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Think of your favorite model or metrics for measuring social media activity. Flip through Olivier Blanchard's presentation on social media ROI. Now, with that in your head, read Tom Davenport's 2007 book, Competing on Analytics. How far do you get before realizing that the enterprise analytics crowd is asking some of the same questions as the social media crowd, but looking for answers in different data? What if the two groups met?

When we asked CIOs to identify their visionary plans for enhancing their enterprises' competitiveness, business intelligence and analytics was the top answer, selected by 83 percent of our sample... "Facts drive decisions," said an Insurance CIO. "Plans for imbedded analytics need to enable data capture at the customer touch point."
— IBM's 2009 Global CIO Study (via KDnuggets)

Are you doing this now? I'm looking for companies to interview for my research.

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

Everyone says that listening is central to social media success, but over time, we've fallen into a too-narrow interpretation of the metaphor. Think about it: if listening means monitoring, then we have too many words. Fortunately, they don't need to mean the same thing. We just need to expand the way we think about listening.

Here's the definition of listening implied by many posts and presentations:

Defensive keyword monitoring of social media for customer problems and complaints that need a communications or customer service response.

How can we expand the definition of listening?

• From a defensive posture to developing valuable market intelligence.

• From keyword monitoring to applying all of the technologies available to discover and analyze relevant online content and activity.

• From monitoring to metrics, mining, and interpretation. It's a metaphor, so there's no reason to be stuck with the word's literal meaning.

• From social media to all media and customer communications.

• From a focus on problems and complaints to an interest in all relevant conversations.

• From PR, marketing, and customer service to anywhere the information has value to the business.

• By collaborating across measurement silos to find the right methodology for the task.
  

All together, now: "Companies should listen to social media." We all know the advice, but do you have the impression that listening is a purely defensive strategy? It's not. You just have to move beyond the common, but limited, interpretation of listening.

How often does your defense score?
In a recent survey of management, marketing and HR executives in the US, Russell Herder and Ethos Business Law found a strong defensive leaning in respondents' current use of social media. The top reasons they use social media?

1. Read what customers may be saying about our company (52%)
   
2. Monitor a competitor's use of social media (47%)
   
3. See what current employees may be sharing (36%)
   
4. Check the background of a prospective employee (25%)
   
5. None/personal use only (16%)
   

Put your listening on offense
Think about my earlier list of conversations you should care about, and let's come up with some things you can do with the information you find. Defensive ideas are easy (and rampant). Let's focus on putting some points on the board. I'll start:

• Spot sales leads where prospects ask questions or contact you through public channels.

• Figure out a competitor's plans from their public statements and personnel changes.

• Figure out a customer's plans (and needs) from their public statements.

• Identify a competitor's weakness in online complaints; launch a product or program to exploit it.

• Identify a product or service opportunity in online discussions; fill the gap before competitors notice it.

Listening can be defensive—and if you're not monitoring for customer complaints and other problems, start. But don't stop with defense; think about how to apply it to advantage, too. Although it sounds passive, listening doesn't have to be either passive or defensive. Don't be satisfied until you find the path to profit for your business.

Thanks to Deni Kasrel and Bill Ives for pointing out the report and the defensive tone of responses to the question.

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?

This morning, Dow Jones will release the first instance of its new Economic Sentiment Indicator (ESI), an economic indicator based on language patterns in news media. I've heard of several strategies for finding investment intelligence in media content, but this is the first I can recall that aims to predict the performance of the overall economy. Naturally, it reminded me of earlier discussions of which media metrics might be useful as economic tea leaves.

The math behind DJ's ESI is astoundingly simple—"a ratio between the number of appearances of the word recession (and some synonyms) and the number of appearances of the word recovery (and some synonyms)"—yet the company says it is a reliable, leading indicator of economic performance.

Predicting market moves
If a simple sentiment test based on a few numbers can predict the economy, what does it take to predict the performance of specific investments? I've heard of a few approaches at real companies. Naurally, what works and what doesn't work is somebody's trade secret, but here are some things they're trying:

• Volume of discussion
  More talk means something. I've heard of hedge-fund experiments with this simple indicator.

• Sentiment analysis of discussion
  Take the PR-research metrics and look for correlations with stock prices. Think about mining targeted communities versus the entire social media world.

• Sentiment analysis of influencers
  Ignore the crowd and focus on quotes and public statements of executives, analysts, and others with specialized knowledge of the company.

• Discovery of little-known facts
  Apply technology to read the impossible volume of daily information that may reveal—or hint at—a valuable fact. Keep your text analytics busy with securities filings, patent filings, court records and anything else that might hold material information. It's amazing how much is online now.
  

What else can it do?
If you think about the information generated by most social media analysis companies, it's not hard to imagine looking at the dashboards or reports with an investor's eye. Both quantitative and qualitative views can tell useful stories. If you're the communications person, you might try comparing your media metrics with your company's stock price, in addition to financial metrics. Wouldn't that be an interesting chart to have in your back pocket?

You might try thinking of how SMA might benefit other functional areas, too. Certainly, vendors I'm hearing from are applying similar techniques outside of marketing and communications. Apply a little rocket science and consider that the value of this information might show up somewhere other than where everyone is looking. It's way too interesting to stay in a sandbox for long.

I recently got a call from someone who's working on an unannounced project. I can't talk about the details, but there's a lesson in what I didn't learn when I looked her up online. It's a reminder that online sources aren't always complete, even when they're accurate.

