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.

After spending the last few weeks telling brands and start-ups to consider LinkedIn as a marketing opportunity, I thought it could be helpful to post some of the hard numbers that make this a compelling channel and social network to sell higher-end products and services.

While many Global 1000 marketing departments currently have a reasonably mature Facebook and Twitter strategy, only a select few are working on a robust LinkedIn marketing strategy.  Considering the demographics of the audience that makes up this social network, it is surely an untapped channel that can provide deep targeted messages to reach an affluent audience with the power to spend money.

According to a compilation from the LinkedIn Ad Platform at the beginning of Feb 2012, LinkedIn has over 147 million members of which 44% work in companies that have more than 10,000 employees.  Within these members, about 40% are Managers or higher and 25% have the title of Director, Owner, Chief Officer or Vice President.  More than 26% of the members are in the high-tech and finance industries, and in fact, every single member has identified what he or she does for a living.

As the level of conversation and number of participants across social networks increases, marketers are having a harder time finding and advertising to the right people who may actually be interested in their products and services.  LinkedIn is a simple way to tap into the interest graph, and as a marketer, this is going to tie together ability-to-purchase with demographics and/or interest.

A simple use case could be for a technology brand.  You sell CRM solutions, and you want to market to people who are authorized to buy these tools for their company.  On LinkedIn, you can buy ads that directly target the roles and types of companies that you are looking for.  On top of that, you can create and contribute content to conversations through  LinkedIn’s Company Pages, Groups and Answers.  If the content is engaging, you can get people who actually buy CRM solutions to talk more about your company with other people who are also in the market for buying CRM solutions.  It’s a way to use a single social network across the entire customer decision journey.

But this also works for other scenarios, where you, as a marketer, are not necessarily looking for an interest that is directly related to an individual’s job.

Imagine running a campaign for a luxury car.  While you could advertise on Facebook, to people in a certain age group or maybe even individuals who have included a reference to cars in their profiles, is that really the right set of people to target for your campaign?  Instead, you could show your campaign ad to every member on LinkedIn who is a Director or above.    Luxury cars are not necessarily for car enthusiasts or people over 50.  Luxury cars are for people who can afford them, so use the tools and channels that can better help find these people.

This is not to say that Facebook advertising does not work, but rather that LinkedIn as an advertising platform is too often ignored.  While a number of technology companies are already trying to figure out how they can use the channel, there are still too many companies with big ticket products and services who are not paying enough attention.  This is probably not the best channel to sell Hanes underwear or Cherry Coke, but it is worth looking into for any company or start-up that wants to target an affluent audience with the ability to spend money.

A friend recently commented that social media monitoring as a stand-alone product is a “race to the bottom”.  While it may be a bit of a provocative statement, the fact of the matter is that any company which sells only a listening tool is primarily competing on cost.  Sure there are differences between products which include better algorithms to filter and an enhanced ability to process sentiment and semantic clues, but who among the top-tier companies can claim that they are really doing anything that is significantly different than their closest competitors?

And for those who are not “top-tier”, there is little question that this game is about lowering costs, increasing usage and hoping for an acquisition.  Considering that Lithium bought Scout Labs, Salesforce bought Radian6, Marketwire bought Sysomos and Neilsen bought Buzz Metrics and partnered with McKinsey, even the biggest and strongest fish in the monitoring space are choosing to take the money and become a part of the inevitable consolidation and integration across the social media marketing product set.

Looking at these acquisitions, there are two major use cases for integrated listening: social enterprise and PR monitoring.

Social enterprise is going to be the place to extract the most business value out of listening.  This is about figuring out how you can listen for insights that drive business KPIs, whether it’s around digital marketing, customer service or CRM.  I think the last few acquisitions help to illustrate the possibilities.  Lithium is beginning to use their acquisition of Scout to help with a lot of their community tools, and they’re also starting to power their new LevelUp offering with Scout’s NLP technology to bridge the gap between listening, engagement and measurement.  Salesforce is making huge bets in social, and right now they are using Radian primarily to augment social CRM and customer service.  As Data.com, Desk.com and Rypple become more integrated with the Saleforce product set, we’re going to see even more applications of Radian’s technology across the company.

It’s also helpful to note that the partnership between Neilsen and McKinsey to form the NMIncite offering will offer a way to really dig into the data and provide strategic direction based on listening.  According to a recent Consulting Magazine article, there’s a ton of money to be made by consulting firms like NMIncite and McKinsey, and this is probably another great exit option for a “value priced” social media monitoring tool.

PR monitoring has and will continue to be a significant consumer of social media monitoring.  But as Neilsen’s acquisition of Buzz Metrics and Marketwire’s acquisition of Sysomos prove, social media monitoring on its own can only be so interesting.  In order to be valuable from a PR perspective, you have to combine social listening with traditional PR monitoring in a single dashboard.  We are starting to get to the point where it will matter less about who is creating the content (PR professionals or anyone else) and more about what the content is saying about a company.

In the next couple months, I think we are going to start seeing the acquisition of smaller monitoring tools by the Buddy Media and Wildfire’s of the world.  We also have companies like Adobe getting into social analytics with Omniture and Oracle beefing up their customer service technology with Right Now.  To my knowledge, Deloitte and Accenture have yet to buy or partner with a listening tool to help with work they do around marketing strategy.

I know there are lots of developers in India and across the globe working on different social media monitoring tools that each have their own take on where and how to focus.  With such high interest from companies ready to spend money, I wonder if this is the year that those small start-ups work around the clock on their product, sell their services for essentially nothing, build as big a base of customers as they can, and begin pitching to possible acquirers.  Thoughts?

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.

As social media strategies mature, many clients now recognize that business goals should drive the technology and not the other way around.  Few organizations are going out and buying social media products and services without thinking about how these purchases will help their business.  But, often, we are seeing a lack of actually integrating the data or productivity gain to the overall business process.

Granted that BPR (business process re-engineering) has been long forgotten and often maligned since Hammer and Champy wrote their best selling book, Re-engineering the Corporation, I thought it was worth revisiting some of the main principles of their work.  The idea is that by rethinking business processes in light of a new technology, like social media technologies, we can make increase revenue and reduce costs.

So here are 7 of their principles:

1. Organize around outcomes, not tasks.
2. Identify all the processes in an organization and prioritize them in order of redesign urgency.
3. Integrate information processing work into the real work that produces the information.
4. Treat geographically dispersed resources as though they were centralized.
5. Link parallel activities in the workflow instead of just integrating their results.
6. Put the decision point where the work is performed, and build control into the process.
7. Capture information once and at the source.

In the recent Web 2.0 McKinsey Global Study, Jacques Bughin and Michael Chui, describe major gains for companies that employ social-enabled business processes.  While, it is worth looking at the full study, a few of the larger gains include:

Internal purposes

Increasing speed of knowledge – 30% median improvement

Reducing communication costs – 20% median improvement

Increasing employee satisfaction – 20% median improvement

Customer related purposes

Awareness – 25% median improvement

Customer satisfaction and loyalty – 20% media improvement

These are some substantial improvements.  Once a social technology has been implemented, take time to think through the overall business process, perhaps with the help of Hammer and Champy’s principles listed above.  If there’s anything that can be changed, redirected or reduced as a result of the technology, you just may be able to achieve more gains than you thought.