The subtitle is 18 Use Cases That Show Business How to Finally Put Customers First, and the purpose of the study is to help companies find an “organized approach using enterprise software that connects business units to the social web – giving them the opportunity to respond in near-real time, and in a coordinated fashion.”

Owyang writes: “Regardless if you’re in IT or in a business unit, we wrote this to meet the needs of both groups. This architecture lays out all the possibilities (18 use cases) defines the problem and goal for each, and suggests some vendors to watch. It’s also pragmatic, as it lays out a process on how to get started, baseline needs (listening) and what to do next.”

The full report is below:

Let’s do a quick recap – Last Tuesday we asked “Can online monitoring predict the Oscars?“, a question we set out to answer by tracking the top three contenders for Best Picture: box office powerhouse Avatar, fan-favorite Inglourious Basterds, and critical darling The Hurt Locker (which just so happened to be this humble author’s favorite film of the year).

Specifically we wanted to see if online discussion could in any way act as a predictor towards who would walk away with that oh-so-coveted little gold man.

Throughout the week, a consistent trend occurred with Avatar leading, The Hurt Locker usually about 11-15% behind, and Inglourious Basterds typically another 3% behind The Hurt Locker.

Was there was a direct correlation between online chatter about three Best Picture candidates and those movies’ chances of winning the big prize at the Oscars Sunday night?

The short answer: not exactly.

In February, I predicted on my own that The Hurt Locker would win Best Picture in a YouTube video for my movie site. It was kind of a no-brainer. But I was curious to see what overall volume would show us.

Avatar had 42% of all online Best Picture discussion among the top three contenders (not surprising since its the highest grossing movie of all time), but it came up short Sunday night.

The Academy is a closed voting body and clearly online buzz didn’t affect their voting to the point of predicting the correct winner. That said, Avatar’s loss Sunday night does illustrate the need to look at social media from the perspective of how it fits with outside datasets, and not merely as a stand-alone source.

In this case, maybe combining picks from industry analysts and movie critics (who championed underdog winner The Hurt Locker since its release last July) with the online data could have bolstered The Hurt Locker’s position. Among those groups, The Hurt Locker was the clear favorite.

Some other takeaways:

Semantically, The Hurt Locker had a very solid presence, even beating out Avatar in the Inglourious Basterds insight.

To wrap-up: This recent Twitter study from NMS also bears out the notion that Avatar was clearly the most discussed film online. In that respect, our study was a success.

While online samplings didn’t predict the Best Picture winner, they are certainly valuable in finding out all kinds of other metrics. After all, isn’t the Oscar supposed to be about artistic merit and not popularity?

The Academy of Motion Picture Arts and Sciences (AMPAS) is obviously a closed voting body, but they are influenced by the media just like everybody else. The study started on Feb. 10, the day ballots were mailed to members of the Academy.

Using Spiral16’s online listening platform Spark, we tracked the top three contenders for Best Picture this year: Avatar, The Hurt Locker, and Inglourious Basterds.

This kind of measurement only takes into consideration volume and not the sentiment of the collected URLs. Unsurprisingly, a huge majority of all of the sentiment surrounding all three movies is positive.

Spark has two sentiment scoring capabilities.

The first is an average score based on all the words on the page and the second automatically extracts the most positive and negative passages.

Among the overt sentiment samples, all three movies have a higher than 97% positive rating. This means that of the loud voices on the Internet, the positive ones far outweigh the negative.

When URLs are scored for an average amount of sentiment, Inglourious Basterds leads the pack, with a 79% positive rating. Avatar is second with 74%, and The Hurt Locker is third with 71%.

Could it be that the multiple last-minute controversies surrounding The Hurt Locker are affecting its overall sentiment score? Is Basterds poised to play spoiler, riding a wave of positive sentiment, and challenge Avatar?

We’ll find out Sunday night! Stay tuned Monday for our Oscar report wrap-up.

Below are some random sentiment excerpts from the study. Click for a larger image:

Two things about the way Spark is collecting data:

Avatar is currently in the overall lead, so the graphic to the right represents the site type breakdown for URLs mentioning Avatar and “best picture.”

Below, however, we’ll combine the stats for all three movies and view the study as a whole.

