Why we Should Worry about Response Rates in Enterprise Social Systems

Why we Should Worry about Response Rates in Enterprise Social Systems 

response-rates-cartoonThis post continues our series on key SWOOP indicators. We have %Response Rate as a key performance indicator for organisations embracing problem solving and innovation within their Enterprise Social Networking (ESN) platforms. Difficult problems require deep dialogue, discussion and debate to be effectively solved. A response to a posting is hopefully the beginning of a constructive discussion, hence an important indicator of the degree to which an organisation is predisposed to solving problems online. Our ESN benchmarking of close to 50 organisations has the average response rate at 72%, but with a large range from a low of 32% to a high of 93%. response-rate-chartresponse-rate

The Response Rate widget identifies the percentage of posts that have received a written ‘reply’ and/or a ‘like’, for the period selected. It will also identify the % posts that have received no response; a measure that community managers need to monitor closely. The timeliness of the response is also reported.  

The Response Rate widget is available at all SWOOP reporting levels, from the individual, right through to the Enterprise overall. While not all posts are framed as problems, the response rate does reflect how responsive an organisation is overall. A response is a tangible signal of value received. In the absence of specific value stories, it is the most direct measure of value being facilitated on the ESN platform.  

For the individual, a poor response rate can indicate that your postings are not framed appropriately for attracting a response. For a group, a poor response rate may indicate a lack of a critical mass of members, or inadequate community management. 

Business Imperative 

It sounds obvious, but before problems can be solved, they need to be shared. Sharing a problem can be construed as a weakness. When senior management openly share a problem, they run the risk of ‘losing face’. Isn’t solving difficult problems what they are being paid to do?  Yet it is the senior management that need to lead the way in generating a culture for collaborative problem solving. As David Thodey, the former CEO of Telstra told us,Management don’t know everything…we have been guilty of releasing poor policies that have taken us years to recover from’. Thodey used the ESN to share problems that new policies were required for, and then getting feedback before finally releasing a new policy. 

The first challenge therefore is to develop a culture which respects that sharing a problem is not a weakness but a strength of character. Think about using hash tags to monitor problems posted, and their journey to a hopeful resolution. Once problems are shared freely on the ESN, the Response Rate measure can be used to measure problems solved. Many of the online technical forms are established specifically for tracking problem resolutions. There is no reason that the ESN cannot be used in a similar way. 

 

Influential People – SWOOP Style

 

In this series of articles, we are profiling each of the SWOOP Analytics Widgets by referencing them to the Enterprise Social Maturity Framework, that we introduced previously. The SWOOP analytics widgets are designed to guide our end users through each stage of the maturity journey. The ‘Influential People’ widget is seen to be most valuable when you are looking to solve difficult problems and/or driving new innovations to positive outcomes: 

enter-collab-3

influential-peopleInfluential people, as the name suggests, are those people that are best positioned to influence others through their interactions. Platforms like LinkedIn and Twitter typically use the popularity of content published to measure influence. In LinkedIn’s  case, profile views contribute strongly to your perceived influence. SWOOP uses a different basis for measuring influence, drawn from the science of social network analysis (SNA). SNA bases influence measured on the size and nature of one’s connections. An individual’s influence in SWOOP is measured by the size of their personal network.  

Your Personal Network Map, which can be viewed on your  personal tab, is a visual representation of your full network. At the Group, Business Unit or Topic level, influence is measured by an individual’s network within the Group, Business Unit or those engaging with a given topic.  

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A network connection is formed when you interact with someone online. It could be a ‘reply’ or ‘like’ you have made to a post, or vice versa. Activity levels are not considered; only the unique connections made.  

Business Imperative 

If you want to influence the activities of a group of people, the most efficient way is to engage with those that are best placed to influence them. Influence propagates through relationship links. Enrolling the influencers in your target audience can accelerate the change that you are seeking. You can aim to become an influencer yourself by looking to expand your network within your target audience. If you are identified as an influencer yourself, it is important to use your privileged location in the network to bring others into the network i.e. being the Catalyst/Engager, ensuring  diverse points of view are accommodated. 

