How Healthy is your Enterprise Social Network?

At the heart of any Enterprise Social Network (ESN) are the groups or communities formed within them. Understanding the health and productivity of these groups should therefore be front of mind. For ESNs we can look again to the more mature experiences with consumer and external customer communities for guidance. We have written previously about the need to take care when translating consumer network metrics to the Enterprise. But in the case of community health, we believe the mapping from external community to internal community can be fairly close.

What can we learn from consumer and customer networks?

Arguably the gold standard for community health measures was published several years ago by Lithium, a company that specialises in customer facing communities. Lithium used aggregate data from a decade’s worth of community activity (15 billion actions and 6 million users) to identify key measures of a community’s health:

  • Growth = Members (registrations)
  • Useful  = Content (post and page views)
  • Popular = Traffic (visits)
  • Responsiveness (speed of responsiveness of community members to each other)
  • Interactivity = Topic Interaction (depth of discussion threads taking into account number of contributors)
  • Liveliness (tracking a critical threshold of posting activity in any given area)

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At the time of publishing, Lithium was hoping to facilitate the creation of an industry standard for measuring community health.

Other contributors to the measurement of online community health include online community consultancy Feverbee with their preferred measures as:

  • New visitors – a form of growth measure
  • New visitors to new registered members– conversion rate measure
  • % members which make a contribution– active participants
  • Members active within the past 30 days– time based activity
  • Contributions per active member per month– diversity and intensity measure
  • Visits per active member per month – traffic measure
  • Content popularity-useful content

Marketing firm Digital Marketer health measure recommendations include:

  • Measuring the total number of active members, rather than including passive members.
  • Number of members who made their first contribution as a proxy for growth.
  • A sense of community (using traditional survey methods).
  • Retention of active members i.e. minimal loss of active members (churn rate).
  • Diversity of membership, especially with respect to innovation communities.
  • Maturity, with reference to the Community Roundtable Maturity Model.

Using SWOOP for Assessing Enterprise Community/Group Health

SWOOP is focused on the Enterprise market and is therefore very interested in what we can usefully draw from the experiences of online consumer and customer networks. The following table summarises the experiences identified above and how SWOOP currently addresses these measures, or not:

Customer Community Health Measures SWOOP Enterprise Health Measures
Growth in Membership Measures active membership and provides a trend chart to monitor both growth and decline.
Useful Content Provides a most engaging posts widget to assess the usefulness of content posted.  We are currently developing a sentiment assessment for content.
Popularity/Traffic SWOOP does not currently measure views or reads. Our focus is more on connections that may result from content viewing.
Responsiveness Has a response rate widget that identifies overall response rate and the type of response e.g. like, reply and the time period within which responses are made.
Interactivity Has several rich measures for interactivity, including network connectivity and a network map, give-receive balance and two way connections. The Topic tab also identifies interactivity around tagged topics.
Liveliness The activity per user widget provides the closest to a liveliness (or lack of liveliness) indicator.
Activity over time The Active Users and Activity per User widgets report on this measure.
Contributions per member The Activity per User widget provides this. The New Community Health Index provides a 12 month history as well as alarms when certain thresholds are breached.
Sense of community Requires a survey, which is outside the scope of SWOOP.
Retention Not currently measured directly. The active members trend chart gives a sense of retention, but does not specifically measure individual retention rates.
Diversity Not provided on the SWOOP dashboard, but is now included in the SWOOP benchmarking service. Diversity can be measured across several dimensions, depending on the profile data provided to SWOOP e.g. formal lines of business, geography, gender etc. In the absence of profile data, diversity is measured by the diversity of individual membership of groups.
Maturity The Community Roundtable maturity assessment is a generic one for both online and offline communities. Our preference is to use a maturity framework that is more aligned to ESN, which we have reported on earlier. How the SWOOP measures can be related to this maturity curve is shown below.

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Thresholds for What’s Good, Not so good and Bad

We know that health measures are important, but they are of little use without providing some sense of what a good, bad or neutral score is. In the human health scenario, it is easy to find out what these thresholds are for basic health measures like BMI and Blood Pressure. This is because the medical research community has been able to access masses of data to correlate with actual health outcomes, to determine these thresholds with some degree of confidence. Online communities have yet to reach such a level of maturity, but the same ‘big data’ approach for determining health thresholds still applies.

