You may have read a lot about Foxconn last week. Tl;DR summary: Foxconn is the subcontractor that makes the iPhone and iPad. Foxconn’s CEO called his workers animals. Foxconn, is probably racking up big time ILO violations. Here’s the key tl;dr quote, from a current Apple executive: “We don’t have an obligation to solve America’s problems. Our only obligation is making the best product possible.” That’s a lot of focus. That’s laser-like precision on a given mission. Because it follows that if Apple produces the best product possible, people won’t care about anything else. Indeed, isn’t s/he right? Doesn’t free market and price competition makes hypocrites of us all? (Two-Buck-Chuck anybody?) What if it didn’t have to? Implications for Analytics[…]
Author: Christopher Berry
You may be familiar with smart grids, open data, and Pachube. I was reading a piece on smart data, when suddenly a wild quote appears: “…a group of hackers who demonstrated in early 2012 that it is possible to discern exactly what film someone is watching by analysing the power consumption of their TV via their smart meter, as every film has a unique ‘fingerprint’ of electricity usage.” Oh yes. Confirmed. It happened at a hacking for privacy event. Reactions and Questions: What an unintended consequence of the technology! What other hidden signals might there be in other sources of data? What good might come of re-purposing seemingly noisy/garbage data? Just as William H. Perkin discovered purple dye in waste[…]
Thomas L. Friedman wrote a fairly good piece for the New York Times. The theme is linked to something that has kept public policy makers awake for a very long time – the Productivity Trilemma. These two themes explain part of the reason for the rise of Data Science and how Web Analytics must evolve. To summarize Friedman: The era of average people relying on doing an average job for average pay is over. Technology is more efficient than ever at destroying average jobs. Everybody has to get smarter. To summarize the Productivity Trilemma: Productivity growth causes growth in GDP, producing negative employment effects. Real interest rates outpace real growth rate of GDP, causing regressive redistribution effects, leading to the[…]
According to Chris Dodd, the response against SOPA was unlike anything he’s seen in his thirty years in politics. He called it a ‘watershed event’. Possibly. Proponents of SOPA argue: Americans are losing their jobs to foreign pirates. National security!!! It’s Caucasian looting – all you kids just want free crap. Opponents of SOPA argue: Externalities. Proponents want to believe that somehow Google made me oppose SOPA. What should be of even more concern to Chris Dodd was that Google had very little incremental effect. Their contribution to the movement was weak compared to what the real grassroots did. There was no astroturf: I learned of SOPA from one of the image boards. It led to a slow moving reddit[…]
Google has announced changes to its privacy policy. TL;DR: They’re rewriting it into human language. Nothing about your Google Analytics account data changes. “The only change for Google Analytics users under the new privacy policy is that now, information about how you interact with the Google Analytics interface may be shared with our other products.” Implications: If the product is free, you are the product. Software-As-A-Service (SaaS) analytics on SaaS users (meta-meta) is a major input in the product development lifecycle, so you can expect Google products to get better. This paves the way towards a single Google Center of Excellence for internal SaaS Analytics. Predictions: Initial uproar. Diminishing interest. Business-as-usual. Carry on.
There’s a big difference between skepticism and blind negativity. It’s through negativity that many experts attempt to differentiate themselves from a herd. Expertise is often some sort of competition – a game by which some people are more expert than others. Over time, that negativity can accumulate in a community, causing stasis and then retreat. Skepticism: The sample size involved seems awfully low. We need more evidence that this relationship holds up before declaring that this is a natural law of marketing. The author didn’t consider a few factors from prior work in this field – probably a genuine oversight on their part – so I’d like to see this report replicated with those factors to see the link. If[…]
Effective analytics is disruptive because being smarter causes smarter actions. Organizations do not, and probably can not, change as rapidly as the intelligence suggests. This alone can be a massive source of frustration, both for the analytics professionals, and for other areas of management within the organization. Three key questions to consider: Small failure is likely and common within most organizations – are you comfortable with those getting surfaced? Small success is likely and common within most organizations – are you more concerned with sharing the resulting insights instead of investing in assigning credit? Do you have a system for change management and updating strategy? Analytics shines a harsh light on previously dark corners. And yet, knowing what you don’t[…]
No fewer than four companies in Toronto looking to build analytics departments. I’m excited for them. A few points of advice as they move forward: If you don’t like the truth, you’re not going to like analytics. Effective analytics is disruptive and prompts change. If you’re not open to changing, then there’s no point in being smarter. You’re better off being dumb. You’ll hit a trough of disillusionment, usually because too many of the wrong people in the organization are looking for too many of the wrong numbers, getting too frustrated that nothing is telling them anything (that they want to hear or see), and that there isn’t a transformation. Some organizations never get out of that trough and give[…]
Marketing Science isn’t Physics. One of the great things about the Marketing Science community is the sane approach to assumptions. Unlike economics, marketing science aims to make reliable predictions about the world, just like the other grown up sciences.Consider: Physics is just called physics. Chemistry is just called chemistry. Biology is just called biology. None of these three fields use the description ‘science’ to affirm that they’re science. They just are ‘science’. Next, consider: Marketing. Politics. These are two sciences which are very young – especially as compared to physics. Marketing Science is really only 50 years old. Marketing Science has special problems that are created by the subject matter itself. Consider: The same laws of motion that put a[…]
I believe this classification was first enunciated by Alex Langhshur at eMetrics Toronto (2008). It’s worth expanding upon. Consider the following classification for web analytics metrics: Pre-Click Metrics On-Site Metrics Post-Click Metrics To unpack that: Pre-Click Metrics refer to all activities that led to a visit to a digital owned property. (E.G. paid search keywords, referring domain, any traditional spend) On-Site Metrics refer to all the activities that can be observed on the site. (E.G. Visits to specific pages, graph analysis / path analysis, time spent on site, and a host of very specific things like the nebulous world of engagement.) Post-Click Metrics refer to all the activities that occur after the visit. (E.G. Money getting transferred to your bank[…]