Influencers can play a big part in helping their organisations to become more responsive. Their central position in the network enables them to become important role models by being personally responsive to problems they see. Influencers need not be able to solve the problems themselves, but they are ideally placed to identify those in their network that can. 

 

Yammer Benchmarking Insights #3 – Collaboration at the Personal Level

 In this episode we drill down to the most detailed level. That’s you, the individual collaborator.

At SWOOP we have designed behavioural personas to characterise individual collaboration patterns based on your pattern of activity.For example, if you are a Catalyst, you are good at getting responses to your posts. Catalysts are important for energizing a community and driving the engagement of others. If you are a Responder, you are good at responding to other people’s posts. Responders are important for sustaining a community and extending the discussions. An Engager is able to balance their Catalyst and Responder behaviour and is seen as the Persona to aspire to, as the Engager effectively balances what they give to others in the form of posts, replies, likes etc. and those that they receive from others. Therefore they are well placed to broker new relationships. Broadcasters tend to post without engaging in conversations. Observers are simply not very active, with less than a single activity every 2 weeks. We see Broadcasting and Observing as negative personas.

behavioural-personasWhat does an organisation’s portfolio of Personas typically look like? The results below are generated from our benchmarking results from close to 40 organisations. The lines indicate the minimum-maximum range and the blue square is the average score.

persona-proportions

The large range of % Observers, between less than 10% to over 70%, may reflect the large variation in maturity amongst the organisations we have benchmarked. It may not only be the case of maturity though, as it is fair to say that the smaller organisations have an easier time engaging a higher proportion of their staff with the Enterprise Social network (ESN).  We show the break-up of the active (non-observer) Personas, which shows that Catalysts lead the way with just over 40%, followed by Responders at just under 30%, Engagers just over 20% and Broadcasters at 10%. This would indicate that in general, ESNs are relying on Catalysts to continue to drive participation and then Responders to sustain it.

Personas within Groups

Given that groups are the space where most of the intense collaboration is likely to happen, we were interested in what the Persona patterns were for the leaders of the best performing groups. We used a combination of two-way connection scores and activity scores to identify the strongest groups. We then applied the same measures to the group members to identify the group leaders. In other words, a group leader is someone who has a high number of two-way connections with other group members, and meets a threshold level of overall activity.

Firstly, we plotted all members on a graph, locating them by the size of their network (y-axis) within the group and the number of 2-way connections they have in the group (x-axis). The bubble is sized by their relative levels of interactions (activity). As you can see, the group leaders are clearly identified in the top right hand corner of the graph as different coloured nodes.

persona-tracking

Secondly, we then plotted the top 5 leader’s Persona movements in 1 week intervals, over a 6-month period. In the example above you can see that the leaders played the Catalyst, Engager and Responder roles primarily. The size of the bubbles reflects their relative number of connections made (breadth of influence), for that week. Not all leaders were active every week. What becomes interesting is that we find some leaders have preferred Personas that are sustained over time. Leaders 1 and 4 in this case have a preference for Catalysing and Engaging. Leader 5 prefers Responding. Leaders 2 and 3 appear to be comfortable switching between Personas.

What appears to be important here is that high performing groups need leaders that can cover the spectrum of positive Personas i.e. Catalyst, Engager, Responder. While it’s fine to have leaders who have a preference for a certain behavioural Persona, it is useful to have leaders who can adapt their Persona to the situation or context at hand.

Personal Networking Performance

At SWOOP we use a fundamental network performance framework, which measures performance against the complementary dimensions of cohesion and diversity. We have indicated that individuals with a large number of two-way connections are likely to have more closed and cohesive networks. Cohesive networks are good for getting things done (executing/implementing). From an innovation perspective however, closed networks can be impervious to new ideas. The best ideas come from more open and diverse networks. In our view therefore, maximum network performance occurs by optimising diversity and cohesion. In other words, it’s good to be part of a strong cohesive network, but this should not be at the expense of maintaining a healthy suite of more diverse connections.

