Understanding alumni communities through social network analysis
As program coordinators, we often believe - like many who are professionally managing alumni programs and communities - to be quite well informed about what is going on in our respective networks: We consider ourselves capable of assessing how vital some or all of our alumni communities are, whether the ties established are based on professional interests or personal friendships, and which individuals among the many alumni have become particularly well-connected. However, sometimes these assumptions can be faulty and based on our biased perceptions rather than valid data.
As we at the iac Berlin started building the alumni network for the Robert Bosch Stiftung, we had to take a step away from what we thought we knew to critically ask ourselves in how far an ‘outsider perspective’ could be accurate and what we really knew about the various sub-networks we were aiming to connect. We wanted to stop guessing and base our strategic alumni work on more solid foundations.
Our questions were manifold and detailed: How strongly connected are our alumni years after their program participation? Who are the influential individuals within the networks and how is this influence distributed (e.g. Is there one ‘star alumna’ the rest of the network is circling around)? How "central" is the Robert Bosch Stiftung in those networks? What differences exist between various alumni networks? And most critically: What is the particular "glue" that holds our different alumni networks together and motivates them to stay engaged years after their program has ended?
To find the answers to these questions, we decided to turn to social network analysis which is increasingly used in the business world to analyze teams, foster collaboration, build networks and to support organizational change.
How Social Network Analysis Works
Social network analysis is a data-driven, sociological approach that allows an analysis of the connections (so called "ties") between various actors in the network (so called "nodes"). To get to the right data, the process usually relies on an online survey among a pre-defined group that asks questions about different forms of connections, for example how many connections are based on contacts, how information travels or how high or low levels of friendships are within these groups. Those completing the survey are simply asked to name those individuals they are connected with and so - based on the results from everyone participating in the survey - it is possible to draw network maps using specific social network analysis software.
In the case of smaller alumni networks of up to 60 individuals, it is possible to aim for a complete picture of the network. In this case, every single network member is asked to indicate his/her connection to all other network members. If the network to be analyzed is larger, sub-categories are necessary (e.g. participants are given a list with names of their program year and asked to name ten additional people from the network they are regularly in touch with). This way, it is possible to get a fairly accurate picture of the network as a whole. However, a very high response rate is usually necessary, and it is vital to ensure that everyone has given their consent to be part of the analysis. This requires extensive communication prior to the survey which in itself is already a great way to activate the network.
What the Data Can Tell You
Once we have analyzed the network in this way, the true beauty of network analysis comes into play: You receive an actual picture (a so-called “network map”) of your network. From the outset, it is easy to tell whether a network has many connections or not, if smaller, more separated clusters exist or if the network is a closely connected web.
An examplary network program. The last two program cycles are depicted in light blue and pink.
Their network is relatively dense, for instance, but is mainly limited to their own program cycle.
Beyond the visual overview that can already highlight some general patterns, network analysis allows us to dive much deeper. A number of key indicators or "metrics", such as density or in/out-degree explained below, further help to better understand the network at hand:
- Density: How dense is the network/how closely connected are the network members? Density is calculated by dividing the number of existing ties by the number of ties that are theoretically possible. In case of a 100% density, every individual within the network is connected to every other individual.
- In-Degree: Who are the people that were most often mentioned by others? We consider these individuals to be ‘Influencers’ simply because they are perceived by others as important. They can be a great resource if one seeks to influence the network because these influencers have great reach within the network.
- Out-Degree: Who are the people that indicated most connections to others? We consider these individuals to be ‘networkers’ because the more connections one actively seeks, the more reach - similar to influencers - this person has in the network.
Influcencers: the larger the nodes, the more often the person was mentioned by other alumni from the program.
Working with these metrics brings more accuracy to the analysis and allows for a more strategic management of alumni communities. One the one hand, network density can for instance serve as a benchmark to determine if activities to strengthen the network bear fruit. If one analyzes the same network after some months or years and finds greater density, this is a strong indicator of successful alumni activation. On the other hand, in/out-degree allows to zoom in on individuals who hold special roles in the network based on the connections they have. Both influencers and networkers are key individuals to include when thinking about network interventions because those few can help reaching many others in the network.
