Do we lack the tools to steer ESN adoption?

It’s been a long time since I last used Facebook. I’d like to say it’s totally out of a philosophical belief that we aren’t meant to be the product – don’t get me wrong, it’s partly because of that-, but it’s got more to do with my dwindling motivation. I like to assess my use of any app through the Return On my Time Investment. And because of its recent (or not so recent) algorithmic updates, my FB newsfeed has lost its edge when compared to my Twitter timeline.

But this is just FB. Leaving it might disconnect you from the latest updates in your friends’ social lives, but it won’t hinder you professionally (unless you’re a web marketer or a Facebook employee that is). But what is of an Enterprise Social Network (ESN)?

Many vendors boast about the gain in productivity and the rise of innovation following the implementation of ESNs. Yet, what business value can you extract from a deserted social network? It is no surprise that adoption is the main issue with many ESN implementations. But how do you get people who are already swamped with work-related tasks to fully engage within a social network? And once there, how do you retain them on the network?

This is a heavy loaded question with no easy answer. A first step would require understanding the motivation behind our staying or leaving (aka churning) an online social network. A rather comprehensive presentation of the question has been issued by Karnstedt et al. in their paper “Churn in Social Networks”:

A key observation of user behaviour in online networks is that users, with the exception of spammers, make contributions to online discourse without expecting any immediate return [39,11]. In sociological discourse, this type of activity is described in terms of the ‘gift economy’ [58]. In contrast to the commodity or service economy, which is driven by the exchange of good/services for money, economic exchange in the gift economy is defined in terms of an im- plicit social contract. In a gift transaction, there is an unstated expectation that the benefits of a gift will be reciprocated by the recipient at some reasonable time in the future. A more risky transaction involves ‘generalised exchange’, whereby the giver’s generosity is reciprocated, not by the recipient, but by someone else in the group. In social networks, this exchange mechanism applies to those contributors who give of their time and expertise but do not appear to receive immediate benefits. However, there is a risk that the group will not assume responsibility for the debt and the contributor will never be reimbursed in kind. In the worst case, if all members of the group never contribute (free-load), no one benefits and the exchange system breaks down.

This gets more delicate when applied within an enterprise because, well the stakes are higher for obvious reasons, and because what’s going on offline (office politics and such) is bound to affect the dynamics within the social network. Fingers are often pointed towards Enterprise culture and justifiably so. Culture does eat technology for breakfast!

Some argue that internal community management could help ESNs thrive, but it can only do as much. Data-driven approaches that proclaim the capacity of steering the community through web-based analytics are abundant. They could help understand the dynamics of the network, if only they focused equally on the relational aspects of the social network as they do on the content and activities occurring within the network.

Maybe the difficulties of adoption are only made more poignant because of the lack of pertinent methodologies to support the endeavor. What if we could visualize the network in real-time (through Social Network Analysis)? Augment it with activity-based indicators (number of posts of a user, numbers of views of a profile etc.)? What if we could even envision the future state of the network based on the patterns in its historical data and thus predict the likeliest users to churn (As is the case for online games platforms or telecom companies)?

Maybe that will steer the adoption efforts in a more accurate manner and maybe it won’t. I’m nothing saying it’s not a complex question, but wouldn’t hurt to dwell on it, would it?

Social data interpretation: The human factor

Research claim that A full 90% of all the data in the world has been generated over the last two years. This tsunami of digital data have brought along incredible insights but also many many headaches. One of the most prominent challenges relates to the inferences we draw from these data. Kate Crawford argues in “The Hidden Biases in Big Data” on Harvard Business Review that “We give numbers their voice, draw inferences from them, and define their meaning through our interpretations. Hidden biases in both the collection and analysis stages present considerable risks, and are as important to the big-data equation as the numbers themselves.” O’Reilly Radar’s Mike Loukides stresses, in “Data Skepticism“, that “even when you have unlimited data, you have to be very careful about the conclusions you draw from that data. It is in conflict with the all-too-common idea that, if you have lots and lots of data, correlation is as good as causation.”

This particularly strikes a nerve when it comes to Social data (and Social Network Analysis). You must have, at least once, come across titles such as “Social Networks are making us anti-social” or “Facebook causes divorce” or “Twitter moods help predict stock markets” etc. This might reflect a mere misinterpretation of the original studies (causation sells way better than correlation as the comic promptly illustrates) or a defect in the analysis and interpretation processes of said studies. We have a tendency to jump into such conclusions because our minds react better to narratives and “because” is a good ideas’ connector.

When examining the virality of content in social networks, a tweet for instance, the observed contagion phenomena is often explained through ‘Peer influence’. It could be the case, but it’s good to stop and think ‘Maybe the answer isn’t that simple’, maybe there are alternative explanations. Sinan Arial, an associate professor at the MIT Sloan School of Management, delivers a compelling talk about social contagion and highlights just how Homophily (the tendency of similar people to bond together) is a viable explanation for some diffusion phenomena often attributed to peer-influence.

When stressing how ‘Big Data’ (Social Data) will revolutionize business or how ‘visualization will save big data’, vendors fail to stress these interpretation issues (which is understandable). Human intervention is an omnipresent part of the conception and analysis process. And unless our analysts (or data scientists if you like) are open minded enough to consider alternative explanations, or we come to find more appropriate models, we might just be digging ourselves into a much bigger hole.