Tyranny of the ‘Long Tail’

Longtail

The advent of Internet enabled e-commerce brought an increased focus on ‘Long Tail’ distributions . Internet organisations like Amazon are able to exploit their low marginal costs by selling low volumes to the Long Tail of buyers with unique non-mainstream needs. The Long Tail has therefore been celebrated as the new opportunity of the Internet age. Even knowledge sharing systems e.g. blogs, podcasts, video have celebrated the increased reach that the Internet facilitates. The ubiquitous 90/9/1 rule acknowledges that 90% of participants are simply consumers of content.

The Pervasive “Long Tail” Distribution

Our own work with communities and social networks identifies the Long Tail effect. Our benchmarking of ‘Key Players’ with ‘off-line’ social networks identified that the majority of those with large personal networks is confined to a selected few. Our Key Player index identifies how concentrated the core of the network is by measuring the % of participants that represent 50% of all connections. For off-line communities we found that the key player index is typically between 11% and 32%. However, when we applied this measure to online Enterprise Social Networks (ESN), this range drops to from 4% to 12%, meaning as little as 4% of the community members are responsible for 50% of all connections, accentuating how online communities amplify the Long Tail effects. To further demonstrate how pervasive this long tail distribution is, in an earlier post we showed how the social cohesion within Yammer groups in one Enterprise followed the long tail power curve distribution. In a follow up analysis we dug deeper into the group we identified as the most cohesive, to better understand what was happening inside. And what we found was another long tail distribution. Of the 243 staff who had been active in this group, over a period of 18 months since launching, 70 had only a single interaction, while 12 members (5%) were responsible for nearly 68% of all interactions. So even in what are perceived the ‘best’ community groups, most of the connecting is being done by only a selected few.

Knowledge Sharing is not Enough?

Here is the issue. Just because more people are exposed to new information and knowledge, can we assume that new enterprise value is being generated? Perhaps for those organisations that measure their success through increased readership, this is fine. But I would argue that increased readership, if it doesn’t result in increased actions, is a shallow benefit at best. We experienced the same issues with Knowledge Management (KM) in the 1990s. In those days KM solutions were largely content centric. It was common to celebrate shared content. Those of us at the centre of KM programmes of the day were however continuously challenged by our executive to demonstrate real value. I can still recall our CEO addressing the knowledge team by saying “I’m not interested in awards or newspaper articles about how great our KM programme is. What I want to see is real, on the ground, impacts”. While we could see a real change in the level of knowledge sharing that was happening, evidence of real impact was limited to selected anecdotes and one off case studies. As impressive as some of these were, they were far from representative of a sustainable enterprise wide change. Interestingly, this is where many Enterprise Social Network community managers now find themselves today.

Engaging the ESN “Long Tail”

It appears that we cannot escape the ESN “Long Tail”, so what can we do to engage them in more active collaboration? We will be addressing this more comprehensively in future posts, but its suffice to say that simply appreciating the extent of its existence and then creating some targeted interventions is a good start. Taking a leaf out of Amazon’s play book, we need to accept that the needs of the “Tail” are not the same as those at the core. Likely their needs will be more diverse and unique. It’s therefore incumbent of the community leaders to ensure that there is a sufficient richness and diversity in the conversations they seed, to attract greater participation from the ‘Tail’.

Image citation: http://www.longtail.com/about.html

Need to convince someone? Bring Data (and a good story)

Big data

As Daniel Pink suggests “to sell is human”.  Even if we do not have a formal ‘selling’ role we are always looking to ‘sell’ someone on our point of view, our recommendations, our need for their help etc.. As data analysts we live and breathe data every day, whether we are looking to develop some new insights, prove a case or simply explore possibilities. In the end we are doing it to influence someone or some group. In these days of ‘evidence based decision-making’ I am wary that one person’s ‘evidence’ is another person’s ‘garbage’. You don’t have to look much further than climate change sceptics to appreciate that. I was therefore intrigued when I came across Shawn Callahan’s recent blog post on “The role of stories in data storytelling”. Shawn talks about the use of ‘story’ before, during and after data analysis.

