NetBase: Insight Delivered

Our internal documentation expert, Matthew Lindgren, recently produced an in-depth PowerPoint to summarize the results of a netnography on consumers’ perception of diaper shopping at Walmart. (We did this as an example of a netnography on a well-know brand; it wasn’t done at Wal-Mart’s request.) I wanted to post a couple of slides from the presentation to show how effective they are in making it easy for anyone to quickly grasp key results.

The presentation summarizes findings, explains our research approach, defines our social media sources for consumer insights, describes the overall sentiment regarding Walmart diaper shopping, analyzes main opportunities and threats, and offers ideas for potential actions.

Here’s a sample slide from the presentation. The pie chart on the left shows all the negative themes associated with diaper shopping at Walmart, while the chart on the right drills down into the biggest negative, quality, and provides specific information on what consumers don’t like about Walmart-brand diapers.

The benefit of documentation like this is that researchers and marketers aren’t simply left with a general conclusion—shoppers are unhappy about the quality of Walmart-brand diapers. Instead, they can look at highly specific reasons why consumers are unhappy—and can analyze those reasons and develop action plans to remedy the problem.

The slide below provides further insight and information on the quality issue. Note the negative “sound bites” quoted at right—they’re the source material that generates the pie charts, they’re easy to scan, and they exemplify the kind of authentic feedback ConsumerBase gathers from social media.

By the way, a great way to find visuals that reinforce the insights in a netnography presentation is to use Google image search http://images.google.com/. We entered “leaky diaper” and found just what we needed to make our netnography presentation more engaging.

The slide below focuses on the insight that consumers have highly ambivalent feelings toward Walmart as a retailer. In both this slide and the one above, note that after presenting the insight from the netnography, we present Potential Actions the company could take to help remedy the problem.

Thanks again to Matt Lindgren for doing the legwork on this example.

This gives you a glimpse into our style for netnography presentations. What’s yours? Do you incorporate images? How do you present your information visually to make it clear and give it impact?

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I’ve long been interested in the problem of digital photo sharing. Back when I was an MBA student at MIT, another business plan I worked on besides NetBase was for a photo-sharing site enabling groups to share photos after events. See, in b-school there are lots of parties, which couldn’t have been good for our grades—that’s why they call it “B” school.

What would inevitably happen after events is students would clog the school’s email system sending each other pix. Some might try to post the photos to a photo-sharing site, but it was a mess. I imagined others would have this problem. For example, what about weddings? Don’t the bride and groom want all their guests to put their photos in one place? I thought there would be some cool things we could do to preserve photos and memories with a group photo-sharing site. Alas, I had to choose between NetBase and that other idea and thankfully I chose NetBase because I think we’ve created some pretty useful technology.

What brought this issue to mind is that I just returned from a family vacation and was struck again by how hard it is for our family to share the photos. One person says to use Facebook. But the kids in the family aren’t allowed to go on Facebook. Someone else wants to use Flickr; someone else wants to use Shutterfly. Some of us just didn’t even want to be bothered about uploading the photos. A mess. Again. Six years later. I was incredulous.

A Quick Netnography on Shutterfly

So I thought I ought to research the market for photo-sharing sites again. Why hasn’t someone implemented the idea I had in mind? I know I’d buy it. Maybe the biggest issue isn’t in fact the inability for groups to easily share photos. Maybe that’s just a niche problem. Time for some proper market research. Oh, but I’ve got a day job, I don’t have time for market research. Well it just so happens that netnography is the perfect way to do market research on a shoe-string budget, when you need answers fast and don’t have a lot of capacity. Faster, better, cheaper.

So I decided to take a look at Shutterfly, which I really like for making photo albums. I expected to see people complaining about sharing photos after events. Well, turns out I’m wrong. The difficulty of sharing photos after events did not emerge as the top issue for Shutterfly. See below for what did emerge as the top issues. There was pretty much a tie between quality, usability, and functionality. I’ve broken out the quality issue into its various sub-issues, but they mainly had to do with the quality of the prints (flimsy paper, color issues, etc.) and of the albums (bindings fall apart, bad alignment of images, etc.).

