Recently in Social media analysis Category

Is it a problem of overpromising/underdelivering, or are people developing these unrealistic expectations on their own? Either way, I'm seeing more examples of people who seem surprised that software doesn't do all of the work in social media analysis. I really don't think this is controversial: regardless of your choice of tool, there's a necessary human contribution to the process.

This notion that the software isn't good enough because it requires a person to do something with it seems to be picking up speed. The first post that really kicked off a conversation was probably Asi Sharabi's. I saw a couple this morning, including one from Mark Schaefer that focuses on the graphics:

I’ve been spending time studying the trends in social media monitoring and have been impressed with the rapid progress. But there is still a lot of noise like this chart that really tells us nothing. The fact is, the most meaningful keyword and sentiment analysis is all still being done MANUALLY.

Building the social media spreadsheet
Think about spreadsheet software. When you first open a new spreadsheet, the software gives you a blank page. In the right hands, the software is a power tool for running financials, forecasting results, analyzing historical data... I've seen some impressive examples, but the most powerful spreadsheet software is useless without someone who knows how to use it. Which, if you think about it, is true of most software.

Social media analysis tools are software; they do some of the work, but to get the most out of them, you need someone who knows how to use them. The more you expect from your tool, the more the user needs to know. Anything that's fully automated either isn't doing much, or it has a lot of human effort baked into it.

Regardless of the tools used, at some point people take over. It may be earlier in the process (manual content analysis) or later (analysis and reporting), but eventually, a person takes what the computer produces and does something with it. All of those agencies that sell services based on the same SMA platforms presumably think this is where they add value.

If you want it done for you...
There is an answer for the company that wants the insights without putting in the effort, of course. Have someone else do it. You can't bypass the requirement for a human analyst, but you don't have to do it yourself. When you're shopping for social media analysis, just be sure to include analyst services among your requirements. That eliminates some of the best-known software companies, but it opens the door to an entirely different set of service providers.

If you want to make spreadsheets, you buy Excel. If you want financial projections, you roll up your sleeves or hire a financial analyst. It's up to you to decide whether that analyst will be an employee or work for an external service provider.

Where's the disconnect? Are unrealistic customer expectations coming from vendor hype, or is it just hope that things will be easy?

I try not to be too obnoxious with my iPhone, but it's hard to avoid being impressed with how it's changed my expectations about mobile computing. I've been wondering when social media analysis apps would start showing up, and now I've found one to play with: iCrossing's new Say What?

Compared to the tools I usually look at, Say What? (iTunes link) is pretty basic. It runs searches across Twitter, Digg, forums and blogs, returning a sampling of the results from each. It's not much, but it might be enough to get a quick sense of what's going on with a topic (especially if it's currently controversial).

Why are people talking about that?
Say What? is best used for getting a clue about a current topic—especially if it's controversial or newsworthy. Looking at results, four per screen, you're looking for someone to provide a hint about what's going on.

Looking up (Rush) Limbaugh, I immediately found mentions of his interest in buying into an NFL franchise. Ford returned items about the latest product recall. But when the company isn't making news, the results are far less interesting—people are apparently having breakfast at Dunkin' Donuts.

From Search to Analysis
I've been looking into web-based collaboration and project management tools for my own company, and as soon as I realized that some of these tools have iPhone apps, that became a requirement. We have so many web-based tools for monitoring and analyzing social media; who's going to be the first to offer a simple dashboard that delivers clients' data to smart phones?

I'm working on a theme that's all about expanding our idea of listening—it's so much more than defensive monitoring, but we need to get beyond first steps. After the last post, Sam Flemming commented on the importance of distinct terms for communicating outside of the bubble, and he's right. After we expand the concept of listening, we need to break it into manageable pieces. Fortunately, the pieces will look familiar.

As a set of activities, listening breaks down into these five modes:

• Searching
  Search is so familiar that we don't always think about it, but look at the advice on getting started in social media. That first step: find out what people are saying, where they meet—you know, the 5 Ws—when you do that as a snapshot, that's search. Don't neglect the value of familiar methods.