In the graphic below we have plotted the members of one large group on the Network Performance graph. In this case the diversity is measured by the number of different groups that an individual has participated in. The size of the bubbles reflects the size of the individual’s network (breadth of influence).

personal-network

We have labelled regions in the graph according to our Explore/Engage/Exploit model of innovation through networks. We can see that the majority of group members exist in the ‘High Diversity/Low Cohesion’ Explore region. This is consistent with what many people give for their reasons for joining a group. The ‘Engage’ region shows those members who are optimising their diversity/cohesion balance. These are the most important leaders in the group. In an innovation context, these people are best placed to broker the connections required to take a good idea into implementation. The bottom right corner is the Exploit region, which for this group is fairly vacant. This might suggest that this group would have difficulty organically deploying an innovation. They would need to take explicit steps to engage an implementation team to execute on the new products, services or practices that they initiate.

The Innovation Cycle – Create New Value for Your Organisation

We conclude this third edition of Yammer Benchmarking insights be reinforcing the role that individuals can play in creating new value for their organisations. For many organisations, the ESNs like Yammer are seen as a means for accelerating the level of innovation that is often stagnating within the formal lines of business.

As individual’s we may have a preference for a given style of working, as characterised by our Personas. Your personal networks may be large, open and diverse; or smaller, closed and cohesive; or indeed somewhere in between. It is important however to see how your collaboration behaviours contribute to the innovation performance of your organisation. Innovation is a collaborative activity, and therefore we recommend that in your groups you:

  1. Avoid lone work (Observing/Broadcasting) and look to explore new ideas and opportunities collaboratively, online (Catalysing/Engaging/Responding).
  2. Recognise that implementing good ideas needs resources, and those resources are owned by the formal lines of business. Use your network to engage with the resource holders. Make the connections. Influence on-line and off-line.
  3. When you have organisational resources behind you, it’s time to go into exploit mode. Build the cohesive focussed teams to execute/implement, avoiding distractions until the job is done.

 

Diversity is Essential but not Sufficient

diversity-imageDiversity is a big word in business today. We are preached to continuously about how important having diverse leadership is to improving your performance. HBR in their article on ‘Why Diverse teams are Smarter”, identify studies showing that diversity based on both ethnicity and/or gender can lead to above average returns. In our own work with networks, research has shown that individuals with more diverse personal networks are more likely to be promoted and succeed in their occupations. Although I’ve always thought that my own personal network was quite diverse, I received a wake-up call from the recent US elections. I was not aware of any of my fairly extensive US citizen network that were voting for Trump! So it does take a conscious effort to build and sustain a diverse network of connections. It’s far too easy to fall back to the comfortable relationships with those just like us.

But diversity alone is only a pre-condition to high performance. One must be able to exploit the diversity in one’s network to actually deliver the superior results that it promises. In a previous post we introduced our network performance framework, which identifies a balance between Diversity and Cohesion in networks, for maximizing performance:

swoop-diversity

In this framework we identify that high performers are those that can effectively balance their diverse connections i.e. identifying high potential opportunities, with their close connections, with whom they can collaborate to exploit those opportunities. From our project consulting experiences these people are either recognised as organisational ‘ambassadors’ or are completely invisible i.e. the quiet achievers. The fact that we find so few people in this quadrant is testimony as to how hard achieving this balance can be.

UGM Consulting explores this tension in their recent article on Innovation and the Diversity Paradox. They nominate the following attributes for those diverse networks that can successfully exploit the opportunities that they identify:

They have a sense of shared common goals and purpose;

  1. They know how to genuinely listen to each other, seeking out elaboration and novel combinations;
  2. They have high levels of mutual trust, so speaking up and disagreements can be had, risk free;
  3. They have the skills to constructively explore alternatives and agree on a direction; and
  4. There exists a strong co-operative atmosphere at both the team and enterprise levels.

For leaders this will mean actively enabling or creating such conditions. For the individual it could boil down to simply developing a diverse network that you actively consult with.  At times you may leverage these relationships by enrolling others in selected joint activities, to bring about positive change in your own areas of influence.

Top Image credit: http://www.ispt-innovationacademy.eu/innovation-research.html

What can we Learn from Artificial Intelligence?

