Everyone says it, and yet few social media marketers seem to take their own advice to heart.  “It all starts with listening,” they say.  And everyone nods their heads in agreement.  But how many follow through?

In a micro-second world where focus and attention is often marginally greater than a cursory study, it seems to make sense that professionals deep inside the world of social media would have even less time to really listen.  I think that we have made the mistake of confusing hearing with listening, and buying into the jargon that social media monitoring really is the same thing as social listening.

In 7 Habits of Highly Effective People, Stephen Covey wrote, “Most people do not listen with the intent to understand; they listen with the intent to reply.”  As we put greater emphasis on engagement, are we thinking more about the response than the learning?

Listening is about taking the next step from simply hearing what people are saying to internalizing what they are trying to convey.  It is about finding context within content and matching feeling with thoughts.  Social media monitoring in itself is just data.  It is the content and the thoughts, but it does not convey intention.  For marketers to take full advantage of social data, they need to look at more than just what people are saying and how often.  Graphs that show volume fluctuations are great, but how does that help you figure out what you should do based on what your customers are telling you?

Lee Iacocca, the former CEO of Chrysler, once said, “I only wish I could find an institute that teaches people how to listen. Business people need to listen at least as much as they need to talk. Too many people fail to realize that real communication goes in both directions.”

If you can’t show that you have listened (not just to your customers, but also to yourself and your company), then how can you improve to meet their needs?  Some people think that this type of listening is mainly about driving product innovation.  But it is much more.  Analytical rigor applied to social media data can help improve future marketing campaigns, fix customer service issues and pinpoint better ways to generate interest for products and services.

This cannot be done within the confines of a social media monitoring product.  It is, however, a necessary first step.  So, monitor.  Hear what people are saying.  Then, take the data out.  Put the data into visualization or business intelligence software.  Have a data analyst look for trends that match content with context.  I know that this is not easy, but at least you won’t be tricking yourself into thinking that hearing is the same thing as listening.

With the considerable attention that Pinterest is getting these days, many individuals and companies are wondering whether and how they should be developing their “Pinterest strategy”.  Rather than thinking about a Pinterest strategy, I would challenge companies to use this time to figure out which types of interest-based social networks are optimal for engagement and participation for their particular brands.  Influence within interest graphs is often coveted by companies trying to reach their target customers through social technologies.  But if the interests of a specific network are not aligned with the products and services of the brand, then spending time defining a strategy for that network may not be the best use of anyone’s time.

To be sure, Pinterest is clearly showing some impressive numbers.  Last month, according to ComScore, the site had over 11.7M monthly unique visitors.  In addition, a Shareaholic study placed the percentage of referrals from Pinterest at a higher number than Google Plus, LinkedIn and YouTube combined.  But looking deeper into the metrics, we see that the most engaged users of the social network are upper-middle class women in middle America.  Referral traffic from the site is promising, but it is mainly to lifestyle-focused retailers and it is unlikely that anyone in the web metrics world thought that Google Plus, LinkedIn or YouTube were driving tons of traffic.

The point is that Pinterest is great for companies whose customers fall within the demographics and interest graph where people are having related exchanges.  If when you listen, your customers are not engaging on Pinterest conversations, then you can probably hold off on this specific social network.  Don’t pay attention to those who say you might miss the train.  If you have a strategy for current and future interest-based social networks, you should be fine.  But focus on what matters today.

We should also keep in mind that there are a number of interest-based social networks besides Pinterest which already exist: including Instagram, Foodspotting and Fitocracy.  In addition to these networks, there are clearly forums and web communities that target on specific interests from maternity to motorcycle racing.  If talk about Pinterest has raised awareness for being a part of communities that are not Facebook, Twitter and YouTube, this is a good opportunity to identify the types of communities which are directly to your business.

And it all inevitably comes back to listening.  It is a lot easier to be where your customers are talking than to force them to find you on networks that your company thinks are important.  Monitor conversations about your company and your competitors, figure out which communities have the biggest bulk of posts, and define your strategy based on this analysis.

If there’s all this great information in big data, why do we have such a hard time getting to the part with the deep business insights? One issue is related to getting a better handle on influence.  But it’s also about structuring and tagging data in a way that allows you to see connections not only between people, but also concepts, companies and innovation.  For a long time, companies have been conducting business intelligence on their internal data, but now a few start-ups are tackling the task of conducting external business intelligence using publicly available data (social media, PR reports, investment disclosures, etc.).

