A quick guide to social listening

If you haven’t been exercising your right to fast forward through commercials lately, you might have noticed a few IBM ads on TV about social analytics and how it will help ‘create a smarter planet’. Or you might have read Dell’s plans to expand their services offering with social listening for brands.

The adoption of social listening platforms has grown at a tremendous rate in the last three years, even though the technology has been around for a while. Dell didn’t unveil their famed listening command center until 2010. Why? Because it took early adopters like Dell, IBM and others to really understand how to use these platforms effectively and strategically.

When we started beta testing listening platforms back in 2006, our challenge was to cull out actionable information from a bunch of disparate data points. Key word mentions, share of voice and sentiment didn’t provide the level of granularity we needed to make actionable decisions. We knew that the human side could offer more insights than pure automation. Through trial and error, we developed a replicable process and approach to social listening that bridged technology and thought.

Today, brands have become much more sophisticated with social listening to drive engagement. A plethora of platforms are available to help with any number of the following programs:

• Content strategy
• Influencer identification
• Advocate engagement
• Customer care
• Crisis management
• Campaign tracking
• Market research

If you are thinking about beginning a social listening program or recalibrating your current one, I offer a few tips to keep in mind.

Have a program goal in mind before you evaluate or adopt a platform.

Platforms have greatly improved their functionality and usability. However, they all have strengths, weaknesses and a breadth of offerings. Based on your goal(s), create a simple assessment tracker that allows you to look across and compare multiple platforms and evaluate them against your specific needs. I have included a sample below. Get your key questions answered along the way. Remember everything looks flashy and exciting in that first demo.

Don’t rely on data alone.

The output of social listening should be more qualitative than quantitative. Numbers give you a baseline, a cluster to investigate and a way to gauge if you are moving the proverbial needle. However, metrics in and of themselves are often interesting, but not always useful.

The real value lies in the interpretation of the results. Therefore assign a SME or partner to the project. Someone with deep knowledge and expertise on your products, services, target industries and audience personas will help make the leap from general observation à insight à opportunity.

Map out your approach.

I don’t know how many times I have heard, “Can I get a listening report?” Well, that could mean many things. Take the time upfront to figure out exactly what insights you’re looking for. Start by listing out your objectives for the program. It could be a simple list of questions you want answered so that you can:

• Validate what you know
• Identify tangents and emerging trends
• Bubble up hot topics

Here is an example. Let’s say you want insights to drive your content marketing strategy for a particular product. Below are some key questions to ask:

• What is the nature of the conversation specific to the product?
• What are the hot topics associated with the product?
• How mature is the conversation?
• Who is talking?
• What are the hot button issues?
• What is the share of voice for key competitors?
• How does the messaging differ by media channel?

Go beyond what is #trending now.

Mine content as far back as a year old. It may seem a little counterintuitive, but it is important to understand the development (or maturity level) of your topic areas so that your actions are relevant based on what your audience cares about. How has the social content on a particular topic or theme evolved over the course of the last year, compared to six months ago and compared to today? Have the conversations increased, stayed flat or dropped? This is where some of your metrics come in handy. Let’s look at an example below.

The goal of this particular project was to inform a content marketing roadmap in a specific industry. We wanted to create an effective content creation strategy relevant to specific points on the decision journey. We compared core topics by quarter over a year’s time. The numbers indicated greater traction for topics A and D, while B and C were emerging. By overlaying the context of the social conversation sample, we determined how they were talking in addition to how much. With aligned data points and context, we recommended that the content direction for A and D should be geared towards consideration and preference, while B and C would focus on promoting adoption and awareness. Below is a peek into what we found.

[Note: Some tools are limited in the amount of historical data they store so add this criteria to your evaluation checklist.]

Without question, social listening platforms are becoming business as usual. If you are currently struggling with your listening program ask yourself some key questions on your strategy and approach. If you are not currently listening, but know that you should have a plan in mind before you just dive in.

Have a question? I’m listening!

By now, everyone has seen this Infographic.  Yes, it is complex and confusing and it should give you a headache. 

The tools to manage and measure activity on a socially enabled web today are growing at substantial rate.  Today, you can track and measure virtually any activity you would want to or deploy a tool to help you manage social campaigns of virtually any type.  A tool or an app has been built to address almost anything you might want to do.

This explosion of social tool development, while innovative and necessary, does little to solve the bigger problems of social engagement, which essentially boil down to understanding why people act the way they act in a social brand encounter and then helping to facilitate the right engagement and then understanding in simple clear terms the value and outcome of that encounter.  That’s the bad news.  The good news is this will change.

If you follow any innovation curve in virtually any industry, things tend to get harder and more complex before they get simpler and easier.  Why?  Because, during the early phases of innovation, the processes and rules and infrastructure that will later support new inventions are getting built right along with what’s being invented.  Solving any one problem on its own is the goal.  Later, problems get grouped together and a new smarter solution addresses them all.

Take the Model T automobile for example.  Building the car on a mass scale was one thing.  Tough enough to be sure, but what about manufacturing and distributing replacement parts as those cars began to break down?  Sourcing, distributing and stocking virtually every part on the car separately was likely a daunting, painful and expensive exercise at the outset, for the supplier and the consumer.

In the end, things improved.  They had to for the fledgling automotive industry to remain viable.  All the confusion, competing systems and supply chain gaps needed to be streamlined and refined and new smarter and more innovative solutions and options were layered on based on understanding and meeting customer needs. 

Innovators moved from activity metrics (which parts are needed) to value metrics (when to make and distribute them so that inventories matched demand) as the industry evolved.