As noted earlier, Lithium has gone furthest in achieving this, from the large data sets that they have available to them on their customer platform. At SWOOP we are also collecting similar data for ESNs but as yet, not to the level that Lithium has been able to achieve. Nevertheless, we believe we have achieved a starting point now with our new Community Health Index Widget. While we are only using a single ‘activity per active user’ measure, we have been able to establish some initial thresholds by analysing hundreds of groups across several Yammer installations.

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Our intent is to provide community/group leaders with an early warning system for when their groups may require some added attention. The effects of this attention can then be monitored in the widget itself, or more comprehensively through the suite of SWOOP measures identified in the table above.

Communities are the core value drivers of any ESN. Healthy enterprise communities lead to healthy businesses, so it’s worth taking the trouble to actively monitor it.

 

 

 

 

 

 

 

 

 

 

Bridging the Knowledge Sharing/Problem Solving Divide

problem-solvingWorking across organisational boundaries

One of the most frequently cited reasons we hear for implementing an enterprise social network platform is to “enable our organisation to better communicate and collaborate across organisational boundaries”.

The real objective is to let information and knowledge flow more freely to solve challenge business problems. This is the point where the focus changes from generic SHARING to business focused (problem-) SOLVING:

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We’re previously introduced this maturity framework that incorporates the 4 stages of Simon Terry’s model, and in a recent discussion with Simon he shared with us with some constructive insights that he has drawn from the application of his maturity model.

He indicated to us that:

“Up to SHARING, people are just engaged in social exchange. It is chat. That can be entirely internal to the ESN and not connected to the business. Beyond that point they are delivering benefits from collaborative work. Moving over that transition and understanding the behaviours beyond that point is essential.

Simon then proceeded to describe the key things to consider in the ‘SOLVING’ stage as:

“Value chains and projects and their relationships to the silos captured in your Cross-team collaboration widget”.

In this post we will therefore review the SWOOP ‘Cross-Team Collaboration’ widget and give you insights about how this can help you in your enterprise social adoption efforts. Together with the recently reviewed Influential People and Response Rate widgets they collectively support the ‘SOLVE’ Stage.

solve

The Cross-Team collaboration widget identifies the levels of interaction between selected organisational dimensions. The most common use is to identify interactions between the formal lines of business.

Two representations are offered:

  • The matrix view shades the intersecting squares by the relative interaction levels. The diagonal represents intra-unit interactions.
  • The map view (see below) more succinctly illustrates the degree to which different units are interacting.

collab

If you have created a cross-enterprise group, or community of practice, it will tell you the degree to which all divisions have been engaged. If you have a corporate initiative that has been launched with a topic hash tag, it will also tell you the degree of cross-divisional engagement.

In a typical hierarchy, we would anticipate that most interactions would occur inside the formal structures, or between divisions along a defined value chain e.g. marketing interactions with sales. Cross organisational groups or teams are usually formed to facilitate interactions across the formal lines of business, for example a Supply/Value chain.

The Cross-Team Collaboration widget provides a view into the degree to which these cross organisational teams are effective. While interactions between formal departments is the most common, geographic location is also a popular dimension to explore interaction levels.

What is the Business Imperative?

It is the apparent inflexibility and poor responsiveness of the formal hierarchy that motivates many organisations to adopt enterprise social networks. Formal hierarchies are designed for efficient execution of pre-determined processes. However, CEOs are now looking for more than this. David Thodey, the former CEO of Australia’s largest Telco, summed up the sentiment by indicating that he wanted to short circuit the entrenched communication channels. He wanted his management team to be able to have authentic conversations with staff at all levels. Similarly, we recall a statement made by a former CEOs at BHP Billiton, an industrial resources conglomerate that was very process driven:

“Silos are not bad, this is how we get work done. We just need to dig some holes in the sides!” (please excuse the mining analogy)

Another of our favourite thought leaders is Heidi Gardner, a former McKinsey consultant and Harvard Business School professor now lecturing at Harvard Law School. She has spent over a decade conducting in-depth studies of numerous global professional service firms. Her research with clients and the empirical results of her studies demonstrate clearly and convincingly that collaboration pays, for both professionals and their firms. In her book Smart Collaboration, she shows that firms earn higher margins, inspire greater client loyalty, attract and retain the best talent, and gain a competitive edge when specialists collaborate across functional boundaries. The Cross-Team Collaboration widget enables you to measure if this is actually happening, and is one of the most important widgets connecting business outcomes with the adoption of your enterprise social network.

