Smart Collaboration = Smart Money

smart-collaboration-artwork

Smart Collaboration’ is the title of Harvard’s Heidi Gardner’s latest book. The book builds and expands on her well cited HBR article  “When Senior Managers Won’t Collaborate” , smart-collab-1where she presents some compelling data demonstrating that collaboration does pay, big time. Her network representation comparing the networks of two lawyers, with Lawyer 2 responsible for generating much higher revenues from her larger and more diverse network, may seem quite logical. Additionally, she shows that greater peer-to-peer collaboration does indeed generate much higher revenue levels; the key measure of success for most advisory firms. But those whom have worked in Partner led advisory firms, will understand the tribal norm of ‘Eat what you Kill’, can actively work against cross-enterprise collaboration. Gardner’s research will hopefully go a long way toward convincing the leaders of advisory organisations that it is time to abandon this tradition. But in the book she acknowledges and addresses head on, the challenges ahead.

In a decade of conducting survey based Organisational Network Analyses (ONA) projects around the world and across many industry sectors, we have found that it is the partner led organisations that fall most strongly into the ‘tribal’ area (High cohesion/Low Diversity) of our Network Performance framework.

smart-collab-2

The above graph plots a representative set of results from client projects undertaken over a decade. In our surveys we use a common question of “Who do you rely on to get your work done?”; to identify people to people relationships. We then look at the proportion of reciprocated (two-way) relationships to devise a cohesion score. The y-axis diversity score is determined by the proportion of cross-departmental activity, similar to Gardner’s ‘cross practice’ measures for consulting organisations. The bottom right region (High Cohesion/Low Diversity) is populated by advisory firms i.e. consulting/engineering etc.. Our advice to these firms mimics that of Gardner’s, to grow the diversity of their work teams, without sacrificing the existing levels of cohesion. This is easier to say than do, as you can see from the above data; diversity and cohesion are often traded off against each other, yet this doesn’t have to be the case.

So How can Partner-Led firms be Disrupted by Smart Collaboration?

In her book, Gardner uses role archetypes to characterise the different behavioural dimensions typically found in partner led organisations. Interestingly, we find a strong correspondence to our own ONA characterizations, and more recently our on-line collaboration personas.

Gardner also identifies the increasing use of collaborative software platforms by professional services firms to help ‘break down the silos’, to better facilitate ‘smart collaboration’. While we agree with the principle, the devil can be in the detail. Without a supporting collaborative culture, these platforms can be used to actually reinforce existing silos. We have seen many instances of teams creating private groups on the pretense of ‘competitive sensitivities’; sometimes warranted, but more often not.

The following chart identifies the synergies between Gardner’s archetypes overlaid onto our Personal Networking Performance framework, to provide a link to our network centric perspective. Additionally, we also overlay our online networking personas to extend the view further to the online collaboration environment within which SWOOP’s analytics operate.

collab-3

As we can see, there is a direct mapping between Gardner’s archetypes and our Networking archetypes. The ‘Seasoned Collaborator’ is Gardner’s key role supporting ‘Smart Collaboration’. Likewise, our ‘Ambassador’ role plays the key brokering role in networks, bridging the diversity/cohesion divide. The ‘Solo Specialists’, like our ‘Specialists’ have strong, cohesive, yet localised networks. The ‘Ring Master’, like our ‘Agent’ are playing an oversight role. They have diverse networks, but not the power to necessarily drive positive actions to the same degree as the ‘Seasoned Collaborators/Ambassadors’. Finally, the ‘Contributor’ / ‘Practitioner’ have both limited diversity and cohesion in their networks. They are more regularly younger or new to the organisation staff; or staff that are comfortable to ‘do their bit’, without trying to ‘push the envelope’.

By extending this framework to the online world, we are escalating the analytics from ‘snapshot’ project based analyses, to the real-time online analytics that SWOOP provides.  Online analytics can measure and monitor ‘Smart Collaboration’ in process. We have now benchmarked close to 50 organisations on a series of collaboration indices, which include the behavioural personas indicated. The correspondence is not one-to-one, but nevertheless still informative.