Quid and Recorded Future are two such companies, and they are raising money and building teams to tackle this very real problem of pulling insight out of all this digital data floating around in cyberspace.  Large corporations, especially those in the technology sector, are using their services to analyze competitor moves, trends in the sector, opportunities for growth and acquisition targets, while traders at financial firms are using these services to analyze events, media-flow, and market behavior to adjust their trading strategies.

It’s always easier to see these things with concrete examples, and there is a great example using Smart TVs on Recorded Future’s website to provide a glimpse into the possibilities.  Imagine you are a corporate strategist at Samsung trying to map out the Smart TV landscape.  You know that people are talking about Smart TVs, and you could probably use a social media or traditional PR listening tool to figure out which forums and which blogs are discussing the topic.  But then what?  What about all of the financial documents, analyst reports and predictions that exist across other channels.  And how do you figure out how it all maps out in terms of sources, relationships and timing?

These tools essentially take as much publicly available content as possible and apply complex natural language processing to identify specific references to ideas, people and events.   These references are then structured and organized by things like time, topics and concepts.  By visualizing this data onto a dashboard that can be played with and manipulated, a corporate strategist at Samsung can now quickly see which companies are currently investing in SmartTVs and where the largest sums of investment are going to.  If he or she wants to find out how soon Samsung needs to begin thinking about R&D or a possible acquisition target, that can be done as well.

As the volume of data and the speed of innovation increases, these types of services will become a necessary tool for any strategic minded professional who wants to get validated insight into the future.

While Quid is currently not available for trial, Recorded Future offers those who are curious, a 15 day trial of the service.  Even if this is not exactly your thing, it’s worth seeing where we are headed.  Pretty cool stuff.

For the last couple posts, I have focused a lot of energy into trying to illustrate the power of influence on engagement strategies.  With so many voices contributing content, how can we focus on the conversations which are going to impact our brands to the greatest degree?  By knowing who is influential, we can ensure that we are paying the most attention to the people who can influence their own audiences.  This is often about figuring out a better engagement strategy on social media, so that company messaging reaches the largest audiences.  You can also understand what people think about your current products and campaigns, and it’s much better to focus on influencer conversations than wade through a billion conversations.

Companies like Klout and PeerIndex are giving companies the opportunity to see scores of influence for every individual, and they are starting to drill down onto the topics that these individuals influence.  Professional networks like LinkedIn allow brands to find and engage people based on what they do for work, and that’s a great way to tap into interest graphs, especially when it comes to people and topics where big purchases are being made.  And then you have a whole ecosystem of companies like Radian6, eCairn and Traackr who help to filter conversations based on influencer mapping using partner or home-grown methods.

But what’s the next step?  How do we help social media conversations achieve the promise of looking into the future?  I would make the case that influence is the answer.  It is one the primary keys to getting better interest graphs.  And interest graphs will then be the key to making sense of the insights hidden in big data.  So then instead of using influence just to figure out who should be engaged, rewarded and marketed to, we can also begin to use influence to find out where we need to go.

This is more strategic than simply finding out that Taco Bell’s Black Jack taco was a bad idea, because “I hate eating black colored food”.  Once we can accurately measure influence and match it to interest, we can use big data to figure out where thought leaders across the globe think the tablet market is going.  What do influencers in fashion think will be next year’s trends?  Which innovations are CIOs excited about over the next five years, and how can that affect the long term strategy of a Google or Salesforce?

I think these are the really big strategies that we can hope to understand from big data as influence and interest graphs are better understood.  What do you think?

Over the last five years, the level of conversation on the larger social networks has increased exponentially, and when you  combine it with all of the other user-generated channels, that’s a lot of content being created on a daily basis.  But what does that mean to the quality of insights that can derived from the larger conversation ?  Is there more noise on a percentage basis or is there more insight as a result of these increases?

There was interesting point raised at the end of a recent article in the Economist that discussed the value of social media buzz, and the diminishing returns that come from an ever increasing volume of posts.

“Most commentary on social media ignores an obvious truth—that the value of things is largely determined by their rarity. The more people tweet, the less attention people will pay to any individual tweet. The more people “friend” even passing acquaintances, the less meaning such connections have. As communication grows ever easier, the important thing is detecting whispers of useful information in a howling hurricane of noise. ”

While the value of each individual tweet will inevitably be reduced by the sheer volume of tweeting, the high volume of created content actually can help to improve overall insights.  Based on our work with clients, we notice that general trends and insights are in fact better and more substantial when the volume of conversations go up.  But as the article states, “Everyone will need better filters—editors, analysts, middle managers and so on—to help them extract meaning from the blizzard of buzz.”