Specifically, in terms of problem solving, there will be problems that traverse the business unit boundaries. For example, a customer support problem may appear to be an operations problem, but perhaps the genesis of the problem is with Sales or Marketing, by how a product or service was represented to the customer in the first place. Also, supply chain problems are by definition, inter-dependent and cannot be solved by a single business unit. The Cross Team Collaboration widget can signal whether these cross-business unit problems are being addressed as a shared problem. If a cross-business unit problem has been hash tagged, it is also possible to use the SWOOP Topic tab to identify where the participants in the tagged problem solving activity are coming from. Are they appropriately cross-business unit?

Summary

Bridging the ‘sharing’ to ‘solving’ divide requires a stronger focus on what the business is trying to achieve. What are the key problems or challenges that must be met? What are the specific and identified collaborative interactions between the different organisational units, that will be required to solve them? The SWOOP Cross-unit Collaboration widget, along with the Response Rate and Influential People widgets have been designed to help you bridge the ‘Sharing’ to ‘Solving’ divide.

This post continues our series on key SWOOP indicators.

 

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: 

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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. 

 

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.

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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.

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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.

 

 

 

 

 

 

 

Data-Driven Collaboration Part 3: Sustaining Performance through Continuous Value Delivery

In Part 1 of our series on Data-Driven Collaboration, “How Rich Data Can Improve Your Communication,” we identified how to plan for collaboration by ensuring that goals were established and aligned with our organizational strategy. We then moved on to Part 2, “Recognizing Personas and Behaviors to Improve Engagement,” to explain how you can build engagement by managing behaviors. In this, the final post in our series, co-authored by Swoop Analytics and Carpool Agency, we will identify how to sustain the momentum to ensure that value is continuously delivered as a matter of course.

Previously, we identified the importance of migrating from simple activity measures to those that signify when collaborative relationships are being formed. It is through these relationships that tangible outcomes are achieved. Therefore, it is not surprising that analytics—as applied to sustained relationship-building—plays an important role in continuous value delivery from collaboration.

For example, a CEO from one of Carpool’s clients had been using Yammer to receive questions for a regular Q&A session, but they’d grown concerned that the CEO’s infrequent posts in the group were creating an echo chamber among the same small group of contributors. Careful analysis showed that this was more perception than reality, and the group showed a great deal of variety in cross-organization conversation. As this was precisely the executive’s goal in forming the group, the team doubled down on their investment in this executive-to-company relationship.

Monitoring Maturation Using Analytics

At SWOOP, we have been benchmarking Yammer Installations from start-up to ‘normal operations’ for some time. With Yammer, the typical pattern of start-up is a bottom-up use of ‘Free’ Yammer, which for some, lasts for many years. Without exception, however, sustained usage only occurred after a formal launch and the tacit approval of senior management. We observed different patterns of start-ups from the ‘big-bang’ public launch, through to more organic, yet managed approaches. Whatever strategy is used, organizations always reach a stage of steady-state operations or, at worst, a slow decline.

CLASSIC YAMMER

For an Enterprise Social Network (ESN) like Yammer, we have found that the average engagement rate of the 35+ organizations in our benchmark set is around 29% (i.e., non-observers) with the best at around 75%. It is evident from our benchmarking that for larger organizations—for example, more than say 5,000 participants—it can be hard to achieve engagement levels above 30%. However, this doesn’t mean that staff aren’t collaborating.