• Monitoring
  The usual starting point for a discussion of listening. Through automated methods (typically a dashboard or RSS reader), find and read new posts, comments, tweets, etc. that are relevant to your business. Focus on individual items for action.

• Alerting
  Similar to monitoring, but the system notifies you through email, instant messaging or text when a new item is discovered. Alerts can also be based on measurement thresholds, such as a sudden increase in negative commentary. No requirement to revisit the platform to receive alerts.

• Measuring
  Add a quantitative element to monitoring. Whatever your choice of metrics or measurement silo, measurement is about aggregation and numbers. For the purposes of this list, I use measurement to refer to the generation of regularly updated metrics.

• Mining
  Add a quantitative element to search, and you have data mining, which looks for meaningful patterns in archival data. Although it has a lot in common with measurement (as used above), I'm seeing different practices and benefits that justify separating the two.

For example, if you're doing routine measurement—you're looking at meaningful metrics on a regular basis—is there an opportunity to find different value by taking a mining approach, looking for insight in a snapshot of historical data? A slim distinction, but the point is to step back, walk around a bit, and look at the data from another angle.

Actually, lots of other angles, but more on that later.

Photo by bdu

Find a topic that a lot of people care about, and you'll find a great pool of data for social media analysis companies to use in a demonstration of their work. Over the last few years, we've seen examples based on Super Bowl ads, American Idol, and national elections. Now it's healthcare reform in the US, where discussions are—uh, generously seasoned with sentiment. Just the thing to show off your analysis chops.

Here's what's shown up so far:

• Obama Health Care Speech: What Did Twitter Think? (Crimson Hexagon)

• Reactions to Obama’s Health Care Speech to Congress (Echometrix)

• How Obama lost the healthcare debate online (Market Sentinel)

• Healthcare debate analysis (MotiveQuest)
  

photo by Rob Stemple.

One thing about sentiment analysis: it really stirs up the opinions. Apparently, it's good for attention, too, because yesterday's post has gotten a lot of it. So what is it about automated analysis that's so controversial, and what can human analysts do to offset the advantages of automation?

Automation offers four basic benefits:

• Scale
  Keeping up with all of the relevant conversations as volume grows.

• Speed
  Processing new items sooner; computers "read" faster than humans read.

• Consistency
  Software doesn't get less accurate with fatigue or mood, and it doesn't consider contextual knowledge if it's not supposed to. It just follows instructions, over and over.

• Availability
  Automated systems don't sleep, so they won't be the limiting factor in determing your 24/7/365 operations plan.

Competing with automated systems
Scale and speed are related, and the hands-on approach is simple: add more people to the process. Speed (latency) will still be limited by the ability of your analysts to read quickly. You won't compete in the sub-second latency market, but you can get ahead of the daily-update crowd.

Consistency in human scoring comes down to training and process. I won't pretend to teach the media analysis pros how to do that job, but it's going to be more formal than the eyeball ratings I gave out yesterday.

Availability is an interesting challenge, but it's not the first time companies have addressed the issue. If you're going to work through nights and weekends, your choices are to create some undesirable jobs at home or switch to follow-the-sun operations overseas.

The rise of offshore outsourcers
Combine the need for a lot of people (analysts) with the desirability of around-the-clock operations, and a lot of people will reach the same conclusion. From the beginning of the social media analysis business, some of the better-known vendors have had development and analyst groups in India. Now, I'm starting to hear from companies that are offering offshore human analyst services as a specialized service.

The interesting bit is that they're unbundling the content coding, so clients (or vendors) can add human-powered sentiment analysis to any platform that provides user coding or tagging.

This won't be an easy group to track. It's largely a traditional outsourcing approach, and any company that provides full-service social media analysis using human analysts could unbundle the coding piece, too. But if clients end up selecting human analysis over the automated version, expect more offshoring of the manual effort.

