This week we looked at social media technologies, covering an overview of social media technology, social media graphs, finding network communities, and similarity of nodes.
First, we looked generically at social media technologies. For me, the interesting point was organisation’s shift away from more formal knowledge management systems (like Moodle) towards more social knowledge management systems like Wikis. This reflects in my opinion that knowledge in an organisation is held by everyone, and should be updated by everyone where appropriate.
One of the more up and coming uses of social media is a new customer service management channel. http://thenextweb.com/socialforbusiness/2014/10/21/social-media-in-unexpected-parts-of-business/ discusses some interesting opportunities for social media in organisations, specifically:
- Cross-team relationships
- Monitoring customer conversations
- Customer behavioural targeting
Of the three, I’ve had recent experience evaluating an SAP product called Hybris (https://www.hybris.com/en/) which is focused on monitoring customer conversations, and customer behavioural targeting. I think it’s important to note that we live in a multi-channel world where customers choose the social networks they wish to interact with an organisation with, whether that be in real time on Twitter, a Facebook group, sending an email, or using a feedback form. In this scenario, a customer posting a comment on Facebook should be treated no differently than them using a form on the website, and expects the same service. More interestingly is the use of social media for sentiment analysis, which filters all the data from sites like Twitter, and searches for both positive and negative words. This way organisations can see as news is released, or campaigns are released, whether there is positive or negative sentiment towards the brand. This allows organisations in real time to adjust their marketing to amplify positive effects, or counter negative effects.
There were three readings this week, the first being Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM. CRM is Customer Relationship Management software, which is used to capture the relationship a customer has with an organisation. Social CRM augments a traditional CRM with social connectivity. I think some organisations would view this as a communication revolution. The key takeaway for me was noted by Trainor, Andzulis, Rapp, & Agnihotri (2014) as social CRM as a technology “alone may not be sufficient to gain a competitive advantage. Instead, social media technologies merely facilitate capabilities that allow firms to better meet the needs of a customer”. This wasn’t a surprising outcome for myself. I view social media as a natural collaborative extension to email, and really the modern version of bulletin boards and newsgroups. So tooling that takes that into account will really help to gain insight, and at best, amplify a conversation with a customer, but in itself won’t do too much.
The second reading was Social media? Get serious! Understanding the functional building blocks of social media. Kietzmann, Hermkens, McCarthy, & Silvestre (2011) defined seven functional blocks of social media:
- Presence – The extent to which users know if others are available
- Relationships – The extent to which users relate to each other
- Reputation – The extent to which users know the social standing of others and content
- Groups – The extent to which users are ordered or form communities
- Conversations – The extent to which users communicate with each other
- Sharing – The extent to which users exchange, distribute, and receive content
- Identity – The extent to which users reveal themselves
All social media sites have these aspects, just in varying degrees. My own personal belief is that Identity is the next big thing on the Internet, and unlike Mark Zuckerberg’s view that “Having two identities for yourself is an example of a lack of integrity” (http://www.michaelzimmer.org/2010/05/14/facebooks-zuckerberg-having-two-identities-for-yourself-is-an-example-of-a-lack-of-integrity/), I believe that we all have multiple identities, one for work (LinkedIn), one for family (Facebook), one for close friends (SnapChat), one for dating etc.
The final reading was Chapter 10 of Mining of Massive Datasets. This, and a few of the videos for this week covered some of the algorithms used as a foundation of grouping nodes together, and understanding their relationships. We looked at relationships between sets of clusters, or Betweenness, and then went into some detail on the Girvan-Newman algorithm, which Rajaraman, Leskovec, & Ullman notes “visits each node X once and computes the number of shortest paths from X to each of the other nodes that go through each of the edges”. While this sounds scary, really it all means how do we infer useful information from a social network? Well, like-minding topics or people like to cluster around each other. So how do you know there’s a cluster? How do you know the relationship between that cluster and other clusters, so you can decide which content to show? Algorithms exist to help us make sense of all this related information.
Trainor, K., Andzulis, J., Rapp, A., & Agnihotri, R. (2014). Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM. Journal of Business Research, 67(2014), 1201-1208.
Kietzmann, J., Hermkens, K., McCarthy, I., & Silverstre, B. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(2011), 241-251.
Rajaraman, A., Leskovec, J., & Ullman, J. (2010). Mining of Massive Datasets.