Shutterfly—Negative Themes

In my hour of research—remember all this research only took me an hour to do!—I did find one discussion of the difficulty of sharing photos after events. The event type happened to be weddings, just as I had predicted. I decided to join the forum, www.makeupalley.com, to ask the group some questions. Following on Rob Kozinets’ ethical guidelines for participatory netnography, I identified myself as someone doing research and posed some questions to the group about their difficulty sharing photos. The impression I got from posters was that they had gotten by with just creating a Shutterfly account (or the like) and giving the password out to their guests to upload photos. One person complained that it was difficult to get the guests to actually upload the pictures.

But it wasn’t a major problem. Perhaps that explains why nobody else has gone ahead with the idea. Glad I chose to build out NetBase instead To tie this back to the benefits of netnography, sometimes people say that it helps you “fail faster, cheaper.” Indeed, innovation is a game of chance so if you can lower the cost of failure, you can spend more effort on the good leads than the bad ones.

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Interested in business challenges, technologies, and solutions in Content Analytics, where Content Management and Publishing, Search, and Analytics intersect? Then the Smart Content Conference is the place for you.

Seth Grimes, contributing editor for TechWeb’s Intelligent Enterprise and analytics strategist for Alta Plana Corp., just let me know he is organizing Smart Content: The Content Analytics Conference. It’s slated for October 19, 2010 in New York.

The conference is about digital transformation and enhancing the business value of information, both enterprise content and social media. You can learn more at smartcontentconference.com.

Seth is also looking for speakers and is accepting speaking proposals. You’ll find information on the conference website under the Call for Speakers tab, but here’s most of it:

“Presentation proposals from end users, analysts, researchers, and consultants in one of the areas that follow are especially welcome, but we will happily consider all proposals. Topic examples:

• Content management, enterprise search, and findability
• Content tagging and enrichment
• Content targeting including contextual advertising
• Analytics-driven social-media and publishing strategies
• Brand and content tracking, measurement, and optimization
• Competitive and market intelligence
• Knowledge bases and data services
• Sentiment and social-media analysis
• Rich media
• Semantics, Web 3.0, and the Semantic Web
• Taxonomies, ontologies, and knowledge management
• Content analytics technologies: theme and topic extraction, metadata, classification, automated summarization, machine translation, plagiarism detection

Please submit your proposal by Monday, July 26, 2010. We will aim to notify you by July 30 whether your proposal is accepted. Selected speakers— one per presentation—will receive a free conference pass.

If you’re from a solution provider—a start-up or established vendor—please consider submitting a proposal for a lightning talk: a 5-8 minute presentation/demo of your content analytics technology, solution, or service (exact length to be determined based on the number of accepted submissions). Just use the form to tell us who you are and what you propose to demonstrate.”

I think content analytics is a great logical next step for text analytics.  It brings it up a level from technology to business value.  Should be a great conference!  Thank you Seth for all the great events you do!

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The Wall Street Journal recently ran an article entitled “Are the Yankees Truly the Most-Despised Ballclub?” It states that “Contrary to popular belief, the Yankees are only the fifth-most despised team in the majors, according to an Internet algorithm built by Nielsen Co. that analyzes how people feel about certain things.” Here’s the article: http://online.wsj.com/article/SB10001424052748704471204575210384180269378.html

Interesting topic, but I had this reaction:

• How transparent is that method?
• Can you run the analysis yourself using Nielsen’s algorithm?
• How willing am I to trust results when I can’t see the underlying data myself, can’t evaluate the algorithm, and can’t do the analysis?

With our PreferenceSphere tool, you can drill down from the high-level 2×2 map showing Net Preference and Influence to the underlying Directional Graph, and drill down from there to see every one of the actual sound bites from consumers that generated the graphs. It’s an affordable, self-service approach that’s transparent and lets you examine source data and understand how it’s presented in graphical form. We believe such an approach gives you a great deal of confidence in the tool’s findings.