Before data analysis stories

Before data analysis is about understanding the dominant ‘story’ before your analysis. For us a good example of this is our recent work on comparing relationship analytics with activity analytics. The dominant storyline was (and probably still is) that social analytics used in the consumer world i.e. activity measures, are sufficient for use inside the enterprise.

During data analysis stories

The ‘during the data analysis’ story is about how stories evolve from your act of data analysis. Our story in the interactions vs activity debate was about one of our clients observing some analytics provided by Swoop and finding that the measure for social cohesion was far more reflective of their view of how different communities were collaborating and performing than the activity measures reported beside them. For us the ‘stories during data analysis’ is continual. We are always looking to find the ‘story behind the data’. And this usually comes when we can talk directly to the owners of the data, in what we call ‘sense making’ sessions. As an example, we are currently looking at adoption patterns for Yammer using some of the benchmarking data that we have collected. We have learnt from experience that collaboration happens best within ‘groups’. Our prior analysis showed that the social cohesion between groups varies a lot and follows a typical ‘power curve’ distribution when sorted from best to worst. We are now looking at how these groups evolved over time. What patterns existed for those highly cohesive groups versus those that were less cohesive? Is there a story behind these different groups? Our evolving stories are merely speculations at the moment, until we can validate them with the owners of the data.

After data analysis stories

Knowing Doing GapShawn Callahan identifies these stories as needed to bridge the gap between what the data analyst ‘knows’ and what the decision makers need to act on.  He goes on to describe types of data stories, being a chronological change, explanation or discovery stories. He recommends that if you are trying to instigate change from a dominant current story, then it has to be a better story than that one. Thankfully in our case we don’t believe there is a dominant story for the use of activity analytics with Enterprise Social Networking (ESN) implementations. Of course there are supporters, some quite passionate, but the majority point of view is that they are insufficient for the needs of the Enterprise. That said, you still need to come up with a good story. And that is still work in progress for us. We can use a discovery story to relate the trigger for the data analysis we conducted being a simple comment from a client. But our sense is that we will need even more data (evidence) couched in some powerful stories told by individuals, who have changed their interaction behaviours for the better, based on the analytics that they were provided with.

I should finish by giving Shawn’s recent book “Putting Stories to Work” a plug, since I have just completed reading it to help us develop that story. So watch this space!

How can you tell if your Enterprise Network is Innovative?

 

One of the most commonly stated objectives for establishing online enterprise social networks (ESN) is to facilitate greater levels of innovation. But how do we know if we are being more innovative or not? One way is to wait to see what sorts of tangible outputs emerge from the cross enterprise communities within the network.  This may result in some good individual cases, perhaps enough to claim an increase innovation capability. More likely these may be seen as random outcomes if the organisation doesn’t ‘feel’ like its being more innovative. In a recent article on behaviours that can create an innovation culture, Rob Shelton identifies five key behaviours that can lead to creating an innovating culture. The behaviours were: broad based collaboration, measuring and rewarding intrapreneurs, emphasising speed and agility, thinking like a venture capitalist and balancing operational excellence and innovation.

There are now many online tools designed specifically to support innovation activities e.g. Spigit, Innovation Cast, Hype and some that are built on top of existing Enterprise Social Networking (ESN) platforms e.g. Sideways6. Typically, these systems are used to facilitate pre-defined innovation challenges or campaigns; if you like, ‘programmed’ innovation. The heavy use of tools and innovation programmes may also result in an organisation ‘feeling’ more innovative.

In the absence of specific innovation tools or programmes how could we identify if our organisation is developing an innovation culture? To guide our thinking we will use the five behaviours identified by Shelton and our own Swoop Personas (Engagers, Catalysts, Responders, Broadcasters and Observers) to explore this:

  1. Build collaboration across your ecosystem

This is the most straightforward one. The Swoop social cohesion measure  applied across the whole enterprise and measured over time will provide an accurate assessment of this.

