Recently in Social media analysis Category

In late 2006, I decided it would be interesting to find every company in the world that offered social media listening tools or services. I thought I'd find a few dozen companies. Five years on, I've found hundreds of companies, and I'm still finding more. Today, I'm sharing the database that I've been using to track them: Companies in Social Media Analysis.

The directory is a bit more than a list. Each company gets its own page, which includes:

• A link to the company's main website
  
• A link to their Twitter account
  
• The location of the company's main office
  
• A description of the company and its social media analysis products and services, provided by the company itself
  
• Links to recent news items on Social Media Analysis that mention the company
  
• A Twitter widget showing the company's most recent tweets

You could browse the list of companies, but it would take you a while—the directory is launching with 292 entries. Instead, I recommend the search function, which has a few tricks up its sleeve. In addition to searching the company descriptions, it can find keywords that don't show up on the company pages, such as states and provinces (spelled out), names that have changed, and companies that have been acquired.

But wait, there's more
Building the directory gave me the push I needed to redesign SMA. It still has the industry news it's always had, and I've made it easier to find the acquisitions scorecard, where I keep track of M&A activity, and the roundup of third-party product reviews. There's a new roundup of investments in social media analysis, and I've added a job board (yes, it accepts international listings). Finally, I added the social network icons that are a required part of social media web sites in 2011.

I always intended for SMA to be a sort of online trade journal, the best source of information about what's going on in the market. The new sections are a step in that direction, and I hope you like them.

The full database just passed 400 companies, which includes many that are no longer active in the market. Listening companies, are you on the wrong list?

Every few seconds this morning, TweetDeck brings another comment on today's announcement that Salesforce.com is buying Radian6. The announcement's not exactly a surprise—Radian6 was the obvious acquisition target in social media analysis (SMA), and their platform had supported Salesforce integration since mid-2009. The price ($340 million in cash and stock, including retention bonuses for the founders) is larger than expected, but overall, it's a logical deal that surprises no one.

Blogging about the day's big news is sort of obvious, so I'll focus on the question I haven't seen raised yet: what does the Radian6 acquisition mean to the other companies in social media analysis? A few thoughts for discussion:

1. Radian6 just took a big step toward solidifying its position as the standard.
   Radian6 was already the company most likely to be mentioned in any discussion of social media monitoring (note the careful use of the term). The Salesforce endorsement makes them the default choice for 92,000 Salesforce customers. Competitors need more than a me-too monitoring platform to win.

2. Aquisitions say something about the segmentation of social media analysis.
   The Radian6 deal says a lot about interest in social CRM, or the integration of social media monitoring and customer relationship management. Other acquisitions have tied SMA firms to PR/media (Sysomos/Marketwire, Brandtology/Media Monitors), market research (Cymfony/TNS, Umbria/JD Power, Evolve24/Maritz), and marketing management (Techrigy/Alterian). SMA is a feature set that can work into multiple categories; look for SMA companies to focus their feature sets on specific use cases, and expect acquisitions that work into acquirer's existing businesses.

3. Enterprise software has noticed social media analysis.
   Salesforce joins SAS in making a serious move to tie social media analysis to nuts-and-bolts business operations. Social business software companies Jive and Lithium have picked up their own listening platforms. Any acquisitions or product announcements by IBM, Oracle, and SAP should be completely expected, but pay attention to the emerging distinctions between social media analytics and social media monitoring (see #2).

4. Obvious acquisition candidates are getting harder to find.
   Despite the presence of 300+ companies in the space, only a handful of the product leaders are still independent companies. Radian6 is easily the most recognized name in SMA, so most of the remaining independents are not widely known. Looking back to my report on social media analysis platforms for workgroups (March 2010), six of the 21 companies have been bought in the last year.

I don't think there's one obvious candidate for the next acquisition, and in any case, any deal has to start with specific goals (just like any purchase). If it's not already obvious, the many companies in SMA are not the same. The differences in what they do and why is what makes the space so interesting. I know you want the list, though, so here's a quick reaction on who I might look at:

• Attentio, Brandwatch, Sentiment Metrics and Synthesio don't usually come up in the social media conversation in the US, but they all have solid SMA platforms.

• Converseon blurs the lines that divide research, creative marketing, and management consulting services. They drive me crazy because they're always working out the same things I am. Nobody else does the one-stop social media shop like they do.

• Visible Technologies has unique capabilities in managing the response component in social media monitoring, as well as a nicely designed interface for working with solid analytics capabilities.
  