This might seem strange, suggesting that a science dedicated to learning from how we humans operate, could actually return the favour by teaching us about ourselves? As strange as this may sound, this is precisely what I am suggesting.

Having spent a good deal of my early career in the “first wave of AI” I had developed a healthy scepticism of many of the capability claims for AI. From the decade or more I spent as an AI researcher and developer I had come to the conclusion that AI worked best when the domains of endeavour were contained within discrete and well bounded ‘solution spaces’. In other words, despite the sophistication of mathematical techniques developed for dealing with uncertainty, AI was simply not that good in the “grey” areas.

AI’s Second Wave

alphago

The “second wave of AI” received a big boost when Google company Deep Mind managed to up the ante on IBM’s chess playing Deep Blue  by defeating the world Go champion Lee Sedol. According to Founder and CEO of Deep Mind Demis Hassabisis,  the success of their program AlphaGo could be attributed to the deeper learning capabilities built into the program, as opposed to Deep Blue’s largely brute force searching approach. Hassabisis emphasizes the ‘greyness’ in the game of Go, as compared to Chess. For those familiar with this ancient Chinese game, unlike chess, it has almost a spiritual dimension. I can vividly recall a research colleague of mine, who happened to be a Go master, teaching a novice colleague the game in a lunchtime session, and chastising him for what he called a “disrespectful move”. So AplhaGo’s success is indeed a leap forward for AI in conquering “grey”.

So what is this “deep learning” all about? You can certainly get tied up in a lot of academic rhetoric if you Google this, but for me it’s simply about learning from examples. The two critical requirements are the availability of lots of examples to learn from, and the development of what we call an “evaluation function”, i.e. something that can assess and rate an action we are considering on taking. The ‘secret sauce’ in AlphaGo is definitely the evaluation function. It has to be sophisticated enough be able to look many moves ahead and assess many competitive scenarios before evaluating its own next move. But this evaluation function, which takes the form of a neural network, has the benefit of being trained on thousands of examples drawn from online Go gaming sites, where the final result is known.

Deep Learning in Business

books

We can see many similarities to this context in business. For example, the law profession is founded on precedents, where there are libraries of cases available, for which the final result is known.  Our business schools regularly educate their students by working through case studies and connecting them to the underlying theories. Business improvement programs are founded on prior experience or business cases from which to learn. AI researchers have taken a lead from this and built machine learning techniques into their algorithms. An early technique that we had some success with is called “Case Based Reasoning”. Using this approach, it wasn’t necessary to articulate all the possible solution paths, which in most business scenarios, is infeasible.  All we needed to have was sufficient prior example cases to search through, to provide the cases that most matched the current context, leaving the human user to fill any gaps.

The Student Becomes the Teacher

Now back to my question; what can AI now teach us about ourselves? Perhaps the most vivid learnings are contained in the reflections of the Go champions that AlphaGo had defeated. The common theme was that AlphaGo was making many unconventional moves, that only appeared sensible in hindsight. Lee Sedol has stated his personal learning from his 4-1 defeat by AlphaGo in these comments: “My thoughts have become more flexible after the game with AlphaGo, I have a lot of ideas, so I expect good results” and “I decided to more accurately predict the next move instead of depending on my intuition”. So the teacher has now become the student!

It is common for us as human beings to become subjects of unconscious bias. We see what is being promoted as a “best practice”, perhaps reinforced by a selected few of our own personal experiences, and are then willing to swear by it as the “right” thing to do. We forget that there may be hundreds or even thousands of contrary cases that could prove us wrong, but we stubbornly stick to our original theses. Computers don’t suffer from these very human traits. What’s more they have the patience to trawl through thousands of cases to fine tune their learnings. So in summary, what can we learn from AI?

  • Remember that a handful of cases is not a justification for developing hard and fast rules;
  • Before you discount a ‘left field’ suggestion, try to understand the experience base that it is coming from. Do they have experiences and insights that are beyond those of your own close network?
  • Don’t be afraid to “push the envelope” on your own decision making, but be sure to treat each result, good or bad, as contributing to your own growing expertise; and
  • Push yourself to work in increasingly greyer areas. Despite the success of AlphaGo, it is still a game, with artificial rules and boundaries. Humans are still better at doing the grey stuff!