Since the recent New York Times piece on sentiment analysis, it seems everyone has an opinion on sentiment analysis (how appropriate, yes?). Without actually counting, I'm getting the impression that the overall score is negative, although with the colloquialisms and subtle innuendo, I'm not always sure. :-)

This is a round-up post, so I'm going to start linking to posts I've seen in a minute, but first, we have a problem: a buzzword alignment problem on what to call companies who monitor and analyze social media content. The article uses sentiment analysis to refer to the industry, but sentiment analysis is better understood as just one of the types of analysis used in the field.

This industry has a history of picking up a new label almost every time someone new writes about it. Forrester Research has called it brand monitoring and listening platforms, depending on which year and analyst you ask. I picked social media analysis when I had to choose, but even that is more limited than the state of the art tools and services. I don't have an answer to that one that makes me happy just yet.

Scoring the conversation
Oh, OK, I'll count. Really, how could I resist? Isn't this the obvious way to collect the posts on this topic?

Positive

• Sentiment First, Ask Questions Later, Joseph Fiore (RepuMetrix)
  
• Text Analytics for Ye of Little Faith, Manya Mayes (SAS)

• Sentiment Analysis, Scott Berkun
  
• Sentiment analysis for online content: Honest?, Urs Gattiker (CyTRAP Labs)
  
• La tarte à la crème du sentiment analysis, Guilhem Fouetillou (Linkfluence)
  
• Sentiment analysis crap in social media, Laurent François (Ogilvy)
  
• Is Sentiment Analysis Reliable? Andy Beal (Trackur)

• Sentiment mining: new term, new field. A new web? Claude Théoret (Exvisu)
  
• The sentiment analysis party has plenty of elbow room, Christine Sierra (Lexalytics)
  
• Sentiment Analysis, Eric Friedman (Union Square Ventures)
  

This list is an example of document-level sentiment analysis by a human. Anyone want to make the case that it might not be 100% accurate?

Do you monitor social media for mentions of your brand? Is that all you're looking for? If so, you're just getting started. You'll get more out of your listening activities if you cast a wider net.

If you've heard me talk about listening in social media, you know that I apply an expansive definition to the metaphor. It starts with basic monitoring to detect items that need a response, but the really interesting part is when you start to think of listening for its intelligence-gathering value. Given all of this public sharing of fact and opinion, what can you discover that will help your business?

1. Customers talking to you
   Call it Social CRM, customer service, or just meeting the customer where she is—if your customers are trying to reach you through social media, you want to be there. As for metrics and analysis, consider rolling the data from these contacts into a broader voice of the customer activity for a comprehensive view of what customers are telling you directly.

2. People talking about you
   Everyone in social media preaches this point. If people (not just customers) are talking about you and making it easy for you by using your brand names, you should be paying attention.

3. People talking about your competitors
   This one's easy to figure out, too. You might find immediate opportunities or longer-term insights, but you will find something useful in what people have to say about the competition.

4. People talking about your customers, suppliers and partners
   No business exists in a vacuum—who's critical to your success? If your customers are businesses, what can you learn by listening to their customers? What issues in your supply chain may affect you?

5. People talking about your market without mentioning names
   Tom O'Brien likes to point out that most conversations don't mention brands. Lots of conversations about your market are probably happening without mentioning brand names. If you're looking for insights—and not just complaints that need a response—you'll want to follow these conversations, even if that makes the queries harder to set up.

Update: Here's a twist: how about a category for what your employees are saying? Not necessarily as a Big Brother, monitoring the employees thing, but as a management of company communications thing?

I've said that opposing viewpoints over human vs. computer analysis of social media content don't constitute a debate, because I've never heard both sides at the same time and place. Now, thanks to an email exchange between Mike Daniels (Report International) and Mark Westaby (Spectrum) for Research magazine, I have to stop using that little observation. It's now—finally—a debate.