As usual, I looked up this person before our call, but what I found was actually misleading. We met through LinkedIn, but her profile still lists her previous employer as current. If you combined (a) her work history with (b) her new employer, you might piece together the subject of our NDA. Keeping the new job quiet goes a long way toward preserving the confidentiality of the initiative.

New hires outside of a company's existing business suggest questions: "What could Microsoft want with a chip guy?"

Our online culture of self-promotion leads to lots of disclosure. I wonder how many secrets have been pieced together as a result of an innocent job change announcement?

Photo by Marcin Wichary.

The usual starting point for social media analysis—whether you're more interested in the monitoring or measurement variants—is to ask, "what are people saying about us?" That's a reasonable starting point, but if we take a few steps around to other parts of the elephant, we discover other applicatons. Today, for example, I talked with a marketing exec at a capital equipment supplier who was interested in consumer intelligence as a major account sales tool. I can think of quite a few companies who could do what he described, but I couldn't think of any who have.

The idea is simple. Use a common snapshot report to generate insights about a major customer account, based on what their customers have to say about them. Package the results for your major account team. You could also use a more general view of your customers' industry for your entire sales force.

What are my customer's customers saying?
Start with a typical reputation snapshot report—volume, sentiment, leading topics, trends. Instead of focusing on your own company, focus on your customer (bonus points for finding trends that mention both your customer and you or a competitor). It's a simple keyword substitution away from the traditional question: "what are people saying about them?"

When you get the report, the first benefit will be in growing your understanding of your customer's business. But the eye-openers will probably be in a leading dissatisfiers list (top issues filtered by negative sentiment). How would your sales team like to know about your customer's issues with:

• Problems associated with your products
  
• Problems associated with competitors' products (bonus!)
  
• Problems your products can help solve
  
• Emerging opportunities for your customer supported by your products
  

I did a lot of this kind of demand-chain analysis in my previous career—looking at consumer trends and their impact on customer demand for my products. This is valuable intelligence for any company whose customers use their products to deliver their own products or services. If you do this for sales, just remember to share the results with your marketing and product groups.

(Update: It may be more accurate to describe this as industrial marketing, as opposed to B2B.)

Nice theory, but who's doing it?
I suspect I could dig through my files and find examples of vendors who offer this type of report, but off the top of my head... nothing. The usual, $10–15K report is overkill, but a package of basic customer snapshots focused on identifying sales opportunities might have potential. Is anyone doing this with clients now?

I'm also curious what this topic does to your comfort meter. Does the thought of running analytics on your customer's market make you uncomfortable?

For those of you in the business, this came out of a casual conversation, but if it were a live request from a client, it's the kind of question I would send out on my new mailing list for vendors. If you don't have your invitation, drop me a note and I'll add you. And yes, I do plan to follow up on today's conversation with what I learn.

A big part of my approach to understanding the market is to explore the edges, and one of the edges is information arbitrage—where social media analysis meets investment research. Think of a portfolio manager or analyst using social media intelligence to analyze investments and find trading signals. It'a pretty easy to understand the ROI in that application, and it's the kind of application that may just get your CFO's attention.

I've talked with people with some interesting approaches to this specialized market, and I recently started a project to categorize the strategies in the market. There are a number of different angles in play, which makes it a particularly interesting space:

• Atomic Intelligence
  
• ClearForest
  
• Connotate
  
• FirstRain
  
• InfoNgen
  
• Jodange
  
• Palantir
  
• Ravenpack
  
• SkyGrid
  
• StockMood

Given recent trends in financial markets, I've decided to prioritize other projects, but I expect to come back to this one (so if I missed your company, let me know). It's a potent reminder that there's more to social media than marketing and communications.

Related posts:

• Financial Week on Info-Arbitrage
  
• Trading on social media
  
• Jodange mines sentiment for investors
  
• Monitor 110 Exits

With the big project completed, I finally have time to start catching up on my reading pile, and the first book was a winner: Super Crunchers, by Yale Law professor Ian Ayres. If you're interested in analytics—and face it, you're reading this blog—you should read this one. Ayres looks at analytics across multiple fields, so virtually everyone will learn something. And if you hold strong opinions on the human vs. computer analysis question, the results in these other fields might challenge you.

If we've talked, you probably noticed that I'm a big fan of cross-pollination—looking for lessons in advances in other fields (such as the hospitals who learned process improvement from racing teams). Super Crunchers tells how analytics are helping companies make more effective decisions, and in the process, changing the role of expert opinion.

The examples are all over the place: e-commerce recommendation systems, individualized offers and price discrimination, evidence-based medicine, social programs, dog racing. Example after example of how analytics create new opportunities and challenge the experts in the predicting game.

The nice thing about the book is that you don't need to understand any of the underlying technology going in. Ayres writes for a non-technical audience, and if it's entirely new to you, he won't leave you in the dust. Still, he's a law prof, so if you want documentation, you'll find plenty in the notes. Either way, I recommend reading it to the end, as he eventually switches from examples to explanation—and yes, he explores the risks to privacy and individual freedom, too.

Teaching the machine to read
Analyzing media content—online or otherwise—requires extracting meaning from unstructured text, and that's not in Super Crunchers. Text analytics are just a bit bleeding edge. For now, we can still argue about whether automated analysis is good enough to use. But the success of the algorithms against the experts in purchasing, sports, medicine and law suggests where text analysis is going.

As for the cross-pollination angle, a book this full of examples should be able to inspire some ideas in the reader, whether for your business or your career. For me, the big takeaway was the confirmation of my new favorite question.