 

 

 

 

Q&A: Start-ups vs Large Corporates

start-up-versus-corporate

SWOOP Analytics celebrated its 2nd Birthday late last month with our distributed workforce face to face, many for the first time; and also many of our early adopter partners and clients. Unlike most start-ups addressing the consumer market, SWOOP Analytics targets the ‘big end of town” i.e. large corporates and public institutions who’s procurement practices go far beyond someone simply pushing the ‘buy’ button. We have been fortunate to have several highly experienced executives and consultants advising us on our product startup journey. We thought we would take advantage of their presence to conduct a mock Q & A panel session, modelled on the ABC show Q & A. We chose our panel members based on their experience with working and advising both start-ups and large corporations. Our panel topic was “How can Startups work Effectively with Large Corporates”.

Here were our selected panel members:

Dr. Eileen Doyle

Eileen is an experienced executive and company director for big end of town companies like BHP, OneSteel, Boral, GPT, Port Waratah Services, Oil Search and the CSIRO. We also identified Eileen as one of the most connected female company directors on the ASX in our ASX networking studies. But most importantly she is also an Angel investor in Swoop and a former chair of Hunter Angels, so she was well qualified to join our panel.

Ross Dawson

Ross is recognized as one of the world’s leading futurists. He is regularly engaged for keynote speeches and consulting advice by the ‘big end of town’ clients like Macquarie Bank, Ernst & Young, Proctor & Gamble, News Ltd and many more about what is coming ‘down the pipeline of future technologies’. A long term friend of the Swoop founders, Ross is an entrepreneur himself, with several startup initiatives on the go.

Allan Ryan

Allan is the founding director of the Hargraves Institute, celebrating its 10th birthday this year as a leading community for major corporations focusing on innovation.  Many of Australia’s leading organisations have been sharing their innovation experiences and practices in the Hargraves community. And Allan has had a front row seat in observing how large and complex organisations are addressing the innovation challenge.

swoop-panelists

The panel were actively ‘grilled’ by an enthusiastic audience. And the panel to their credit, responded in good spirit. Here are some nuggets of wisdom shared by our panel:

  1. How can big corporations work more effectively with start-ups?

Eileen shared the mindset is different in a large corporate, where you have to look at risk in a different way. The balance between risk and reward is tilted to risk in a large corporate and reward in a start-up, which is why the majority of start-ups fail. Interaction between the two works well when there’s a genuine need that the large corporate has, which aligns with what start up is doing. Her advice is investors will not get rewarded if corporations don’t take risks, it’s ok to fail which we need to learn to celebrate.

Ross shared that it’s key for big corporations to set up mechanisms to deal with start-ups, like accelerators, incubators and hackathons. There needs to be more structures and governance to support transformation. As a Futurist he helps people think about the future to make better decisions today, that will make a different in the future.

From his work at Hargraves Institute, Allan shared that large organisations are maturing rapidly. His advice to start ups was to find the most mature area which has the need for your service and give them a solution that doesn’t give them great risk to test and try.

  1. Quality versus innovation?

An audience member asked about the importance of IT security for starts ups and another shared it can be boring to get the basics right, how crucial is this for successful innovation? Panelist’s shared:

  • Start-ups need to get their disaster recovery and IT security right, at least to the level of the Organisation they’re engaging with.
  • Start–up products need to have their quality right and be tried and tested. Quality is more important than innovation where there are winners and losers.
  • Start-ups need to adopt a philosophy of forever getting better in the basics and making sure they’re improving.
  1. Can Australia become the Silicon Valley of the Southern Hemisphere?

For Australia to further foster the success of start-ups Panelist’s suggestions included:

  • Linking the quality of Australia’s research to effective commercialisation on a global scale
  • Promoting innovation as ‘invention accepted by the market’ by private and public businesses spending more in this area.
  • The Government providing tax breaks and recognition of greater risk.
  • Universities taking a what’s best for the whole country mindset versus what individual academics might want to do.
  • Encouraging small businesses to be more innovative and teaching kids how to have fun doing new things.