This is how the breakdown of sites looks so far for frontrunners Avatar, The Hurt Locker, and Inglourious Basterds It’s actually very similar to the Avatar study:

Also, it’s important to mention that each of these samplings are being done independently of each other. This means that if Avatar is mentioned in the same post as The Hurt Locker, that URL will be accepted as a relevant result in the individual studies for each movie. That way, one film is not able to “steal any votes” away from another.

To illustrate just how widely spread out Oscar talk is all over the web, take a look at our 3D visual map of the Avatar study. The spheres represent URLs and the lines are links between them.

Green spheres have an average of positive sentiment, red are average negative, and gray are URLs with an average neutral sentiment.

This is a visual illustration of how many websites have Oscar talk related to these movies and how they all are fairly disconnected to each other. To explore a working Spark 3D Virtualization for yourself and see what this is really like, click here.

Tomorrow, we’ll look at sentiment—some specific overt stuff and how sentiment relates to the big picture.

The overall percentage for each of the movies hasn’t changed that much since yesterday. Remember, the URLs deemed relevant for this study are only the ones that also mention “best picture.”

Now let’s look at how the results differ in the three separate searches related to each movie. Looking at the Semantic Cloud, you can get a good idea of the language people that are using when writing about the Best Picture race. Although Inglourious Basterds has only 7% less of the share of conversation overall, it’s status as a distant third place is solidified by looking at the total count of words used.

Looking at this, it’s pretty clear that Basterds is running a distant third. It should also be noted that no other nominated movies’ keywords (Precious, Up, Up in the Air, The Blind Side, etc.) showed up before Basterds, further solidifying its place as the odds-on unrewarded bronze medalist .

It’s also worth mentioning that The Hurt Locker has such a solid presence in the word cloud overall.

Other interesting sidenotes:

In all three studies, “oscar” outranks “academy” and “awards,” while “oscars” is right behind them.

This is no big surprise to learn that people are using the slang terminology more than the official title of the ceremony.

With another search today, I also confirmed my suspicions from yesterday with data from Spark. I contended that:

Using two different queries, one for “inglourious basterds” and one for the more subtly misspelled title “inglorious basterds,” I discovered that 46% of all people referencing the film spelled it wrong like Neilsen did. This means their study didn’t pick up a whopping 55% of the traffic that spelled the film’s title right.

We’ll have more data insight tomorrow, so stay tuned.

Or not.

Nobody really knows, since it is more than just the number of nominees that has changed. The Oscars are also employing a tiered voting system where Academy members rank the 10 films from best to worst and the scores determine the winner.

Even though the Academy of Motion Picture Arts and Sciences are the only ones whose votes actually count, I thought I’d take a look at the amount of online chatter about some of the nominees to see if there is any correlation between the movies people are talking about and the end result of the Academy’s voting.

Using Spiral16’s Internet monitoring platform Spark, I started tracking posts about the two movies that are generally considered to be the frontrunners—Avatar and The Hurt Locker—and the film that many say could be the spoiler: Inglourious Basterds.

Remember, Spark is not limited to merely social media sites. Also, because Spark is not limited to RSS-generated results, it pulls in a huge amount of data.

On Monday, I posted about setting up good search terms to get great results. This is absolutely key. For this study, I searched the title of each film and the words “best picture” to see only the posts that were equating the two in some fashion.

For The Hurt Locker, I left out the word “the” and for Inglourious Basterds, I searched for both “inglourious” and “inglorious,” knowing that everybody will catch the obvious misspelling of “bastards” in the film’s title, but might miss the more subtle misspelling of “inglorious.”

(The Nielsen Company, in a post from yesterday, appears to have made a major goof in their flawed Best Picture study. Not only did Inglourious Basterds come in last in terms of total buzz—not a chance that is accurate—but in their data chart, the movie is spelled incorrectly as well. Guess we know why it came in last! I’m also wondering how they solved the Up/Up in the Air problem, but that’s another story.)

Thankful that they are not considered to be in the top three, I purposefully ignored nominees Up and Up in the Air because differentiating the two would be nearly impossible and lead to a lot of bad data. (You can add “Pixar” as a search term for Up, but how many people are actually mentioning the studio when they talk about the movie’s Best Picture chances? 50 percent? 40?)