The ‘Engager’ maps closely to the ‘Seasoned Collaborator / Ambassador’ archetype, by identifying those participants whose online networks are both diverse and cohesive. The important ‘Catalyst’ persona instigates interactions. They are key to growing online communities, but are not always those that broker connections. Hence they are located between the ‘Agents/Ringmaster’ and ‘Ambassadors/Seasoned Collaborator’. The ‘Responder’ persona will regularly have a diverse, yet less cohesive online network. There is not a correspondence with the pro-active ‘Agent/Ringmaster’ role, as the role is more re-active, than pro-active. The ‘Broadcaster’ tends to prioritise ‘telling’ over ‘discussing’. In this sense the behaviour is similar to the solo specialist, but ‘Broadcasters’ do not have highly cohesive networks; hence their positioning toward the ‘Contributor’/’Practitioner’. Finally, the ‘Observer’ persona has minimal participation in the online platform and therefore has low or non-existing online diversity and cohesion. The ‘Observer’ is an artefact of online platforms and there is little, if any, correspondence to the ‘Contributor / Practitioner’ archetype.

So can digital ‘Smart Collaboration’ disrupt the current status quo of the big end consulting companies? Well Harvard Professor Clayton Christensen thinks so. He wrote an article on Consulting on the cusp of Disruption back in 2013, citing the clients’ drive for more transparency and also the increased availability of big data analytics and predictive analytics. More recently, this article on Big Four Firms face tsunami of threats from Digital Groups’ also explores the digital disruption potential. And of course Heidi Gardner’s ‘Smart Collaboration’ might be framed as a helpful guide to partner led advisory firms, but could also be read as a ‘if you don’t, someone else will’ warning to the incumbents.

Final Comments

In this article we aimed to draw linkages between Heidi Gardner’s recent work on ‘Smart Collaboration’ and firstly, our own organisational network analysis consulting work. We both used survey techniques to elicit our insights, though Gardner also drew from personal interviews and observations. The extension of these insights toward insights that can be drawn from online interactions is still ‘work in progress’. Unlike surveys, interviews and observation; online analytics has to create its insights through more indirect means. That said, the wealth and volume of data available online swamps the data that can be gained from traditional surveys and interviews. At SWOOP we have now collected and analysed collaboration data from more organisations in less than two years, than from a decade of consulting projects. While consulting projects are often necessarily constrained to a limited scope, the online analysis, drawing its data from the collaborative online platforms, covers the full breadth of these organisations.

We are excited by the potential for online analytics to facilitate ‘Smart Collaboration’ in real-time. Watch this space for updates on our collaboration benchmarking research.

 

 

 

 

 

 

 

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.

 

Data-Driven Collaboration Part 2: Recognizing Personas and Behaviors to Improve Engagement

In Part 1 of this series, “Data-Driven Collaboration Design”—a collaboration between Swoop Analytics and Carpool Agency—we demonstrated how data can be used as a diagnostic tool to inform the goals and strategies that drive your business’ internal communication and collaboration. 

In this post, we will take that thought one step further and show how, after your course is charted to improve internal communication and collaboration, your data continues to play a vital role in shaping your journey.

Monitoring More Than participation

Only in the very initial stages of the launch of a new Enterprise Social Network (ESN) or group do we pay any attention to how much activity we see. Quickly, we move to watching such metrics as average response time; breadth of participation across the organization, teams, roles, or regions; and whether conversations are crossing those boundaries. We focus on measures that show something much closer to business value and motivate organizations to strengthen communities.
For our purposes in this post, it will be useful to pivot our strategy to one that focuses on influential individuals. The community or team—whether it’s a community of practice, a community of shared interest, or a working team—isn’t a “group” or “si te,” but a collection of individuals, with all the messiness, pride, altruism, and politics implied. Data can be used to layer some purpose and direction over the messiness.

Patterns Become Personas

The Swoop Social Network Analytics dashboard uniquely provides analytics that are customized to each person who is part of an organization’s ESN. Using the principle of “when you can see how you work, you are better placed to change how you work”, the intent is for individual collaborators to receive real-time feedback on their online collaboration patterns so they can respond appropriately in real-time.
We analyzed the individual online collaboration patterns across several organizations and identified a number of distinct trends that reflect the majority of personal collaboration behaviors. With that data, we were able to identify five distinct personas: Observers, Engagers, Catalysts, Responders, and Broadcasters.