Our takeaway message was large corporates have multiple entry points, so it’s important not to get discouraged and keep looking for the people that have roles with a larger risk profile in them.

Image citation: https://www.tnooz.com/article/startup-chic-vs-corporate-geek-can-gen-y-retention-predict-success/

 

 

Identifying Key Connectors/Informal Leaders at Scale

Informal Leaders Blog Image

This recent article by Reid Carpenter  on uncovering the authentic informal leaders reminds us again that in a post industrial economy, the powerbrokers are less likely to be identified by their C-Level formal titles, and more likely to be identified through word of mouth. New emerging organisational forms like Holocracies  and Business Networks  will live and die by the strength of their informal leaders. The importance of the connector is nothing new. Seth Godin wrote a book about ‘linchpins’; we have also blogged about the Quiet Achiever. There are now many sources of advice on how to recognise a genuine connector/informal leader. The challenge exists however, on how we identify these new informal leaders at scale?

Business and stock exchange directories can still provide us with those that occupy the formal power roles. In today’s economy however, it is often the next layer of powerful connectors and invisible leaders that dictate success or failure; the equivalent of the industrial age ‘middle management’. Let’s consider the world’s largest business network Linkedin. How would one identify a ranked list of informal leaders from this massive network? Is it the ones with the most connections? the most followers? the most read posts? the most diverse suite of connections? the ones who are most regularly asked to broker a connection? Perhaps it’s a combination of all of these, or perhaps none at all. What is problematic is that we don’t have a simple directory to look up. We are therefore left to explore the ‘word of mouth’ network. As effective as this can be, is there an alternative that can work at scale?

While we don’t yet have the answer, it is certainly something that consistently exercises our minds and ongoing research activities. Let’s take for instance, Microsoft’s Yammer network as a source of data for identifying informal leaders. By this we mean those that don’t have an acknowledged or formal role as a connector/leader e.g. a general manager, community leader, business coach, business improvement leader etc.. On first thought we could look at who gets ‘mentioned’ or ‘notified’ a lot. The ‘mention’ function is a ‘word of mouth’ proxy. The ‘notify’ function we have observed can be used by formal line management to direct the attention of their staff, but is also used to direct attention up the formal lines of management. It tends to work like an email ‘cc’ equivalent. The question is whether these message ‘tags’ can be used to profile connectors and informal leaders i.e. are the people that use the ‘mention’ and/or ‘notify’ functions really representative of connectors or informal leadership? Are the people who are the subject of these functions the real informal leaders? Perhaps those doing the mentioning and notifying are the ‘connectors’ and the subjects are the ‘informal leaders’? i.e. connectors are separable from informal leaders.

Taking these thoughts further, a connection is not a connection unless it is acknowledged by the parties being connected. For example, a Linkedin connection has to be formally acknowledged by both parties. A twitter follow is therefore not a connection, unless of course it is reciprocated. Therefore, simply mentioning or notifying someone is not a connection unless it is acknowledged by the subject.

While a ‘connector’ is often seen as an informal leader, is just connecting enough? This is where I start to qualify my earlier assertions  that activity measures are no indication of collaborative performance. If we adopt a ‘connections before activity’ perspective, then activity rates between connections becomes a useful proxy for connection strength and even relationship strength. It’s not hard to accept that if two connected people are conversing a lot i.e. have a highly active connection; then it is likely that they are more strongly related (even if the relationship is argumentative). And those individuals who sustain many highly active and diverse connections, are more likely to be the authentic informal leaders that Reid Carpenter describes.

Using our Yammer benchmarking data  we are able to make the measurements described above, at scale using reciprocated interactions and activity counts within connections. That said, we will still need to validate these indicators against some of the more qualitative attributes identified by Carpenter and other commentators, to be sure. So watch this space!