Thinking Beyond Network Analysis
Social network analysis is a great tool to accurately analyze the status quo of the alumni community and the interactions between members. Another way of seeing it: A network graph map the result of the alumni management up to now. To invigorate passive networks and foster interaction, it is important to keep in mind that a functional community is the result of many contextual factors that, to a great part, can be influenced with strategic measures. To use network analysis as a strategic tool, it helps to complement it with contextual information about the community. This data can be collected via the same survey and refer, for example, to questions regarding the importance of the network for the alumni, what alumni want to contribute to the community, preferred event formats and thematic issues high in demand. Knowing these contextual factors enables community managers to address alumni in a compelling manner and design future program offers, events and activities in a way that attracts previously passive target groups. Eventually, these measures will foster an inclusive, vital and cohesive alumni community that enables members to increase their interaction. Collected in multiple intervals, this data can be used to assess the long-time health and attractiveness of the community and monitor the success of strategic measures.
Key Learnings For Us
In our particular case, we have done four such analyses within our Bosch alumni community and we are excited to use the results gained to better communicate with our alumni and engage them in a more strategic way. For us, three key learnings stand out:
First, the way in which a program is set up ultimately imprints itself onto the structure of the alumni network. If you have a decentralized program which is managed from the outset and driven to a large extent by the program participants, your alumni network will reflect this. It will likely be self-sustaining and largely centered around contacts among alumni. In contrast, a program that is heavily guided and managed externally with little involvement of the program participants will likely also have an alumni network that revolves around the program's organizers. Whether a network is self-organized or externally driven is in itself neither "good" or "bad" but important to know to design alumni activities accordingly. What we have drawn from this is that the composition of the network greatly affects how we communicate with alumni and that a dedicated core at the center of the network - whether made up of alumni or funders supporting the community - is essential to a solid network.
The program staff (blue) as the central component within the network.
Second, we found that our alumni network is most clearly structured along cohorts, thus, along the annual participant groups of our programs. Meaning that people are mostly in touch with people they have personally met and share a common experience with. While this insight is unsurprising as such, it raises a common question among alumni organizations: What does it take to encourage alumni to get in touch with other peers they haven’t met yet? This question is particularly urgent for alumni communities that define themselves as a network of experts who support each other with their expertise and experience. Here, complementing network analysis with contextual data can help to create attractive program formats and platforms for alumni to meet and exchange. The key is to ask alumni what they expect from their alumni network, how they want to engage and what is currently keeping them from engaging.
Lastly, we learned that alumni networks are held together by different "glues". A "glue" describes the underlying sentiments or incentives that compel alumni to engage with each other. Much to our surprise, the potential career benefits were hardly mentioned as an important network glue. To the contrary, it appears that values and shared norms play a paramount role in our networks. Community members are motivated to actively engage in their alumni networks because they feel a connection to others that is based on a similar outlook on topics and issues or because of the shared programmatic experience each alumnus has gone through. Being aware of the exact reasons that hold alumni together helps to design and offer the right alumni activities. In our case, we focus particularly on value-driven activities and on communicating those values to highlight that our network serves a greater purpose.
Network analysis is a technique that answers many questions alumni managers are currently facing. The accurate and empirically robust network maps show the patterns of relationships within the alumni community in an intuitive, graphic way. Network analytics unveil the underlying structures of alumni interaction and indicate how dense and how intense alumni networks are. It also serves as a tool to identify the most well-connected and the most trusted persons in a network that can serve as multipliers for future network events. Our experience shows that network analysis is particularly insightful when combined with contextual data about e.g. the experiences, expectations and motivations of the community members. Enriched with contextual data, network analysis can serve to develop effective communication strategies and attractive offers that eventually lead to a vital and self-sustaining alumni community.
This post is jointly written with Between|.|ness, a Berlin-based consultancy specialized in social network analysis and community building. For more information on their approach, please visit www.betweenness.net or contact Raffael Hanschmann or Alexander Gaus at firstname.lastname@example.org.
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