Disclosure: Radian6 is paying my way to their user conference next week. I consult with companies (usually buyers) on partnerships and acquisitions in social media analysis, but I do not represent the companies listed here.

Related:

• Brand Monitoring: Where Are They Now?
  
• All Acquisitions, All the Time
  

Judging from the way people are talking about it, social media analysis is segmenting into at least three subspecialties. As usual, we're using multiple labels that occasionally overlap, so the potential for miscommunication is great. Whatever the utility of any one approach, companies need a complete set of tools, so let's keep these emerging specializations in context.

In 2007, I asked for opinions on a generic term for social media monitoring, analysis, research, etc. I settled on social media analysis as an existing term that could stretch to fit the tools and services then on the market. Since then, I've also argued for an expansive interpration of the listening metaphor. Lately, though, I'm seeing a lot more of these labels:

• Social media monitoring
  In 2005, companies started to learn that people were talking about them online and they needed to pay attention. Today, we have tools and case studies, and more companies are prepared to notice and respond when someone mentions them. The response might come from a customer service or PR function, but the basic idea is what Radian6 calls "the social phone:" social media represent a new customer-service touchpoint, and companies need to respond to every mention that merits or requires a response.

• Social media analytics
  Every 15 minutes, someone announces a new tool for measuring social media. Most of these focus on the structured data of social media: seemingly hard numbers, such as friend/follower counts, mentions, shares, likes, and Facebook pageviews. This approach blends social media and web analytics, and it's good for questions such as, "is my Facebook campaign working?" If your ROI comes from online sales, this approach is an especially powerful tool for managing social media marketing efforts.

• Social media intelligence
  Analyzing the content of what people say online—topics, sentiment, emotions, and the trends and underlying causes—is starting to be called social media intelligence (I refuse to use the unfortunately abbreviated buzzword, social intelligence, in this context). This is perhaps the least consistently applied label, but whatever you call it, measuring and analyzing online content looks increasingly distinct from measuring online activity (the analytics view).
  

This doesn't begin to cover all of the variations in terminology we're using, and these categories aren't even mutually exclusive. But they do represent a division I'm seeing in both the thinking about, and the capabilities of the tools for, listening in social media. We're getting better (?) at talking past each other, which is not making it easy for beginners.

Update: All that and I forgot to mention social media research—thanks to Annie Pettit for the reminder in the comments. Also, here are a few of the many posts that inspired the topic:

• Social Media Monitoring is Not Research, Tom O’Brien & David Rabjohns (MotiveQuest)
  
• The Difference Between Social Media Monitoring and Measurement, Eric Melin (Spiral16)
  
• When to say Monitoring Social Media means nothing, Gianandrea Facchini (Buzzdetector)

Photo by Dan Thompson.

Ethics and social media monitoring: so much at stake, but the existing standards are linked to specific business functions. Can we fix that? Converseon suggested some questions for clients to use in avoiding service providers with problematic practices. Let's go a step farther and think about appropriate ethical standards for companies that do the actual monitoring and analysis work, regardless of which functional silo they support.

I have a few suggestions:

1. Obey applicable laws.
   Stay legal—always nice to include that in the code. This will be trickier than it sounds, because (a) the law that applies to online monitoring is "complicated, multi-faceted and unclear," and (b) the Internet is global. Whose laws apply in which situations should be good for generating legal fees somewhere.

2. Match clients' regulatory obligations.
   In addition to government regulations that apply to them directly, service providers should comply with requirements that apply to their clients. Service providers shouldn't be in the business of doing work that clients are prevented from doing themselves. Yes, this requires learning about clients' regulatory environments.

   Clients should extend their own compliance standards to service providers working for them—if you can't do it, don't hire an outside company to do it for you.

3. Honor sites' terms of service.
   Whether terms of service are enforceable is a legal question that will eventually be settled, but the strong ethical position is to monitor sites on their terms. If you need to hide your identity or play cat-and-mouse games with site admins, you're in the wrong.

4. Be transparent in your monitoring.
   Don't conceal your identity, through either technical or non-technical means. Your IP address should map to your company. When using an individual profile to monitor or interact on a site, disclose the individual's affiliation with either the service provider or client.

5. Respect privacy norms in closed settings.
   Blog monitoring was ok because blogs are publicly available. If an individual login is required and community norms are that information is to be kept within a community, don't use it. These sites create an expectation of personal privacy that should be respected.

6. Don't overburden servers with automated requests.
   Sites exist to serve their users, or to reach an audience, or to conduct business. Manage your data collection activities to minimize negative impacts on servers.