In addition to classifying patterns into personas, we developed a means of ranking the preferred personas needed to enhance an organization’s overall collaboration performance. At the top we classify the Engager as a role that can grow and sustain a community or team through their balance of posting and responding. This is closely followed by the Catalyst, who can energize a community by provoking responses and engaging with a broad network of colleagues. The Responder ensures that participants gain feedback, which is an important role in sustaining a community. The Broadcaster is mostly seen as a negative persona: They post content, but tend not to engage in the conversations that are central to productive collaboration. Finally, we have the Observer, who are sometimes also called ‘lurkers’. Observers are seen as a negative persona with respect to collaboration. While they may indeed be achieving individual learning from the contribution of others, they are not explicitly collaborating.
Using Personas to Improve Your Online Collaboration Behavior
Individuals who log in to the Swoop platform are provided with a privacy-protected personal view of their online collaboration behaviors. The user is provided with their persona classification for the selected period, together with the social network of relationships that they have formed through their interactions:

You may notice that the balance between what you receive and what you contribute is central to determining persona classification. Balanced contributions amongst collaboration partners have been shown to be a key characteristic of high performing teams, hence the placement of the ‘Engager’ as the preferred persona.

Our benchmarking of some 35 Yammer installations demonstrates that 71% of participants, on average, are Observers. Of the positive personas, the Catalyst is the most common, followed by Responders, Engagers, and Broadcasters. It’s therefore not surprising that an organization’s priority often involves converting Observers into more active participants. Enrolling Observers into more active personas is a task that falls on the more-active Engagers and Catalysts, with Responders playing a role of keeping them there.
At Carpool, during a recent engagement with a client, we encountered a senior leadership team that was comprised of Broadcasters who relied on traditional internal communications. Through our coaching—all the while showing them data on their own behavior and the engagement of their audience—they have since transformed into Catalysts.
One team, for example, had been recruiting beta testers through more traditional email broadcasts. But after just a few posts in a more interactive and visible environment, where we taught them how to invite an active conversation, they have seen not only the value of more immediate feedback, but a larger turnout for their tests. Now, it’s all we can do to provide them with all the data they’re asking for!
Identifying the Key Players for Building Increased Participation

When Swoop looks at an organization overall, we will typically find that a small number of participants are responsible for the lion’s share of the connecting and networking load. In the social media world, these people are called ‘influencers’ and are typically measured by the size of the audience they can attract. In our Persona characterization, we refer to them as Catalysts. Unlike the world of consumer marketing—and this point is critical—attracting eyeballs is only part of the challenge. In the enterprise, we need people to actively collaborate and produce tangible business outcomes. This can only happen by engaging the audience in active relationship-building and cooperative work. This added dimension of relationship-building is needed to identify who the real key players are.
In our work with clients, Carpool teaches this concept by coaching influencers to focus on being “interested” in the work of others rather than on being “interesting” through the content they share, whether that’s an interesting link or pithy comment. With one client, our strategy is to take an organization’s leader, a solid Engager in the public social media space, and “transplant” him into the internal communications environment where he can not only legitimize the forum, but also model the behavior we want to see.
In the chart below, we show a typical ‘Personal Network Performance’ chart, using Enterprise Social Networking data from the most active participants in an enterprise. The two dimensions broadly capture an individual’s personal network size (number of unique connections) against the depth of relationships they have been able to form with them (number of reciprocated two-way connections). They reflect our Engager persona characteristics. Additionally, we have sized the bubbles by a diversity index assessed by their posting behavior across multiple groups.
The true ‘Key Players’ on this chart can be seen in the top right-hand corner. These individuals have not only been able to attract a large audience, but also engaged with that audience and reciprocated two-way interactions. And the greater their diversity of connections (bubble size), the more effective they are likely to be.

Data like this is useful in identifying current and potential key players and organizational leaders, and helps us shift those online collaboration personas from Catalyst to Engager and scale up as far and as broadly as they can go.

Continuous Coaching

Having data and continuous feedback on your online collaboration performance is one thing, but effectively taking this feedback and using it to build both your online and offline collaboration capability requires planning and, of course, other people to collaborate with! Carpool believes in a phased approach, where change the behavior of a local team, then like ripples in a pond, expand the movement to new ways of working through compelling storytelling, using the data that has driven previous waves of change.
To get started now, think about your own teams. Would you be prepared to have your team share their collaboration performance data and persona classifications? Are you complementing each other, or competing? If that’s a little too aggressive, why not form a “Working Out Loud” circle with some volunteers where you can collectively work on personal goals for personal collaboration capability, sharing, and critiquing one another’s networking performance data as you progress?
Think about what it takes to move from one behavior Persona to another. How would you accomplish such a transformation, personally? What about the teams you work in and with? Then come back for the next, and final, part of this co-authored series between Swoop and Carpool, where we will explain the value in gaining insights from ongoing analytics and the cycle of behavior changes, analysis, and pivoting strategies.

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!