Filtering conversations based on purchase intent and measurable business value can allow companies to get a better handle on what their customers think of their services and products.  In fact, it could be said that the promise of using social media for business intelligence has not been fully realized because there is still not enough volume to do everything that we hope for.  The time for companies to start working and testing their data filters is now, because we’re not too far away from the day where everyone really is talking.  And then there’s going to be a lot of data to sift through, and less time to experiment.

Having a listening solution that works “out-of-the-box” maybe be a bit of an illusion given the high expectations of deep social insight, however we are seeing many clients who are buying social media monitoring only to be frustrated that these tools don’t work as advertised.  The problem is not that these tools don’t work, but rather we were all really hoping for some magic.

Good business intelligence that comes from social media is a little like having a crystal ball.  Tell me the current trends in my industry.  Tell me why customers buy.  What should I do to engage?  But can you really just take a crystal ball from your local magic store and expect it to give you insights about your future?  Two things are going to happen: either the crystal ball is going to tell you something pretty generic or you’re not going to be able to figure out what you’re being told.

Sound familiar?  Many of the insights that come out of social media monitoring tools when set up internally, often are generic or filled with noisy data.  And generally, the problem is not that the tools are useless.  Rather, these tools work best when they are set up by individuals who have worked with them before and know how to get the most of out them.  While most vendors and many consultants provide this service, few clients actually pay the extra money to have a solution that has been calibrated to their business needs.

While some current social monitoring tools may not be as sophisticated as Cognos or Microstrategy business intelligence solutions, they still aim to bring back insights that can affect marketing, crisis communications, customer service, product design, campaign development and more.  Trying to save the initial setup and quarterly fine-tuning costs after committing to a hefty monthly licensing fee almost seems absurd.  Keywords that drive the filtering in these tools can be easy to come up with, but it’s a lot harder for teams to put together profiles that bring back clean data focused on business needs.

Rather than throw out your expensive crystal ball, spend the extra cash and have someone calibrate these tools so that you can use them to learn more about your industry, your competitors and your customers.  You will feel better about the tools, and you will actually be able to use the insights to inform business strategy.

As influence becomes a coveted metric among those who measure and analyze social media data, many are beginning to segment content creators into one of two groups: those with influence and those without.  But considering that our ability to measure influence with true precision is still relatively limited, this may not really be the most effective to way to think about influence.  Rather we may be better served by breaking influence into its component parts: reach and relevance, and then examining the individual business purpose for wanting to know influence.

Reach is about numbers.  How many fans and followers does an individual have across all of his or her online properties?  This is often the easiest way to measure influence, because it is objective and public metric.  Some will say that this metric doesn’t always tell the right story since “tribes” are influenced by very specific people.  However, it can still be a relevant metric, especially when we think about social customer service within retail and consumer products/services.  It doesn’t matter if Justin Bieber has zero relevance to the pizza business and is not an influencer to that industry.  If he says that he hates Dominos, that’s going to have an impact on the brand, and customer service professionals with access to social monitoring and social CRM tools need to pay attention to the absolute reach that each person has online.

Relevance is about topics and interest graphs.  This is a little harder to measure, but  there are already a number of vendor tools that are trying to crack that nut as well.  We’re starting to see Klout scores incorporated into many of the leading social media monitoring tools, and a combination of social mining with topic level influence is going to make this type of analysis important for brands trying to influence the influencer.  Unlike reach metrics around influence which are essential for real-time response and crisis avoidance, relevance metrics can help a brand quickly figure out who is influential to their particular brand or industry and then target that person for outreach.  A food blogger who could potentially rave about the new Dominos pizza sauce is a much better candidate to engage with than a B-list celebrity who never talks about food but technically may have more fans and followers.

Linking reach and relevance clearly allows brands to have a better understanding of influence for customer service, outreach and trending analysis.  But because the methodology of determining accurate relevance is still in its infancy, it may be a bit premature for brands to automatically combine reach with relevance.  Rather, we encourage organizations to look at both reach and relevance individually, and to bring together reach metrics with relevance data and interest graphs when appropriate.

To illustrate the danger of combining these metrics: a food blogger who talks about pizza with 5000 followers is obviously a better candidate for outreach than a food blogger with 500 followers.  But the reality is that we don’t know if we have captured all of the individuals who are relevant to Dominos by focusing just on food bloggers who write content about pizza.  Data analysis of social conversations is still generally keyword based, and there can be large gaps in the types of relevant influencers that automated tools bring back.

We recommend using both methods of measuring influence, but using them separately and as appropriate.  Reach for customer service.  Relevance for influencer engagement and trending.  It’s not always black and white, but it can help focus analysis to those metrics that will have the most impact for your specific business goals.