A final comment on Linkedin. While this network provides authenticated connections, it is missing a ‘strength of connection’ capability. Hence in most cases our Linkedin networks would be what is called a ‘weak tie’ network. Without a reliable way of measuring a strength of connection/relationship, I believe we have no reliable way of identifying authentic informal leaders in this network. The same could be said for other public networks like Twitter and Facebook. There is hope however in the Enterprise Social Networks, where interactions are more focused and the audience more constrained.

Image citation: How to Find and Engage Authentic Informal Leaders – Illustration by Shutterstock/alphaspirit

Does your Community have a Key Player Risk?

Key Player Blog

SWOOP: Key Player Index

An important characteristic of networks is that some individuals are more important to the performance of the network than others. In fact, if we were to plot the relative influence of individuals in a network, the degradation from the most influential to the least follows a power law distribution. This means that the level of influence between the most influential members and the least influential reduces exponentially; emphasizing the importance of these few selected influencers in a network. Networks that have just a few key influencers are clearly at risk if one or more of them were to leave the network. So how can we tell how open your community is to a key player risk?

This post continues the series of deeper dives into the specific measures included in the SWOOP Collaboration Framework #swoopframework. We have previously addressed individual behavioural personas and the important social cohesion measure.

How is this Measured?

The key player index is a measure of the degree to which a network is reliant on a ‘selected few’. To compare networks we measure the proportion of members that are responsible for 50% of all connections. The higher the proportion, the higher the key player index is and the lower the key player risk is. The range of scores from our 20+ benchmarking sample is between 4% and 12% for online communities, with a mean score of 6.4.

Interpretation

What we have ascertained from our online networking studies is that online communities are much more susceptible to key player risk than off-line communities/networks. This may potentially be attributed to an existing ‘digital divide’, where by only a proportion of community members choose to be active online. Alternatively, it could simply be the online medium makes it easy to attract a larger, only marginally active, membership. That said, we think that the relative scores are still a good indication of key player risk.

What should this mean to you?

If your community/network has a low key player index, meaning a high key player risk, it is important to start to address this by encouraging more members to act as hubs in the network, by actively connecting others. If you notice that selected individuals are doing all the ‘work’ in keeping the community active and vibrant, start trying to lend a hand. If you are one the ‘selected few’ key players, try and encourage others to join you and become more active in connecting others. Perhaps ask others to host online events or initiatives as a way of broadening the community leadership responsibilities and increasing the visibility of others.

In summary, a strong, sustainable community has built in redundancy, so that it can remain active, vibrant and productive, even if some the key players were to leave or be absent for an extended period. By ensuring that your community has many hubs and/or alternative sources for brokering and connecting the community, the longevity of your community will be more assured.

 

 

How Cohesive is your Community?

Cohesive community

SWOOP: Mean 2-Way Connections

Social cohesion is synonymous with ‘community’. Intuitively we experience social cohesion when we participate in high performing communities. Experienced ‘networkers’ lead these communities. New members are made to feel welcome. Community objectives are met through active engagement between members. High performing online communities are a fertile field for knowledge sharing amongst its members. While qualitatively we can experience and differentiate a good community from a poor one, what measures are available to assist leaders in monitoring social cohesion in their communities? How can these measures be used to help grow social cohesion?

This post continues the series of deeper dives into the specific measures included in the SWOOP Collaboration Framework #swoopframework. We have previously addressed individual behavioural personas. The Mean 2-way Connections measure is community wide, and our measure for social cohesion.

How is this Measured?

The Mean 2-Way Connections measure calculates the average number of reciprocated connections each community member has.  An example of a reciprocated connection created is if say, you reply to a post by person A and then person A replies to a post of yours. A community rich with members having a high number of two-way connections is going to be a highly cohesive one. On the other hand, a community with a low Mean 2-Way Connections score will have created few sustainable relationships between its members and therefore much less cohesion.

Interpretation

The Mean 2-Way Connections is our measure of social cohesion. For the majority of communities, the higher this score, the better. A high score means that it is highly likely that strong relationships are being developed amongst the membership. We know that strong relationships underpin trust, and with trust we get speed and tangible results, as Stephen Covey elegantly represents in his book ‘The Speed of Trust’. Where we have been able to compare online communities/groups within the same enterprise on this measure, we have found qualitative reinforcement that the more cohesive a community is, the more value that it is creating for its members and the enterprise.