7. Where multiple codes of ethics may apply, observe the more restrictive code.
   Existing codes from other fields may impose extra requirements that still apply. For example, entering a community to observe it is ethnography, which has its own ethical standards.

8. Be honest with clients.
   Don't make promises that your technology can't keep or present insights that aren't supported by the data. If the client wants something you can't do, admit it. If they want something you won't do (or shouldn't), educate them. As Converseon's list suggests, your ethics protect them, too.

9. Don't freak out the natives.
   It's not good for your business, anyway. The more people think of what you do as creepy, the more likely you are to face regulatory pressure or other challenges. Besides, it's not nice.

Do we need an industry standard?
Incidents like the one in yesterday's WSJ, and the attitudes exhibited in some of the quotes in the article, increase the likelihood of government intervention and externally imposed rules. Who'd rather create a clear and relevant ethical standard for the listening business before that happens?

I've already heard that this topic is too sensitive for an open discussion online. If you want to pursue this, let me know, and we can decide on the right venue.

What can you learn online? How about where someone is? Or where they live, where they work, where they hang out… One of the interesting ways to segment social media data is by the contributor's location, but it's a rare feature in social media analysis platforms because of the difficulty of doing it well. More than they realize, though, people are publishing their locations.

I've heard of two main methods to assign locations to social media sources. The easier method, which initially sounds more accurate, tracks down the IP network address of the associated computer. Every computer on the Internet has one, and in principle, the address corresponds somewhat to location. But the goal isn't to find a server, it's to segment online contributors by geography.

If addresses matched locations in some mythical past, they're useless for location now. Facebook is Facebook, wherever an individual user is. Blogs are hosted by a few big players; even with private domains, there's no guarantee that the web host is anywhere near the user. This blog, for example, is hosted on a machine in Pennsylvania—a long way from where I'm sitting. I have accounts on lots of social media sites, none of which are here.

So IP addresses might help you locate a computer server, but they're not a reliable indicator of where an individual user of that system may be.

Revealed demographics
The more interesting process, which I've heard from a handful of SMA companies, is to extract information revealed by the user, linking profiles across services to develop a profile of the person. If someone links a blog to accounts on services like Twitter, Facebook, and LinkedIn, then the combined profiles can build a better picture of the person. Location is one of the major components of that picture.

People have lots of opportunities to announce their location in social media, especially in all those member profiles we fill out. The location field in Twitter might be misleading (remember all the people who changed their location to Tehran in a show of support last year?), but if it agrees with Facebook, Linkedin, or the About Me page on the blog, you have a location.

That's without getting into location-based services like Foursquare. Everyone using those is building a personal tracking database on purpose.

Are you uncomfortable yet? At least this is all based on information that people shared intentionally—so far.

Oops, too much information
The New York Times has an article today, Web Photos That Reveal Secrets, Like Where You Live, which discusses the location metadata attached to digital photographs now. Sarah Perez wrote on the same topic a few weeks ago (Researchers Warn of Geotagging Dangers - Are You Concerned?). Cyberstalking, meet cybercasing: how to reveal your home address on Craigslist.

Both articles emphasize the privacy concerns, as they should. In aggregate, the data creates what Marshall Kirkpatrick calls your new superpower; applied to individuals, it's just creepy.

So, how much location information do you want? Where's the line between constructive location and demographics data and creepy/dangerous? Finally, whose ethics apply to analyzing this data?

Photo by Silver Smith.

"Build your own listening tool" has been a popular topic, with suggestions usually building on free combinations of search feeds and RSS tricks. Twitter, especially, has inspired a whole constellation of free tools. But "build your own" has a deep end of the pool, too. Whether you're building a customized tool for specific, internal requirements or realizing your vision of the perfect entrant to an overcrowded market, building your own tool involves a series of build-or-buy decisions, starting with where you will get your source data.

There's a list below, but first, some background.

I first wrote about the building blocks of social media analysis in 2008. The short version is this: any system for monitoring or measuring social media has three basic components: data collection, analytics, and application. The differences are in the details, and each component can be the subject of its own build-or-buy decision. If your goal is to beat the industry standard in a particular area, you build, but if industry-standard is good enough, you don't have to.

At the time of the original post, all of the components were available separately for companies who were building their own systems, either for their own use or for commercial product development. Now, more options are in the market, especially in data collection. Lots of search engines offer RSS feeds. This is something else: services that aggregate social media data from multiple sources for business or commercial users.

More than just aggregation
The data collection step is about more than mashing together multiple search feeds. For the professional-strength aggregator, the finished product—like paid products in other categories—does something the free tools don't offer.