Making sure that the customer could get the parts they needed when they needed it AFTER they purchased the vehicle helped to ensure that that customer would buy another auto from that manufacturer rather than a competitor’s product.

Social Marketing today is going through the same innovation phase as the early auto industry (and every other one for that matter).  It will get better.  For the industry to survive and remain relevant to users, it has to!  The focus will begin to move away from the tools and the activity to the experience and the value of the relationship we deliver.

As this innovation occurs over the next few years, look for consolidation to speed up and for tools become more expansive, robust…and simpler.  Tomorrow’s Infographic will look drastically different than the one at the beginning of this post.  It will be less about the tools available and more about content, relationship triggers and the decision journey.  That’s what tomorrow’s tools will help to manage-relationships and decision journeys, not just data.

Remember, keep your eye on the prize of understanding what is driving your value metrics; what is moving your constituents through the decision process and what compels them to remain involved with your brand.  The tools which help you manage your social marketing initiatives will get better, be more intuitive and easier to use, I guarantee it. 

Knowing this, social marketing practitioners and their business peers need to start focusing hard on what makes good relationships work. You must now begin the process of blurring the lines between all of your brand experience channels and optimizing those channels for relevance and value.

Get ready, as the next few years will bring marketing innovation and opportunities you have never imagined were possible.

Recently Gartner predicted that by 2017, marketing’s technology spend will exceed that of IT’s within the business enterprise. According to Gartner, 2011 B2B and B2C marketing budgets as a percentage of revenue were almost three times as high (10 percent) as IT budgets (3.6 percent). 2012 IT budgets are expected to grow 4.7 percent, while all marketing budgets, in general, are predicted to grow 9 percent, and high tech marketing budgets, more specifically, are expected to increase 11 percent. On average, nearly one-third (30 percent) of named marketing-related technology and services is bought by marketing already. What’s more, marketing now influences almost half of all purchases.

So why are these facts so important (other than having more money to spend on projects)? In my opinion it is important because of the Paradox of Big Data and its impact on marketers. More money spent on IT means more data streams the marketing teams must manage, right?

Notice I didn’t say ‘information streams’?

Marketing teams can hardly keep up with the fire hose of bits and bytes, metrics, actions, activities and the like today. So how does more make it better? It doesn’t.

Think about your own work. How many dashboards, spread sheets and reports do you see? Do you have the time to study them all and make good decisions or is there simply too much and you do the best you can? Is information overload a reality for you today?

I predict that as more information systems are deployed and the more data that comes online, marketing teams will settle into two camps. 1. High intelligence/managed data and 2. low intelligence/unmanaged data.

The net result of this likely evolution will be organizations that better understand the nuances of their customer segments innovate well and deliver relevant and compelling content, products and experiences to their constituents. That’s the first group. How will they do this?

It is simple in theory, hard in practice (which is why this first group will be smaller than the second).

Members of the high intelligence/managed data group will have forged strong collaborative internal bonds between the various business teams. Marketing, IT, product development, knowledge & insights, HR will all be working together in more efficient ways than in the other group. They will have at their disposal clear and actionable intelligence that comes from their effective Big Data use. It’s important to note that this group will use much more than the standard web data we all use today. They will integrate vast amounts of other transactional information into the intelligence process such as shipping information, call center data, mobile geo-location and usage data, RFID data, etc.

Essentially, these high performing organizations will put the right filters and algorithms in place to provide them with what they need to know to perform well, not what they can know. This is a really important distinction. More data is not better. More intelligence is. Individual data streams will tell you very little. However, when they are paired and bundled together, weighted in terms of importance and linked with certain business goals, patterns and pictures emerge that provide clear insight into what actions might be taken to generate certain outcomes.

By creating intelligence filters, the paradox of big data (more) becomes the power of big data (better). When it is organized against business objective and tracked over time, high performing organizations will excell even further. They will know what activities, campaigns and assets are generating very specific business results. These teams will be able to discern between important activities and unimportant. After all, not all activities or even business goals for that matter are of equal importance.

For instance, below is a filtered report you might find in use in the first group. Note that for this business, generating revenue and driving product innovation are more important objectives than decreasing the cost of support. The question is how much more important and what activities feed into each one of these goals and how important are each of these activities? What if you tracked 800 separate activities or metrics? How would you know? If you take each metric or data stream on its own you wouldn’t.

Which brings me to the second and larger group, low intelligence/unmanaged data organizations. This group collects data like it is going out of style, many times without any rhyme or reason as to why and what to do with it. The problem here isn’t that they will continue to collect more and more of this information but that they will do so while ignoring many other forms of information that is available which can provide critical intelligence. In the end, they will bury themselves in expensive and somewhat useless information.

Customers of firms in this group will grow weary of their disjointed experiences, inconsistent content and lack of understanding of their needs. Firms that fall into the first group will enjoy the benefits of collecting these people as new customers.

There is a relatively recent analog for the potential impact that Big Data will have on marketers in the coming years and the dichotomy between the first and second group I’ve outlined here. In the 1960’s Ed Deming taught the Japanese auto manufactures how to establish and manage a quality process that turned out better parts than their American counter parts. The Japanese listened and adopted the approach, enterprise wide. The result was the reversal of market share, which had a catastrophic effect on Detroit’s automobile dominance.

Currently, neither the first or second group has really formed yet but organizations are already headed one way or the other. For marketers with the ability to be change agents, recognizing which path you are on and doing something to either ensure you remain on that path or quickly change it will impact your organization’s future success.

Big Data will either be your greatest ally or your nemesis. It is up to you to choose which.