To illustrate the difference, here’s The Hatred Index from the WSJ article, created by Nielsen. It does make me wonder about how the algorithm generated these numbers and what the underlying source data had to say.

And here are the 2×2 graph and Directional Graph that shows visually how the numbers were calculated.  When we put this graphing capability into our product it will also show the underlying data, or “sound bites” as we call them.  For more on how to interpret these graphs see part 1 and part 2 of my postings on visually representing brand preference.

Whether you’re from Missouri or not, we think you’ll want to run these analyses yourself and have complete access to the method and data for reaching conclusions about brand preference. Let us know if that’s true. We welcome any other feedback on this evolving tool.

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As we were developing ConsumerBase, one of our customers said to us, “We can’t find any technology that helps us answer our research questions.” We asked for an example of a research question and they said, “More and more men are wearing beard stubble. Why?”

When we asked how they’d typically go about answering that question, they said they’d convene a focus group and ask probing questions about why men wear stubble.

Well, at that time, I was wearing stubble, and it struck me that if I were one of the people invited to their focus group, I’d have nothing insightful or terribly informative to say about the question. It looks good? It’s popular? That’s the best I could do, and that doesn’t help much.

Celebrity Stubble

So we told the customer we’d research the question using our tools and get back to them. I searched the Web using ConsumerBase and found many people saying that stubble gives a more masculine appearance, or celebrities are doing it—not much more helpful than what I would have said. But then I came across a few individuals saying it enhances the profile of the jawline, giving it a more angular, chiseled look. That was an “aha moment” for me, because I realized there were a few people who had the insight to  clarify and pinpoint the real reason behind men’s decision to wear stubble—and that there would be a similar small group of insightful people for virtually any question a researcher could ask.

Reliably finding and understanding “the insightful few” is a huge advantage ConsumerBase has over focus groups. When you convene a focus group, there’s no guarantee that its members will include some of the insightful few—those individuals who have thought about a subject, care about it, have insight into it, and write about it on the Web. You want to hear from them, because they’re the ones who can provide information that goes beyond the obvious and becomes the basis for a product or a solution.

ConsumerBase finds them because it’s essentially convening a focus group of the entire Web and everyone who contributes to it. That means your ConsumerBase “focus group” is guaranteed to include members of the insightful few. With its semantic frame technology and the ability to read and understand sentences, ConsumerBase finds the richest expressions of opinions, needs and emotions. Those are likely to be the comments from the insightful few. After it cuts through all the noise of the predictable comments and finds the perceptive ones, you have results you can use to solve your business problem.

NOTE: As NetBase’s chief innovation officer, I experiment with different applications for our tools, including ConsumerBase, which is for researching brands. In this case, stubble isn’t a brand, but I wanted to assess the ability of the lenses that underpin ConsumerBase to help in searches for insights on things that aren’t brands.

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One of my favorite activities during the summer is watching movies with my husband and kids. Collectively, we’re not quite at the age of Edward and Bella yet so I don’t totally get the Twilight mania. This month’s Brand Passion Index shows that I’m not the only one who loves good ole animated popcorn movies.

As the summer box office heats up, we used our ConsumerBase tool to surface emotions and passion levels associated with some of the biggest summer movie franchises: Toy Story, Shrek, Harry Potter, Twilight, Karate Kid and Iron Man.

The results were surprising: ticket sales don’t equate to love – Twilight’s voluminous chatter (and dominance at the July 4th weekend box office) is eclipsed by the intensely positive feelings expressed for Toy Story and Shrek.

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.

Despite ranking #1 at the box office and pulling in more than $64 million in its first weekend, NetBase’s Brand Passion Index showed that Twilight has a polarizing effect on movie-goers. Check out the range of emotions expressed on both sides of the aisle:

And apparently other people love 15-year-old Toy Story franchise as much as I do, for the same nostalgic effect it has on me and my husband and the pure joy our kids feel when watching it:

Next up: we’re headed back to school!

