‘Thinslicing’ connects the data, to the behaviour that creates it

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.

7 thoughts on “‘Thinslicing’ connects the data, to the behaviour that creates it

  1. Talking about ‘alignment’ reminds me of this quote about the key to influencer analysis from Dr Michael Wu:

    “The missing link of influence: the link from the “potential to influence” to the “potential to be influenced.” Real influence can only occur when there is an alignment between these two. In fact, this is the minimum required state.”

  2. From ‘6 Strategy Lessons from a former chess Prodigy’ is a remarkably similar example to how data can inform the best way to improve customer satisfaction that beats the experts:

    In 2011, Moore’s team was trying to improve customer satisfaction. They worked from the assumption that one metric in particular–case backlog–was the best predictor of customer satisfaction. It seemed reasonable to assume that if you had low or zero backlog, then your customers would be happy. “It turned out we were wrong,” says Moore. After three months of wandering through the weeds, Moore’s team realized that a better predictor of customer satisfaction was the time it took to respond to a customer request, combined with frequency of updates. For months, Axcient had been focusing on the wrong metric.

    In 2012, with a larger customer base and a new playbook, Axcient was again trying to improve its customer satisfaction. It was working from the assumption that time-to-response and update frequency were still the key metrics to watch. And yet even as they lowered their response time, Axcient’s survey data was “not necessarily supporting what we believed.”

    Rather than labor under a false assumption for months, Moore and company quickly recognized a pattern–they needed to question and refine their metrics. They rapidly determined that while time-to-response mattered, Axcient customers also deeply cared about getting access to a live Axcient employee. “Now when people call Axcient, in a matter of minutes, they’re connected to a live person,” explains Moore. Crucially, Axcient put this fix into place in a matter of weeks, rather than months, having refused to fall into the same trap of relying on a false metric.

  3. Fascinating article on use of brain scans to prove the value of emotional connections in marketing, loved the quote “We live in a world where we are taught from the start that we are thinking creatures that feel. The truth is, we are feeling creatures that think.” I think there are valuable insights here, too, for social media analytics. As with marketing we need to better connect the approach to data analytics – to the actual behaviour that creates that ‘data’ – namely with 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.

    BTW the first fMRI use of scanning was reported by the RSNA in early 2007, to measure the impact of brands. Guess I must have spotted at while working at MedicExchange.

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