 

 

 

 

Yammer Benchmarking Edition 1

 

First in a series of SWOOP Yammer Benchmarking video blogs. Swoop has benchmarked some 36 Yammer installations to date. This first video blog shares some insights gained on the important measures that influence collaboration performance.

 

Video script:

SLIDE 1

Hello there

My Name is Laurence Lock Lee, and I’m the Co-Founder and Chief Scientist at Swoop Analytics.

If you are watching this you probably know what we do, but just in case you don’t, Swoop is a social analytics dashboard that draws its raw data from enterprise social networking tools like Yammer and provides collaboration intelligence to its users, who can be anyone in the organisation.

Our plan is to provide an ongoing series of short video blogs specifically on our Yammer benchmarking insights, as we work with the data we collect. We will aim to use this format to keep you appraised of developments as they happen. We have also recently signed a joint research agreement with the Digital Disruption Research Group at the University of Sydney in Australia. So expect to see the results of this initiative covered in future editions.

The Swoop privacy safeguards means its pure context free analysis, no organisational names, group names, individual names…we don’t collect them.

SLIDE 2

This is the “Relationships First” benchmarking framework we designed for our benchmarking. But we also measure traditional activity measures, which we tend not to favour as a collaboration performance measure…but more about that later. The 14 measures  help us characterise the organisations we benchmark by comparing them against the maximum, minimum and average scores of those in our sample set,  which currently sits at 36 organisations and growing rapidly. They represent organisations large and small from a full cross section of industries and geographies.

SLIDE 3

For those of you who have not been exposed to the Swoop behavioural online personas, you will find a number of articles on our blog.

Because I will be referring to them it’s useful to know the connection patterns inferred by each of them. We don’t include the ‘Observer’ persona here as they are basically non-participants.

Starting with the Responder; Responders make connections through responding to other people’s posts or replies. This can be a simple ’like’, mention or notify..…and it often is, but sometimes it can be a full written reply.

In contrast the catalyst makes connections through people replying to their posts. A good catalyst can make many connections through a good post. Responders have to work a bit harder. They mostly only get one connection per interaction.

The Engager as you can see is able to mix their giving and receiving. This is a bit of an art, but important as engagers are often the real connectors in the community or group.

And what about the broadcaster? Well if your posts don’t attract any response, then we can’t identify any connections for you.

SLIDE 4

This is how we present our benchmarking results to the participants. You can see that we have the 14 dimensions normalized such that the ‘best in class’ results are scored at 100 points and the worst performance at zero. The orange points are the score for the organisation with lines connecting their scores to the average scores.

A few points to note are that we only count ‘active users’ being those that have had at least one activity in Yammer over the period we analyze, which is the most recent 6 months.

Some of the measures have asterisks (*) , which means that the score has been reversed for comparison purposes. For example, a high score for %Observers is actually a bad result, so this is reversed for comparison purposes.

Finally, not all of the measures are independent of each other, so it is possible to see recurring patterns amongst organisations. We can therefore tell a story of their journey to date, through seeing these patterns.  For example, a poor post/reply ratio indicates to us that the network is immature and therefore we would also expect a high % observers score.

SLIDE 5

One way of understanding which of the 14 measures are most important to monitor is to look at the relative variances for each measure across the full sample set. Where we see a large relative variance, we might assume that this is an area which provides most opportunity for improvement. In our sample to date it is the two-way connections measure which leads the way. I’ll go into a bit more detail on this later on. The % Direction measure relies solely on the use of the ‘notification’ type, which we know some organisations have asked users to avoid, as it’s really just like a cc in an email. So perhaps we can ignore this one to some extent. The Post/Reply measure is, we believe, an indicator of maturity. Foe a new network we would expect a higher proportion of posts to replies, as community leaders look to grow activity. However, over time we would expect that the ratio would move more toward favoring replies, as participants become more comfortable with online discussions.

It’s not surprising that this measure shows up as we do have quite a mix of organisations at different maturity stages in our sample to date. The area where we have seen less variance are the behavioural personas, perhaps with the exception of the %Broadcasters. This suggests that at least at the Enterprise level, organisations are behaving similarly.

SLIDE 6

This slide is a little more complex, but it is important if you are to gain an appreciation of some of the important relationship measures that SWOOP reports on.

Following this simple example:

Mr Catalyst here makes a post in Yammer. It attracts a response from Ms Responder and Mr Engager. These responses we call interactions, or activities. By undertaking an interaction, we have also created a connection for all three participants.