I set the date range to bring in results going back to Feb. 10, the day the ballots were mailed to members of the Academy. Hopefully, this brings back a total that is more relevant to the actual vote, as it reflects the online culture during the time when voters will be making up their minds.

Here is the big-picture Best Picture overview as of noon CST today:

Below are some random snippets that bear out some of the bigger themes of this race.

Although Avatar is the most talked about so far, it also is the film that’s been seen by the most people. That means we have to assume that not all the conversation surrounding the Best Picture race is about how it’s going to win. (Check out the very first conversation excerpt.)

Inglourious Basterds’ spoiler chances are a popular topic, as you can see by the snippets in the third box below.

Come back tomorrow for more interesting data in our 2010 Best Picture race case study. After the Oscar telecast on Monday night, we’ll know if there’s any connection between online chatter and who wins the big prize.

Your search is more than just a search. It’s the first step of a research project. Our job is to make your project successful, so we’ve had a lot of experience setting up and testing search queries to give clients the best possible statistics.

Here are the steps you need to take to define and refine your query:

1. Know what you’re looking for—have a defined topic.

If you went into a library and told the librarian you were looking for stuff on history, the first question any good librarian would ask is “What kind of history?” Otherwise, you’d be poring through the entire breadth of human history just to find information on poodle skirts.

It’s all about narrowing down the field. If you want to find out whether a used 2003 Ford Expedition truck is a good buy or not, you’re one step ahead of the game because you’re looking for a specific make, model, and year. A search for “Ford” isn’t going to do you any good. You have to define what you are looking for specifically because the search won’t do that for you. It gives you everything.

2. Think like a machine—narrow your topic into a concept.

Let’s say you want to run a query about politicians who put a lot of political pork in the bills they submit to Congress. A machine doesn’t know what “pork” means. Machine language would happily spit back recipes for baconated grapefruit with the same enthusiasm as information about a spending bill. You’re liable to get a lot of irrelevant results unless you bear that in mind.

3. Search for what people are saying, not what you want them to say.

This is a common pitfall. If you are looking to define what customers who frequent movie concession stands are saying about the products there, don’t search for “concessions.” Chances are, few of the relevant URLs will contain this word. Use common language. You’ll get better results if you think about how people actually talk.

People are more likely to be talking about the popcorn, hot dogs, or Cokes that they had at the movies. They may also talk about prices. All of these are important words to consider.
Another example: Use “laid off” or “lay off” before you use “unemployed.”

4. Find them and learn their language.

Sometimes you have to do a little research before you start. You wouldn’t go to a mommy blog to find out what people think of the public option. Do a test queries to find out where the conversations about your topic are happening.

Now it’s time to find out specifically what’s being said and how it relates to your concept. Read the blogs that cover your topic. Even better—use a tool like semantic analysis to learn the language that your targets are using. Now that you know the lingo, it’s time to refine your queries.

5. Know that you may not find what you’re looking for.

Expectation management is important. As shocking as it may be, there probably aren’t a lot of people talking about your original Cap’n Crunch/James Bond fan fiction or the rubberized oscillating spammer widget that you marketed to the Amish.

That could mean many things. Are you not getting the right results because you haven’t used the right terms to find them or because the results you want aren’t there? If nobody is talking about your widget, look at it as an opportunity! Running a query on popular Internet widgets might reveal to you that the Amish aren’t real big on computers—and that’s valuable information.

This section of Spark is laid out in an intuitive fashion and has improved functionality. The new Insight Explorer section gives a more in-depth look at each data gathered for each individual insight by including a complete list of relevant URLs. These URLs can be sorted with the new global filter to find and highlight the material that is the most important to you.

The Bulk Options box can be checked to automatically select the individual boxes to the left of each URL. Use that option or select nodes individually and apply the Delete Selected Nodes feature or the Export Data icon.

Insight Explorer is laid out in an easy-to-read format that gives you a snapshot of the most important information, with the ability to delve further into each URL. Here’s a glance at some of the new features:

Site Type – Each site type is color-coded and has a corresponding abbreviation.

Sentiment – The circle next to the site type graphic will either be colored, red, green, or gray to represent the negative, positive, or neutral average sentiment level of each URL.

