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The power of thinslicing applied to social media and community

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A hard problem to solve

One of the hardest problems faced by clients and agencies alike is how to match up social media activity with bottom line business results. Part of that is because you can’t always easily see a straight cause and effect between levels of engagement on Facebook for example, and numbers of purchases. But perhaps the reason isn’t for lack of effort. Perhaps it helps to think about how customers make purchasing decisions, and how best to capture that? Certainly that’s what I focused on at Sony EU and it led me to move away from the simple numbers game, to include the qualitative.

To explain why I like the term thinslicing to tackle this question of how best to connect with customers using social data first take a look at the cool piece about data interpretation written by Lithium’s Dr Michael Wu, including this neat illustration:

The power of thinslicing

Identifying the value of thinslicing lies in the elegant and powerful way the term thinslicing connects the approach to data analytics to the behaviour that creates that data – namely with the thinslicing of online consumers who “tend to ignore most information available and instead ‘slice off’ a few relevant information or behavioral cues that are often social to make intuitive decisions,” as Brian Solis puts it. 

In other words by thinslicing, rather than using intuition to make decisions, I mean adopting a strategy which is based on the understanding that by connecting the means of analyzing the data with the way the data is created by customers.

The question then is why? While it may be clever to see a way which logically connects the way to analyse data with the way it’s created, why is that potentially so useful to a business? Now there’s a good question! The obvious answer is that by aligning the analytic method used by your business, with the way the data is created by your customers, you are going to produce better results in terms of both better quality actionable recommendations which also produce an increase in ROI. How does that sound?

Less is more

National Express Victoria Coach Station

“Click which photo better represents this place” – foursquare allows people to rank pictures

Not surprising in the gaming world this understanding is already paying serious dividends. A leading example is gaming company wooga which has carefully built its business by monitoring the data gathered by user responses, to tweak aspects of its online games to help boost engagement and thus ROI. In effect they are able to leverage user behaviour to give them what they want. By thinslicing social data effectively, figuring out what matters by understanding what customers want and ignoring the rest, the same benefits are available to your online business too. So by reducing the amount of data provided, you’re actually able to make better decisions about your customers, and you’re able to better understand how they making purchasing decisions online. It’s as simple as that.

A quick example of thinslicing – to find the data and to act on the data

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1. Consider this excerpt from Wikipedia on the Friendship paradox, as way of a quick mathematical -based example of ‘thinslicing’, that helps predict disease epidemics:

The analysis of the friendship paradox implies that the friends of randomly selected individuals are likely to have higher than average centrality. This observation has been used as a way to forecast and slow the course of epidemics, by using this random selection process to choose individuals to immunize or monitor for infection while avoiding the need for a complex computation of the centrality of all nodes in the network.[5][6][7]

2. Then consider that this is probably what happened in one New York community, prior to the full impact of HIV, to quote one study from Dr Sam Friedman:

In the period from 1976 to the early 1980’s, seroprevalence in New York rose from zero to about 50%…The epidemic then entered a period of dynamic stabilization…Although mathematical models have suggested network saturation may have been an important part of the stabilization process (Blower, 1991), the sociometric analysis of drug injectors’ networks conducted during the research for this volume suggest that the extent of network saturation may have been quite limited.

Behaviour change probably made a major contribution to the stabilization of seroprevalence. In spite of a popular image that would suggest that either “slavery to their addiction” or “hedonistic, selfish personalities that ignore risks and social responsibility,” drug injectors in New York (and indeed, throughout the world) have acted both to protect themselves and others against the AIDS epidemic. Thus, by 1984, before there were any programs other than the mass media to inform them about AIDS or to help to protect themselves, drug injectors in New York were engaged in widespread risk reduction…Furthermore, observations on the street confirmed this by showing that drug dealers were competing with others for business by offering free sterile syringes along with their drugs as AIDS-prevention techniques.

BTW if you’ve stumbled on this post and wonder what it all means, join the club. I am still working on myself, but there’s something here about ‘thinslicing’ as an outsider – in this example finding who to immunize in an epidemic; and ‘thinslicing’ from an insider perspective, in this example, who with little information people figured out how to take precautionary measures.Hence the title addition – to find the data and to act on the data..