We are seeing a proliferation of offerings that make up the digital office. For a small organization, Yammer may be their main collaboration tool, where team level activities take place. For larger organizations, however, Yammer may be seen as a place to explore opportunities and build capabilities, rather than as an execution space. Increasingly, tools like Slack, HipChat, and now Microsoft Teams are being used to fill this space for some teams that depend on real-time conversations as their primary mode of communication.

A Collaboration Performance Framework

As organizations mature with their use of collaboration tools, it is critical not to be caught in the ‘collaboration for collaboration sake’ cycle. As we indicated in “How Rich Data Can Improve Your Communication,” collaboration must happen with a purpose and goals in mind. The path to achieving strategic goals is rarely linear. More regularly, we need to adopt a framework of continuous improvement toward our stated goals. For many organizations, this will take the form of a ‘Plan, Do, Check, Act’ cycle of continuous improvement. However, in this age of digital disruptions and transformations, we need a framework that can also accommodate transformational, as well as incremental innovation.

At SWOOP, we have developed a collaboration performance framework drawn from Network Science.

DIVERSITY GRAPHIC

The framework balances two important dimensions for collaborative performance: diversity and cohesion. It identifies a continuous cycle of value delivery, whether it be radical or incremental. Let’s consider an innovation example, with an organizational goal of growing revenue by 200%:

Individuals may have their own ideas for how this radical target could be achieved. By ‘Exploring’ these ideas with others, we can start to get a sense of how feasible our ideas might be, but also have the opportunity to combine ideas to improve their prospects. The important ‘Engaging’ phase would see the ideas brokered between the originators and stakeholders. These stakeholders may be the key beneficiaries and/or providers of the resources needed to exploit a highly prospective idea. Finally, the ‘Exploiting’ phase requires the focus and strong cooperation of a smaller group of participants operating as a team to deliver on the idea.

The performance framework can be deployed at all levels, from enterprise-wide to individual business units, informal groups, teams, and right down to the individual. In a typical Carpool engagement, we work with smaller teams to demonstrate this cycle and then use the success stories to replicate the pattern more broadly. A current client started with a smaller community of interest of 400 people, and is now expanding the pattern to their global, 4,000-member division.

Deploying Analytics and the Performance Framework

Like any performance framework, it can’t operate without data. While the traditional outcome measures need to be present, the important predictors of collaborative success are relationship-centered measures. For example, your personal network can be assessed on its diversity by profiling the members of your network. Your personal network’s cohesiveness can be measured, firstly, by how many of your connections are connected to each other; and secondly, by how many of these connections are two-way (reciprocated). We can then add layers provided from HR systems such as gender, geography, organizational roles, age, ethnicity, etc. to provide a complete picture of diversity beyond typical dimensions.

In the example below, we show the collaboration performance of participants in a large Yammer network over a 12-month period. You can see how challenging it might be to become an ‘Engager’, maximizing both diversity and cohesion.

BUBBLE GRAPH

We profiled their personal networks for their diversity, cohesion, and size, and plotted them on the performance framework. Interestingly the data exposed that the nature of this Yammer network is a place for exploring and, for some, engaging. There is a gap, however, in the Exploiting region. This is not to say that these individuals were poor at putting projects into motion. More likely, at least in this organization, the ESN is not the usual place to collaborate as a team. If there is no easy transition from the ESN to a team environment, then we have a problem that many ESNs experience: lots of activity but a perception of few tangible results directly from the ESN. Carpool’s approach puts this data together with data from other services and sources to create a holistic picture of the results and impact of the organization’s collaboration evolution.

Continuous Monitoring

For many organizations, continuous monitoring simply means monitoring activity on digital platforms. As we indicated in “Recognizing Personas and Behaviors to Improve Engagement,” activity monitoring can be a poor predictor of performance. At SWOOP, we look at activity that establishes or strengthens a relationship. In the screenshot below, you can see measures such as the number of two-way reciprocated relationships; the degree to which relationships are forming between the formal organizational departments; and who is influential, based on the size of their network, not how frequently they contributed. We identify key player risk by looking at how polarized a network may be among a selected few leaders. Even the Activity/User measure inside groups predicts how cohesive that group may be. By providing this data in real-time, we have the best opportunity for both leaders and individuals to adapt their patterns of collaboration as they see fit.