Importantly, the social cohesion measure was the result with the greatest spread from best to worst in our benchmarking studies. The following chart shows the relative spread of results amongst a selected set of 21 enterprises:

Standard Deviation.png

In essence this chart indicates the Mean 2-Way Connections (social cohesion) is the measure that most differentiates good from poor performance. It is also therefore the dimension that offers the most potential for improvement.

There is however an upper limit for social cohesion within a community. This point is where a community reaches, what we commonly call ‘group think’. In these circumstances, highly cohesive communities become immune to ideas from outside the community. Innovation stagnates, and while the community may still be successful, it will find it increasingly hard to deliver further improvements, without introducing more diversity within its membership.

What should this mean to you?

As an individual, one should always be looking to maximize the number of reciprocated relationships one has. Having a high number of 2-way relationship connections should result in your being seen as an ‘Engager’, the most productive behavioural persona. Recall that Engagers are able to effectively manage their ‘give-receive’ balance. They become the glue that binds a community.

As a community leader, a high average 2-way relationship score means that your members are actively engaged in community activities and delivering value for the community and its sponsors. On the other hand, a low score indicates poor social cohesion and therefore much work to do. To build up social cohesion in your community, you need to start with identifying a few important tasks that selected groups of members can actively work and collaborate on. Traction is gained around these activities and value stories are shared amongst the broader community. You will see the 2-Way Relationships score grow, as the membership becomes more engaged in its activities.

In summary, social cohesion and its specific measure of Mean 2–Way Connections is seen as, arguably, the clearest measure of a successful community.  Social cohesion is synonymous with community. Our benchmarking studies have shown that it is also the measure that most differentiates excellent from poor community performance. For those communities exhibiting poor social cohesion, the task is to develop activities that encourage members to reciprocate. There is however price for too much cohesion; and that is a lack of diversity and innovation, which could lead to eventual stagnation, if not managed properly.

 

 

 

Is There a Place for ‘Broadcasters’ on Conversational Platforms? SWOOP: Broadcaster Persona

KEEPIA View from the Top – David Thodey Interview - Part 1- Why Enterprise Social Networking-

This post continues the series the deeper dives into the specific measures included in the SWOOP Collaboration Framework #swoopframework. The ‘Broadcaster’ behavioural persona; is the fourth collaboration persona with the ‘Engager’, ‘Catalyst’  and ‘Responder’ personas.

How Measured?

Like the ‘Responder’ persona, the ‘Broadcaster’ is calculated as having a significant surplus of contributions made, over responses received. Broadcasters are differentiated from Responders due to the original posts outnumbering the number of replies they provide.

Interpretation

‘Broadcasters’ as the name infers, suggest that these people have a bias toward publishing over the more conversational aspects that would include more liberal use of the ‘reply’ and ‘like’ contributions. Because a collaboration platform is founded on active and engaged conversations, the ‘Broadcaster’ persona is generally seen as negative. That said, there would be instances where an individual might correctly adopt a ‘Broadcaster’ persona. For example, if you are responsible for launching a brand new initiative, say like a new marketing campaign, or organizing an event. For a limited time you would be required to undertake substantial original postings to begin the initiative.

What should this mean to you?

While the ‘Broadcaster’ persona may be considered a negative one, it could be that circumstances may require you to adopt this profile from time to time. The important principle however is that it should not be considered a persona to aspire to permanently. If you feel that your role requires you to continuously adopt a ‘Broadcaster’ persona, you may want to consider whether the collaboration platform is the appropriate vehicle for you. It is likely that your organisation will have other platforms for publishing content e.g. Intranet, Document Management Systems etc..

In summary, the ‘Broadcaster’ persona tends to prioritize publishing over conversation. It is generally seen as a negative persona, unless used sparingly for situation specific instances. We do not see productive communities or teams having or needing regular ‘Broadcaster’ members.