  1. Measure and motivate your intrapreneurs

Intrapreneurs are the entrepreneurs inside the enterprise. A true entrepreneur is more than an ‘ideas person’. Successful entrepreneurs are able to deliver on ideas, whether they are their own or not. We would see the behaviour patterns of a successful intrapreneurs as essentially displaying the behaviour of a Swoop Catalyst. Over time, however,  as they drive ideas to successful completion, their networks will become denser and more cohesive i.e. their personal social cohesion measure will increase over time. They also may migrate from being a catalyst to an engager as they become more active and focussed in their contributions to the network, as ideas progress to execution. Successful execution is typically characterised by tighter, more cohesive teams.

  1. Emphasize speed and agility

Speed and AgilityThis is perhaps best observed at the team or community level. We know that speed comes with trust and trust is generated through reciprocated relationships, so again the Swoop social cohesion measure is the one of choice. However, speed does not mean agility. The world’s fastest vehicles achieve their speeds by not having to turn corners! So how do we know if a team or community is agile? Here we would have to observe team goals and how quickly a team or community can adapt to a change of direction. One way of achieving this in Swoop would to get into the habit of hash tagging all community/team initiatives. Using the Swoop Topic analysis, one could monitor over time how quickly, and to what extent, conversations evolve around each tagged imitative. An agile community would see a rapid and broad based network of conversations emerge on the launch of a new tagged initiative. Therefore, a combination of the social cohesion measure across the group (speed) and then indicators like the interactions and relationships built around a new topic/initiative over a short period; perhaps a variety of conversation leaders for different topics; a large and rapidly growing number of conversation threads on the launch of a new tagged initiative (agility), could all inform on progress and performance on this dimension.

  1. Think like a venture capitalist (VC)

Venture Capitalist.tifShelton describes this as coming up with the ‘Big Ideas’, rather than dismissing them out of hand. He suggests protecting them by addressing the potential challenges/barriers. One idea to implement this is to establish a group where these ideas can be captured for discussion. Perhaps the group could be called “Moonshots” and the ideas hash tagged to track engagement around the ideas, as the inevitable challenges are surfaced. For most enterprises, ‘#Moonshots’ rarely make it out of the ‘ideas lab’. The rare ones that do will have been adapted as challenges are addressed. They will also have a consistent suite of discussants that will have to include the senior decision makers. Even a single successful ‘#Moonshot’ is enough to have an organisation labelled as “innovative”. I can think of a hardware company called Sun Microsystems inventing Java; Apple, a company making personal computers creating a game changing mobile phone; the dominant provider of large corporate mainframe computers, IBM, building a highly successful personal computer.

Using SWOOP we would monitor the discussion patterns around #Moonshot ideas and the seniority of the discussants over time, to sense whether the organisation was really thinking like a VC.

  1. Balance operational excellence with innovation

BalanceThis is a nice one to finish with, as many organisations struggle with achieving the right balance between the shorter term and more visible operational excellence initiatives and the less visible and perceived riskier innovation initiatives. In the academic literature this is often coined the “Explore vs Exploit” challenge. I have written extensively on this issue, suggesting that they are not ‘choices’ and that explorations only create value when they are exploited; so it should be thought of more as a ‘flow’. Many years ago we published a short paper on this called the ‘3 E’s of Innovation’  for Explore/Engage/Exploit, where the Engager, was identified as the link between exploration and exploitation. For those with more of an academic bent this journal article “Corporate social capital in business innovation networks” explores it in more detail.

The SWOOP monitoring suggestion for this would mimic the ‘Speed and Agility’ behavior. The social cohesion measures will predict speed of execution, viz Operational Excellence. The innovation aspect would look at the breadth of the conversation topics and the diversity of the participants, across geographies, formal business units and/or levels of seniority. In essence we are characterizing the operational excellence/innovation balance as a social cohesion/diversity balance. The diversity measure in Swoop is yet to be installed, but to operate, will require profile data on business unit membership, location and if available, seniority or job/role classification attributes, to be made available.

So to summarise; how can your ESN analytics flag if you are developing an innovative culture or not?

  • Monitor your enterprise social cohesion measure over time; is it travelling upward?
  • Identify your Catalysts that have highly cohesive personal networks. Are they increasing in number?
  • How diverse are your conversation topics and those participating in these discussions? Is your diversity increasing in line with your social cohesion?
  • Do you have a space where “Moonshot Ideas” can be proposed and discussed? If so, to what extent are the senior leaders engaged in these discussions?