COLLABORATION CHART

At Carpool, our engagements use a set of such dashboards to regularly check in on all the various channels and stakeholders, and make recommendations on an ongoing basis that accounts for the holistic communication picture.

Final Thoughts

In this series, we have taken you on a journey from planning for, launching, and productively operating a digital office. At the very beginning we emphasized the need to collaborate for a purpose. We then emphasized the need to ‘engage’ through relationships and adopting appropriate behavioral personas. Finally, we have explained the importance of adopting a collaboration performance framework that can facilitate continuous delivery of value.

In order to do all of this effectively, we not only need analytics, but interventions triggered by such analytics to improve the way we work. Analytics on their own don’t create change. But in the hands of skilled facilitators, analytics and rich data provide a platform for productive change. Collaboration is not simply about how to get better results for your organization, but also to get better results for yourself, by helping you to be a better collaborator.

Want More?

We hope these insights into data-driven collaboration will give you new ideas to innovate your own approach to internal communication. If you have any questions, or would like to learn how to establish, nurture, and grow deep internal communities, Carpool and SWOOP has a team who are ready to help you grow your business and drive collaboration today.

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.

Data-Driven Collaboration Part 1: How Rich Data Can Improve Your Communication

Originally published on Carpool.

This is the first of a series, coauthored by Laurence Lock Lee of Swoop Analytics and Chris Slemp of Carpool Agency, in which we will explain how you can use rich, people-focused data to enhance communication, increase collaboration, and develop a more efficient and productive workforce.

It’s safe to say that every enterprise hungers for new and better ways of working. It’s even safer to say that the path to those new and better ways is often a struggle.

Many who struggle do so because they are starting from a weak foundation. Some are simply following trends. Others believe they should adopt a new tool or capability simply because it was bundled with another service. Then there are those organizations that focus primarily on “reining in” non-compliant behaviors or tools.

But there’s a way to be innovative and compliant that also improves your adoption: focus instead on the business value of working in new ways—be data-driven. When you incorporate information about your usage patterns to set your goals, you are better positioned to track the value of your efforts and drive the behavior changes that will help you achieve your business objectives.

While it’s assumed that doing market research is critical when marketing to customers, investments in internal audience research have gained less traction, yet they yield the same kinds of return. Data-driven internal communication planning starts at the very beginning of your project.

Here we will demonstrate—using real-world examples—how Carpool and Swoop use data to create better communications environments, nurture those environments, and make iterative improvements to ensure enterprises are always working to their full potential.

Use Data to Identify Your Actual Pain Points

One team Carpool worked with was focused on partnering with customers and consultants to create innovations. They thought they needed a more effective intranet site that would sell their value to internal partners. However, a round of interviews with key stakeholders and end-of-line consumers revealed that a better site wasn’t going to address the core challenge: There were too many places to go for information and each source seemed to tell a slightly different story. We worked with the client to consolidate communications channels and implemented a more manageable content strategy that focused on informal discussion and formal announcements from trusted sources.

In the end, we were able to identify the real pain point for the client and help them address it accordingly because of the research we obtained.

Use Data to Identify New Opportunities

Data can drive even the earliest strategy conversations. In Carpool’s first meeting with a global retail operation, they explained that they wanted to create a new Yammer network as they were trying to curb activity in another, unapproved network. Not only did we agree, but we brought data to that conversation that illustrated the exact size and shape of their compliance situation and the nature of the collaboration that was already happening. This set the tone for a project that is now laser-focused on demonstrating business value and not just bringing their network into compliance.

Use Data to Identify and Enhance Your Strengths

In-depth interviews can be added to the objective data coming from your service usage. Interviews reveal the most important and effective channels, and the responses can be mapped visually to highlight where a communication ecosystem has broadcasters without observers, or groups of catalysts who are sharing knowledge without building any broader consensus or inclusion.

Below, you see one of Carpool’s chord chart diagrams we use to map the interview data we gather. We can filter the information to focus on specific channels and tools, which we then break down further to pinpoint where we have weaknesses, strengths, gaps, and opportunities in our information flow.

