’t have unlimited time—and frankly, even if you do—it’s a more efficient way to do netnography.

To read more about netnography, be sure to check out our white paper written by Rob Kozinets, the father of netnography.

Comments (0)

“Houston, We’ve Had a Problem”–That’s what Apollo 13 astronaut Jack Swigert famously said to Mission Control on April 13, 1970. It’s dramatic, but as a description of a problem, it’s not very useful. If I had been in Houston, I would have been like “okaaaay, what is the problem exactly?” Without more information, you couldn’t be of much help to the crew.

Figuring out a way to get a specific, useful statement of a problem was one of the central challenges in the early days of NetBase. The original idea behind the company was to scour the web for problems and find technologies to solve them. To do that effectively, you need to define a problem precisely. So I was trying to answer the question, How do you create a structured representation of somebody’s problem? That’s what brought to mind the Apollo 13 quote. What if the transmission had been cut off at that point in the conversation between Apollo 13 and Houston? Engineers on the ground would have had no ability to help the astronauts at all. But we know they did help them, so I researched the incident further and found out that Astronaut James Lovell immediately followed up with a much more useful statement: “It’s a main B bus undervolt.”

Even if we don’t understand what a “main B bus undervolt” is, we can tell that this follow-up has a lot of rich information about the problem; enough, in fact, for Houston to start helping the crew.

Apollo 13 Spacecraft

Looking at those two statements made me realize that not all sentences about a problem have the same informational value. A market researcher trying to understand needs is going to find a sentence like the second one much more informative than the first. And that’s when I realized that our technology needed to be able to distinguish between these two types of sentences. We couldn’t simply do basic sentiment analysis. Why not? Because sentiment analysis would look at both sentences and conclude that they both say something negative—the first because it has the word “problem” in it, the second because it has the word “undervolt” in it, which you can infer has some kind of negative connotation.

More research brought us to the concept of frame semantics, which showed us how one can distinguish between these two types of sentences. Frame semantics has developed a wide range of frames to represent concepts, such as the concept of a need, which has several components, including who the sufferer of the problem is, when the problem occurred, and how the situation differs from the ideal situation.

That last component really defines the problem. If you can identify how a situation differs from the way it would be ideally, and subtract the ideal state from the actual state, what you have left is the problem. “Undervolt,” for example, identifies the problem by specifying that the voltage ideally would be higher than it was. That term probably gave Houston a better technical understanding of what the crew’s problem was—one they couldn’t get from the first sentence.

The Apollo 13 quote thus triggered for me an understanding of why NetBase would need a technology like frame semantics to specifically define problems—albeit problems with a lot less at stake.

Comments (0)

I was just trying out our Brand Insight tool, ConsumerBase and I thought i’d take a look at what the “sentiment” is around “sentiment analysis” (BTW this literally took 2 minutes). As you can see our sentiment analysis actually extracts words and phrases so you can see not only the positive/negative trends you can drill into the tool and start asking the question WHY. Of course what makes me smile is that we have, with some accuracy identified the top dislike of sentiment analysis is that the tools are generally “not accurate”.

Also interesting what some of the top sites are talking about sentiment analysis, including Mashable, CustomerThink, Web Strategist, and Read/Write Web.

It’s worth reading Jason Falls’s post about why you shouldn’t trust automated sentiment analysis. What are your thoughts? Does the example above explain why ConsumerBase is a bit different?

Comments (2)