Page title – The title of the URL is displayed just to the right of the sentiment rating. It is bolded and in blue. If you click on the title, you will go to the Site Details page for that particular URL where more detailed information about that web page is shown.

Domain – Under the title on the left side is the domain of the URL. If you click on the domain, a direct link to the specific URL will pop up in a new tab in your web browser. If you roll your cursor over the domain, the full URL will appear.

Influence – This ranking orders each node in your insight according to Spark’s influence algorithm. This is the same proprietary algorithm as the Node Influence ranking on the Top Engagements Targets gadget.

Summary – This displays text from the URL that was indexed from RSS feeds or search engine APIs, and is often—but not always—the first words of the post. Each site will be different.

Options
• The Delete Node icon will delete the URL.
• The Favorite icon highlights that site by adding a favorite icon next to the sentiment score. Favorites can be used to develop a Daily Growth Report. Click the icon a second time to un-favorite a site.
• The Notes/Tasks icon allows you to add notes or tasks to the URL.

If the options exist for that URL, one to four feature icons may appear along the bottom of each listing.

• The Excerpt Available icon appears if Spark has captured at least one excerpt of overt sentiment. Click on the icon and a new box will appear below the listing with the text of the excerpt, its sentiment rating, and the date it was captured by Spark.

• The Notes/Tasks icon appears if one or more notes or tasks have been assigned by a user within the insight. Clicking on this icon takes you to the Site Details page, where all tasks and notes assigned are displayed.

• The Video Embed icon and External Comments icon will appear if a site offers API access to extra information such as videos or comment streams. Clicking on these icons takes you to the Site Details page, where the URL’s embedded video and comments section are displayed.

Thanks to the KC/IABC for inviting us–it was a lively session and Phil and Alex walked everyone through a 3D visual map of the Conan vs. Leno insight we ran just recently. If you were there, you got to see a 3D social media visualization in action. (If you go to the Conan/Leno blog, you can get interactive with it on the website as well.)

Phil and Alex also demonstrated how social network visualizations can be applied to offline data sets by delving into a 3D visualization of quick service restaurant (QSR) customer surveys from different store locations.

Here’s the session description and the PowerPoint from the presentation below:

You’ve seen the graphics—a cluster of colored spheres with seemingly random connections between them, spread out in empty space like a spider’s web. In actuality, it’s not random at all, but a 3D visual map of the links between the key influencers on the Internet talking about your brand. This session will help you understand how to identify the most influential conversations, measure the brand reach of a message, and understand where positive and negative “hot spots” are within your ecosystem. Before you engage with key influencers, you have to first determine how to prioritize your engagement opportunities, and visual mapping is a great place to start.

What surprised me, however, after running a sampling of relevant URLs with Spark, was the amount of anticipation and buildup for the product.

Using our proprietary influence algorithm (which takes inbound links, domain influence, persistence, and search source abundance into consideration), the number-one most influential URL is a category in the Business Insider for “Apple Tablet.”

Look at the screenshot from our 3D visualization and you’ll see the URL right in the center of everything and the other URLs that link to it in the surrounding area.

From there, you can also see the other URLs with inbound links and how that influence spread throughout the Web.

Internet chatter about the iPad began long before last week.

After weeding out spam and non-relevant posts, the average sentiment line graph to the right compares the positive and negative sentiment about Apple’s impending new product during the month of January.

Much of the talk surrounding the iPad concerned what it would be named when Apple finally introduced it. (The word “iPad” was only 11.4% more prevalent than the word “tablet” throughout all the URLs.)

As you can see, positive and negative sentiment was generally even with small amounts of flip-flopping up until the announcement on Jan. 27.

As the total amount of Internet posts fluctuates after the announcement, the positive sentiment continues to outweighs the negative by anywhere from 23% to 46%.

Our automated sentiment extractor finds examples of the most overt sentiment in all relevant posts. Of the 2,500 sentiment extracts up to Jan.31, Spark found that over 45% were negative and over 54% were positive (see screenshot to the left).

When you change that date filter to reflect Jan. 27 to Jan. 31, positive sentiment increases just over 4% and negative sentiment decreases by just over 4% as well (see screenshot to the right).