CHORD CHART

Turning Data Into Action

These kinds of diagnostic exercises can reveal baselines and specific strategies that can be employed with leaders of the project or the organization.

One of the first activities organizations undertake when implementing an Enterprise Social Networking (ESN) platform is to encourage staff to form collaborative groups and then move their collaboration online. This is the first real signal of ‘shop floor empowerment’, where staff are free to form groups and collaborate as they see fit, without the oversight of their line management. As these groups form, the inevitable ‘long tail’ effect kicks in, where the vast majority of these groups fall into disuse, in contrast to a much smaller number that are wildly successful, and achieving all of the expectations for the ESN. So how can organizations increase their Win/Loss ratio? At Swoop Analytics we have started to look at some of the ‘start-up’ patterns of the Yammer installations of our benchmarking partners. These patterns can emerge after as little as 6 months of operations.

Below, we show a typical first 6 months’ network performance chart, which measures group performance on the dimensions of Diversity (Group Size), Cohesion (Mean 2-Way Relationships formed), and Activity (postings, replies, likes etc.). We then overlay the chart with ‘goal state’ regions reflecting the common group types typically found in ESN implementations. The regions reflect the anticipated networking patterns for a well-performing group of the given type. If a group’s stated purpose positions them in the goal-state region, then we would suggest that they are well positioned to deliver tangible business benefits, aligned with their stated purpose. If they are outside of the goal state, then the framework provides them with implicit guidance as to what has to happen to move them there.

BUBBLE GRAPH

At launch, all groups start in the bottom left-hand corner. As you can see, a selected few have ‘exploded out of the blocks’, while the majority are still struggling to make an impact. The 6-month benchmark provides an early opportunity for group leaders to assess their group against their peer groups, learn from each other, and then begin to accelerate their own performances.

Painting the Big Picture

The convergence of multiple data sources paints a holistic picture of communication and collaboration that extends beyond team boundaries. This new picture extends across platforms and prescribes the design for an ecosystem that meets user and business needs, aligns with industry trends, and is informed by actual usage patterns.

ECOSYSTEM DESIGN

The discussion about the ROI of adopting new ways of working, such as ESNs, hasn’t disappeared. While we believe it’s a waste of resources to try measuring a return from new technologies that have already been proven, it’s clear that developing business metrics and holding these projects accountable to them is just as critical as any effort to increase productivity.

The nature of these metrics also needs to shift from a focus on “counts and amounts” to measures of a higher order that tie more closely to business value. For example, knowing that posting activity has risen by 25% in a year may make you feel a little better about your investment in a collaboration platform. Knowing that there is a higher ratio of people engaging vs. those who are simply consuming is much better. Showing a strong correlation in departments that have higher percentages of engaged users with lower attrition rates … that’s gold.

So now is the time to look at your own organization and wonder: “Do I track how my people are connecting? Do I know how to help them become more engaged and productive? When was the last time I measured the impact of my internal communication ecosystem?”

Then take a moment to imagine the possibilities of what you could do with all of that information.

Stay tuned in the coming weeks for Part 2 and Part 3 when we address the topics of driving engagement by identifying types of enterprise social behavior in individuals, and the results we’ve seen from being data-driven in how we shape internal communications and collaboration.

Are we Getting Closer to True Knowledge Sharing Systems?

knowledge-systems

(image credit: https://mariaalbatok.wordpress.com/2015/02/10/religious-knowledge-systems/)

First generation knowledge management (KM) systems were essentially re-labelled content stores. Labelling such content as ‘knowledge’ did much to discredit the whole Knowledge Management movement of the 1990s. During this time, I commonly referred to knowledge management systems as needing to comprise both “collections and connections”, but we had forgotten about the “connections”.  This shortcoming was addressed with the advent of Enterprise Social Networking (ESN) systems like Yammer, Jive, IBM Connect and now Workplace from Facebook. So now we do have both collections and connections. But do we now have true knowledge sharing?

Who do we Rely on for Knowledge Based Support?