Now Mr Engager’s response was a written reply, that mentions Ms Responder, because that’s the sort of guy he is. Mr Catalyst responds in kind , so now you can see that Mr Catalyst and Mr Engager have created a two way connection.

And Ms Responder responds to Mr Engager’s mention with an appreciative like, thereby creating a two-way connection Between Mr Engager and Ms Responder.  Mr Engager is now placed as a broker of the relationship between Mr Catalyst and Ms Responder. Mr Catalyst could create his own two-way connection with Ms Responder, but perhaps she just responded to Mr catalyst with a like…leaving little opportunity for a return response.

So after this little flurry of activity each individual can reflect on connections made…as Mr Engager is doing here.

So in summary, An interaction is any activity on the platform. A connection is created by an interaction and of course strengthened by more interactions with that connection. Finally, we value two-way interactions as this is reciprocity, which we know leads to trust and more productive collaboration

SLIDE 7

Finally I want to show you how the two-way connections scores varies amongst the 36  participants to date. Typically, we would look to build the largest and most cohesive Yammer network as possible, though we accept this might not always be the case. While the data shows that the top 4 cohesive networks were relatively small, there are also 3 organisations that have quite large networks with quite respectable two-way connections scores.

So there is definitely something to be learnt here between the participants.

SLIDE 8

So in summing up, as of September we have 36 participants in our benchmark and growing rapidly now. The two-way connections measure, which is arguably the most important predictor of collaborative performance, was also the most varied amongst the participants.

By looking at the patterns between the measures we can start to see emerging patterns. We hope to explore these patterns in more detail with our research partners in the coming year.

Finally, we show that network size should not be seen as a constraint to building a more cohesive network. We have reported previously that another common measure, network activity levels are also an unreliable measure for predicting collaboration performance.

SLIDE 9

In the next video blog we will be looking at Yammer groups in more detail. We are aware that for many organisations, it’s the Yammer groups that form the heart of the network, so it makes sense to take a deeper dive into looking at them.

Thank you for your attention and look forward to seeing you next time.

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/

 

 

What do Customer Communities have in Common with Employee Communities?

In June we wrote a blog post “Is Bridging the Enterprise-Consumer Social Networking Divide a Bridge too Far”, which went to some length in describing why these two worlds appeared to be operating in different solar systems.  In fact, we pointed out that blindly adopting the media centricity and activity measures from consumer social networking into the Enterprise, could actually cause more harm than good. In this post we want to explore what might be common and potentially useful adoptions from the consumer world to inside the Enterprise. I must say that this post has been influenced by Michael Wu  coming to town and telling us a little about his perspectives on the ‘Science of Social’ . Michael is the chief scientist at Lithium, an organisation that specialises in customer communities. While my interest in customer communities is somewhat less than my interest in Enterprise communities, Michael Wu is well regarded in the world of data science, so I was sure to learn something from him; and I wasn’t disappointed.

The two key insights I took away was that Enterprise Social Networks (ESN) are not social networks as we have come to perceive them; and secondly there is some useful commonality between customer communities and employee communities.

On the first insight, this is how Dr Wu characterised the customer engagement journey:

customer-community

In his commentary he positioned Facebook as a social network of pre-existing relationships, of which only some were based on shared common interests. In his view social networks were good for building awareness and reach, but not in influencing a purchasing action. For this level of influence, he promoted the role of the customer community; where actions could be more effectively influenced by those with a shared context. In essence he was arguing that each played their respective roles at different parts of the engagement funnel. When I look at ESNs like Yammer, there is no explicit connections being built like in Facebook or LinkedIn i.e. connections being sought and accepted. We do have Twitter like ‘Follows’ which can be interpreted as a network; but follower networks are more like one-way subscriptions trails and therefore would only weakly imply a relationship exists. So in essence, ESNs do not have the benefit of an authenticated social graph in the way that Facebook and LinkedIn do.