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My previous post on our prototype for mapping the PreferenceSphere showed preference data for social networking sites expressed as a 2×2 graph, which I said was derived from a directional graph. Here’s that underlying graph.

Social networking directed graph

DISCLAIMER: There are many duplicates in the Internet data for our prototype. Don’t take these scores too seriously until we integrate this feature into our ConsumerBase product, by which time we will have corrected the problem. However, the duplicate problem works in both directions and tends to cancel itself out, so we don’t believe it’s skewing the results.

In this graph, the thickness of the lines corresponds to the number of expressions of preference—the more people who say they prefer one brand over another, the thicker the line. So the thick line originating at Facebook and ending with an arrowhead at MySpace shows that a large segment of people prefer Facebook over MySpace. The much-thinner line going from MySpace to Facebook shows that a much smaller segment has the opposite preference.

A very useful feature of the prototype is that it allows you to drill down into WHY people have these preferences, which you can do by clicking on the lines and viewing the actual sound bites from consumers that were used to draw the graph.

Deriving the 2×2 Graph from the Directional Graph

We use data from this directional graph to draw the 2×2 graph discussed in my earlier post. For example, to derive the data point on the Net Preference axis in the 2×2 graph, we subtract all the preferences for another brand (indegree) from the preferences for the brand in question (outdegree). (For more on graphing terms, see this Wikipedia entry on Directed Graphs http://en.wikipedia.org/wiki/Directed_graph.) In the case of our social networking sites example, we subtracted the values for all the arrows pointing away from Facebook from the values of all the arrows pointing towards it.

Comments? Want to see other features? Please let us know.

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Online researchers are beginning to see the value of replacing focus groups and questionnaires with netnography, which is the online version of traditional ethnography. ConsumerBase is a valuable tool for a key element of netnographic research—namely, discovering consumer likes and dislikes about a product or brand. But that’s only one aspect among many in a comprehensive netnography of a Web subculture. ConsumerBase can also return results on emotions and behaviors, trends over time, and more. Beyond that, there’s a wide range of useful knowledge about a subculture that we could discover with additional ConsumerBase lenses.

For example, we could build lenses to learn more about identities within the subculture. We could identify values, shared experiences, customs, symbols, attitudes, and much more.

Cover for Robert Kozinets' book on netnography

Community Aspects

We envision offering a set of lenses for ConsumerBase that will continue to grow and will enable it to discover many more aspects of online communities for netnographers. Here are examples of lenses we could build:

• Identities
• Values
• Experiences
• Meanings
• Customs
• Rituals
• Beliefs
• Symbols
• Sayings
• Attitudes
• Behaviors
• Associations members have between certain ideas
• Social interactions
• Practices
• Events

Two unusual aspects that have linguistic patterns a ConsumerBase lens could identify are:

• Competitiveness—Knowing to what degree members of a community are competitive has practical applications. For example, Professor Robert Kozinets, netnography pioneer, found that in the PriusChat community, members demonstrate competitiveness in knowledge about the car and the category, and about problem-solving, gas consumption, and so on.
• Helpfulness—It’s also useful to know the degree to which members of a community are helpful to each other. In the PriusChat community, members were found to be helpful.

Regarding the members of a community, we could also identify, for example, who the influential people are, who has been thanked a lot, and other characteristics.

Applications

Using the lenses above, researchers could find communities on a particular subject and rank them according to the richness of the social interactions, competitiveness, helpfulness, and much more. This ranking of communities could help researchers find the best communities for participatory netnography, and could help consumers find the best communities to participate in.

Lens Requests?

We’d like to know what lenses you’d like us to build for ConsumerBase. Are there ones we should add to the above list? Please respond with any requests and ideas—we’re very interested to hear what lenses researchers and marketers believe would help them understand their audiences and do their jobs better.

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