Tracking online word-of-mouth: The people vs machines debate

After an exchange of the usual points and counterpoints (speed, accuracy, sarcasm, synonyms...), the discussion really gets going in the comments. Mark makes a point that may summarize why I find this stuff interesting:

Automated analysis should not be viewed as a replacement for human analysis. Rather, it is a different method that is opening up entirely new and tremendously exciting ways of analysing data.

The usual debate: a closed question
If you're looking at it from a media analysis perspective, this question comes down to quantity and quality. How much media can you analyze in a way that you will trust? The new technologies will let you analyze more media sources faster, if you accept the results. In a world bursting with new publishers, that could be a good thing, and that's where we find the usual—reminding myself to use the word now—debate.

Moving to an open-ended question
Speed and scale benefits come from the application of new tools to old questions—not a bad thing, but not terribly interesting. Coming at it from another angle, the rise of automated analysis suggests a question about the removal of obstacles: What would you do with online information if you could "read" all of it? We're seeing some early ideas; what else is it good for?

Which question are you thinking about? Is "good enough for media analysis" your standard, or does the prospect of a different set of capabilities (with new tradeoffs, yes) inspire new ideas?

Update: T.R. Fitz-Gibbon picks up the discussion on the Networked Insights blog: Social Media Analytics, Humans vs. Machines.

Photo by Narisa.

The entanglement of social media analysis (SMA) and customer relationship management (CRM) is moving right along. It was inevitable, really, as companies realized the need to interact with customers through social media. The interesting question becomes, will they want engagement features in an SMA platform or monitoring and analytics in a CRM platform?

Adding process to SMA
From an SMA perspective, process management is an obvious addition to social media monitoring capabilities—once you find a customer with a problem, you want to fix the problem. Scale that beyond a handful of customers, and you need a system that can track your progress. Pieces to consider:

1. Discovery
   Finding relevant mentions of a company is a fundamental building block of SMA.

2. Tagging
   While we're in an analytics platform, let's assign some metadata to the item for future analysis.

3. Triage
   Decide which items require a response and prioritize. You might filter on sentiment, topic, or influence. Some items might receive an automated response at this stage.

4. Assignment, delegation and reassignment
   Who owns the response? It's not just for accountability, ownership relieves others of spending time on an item. The system should be able to handle ownership changes to support escalation and assignment to specialists.

5. Track to closure
   Classic customer service management—what's the status of an open item? Open-item analysis gives management a tool to manage workload and understand current incidents (the metadata from step 2 will be useful).

6. Measure results
   Now, we're back in the analytics realm and can look at the results through customer-service and media-analysis lenses.
   

The workflow features in some SMA platforms support this type of process, which can also be used in media relations or other contexts (action items aren't just for customer service). Other vendors provide tagging that can be kludged into workflow features. So you could choose to build your processes on an SMA platform.

Adding social media to CRM
The heavyweights of CRM are beginning to add social media features to their products, creating new options. Their existing installations will help them, especially in more conservative customers.

• Salesforce announced Twitter support in Service Cloud, including analytics and interaction. Service Cloud already promised interaction with online communities and social networks.

• SAP demoed Twitter analytics based on BusinessObjects Text Analysis during the Business Suite 7 launch (via KD Nuggets).

• Oracle will have integration of Clarabridge Smart Response with Siebel CRM this fall.
  

You got peanut butter on my chocolate!
Larry Dignan says of SAP's Twitter demo,

...it does show an increasing amount of integration with social networking tools. If corporate data is merged with the anecdotal tips from customers and partners there could be real insight.

This Holy Grail of insight is what a lot of vendors–Salesforce, Oracle and SAP–are chasing.

Here's an interesting challenge from the twitterstream. Can you spot mentions of a brand when the writer makes an effort to disguise the brand name?

Brandname obfuscation (e.g. St*rb*cks) is a recognition that twitter has tipped and that the data will be mined. #hardlife for #twanalytics
—Casper Davies (@drepsac)