By: Angela Ashenden, Principal Analyst, MWD Advisors
Published: 17th February 2014
This work is licensed under a Creative Commons License
When you say the phrase “social analytics”, for many people it can conjure up images of endless pages of Twitter messages sorted into columns accompanied by happy, sad or neutral smiley faces, or maybe stories of how badly particular brands have performed in a social media-based confrontation with a customer. It’s a topic of conversation for most organisations—how to get a handle on how they are performing in the 'social sphere'; to what extent they are addressing their customers’ needs via social media, and how public social media platforms should play in the context of their overall marketing or customer service strategy. (My colleague Helena Schwenk and I published a free report on this topic a while back.)
But few have yet started to think about the potential of social analytics technologies inside an organisation. The problem is that this can be a difficult thing to get your head around—ok, so you might be able to judge the sentiment of your employees by analysing their posts in an internal social network, but what will you do with that information? In fact, while this is actually one of the potential ways of using this technology, the truth is that it is really just scratching the surface of what we can do (and will be able to do in the future). Social analytics of internal networks is a hotbed of innovation that is largely under the radar for most people, and even when the results of this innovation make it into general availability they are often so invisible that they are taken for granted.
One example of this is the use of social analytics technologies to drive the user experience of a social collaboration solution, with the most commonly found feature being the recommendation engine. Though every recommendation system is not equal, the most sophisticated examples analyse activity across the enterprise social network—looking at what individuals are posting/reading/commenting on, what groups they are participating in, who they are interacting with, for example, and mapping that with what their colleagues or 'connections' are doing and what people similar to them are doing—to identify content that might be interesting to them, groups they should know about, and people who should be on their radar. To the user, it’s often viewed just as a convenient widget—a time saver, perhaps—but in the broader scheme of things it can significantly help smooth the user’s adoption process by making it easy to get up to speed more quickly. From the organisation’s perspective, features like recommendations also help to seed the process of knowledge discovery, for example surfacing common pockets of activity in different parts of the organisation and thereby enabling duplicate projects to be consolidated or at least to learn from each other.
Expertise identification is another great opportunity for social analytics technologies—not relying just on the user’s own, explicitly-articulated skills and interests, but leveraging the information about what they are posting/commenting on/answering questions on, for example, to create a richer picture of who the real experts are in a particular area. This can be used for operational purposes, for example to suggest potential experts to answer a particular question or respond to a particular customer issue, and for more strategic, organisational purposes, for example to help support talent management processes by profiling the skillset of the organisation.
This is an area which—as you can probably tell—fascinates me (and leaves me slightly in awe of the great specialists who innovate here), and it’s one of my major areas of focus for research this year. There’s a lot going on—we’ve already seen Jive investing here with a couple of analytics acquisitions (Proximal Labs and CLARA) which are helping to shape its UI product roadmap, and I recently shared my analysis from this year’s IBM Connect conference, which included some interesting projects that bring together its Connections and Kenexa product strategies through analytics. Of course there’s also the other side of analytics in a social collaboration platform—using it to measure adoption and assess the value people are getting from the tool, and I’ll be looking at that too. As part of that research, as ever, I am on the lookout for good examples of how businesses are benefiting from this in the real world, as it were, so if you have any stories you’d like to share, please get in touch!
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