A common occupation for KM professionals is to try and delineate a boundary between information, that can be effectively managed in an information store, and knowledge, which is implicitly and tacitly held by individuals. Tacit knowledge, arguably, can only be shared through direct human interaction. In our Social Network Analysis (SNA) consulting work we regularly surveyed staff on who they relied on to get their work done. We stumbled on the idea of asking them to qualify their selections by choosing only one of:

  • They review and approve my work (infers a line management connection)
  • They provide information that I need (infers an information brokering connection)
  • They provide advice to help me solve difficult problems (infers a knowledge based connection)

The forced choice was key. It proved to be a great way of delineating the information brokers from the true knowledge providers and the pure line managers. When we created our ‘top 10 lists’ for each role, there was regularly very little overlap. For organisations, the critical value in these nominations is that the knowledge providers are the hardest people to replace, and therefore it is critical to know who they are. And who they are, is not always apparent to line management!

So how do staff distribute their connections needs amongst line managers, information brokers and knowledge providers? We collated the results of several organisational surveys, comprising over 35,000 nominations, using this identical question, and came up with the following:

work-done

With 50% of the nominations, the results reinforce the perception that knowledge holders are critical to any organisation.

What do Knowledge Providers Look Like?

So what is special about these peer identified knowledge providers? Are they the ‘wise owls’ of the organisation, with long experiences spanning many different areas? Are they technical specialists with deep knowledge about fairly narrow areas? We took one organisation’s results and assessed the leaders of each of the categories of Approve/review, Information and Knowledge/Advice looking for their breadth or diversity of influence. We measured this by calculating the % of connections, nominating them as an important resource, that came from outside their home business unit. Here are the results:

external-links

As we might anticipate, the inferred line management had the broadest diversity of influence. The lowest % being for the knowledge providers, suggests that it’s not the broadly experienced wise old owls, but those specialising in relatively narrow areas, where people are looking for knowledge/advice from.

Implications for Knowledge Sharing Systems

We have previously written about our Network Performance Framework, where performance is judged based on how individuals, groups, or even full organisations balance diversity and cohesion in their internal networks:

personal-networking

The above framework identifies ‘Specialists’ as those who have limited diversity but a strong following i.e. many nominations as a key resource. These appear to be the people identifying as critical knowledge providers.

The question now is to whether online systems are identifying and supporting specialists to share their knowledge? At SWOOP we have aimed to explore this question initially by using a modification of this performance framework on interactions data drawn from Microsoft Yammer installations:

performance

We measured each individual’s diversity of connections (y-axis) from their activities across multiple Yammer groups. The x-axis identifies the number of reciprocated connections an individual has i.e. stronger ties, together with the size of their personal network, identified by the size of the bubble representing them. We can see here that we have been able to identify those selected few ‘Specialists’ in the lower diversity/stronger cohesion quadrant, from their Yammer activities. These specialists all have relatively large networks of influence.

What we might infer from the above analysis is that an ESN like Yammer can identify those most prospective knowledge providers that staff are seeking out for knowledge transfer. But the bigger question is whether actual knowledge transfer can happen solely through an ESN like Yammer?

Is Having Systems that Provide Connections and Collections Enough to Ensure Effective Knowledge Sharing?

The knowledge management and social networking research is rich with studies addressing the question of how social network structure impacts on effective knowledge sharing. While an exhaustive literature review is beyond the scope of this article, for those inclined, this article on Network Structure and Knowledge Transfer: The Effects of Cohesion and Range is representative. Essentially this research suggests that ‘codified’ knowledge is best transferred through weak ties, but tacit knowledge sharing requires strong tie relationships. Codified knowledge commonly relates to stored artefacts like best practice procedural documents, lessons learned libraries, cases studies and perhaps even archived online Q&A forums. Tacit knowledge by definition cannot be codified, and therefore can only be shared through direct personal interactions.

I would contend that relationships formed solely through ESN interactions, or in fact any electronic systems like chat, email, etc. would be substantially weaker than those generated through regular face to face interactions. Complex tacit knowledge would need frequent and regular human interactions. It is unlikely that the strength of tie required, to effectively share complex knowledge, can be achieved solely through commonly available digital systems. What the ESN’s can do effectively is to help identify who you should be targeting as a knowledge sharing partner. Of course this situation is changing rapidly, as more immersive collaboration experiences are developed. But right now for codified knowledge, yes; for tacit knowledge, not yet

 

Who Should Decide How You Should Collaborate or Not?

