Tagged: data

US Mid-terms: the role of social media in the Republican’s success

I would not consider myself to be a fan of the Republican Party, but I am a fan of this comment by Lori Brownlee, social media director for the Republican National Committee (RNC).  Commenting on the success of their recent campaign she said “rather than simply using Twitter and Facebook as a broadcast tool,  we centered our plan around using social as a strategic listening and data collection tool.”

Check out this article just published in AdAge for more details.  There is so much that brands could learn from this approach – especially the ability to understand, in real-time, what people are talking about or asking.  Social media is a real-time game and it requires that a brand design real-time processes to play it.  This is not a game where you sit down and plan your content in advance – you plan your process in advance and this will then tell you what content you need to have out there right now.  A content strategy needs to be seen as a process that matches brand answers to consumers’ questions in real-time.

Neither do you plan your influencers in advance, people become influential because what it is they are doing or saying right now, and you therefore need to identify them in real-time.  Someone who is influential today, is not necessarily going to be influential tomorrow.

And key to this process are tools and people.  Listening and analysis tools (such as Sprinklr, mentioned in the article), but then places (such as newsrooms or command centres) where the tools can be plugged into people who can then process and share the information and make decisions about what to do.  Rather than spending time and money simply filling up channels with ‘brandfill’, brand should spend time and money creating (and then staffing and managing) command centres.

Privacy: let’s have the right conversation

The whole social media, Big Data, privacy thing is getting an increasing amount of air time. This is good, because this is very important thing to start getting our heads around. However, I don’t think we are really yet having the right conversation.

The pre-dominant conversation out there seems to be focused on the issues concerned with the potential (and reality) of organisations (businesses or governments) ‘spying’ on citizens or consumers by collecting data on them, often without their knowledge or permission.

Our privacy is therefore being ‘invaded’.

But this is an old-fashioned, small data, definition of privacy. It assumes that the way to gain an understanding of an individual, which can then be used in a way which has consequences for that individual, is by collecting the maximum amount of information possible about them: it is about creating an accurate and comprehensive personalised data file. The more comprehensive and accurate the file is, the more useful it is. From a marketing perspective, it is the CRM way of looking at things (it is also the VRM way of looking at things, where the individual has responsibility for managing this data file).  It is also a view that then gives permission to the idea that if you detach the person from the data (i.e. make it anonymous) it stops it being used in a way which will have consequences for the individual concerned and is therefore ‘cleared’ for alternative usage.

But this is not the way that Big Data works. The ‘great’ thing about Big Data (or more specifically algorithms) is that they require almost no information about an individual in order to arrive at potentially very consequential decisions about that individual’s identity.   Instead they use ‘anonymised’ information gathered from everyone else. And increasingly this information is not just coming from other people, it is coming from things (see Internet of Things). The great thing about things is that they have no rights to privacy (yet) and they can produce more data than people.

The name of the game in the world of the algorithm is to create datafied (not digitised) maps of the world. I don’t mean literally geographical maps (although they can often have a geographical / locational component): from a marketing perspective it can be a datafied map of a product sector, or form of consumer behaviour. These maps are three dimensional in that they comprise a potentially limitless numbers of data layers. These layers can be seemingly irrelevant, inconsequential or in no way related to the sector of behaviour that is being mapped. The role of the algorithm is the stitch these layers together, so that a small piece of information in one layer can be related to all the other layers and thus find its position upon the datafied map.

In practical terms, this can mean that you can be refused a loan based on information concerning your usage of electrical appliances, as collected by your ‘smart’ electricity meter. This isn’t a scary, down-the-road sort of thing. Algorithmic lending is already here and the interesting thing about the layers in the datafied maps of algorithmic lenders is the extent to which they don’t rely on traditional ‘consequential’ information such as credit scores and credit histories. As I have said many times before, there is no such thing as inconsequential data anymore: all data has consequences.

Or to put it another way, your identity is defined by other peoples’ (or things’) data: your personal data file (i.e. your life) is simply a matter of personal opinion. It has little relevance to how the world will perceive you, no matter how factually correct or accurate it is. You are who the algorithm says you are, even if the algorithm itself has no idea why you are this (and cannot explain it if anyone comes asking) and has come to this conclusion based in no small part, by the number of times you use your kettle every day.

The world of the algorithm is a deeply scary place. That is why we need the conversation. But it needs to be the right conversation.