• Sources
  Social media comes in lots of flavors, and aggregators need to keep up with the introduction of new services. Commercial aggregators can also deliver content that simply isn't available without a subscription, such as full feeds from traditional media.

• Filtering
  Once you fill the pipe with incoming content, it's time to screen out the junk. Removing duplicate items is a start; removing near-duplicates (such as syndicated content or press releases) helps, too. Spam removal is a big deal.

  Another kind of filtering is prescreening the content for relevance to the customer. How that works is part of the aggregator's secret sauce and will differ by provider.

• Metadata tagging
  We spend a lot of time thinking about analyzing the unstructured text in social media, but most of the content also has structured data around it (such as the source, publication date, and number of comments). Aggregators can also pull information about posts from third-party sites to complete the picture.

• Speed
  Oh, and do all of the above quickly, please. Financial applications go to extremes to reduce latency (the lag between when content is posted and when it shows up in the aggregator), but it's a factor in less demanding environments, too. If you're monitoring Twitter, for example, you need to know in seconds or minutes, or you'll be too late to respond in that near-real-time environment.

The list
Oh, yeah, this is a list post. Companies who offer social media content aggregation as a service (updated 30 Jan 2012):

• 80legs
  
• Beevolve
  
• Collective Intellect
  
• Context Voice (UberVU)
  
• CyberAlert
  
• DataSift
  
• Dow Jones
  
• Effyis (Boardreader)
  
• Gnip
  
• Kapow
  
• Moreover
  
• Omgili
  
• Samepoint
  
• SocialMention
  
• Spinn3r
  
• Superfeedr
  
• Trendiction
  
• Twingly

More posts in the "Build or Buy?" series:

• Building Blocks of Social Media Analysis
  
• Text Analytics in the Cloud
  
• The Rise of the "Influence" Peddlers

Photo by identity chris is.

The aquisitions of Scout Labs, Biz360, and Filtrbox so far this year have people talking about what else is coming. Actually, I count seven acquisitions so far this year—apparently we need a score card.

If you weren't aware, I post industry news at Social Media Analysis. If you care about what the companies in SMA are doing, you should read it.

The M&A list is at SMA, too: Acquisitions in Social Media Analysis.

I've pointed out some of the elements of the learning curve for social media analysts. In the middle of looking at almost 30 social media analysis platforms for my recent report, I realized that the software itself isn't the main challenge—developing the analytical mindset to know what to do with the tool is. The question is, how much of that mindset can be taught? How do we teach people to ask penetrating questions using a simple set of analytical tools?

How's your logic?
Here's an example of the challenge. Most social media analysis tools use keyword searches to define topics or to segment the data with subtopics. The query typically takes one of three forms: a simple search, a Boolean query, or an advanced search that simplifies the process of building the query. (Boolean logic isn't the only technique used to define topics, but other methods are more complex, and the companies that use them set them up for their clients.)

Search using Boolean logic seems simple. You use operators like AND, OR, and NOT to include or exclude keywords from your results. Some tools let you get fancy with proximity operators (x within n words of y), and you can nest your statements for finer control. But many of us think we understand how it works.

So it could be a bit of a shock to see the queries presented by Integrasco's Aleksander Stensby at Monitoring Social Media Bootcamp last week. This little one finds the telephone company Orange in English-language content:

(Orange OR subject:Orange -subject:light -light -"Clockwork Orange" -subject:"Clockwork Orange" -"orange box" -subject:"orange box" -juice -subject:juice -fruit -subject:fruit -peel -subject:peel -"Orange Wednesday" -subject:"Orange Wednesday" -"orange county" -subject:"orange county" -"clock work orange" -subject:"clock work orange" -"orange ink" -subject:"orange ink" -"bright orange" -subject:"bright orange" -"dark orange" -subject:"dark orange" -"light orange" -subject:"light orange" -("color orange"~3) -subject:("color orange"~3) - ("style orange"~3) -subject:("style orange"~3)) AND ( (SMS OR MMS OR HDSPA OR "Mobile Phone" OR GSM OR GPRS OR 3G OR SIM OR handset OR "Sony Ericsson" OR Nokia OR HTC OR Motorola OR BlackBerry OR iPhone OR PAYG OR "pay-as-you-go" OR "Network Provider" OR UMTS OR WAP OR PDA OR "PAC Code" OR Cellphone OR OFCOM OR phones4u OR voda OR vodafone OR tmobile OR tmob OR "T-mobile" OR T-Mob) OR subject:(SMS OR MMS OR HDSPA OR "Mobile Phone" OR GSM OR GPRS OR 3G OR SIM OR handset OR "Sony Ericsson" OR Nokia OR HTC OR Motorola OR BlackBerry OR iPhone OR PAYG OR "pay-as-you-go" OR "Network Provider" OR UMTS OR WAP OR PDA OR "PAC Code" OR Cellphone OR OFCOM OR phones4u OR voda OR vodafone OR tmobile OR tmob OR "T-mobile" OR T-Mob) )