In a recent post pre- Microsoft’s recent Ignite 2016 conference, we intimated that we hoped that in the push to build the ultimate office tool that the core features of the component parts were not sacrificed in the name of standardisation. I can happily say now that post MS Ignite it appears that, at least for the product we are most interested in, Yammer, has re-surfaced as a more integral part of Office 365, without sacrificing its core value proposition. As a Yammer core user, it appears now that as circumstances arise, where our collaboration partners might need to manage content, collaborate in real time, schedule and manage an event, we will be able to seamlessly access these core functions of other components like Sharepoint, Skype, Outlook etc.. Now while of course we know events like MS Ignite are mostly to announce intentions, more so than working products, it is comforting to see a positive roadmap like this.

In effect Office 365 is now offering a whole multiplex of collaboration vehicles. There will be individuals looking for a simple ‘usage matrix’ of what to use when. Yet collaboration can mean different things to different people. Is working in your routine processing team a collaboration? Is reading someone else’s content a collaboration? Is sending an email a collaboration?

How do we define Collaboration?

A couple of years ago Deloitte Australia’s economics unit produced a significant report on the economic value of collaboration to the Australian economy. As part of the process Deloitte surveyed thousands of workers looking for how they spent their time at work, specifically related to collaboration activities:

collab-blog-tif

While the numbers will vary between individuals, we can look at the categories as typical work tasks and then look to map them to O365 components. For me the nearly 10% ‘Collaboration” is a natural home for Yammer, and probably “Socialising”. Routine tasks fit nicely into Sharepoint and Team sites. Outlook for Routine Communication. The individual work maps very nicely to core office 365 tools like Word, Excel and Powerpoint. So what we can see is that O365 can be nicely mapped to the O365 components. But does just knowing this help us use it productively? Who decides how we should interact and how?

Who should control collaboration?

The Deloitte work characterisation separates “collaboration” out from “interactions” as activities that staff engage in to be able to improve the way they work; improvising and innovating. While it may constitute only 10% of their work time on average, the impact is in improving the productivity of say routine tasks, routine communication and even individual work. So is it the role of managers to dictate modes of collaboration for their staff? Maybe its community managers of workplace improvement specialists? As the workplace moves to become more distributed and networked it is quickly becoming beyond that capability of specialist roles to orchestrate collaborative processes, without bloating the middle manager layers.

So what are we left with? I believe that it all comes back to the individual to “negotiate” how they interact and collaborate and how. As it turns out, the one who knows best as to how to improve your productivity is yourself. This comprehensive study on time-wasting by Paychex found that the most effective way to reduce time wasting is more flexible time scheduling or time off. Carpool recently ran an experiment in working from anywhere. Carpool CEO Jarom Reid speaks about the productivity improvements available when you have the flexibility of not being tied to a physical office. In the industrial age we became used to executives jobs being solely about linking and communication. However Reid, being the leader of a digitally enabled organisation, values having personal time where he can feel more productive than in the office. Andrew Pope writes about the dangers of over-collaboration. We all want our collaborations and interactions with colleagues to be productive. We feel we are over-collaborating when we feel we have wasted time in non-essential meetings. Pope suggests that individuals should take control of their collaboration activities to match their natural styles and tendencies, rather than trying to adhere to a particular organisational norm.

How will Office 365 Help?

So how would the new world of Office 365 support individual preference led collaboration? For those of us that have been used to living in Yammer or Sharepoint or Outlook it does put the onus on the individual to become competent in all the key toolsets, if we are to accommodate the potential preferences of our collaboration partners and avoid “tool solos”.

The nice thing about the Office 365 roadmap is that the tool silo walls have become more elastic. We can form a group from Yammer to explore an idea and then form a team to exploit the idea still inside Yammer, without having to move to a Teamsite. Alternatively, we can reach out from a Teamsite into a broader community group inside Yammer, if and when the need arises.  The benefits in making this investment in learning is the flexibility it can afford to enable you, as an individual, to be in charge of your own productivity and performance.