If you’re here for the two examples of companies that improved customer service by allowing people (customers) to talk to people (employees), highlighted in red – and the 2nd example is in the 3rd comment. You can ignore the stuff about thinslicing:-)
To explain why I like the term thinslicing first take a look at the cool piece about data interpretation written today by Lithium’s Dr Michael Wu, including this neat illustration:
Then consider this, that my response to reading this blog post clarified a key thing I have been trying to say. Firstly, that I’ve come to term the business objective of finding the “interpretable, relevant and novel” in data as Michael terms it – through a combination of art and science – namely that of thinslicing.
But now I’ve made the next step. 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.
But perhaps it would help if I made clear what I don’t mean by thinslicing as a strategic tool, is that summed by nicely in these two paragraphs written by Bob Thompson on the CustomerThink community:
“Despite our best efforts to collect and analyze data, good business decisions will always include elements of judgement, intuition or just plain luck. Many day-to-day decisions are made with little or no thought, because the option selected just seems “right.” Gut-feel decisions might be examples of what Malcolm Gladwell called “thin-slicing” in his provocative 2005 bestseller Blink.
“However, the best decision can sometimes be counter-intuitive. For example, the financial services firm Assurant Solutions wanted to improve its “save” rate on customers calling in to cancel their protection insurance. The industry’s conventional wisdom, which resulted in 15-16% retention rates, was to focus on reducing wait time to boost customer satisfaction. But data analysis found a solution that tripled the retention rate: matching customer service reps with customers based on rapport and affinity.”
What I mean is the approach to data as you outline above which I categorize as thinslicing, coupled with the way consumers make purchasing decisions – which like good business “will always include elements of judgment, intuition or just plain luck”.
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?
Update: so there’s a nice response from Dr Michael Wu on that question of linking the too together, the way you approach the data, with the way its created, that connects the two ends of the spectrum together:
Good data scientists must know everything that happen to the data, from its creation, all the way to the point where they get their hands on the data. It is actually a pretty standard practice for hardcore financial/business analysts. Not only you need to “connecting the means of analyzing the data with the way the data is created,” you must know everything that happen to the data along the way, until the data reaches you (or the analyst). Only then can you be certain that your analysis is not biased or confounded by something before you get your hands on it. In statistics term, only then can you know the confidence interval of your result.