The point in common is in Figure 2, showing the customer community. The lack of a social network to create ‘reach’ is less of an issue for the Enterprise, as they have corporate directories for that purpose. The Awareness, Interest, Desire, Action phases in the funnel could equally be applied to the multitude of employee communities established in the ESN. Having an ‘Action’ as the end point we feel is entirely appropriate for an Enterprise community. As we have written previously, without actions, tangible value from an ESN is questionable.

dr-wuA key new message that Dr Wu provided was on his recent work with Geoffrey Moore on a four gears model for viral adoption. Wu suggests that those joining a group or community (acquire gear) immediately gain a ‘weak tie’ with all other members on the strength of their shared interest. The ‘engage’ gear helps turn some of these ‘weak ties’ into ‘strong ties’ and eventually trusted relationships; through the vehicle of online discussions and conversations. The ‘enlist’ gear acknowledges that there will be ‘super users’ who will drive the conversation and facilitate many of the connections. In SWOOP these are our Catalyst  and Engager  personas. In the Customer community, these people become the influencers and advocates. The final gear is ‘monetise’, which means making a sale and earning some revenue. Some would suggest that this is totally appropriate for the Enterprise as well. However, it is fair to say that Employee communities can be much more diverse than a customer community and therefore the action isn’t always as easily connectable to a monetary return. That said, this ‘performance gear’ should be able to connect actions taken by the community members, to the Enterprise’s mission and goals, as a minimum.

So there we have it. While Consumer and Enterprise Social Networks do appear to work in different solar systems, there is just enough of an overlap to make the learning worthwhile.

Seeing How You Work, Changes How You Work – What’s Your Online Persona?

Our SWOOP Personas are having a much bigger impact than I expected. For a quick summary of the five personas see our previous posts: Observer, Broadcaster, Responder, Catalyst and Engager. In summary, these personas provide you with insights into your online behaviour on your enterprise social network.

I recently spoke to a community manager about this, and he told me this wonderful story about the impact the personas has had in his organisation. One of his colleagues, a senior manager, had been receiving help from a communications specialist to write updates on the Enterprise Social Network.

However, when the community manager showed the senior manager how little she had used the ‘Like’ feature, she realised two important things. Firstly, she was missing out on the positive recognition a ‘Like’ can provide the recipient, especially in her role as a senior manager. Secondly, she realised that she couldn’t outsource posting, replying and liking to her communication specialist. Interacting on an enterprise social network is a deeply personal thing, and as ex-CEO of Telstra David Thodey told us in a recent interview, he found the most important thing in generating transformational chance was to have authentic conversations with staff. The senior manager now does her on posting, replying and liking, and for me this really shows that:

Seeing how you work, changes how you work.

In CUA, an Australian bank piloting SWOOP to drive adoption of their Enterprise Social Network, they also saw the power of these simple personas in creating a common language so you can think about what you do, and what collaborative profile would be most effective for you. For instance, a communications specialist might operate best as a Broadcaster and a technical expert as a Responder. We generally consider the Engager to be the persona that all people managers would want to be, but a lot of the real value lies in reflecting over what you are, and what you ideally should be.

What is your SWOOP Persona?

By now you might be wondering what your own persona is. Answer the following questions to get started. Please note that your persona is not dependent on volume of your online activities, but the relative spread of what you do (post/reply/like) and what you get back.

When I am using my Enterprise Social Network… Not like me Some-times like me Like me
1. I post links to, or attach, interesting content I think people want to know about
2. I post updates to my team/colleagues about things they need to know
3. I call out colleagues for a job well done
4. I ask people for help with problems/challenges
5. I reply to requests for input/assistance where I can add value
6. I often prompt people to participate (@mention/notify others)
7. I hit ‘Like’ whenever I see a post/reply that I like, or something I want to show support for
8. My posts always get replies and/or likes
9. I am often encouraged by others to add input (am being @mentioned/notified)
10. I read posts, but don’t participate myself

Now, review your answers and determine which persona you think you are. Is that what you’d like to be?

SWOOP Personas

If you are with an organisation that has SWOOP running, then you should jump in and have a look at if your self-perception mirror reality. I’ve always thought of myself as an Engager, and must admit to you that I was pretty guttered when I saw that I was a Broadcaster on our network. My knee-jerk reaction was “Why aren’t you responding or liking the stuff I post!”, but my wonderful co-founder Laurie Lock Lee calmly said “Well – maybe you need to think about what you’re posting.”. I, of all people, should know this. I mean, we actually created the SWOOP persons to provoke this exact conversation, but it still hit me pretty hard as it was suddenly about what I was doing and not about what other people weren’t doing. It got very personal. I started to reflect over the posts, and replies that I had been making, and thinking about ways to make it more engaging. I tried to ask for more feedback by @ mentioning people, and also started to think more about what actually generates value for others rather than just focusing on things I think they need to know.

By seeing how I worked, I managed to change how I work. For the time being I am an Engager, but I know I’ve got to keep an eye on my persona to ensure that my changed behavior is locked in. This is not set and forget just yet!

Not on SWOOP yet? Try our 2 week free trial to check it out and get your SWOOP persona.

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

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.