With the ever increasing volume of data flowing through the blogosphere, we constantly hear from clients about the challenges of sorting through conversations that will have an impact on their interest graph and those that are just casually commenting about a topic or brand.  In other words, can we isolate only those conversations and individuals that will influence a brand, so that we’re not listening, analyzing and responding to a lot of random chatter?

Many of the leading  tools in the market have already begun to assign influence through a combination of variables including: conversation volume, keywords and high level scores partnering with companies like Klout and ComScore.   Focusing on the conversations of the more influential people can often provide better insight towards trends.  And it can also make the process of engagement more efficient.  As we think about how this relates to Social CRM, a true determination of influence helps to provide an additional layer of knowledge around customer and vendor relationships.  The next step will be mapping the relationships and similarities among the most influential voices to determine how people are interconnected and what that can mean to the business.

One vendor who is working on this today is a company called eCairn.  I sat down with Dominique Lahaix, eCairn’s CEO, a few days ago to go through a quick demo of the product.  Rather than having to find influencers from scratch, eCairn has already identified over 300,000 bloggers who belong to some 300 different “tribes”.  These tribes include everything from people who talk about nails to bloggers interested in healthcare.  Looking at specific topics and conversations within these tribes, a company can listen and quickly put together an influencer map that identifies connections between groups and uncovers relationships that may not be obvious at first glance.  Over the years, I have seen the painstaking process of building similar influencer maps, and the option of having a head start on this type of work is something that should help many strategists.

As we move forward, the need to filter social media conversations based on influence, urgency and impact will become more important to social business.  The more that we can automate the ability to do this, the easier it will be for organizations to develop strategies and processes for business units to take advantage of this additional layer of data.

If you use other vendor technologies that help look at influence, I would love to hear about them, so please comment below.

There’s a new book out by Aaron Shapiro called “Users, Not Customers: Who Really Determines the Success of Your Business“.  Listening to an interview with him promoting the book, I realized that this is one of the fundamental things that a lot of our clients need to consider.  Traditionally, we always think that it is the customer who determines a company’s success.  And we define it as someone who buys or actively interacts with the brand.  It’s a very specific place on the purchase funnel.  But what about all of the people who are not yet customers?

According to Shapiro, a user should be defined as “anyone who interacts with a company through digital media and technology.”  Combine that with a recent Forrester study that says that 48% of all offline retail purchases are researched or conducted online (reaching 50% next year), and it becomes pretty obvious that the pool of people reading, curating and sharing messaging is much larger than “customers” in the traditional sense.

So when we think about trending analysis from social conversation data, we need to stress the importance of understanding what users are thinking about the brand, its products and its services before they become customers.  What factors drive users to go with our brand over a competitor?  Are there specific forums or sites that people rely on to make decisions?

When it comes to users, it is often more important to see the bigger picture and to understand the drivers of high level perceptions.  By accepting this new reality and analyzing conversations with this in mind, we can be better prepared to provide content and interactions that meets the needs of both customers and users.

Active listening across social media channels and responding directly to questions and comments across these channels is a lot different than in-depth trend analysis which can influence business strategy.  But as we work with our clients, we are noticing that there is a push towards engagement over analysis.  And it makes sense due to the immediacy necessary for responding to customer queries, but it may also indicate a dangerous sign from a governance and leadership perspective.

For the last two or three years, many of the social media reports hitting the desks of the C-level have been cluttered with generic volume charts and a hodgepodge of social media mentions from Twitter and Facebook.  In general, these reports (either developed in-house or by a third-party) do not address business KPIs nor do they help executives understand the parts of their business that social is truly affecting.  With few business metrics to analyze, many executives are forced to take away the operational numbers that they are given: increase in Facebook fans, total posts, total interactions.  But what does a higher volume of fans, posts or interactions really mean, if deep statistical analysis is not performed on the conversations?

Listening and reacting to social media posts is extremely important.  There is no question of that.  But organizations need to begin going through the exercise of defining what they want to see from social media conversations and interactions that are going to drive actions, sales and innovation.  From a trending perspective, you might want to find out how things have changed over time and how that affects your business.  What are people talking about in Q3?  Is it different than Q1?  Do we want to influence the conversation to change it, or are there things that we can learn to make marketing or customer service better?

A tactical approach is necessary to engage with your customers and let them know you are listening.  However, a business focused approach to listening analysis can deliver insights that can be actionable across the long term.  It’s not something that needs to be done daily or even monthly.  But once every two or three months, if done right, an aggregate analysis of social media conversations can provide a view of your customers that is extremely hard to see from the trenches.