He showed another one, about nine times as long, that finds discussions in multiple languages of the form factor of a particular mobile phone. You can see that endless query in Aleksander's presentation.

So, yeah, we know Boolean logic, but wow.

It's not difficult, just hard
These intensely focused queries illustrate the difference between the two learning curves. A query like this could be pasted into many—maybe most—of the available tools for social media analysis. Working out the nested Boolean logic is the trick.

Eric Garland puts a competitive spin on things with this note from a discussion of the future of intelligence at GWU:

Asymmetry of analysis will be more important than asymmetry of information—it’s not who collects the most data, but who is the best at deriving insights who will be most effective.

The question is, how easily can we develop the right combination of logic, curiosity, and perseverance in those who would analyze social media? Is it teachable, and how much of it depends on existing inclinations in future analysts? Or is there really, as someone at MSMBC suggested, a business opportunity in crafting complex queries as a service?

I wonder how many on-topic posts include exclusion keywords: "I called Orange to complain about my phone while eating a piece of fruit."

At last week's Monitoring Social Media Bootcamp, several speakers highlighted the importance of the human analyst. Having several of us stress the continued requirement for a knowledgable user to do something useful with the tool was apparently a surprise and a disappointment to some. So it must have been a shock when some later speakers showed examples of just how complicated this stuff can be. For those who aren't shocked, that complexity will be a lingering source of competitive advantage as the tools continue to improve.

Software still doesn't replace people
Jason Falls describes the lack of strategic services as where social media monitoring services fail, but that's not a fair summary. The market has both software companies and service companies, and clients can decide how much of the work they want to outsource. The software model requires that the user do something useful with the tools. The services model adds highly skilled consultants to the mix, at a higher price.

It's a familiar decision: build or buy. In this case, it refers to analytical skills and the ability to link analysis to business insights. In the journey from raw data to insight and strategy, some of the distance can be traveled by software. The rest requires people, no matter how sophisticated the tools. The available choice is whether the people who complete the conversion of data into insight are employees or consultants.

This response from Sysomos nicely summarizes the importance of both the software and the analyst:

When you think about it, neither side can be successful or effective without the other. The technology is interesting but not useful or valuable without people to do something with it, and people are only able to do a limited amount of monitoring without the assistance of technology to sift through millions of conversations.

All of this is in the pursuit of insight—the monitoring and mining modes of listening. In the monitoring and response mode, the emphasis is less on insight than on action, but similar logic applies. A monitoring platform can automate the discovery of items requiring a response. Although a few automated response products are available, most companies will want a person in the customer-service loop.

I'm surprised that people are surprised by this.

As a person myself, I'm glad that computers haven't replaced us.

I've tried a lot of different platforms for monitoring and measuring social media in the last few months. Somewhere in the middle of the project, I realized that new users of these systems face not one, but two learning curves. The obvious one—learning the software itself—is the easy part.

Get new software, and you'll spend some time figuring out the features and user interface. That much is obvious. Some vendors try harder than others to make their social media analysis platforms easy to use, but none of them are all that hard to understand.

Compared to other software products most of us have learned, these systems don't really have many features. Refuse to be intimidated, and you can figure it out.

Getting analytical
The more challenging learning curve for the new user is in using the tool appropriately—developing the analytical mindset to find meaning in the data. It's easy to fixate on the default charts that virtually every tool provides, but the interesting stuff only starts to happen when you go deeper into the data. On a checklist level, that means developing an understanding of:

• Queries and topics
  Most platforms use Boolean logic to find relevant content for analysis. Some use the same method to define subtopics, such as competitors or industry trends. Know what the topic does—and doesn't—cover.

• Analysis and metrics
  The usual list of available analyses is generally short. but it's important to think about how the numbers are calculated and what they mean in a given context.

• Filters and more queries
  Most platforms allow users to focus on subsets of the data, either by clicking on charts or by selecting filters based on metrics or new queries. Filters and queries can lead to useful information, but it's important to understand what applying a particular set of filters to the data tells you.