Gary Morgenthaler had a few interesting statements to make: “Therefore, when Siri was an independent company, its plan was to map these domains deeply and seamlessly to automate transactions for its users within them. For example, “Buy that Steve Jobs biography book and send it to my dad”; “Send a dozen yellow roses to my wife”; “Book me the usual table for 2 tonight at 8 p.m. at Giovanni’s”; and “Get me 2 box seats for the Giants game on Saturday.” Then comes the question of what solves our biggest problems. Ultimately, Siri’s value is that of automation and removing “friction” on the Internet. Siri achieves this by: (1) understanding speech input in natural language form, (2) mapping user requests[…]
Author: Christopher Berry
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Joe Stanhope wrote a good piece for Forrester. If you have a subscription to Forrester, read it. It summarizes the state we’re in, and has a few very good points on the last page. In that piece, web analysts themselves list ‘attribution’ as a major challenge. This is a wicked problem. All the energy you put into untying that knot only causes it to become tighter. But let’s try this again, together. If you haven’t seen this previous post, it’s new to you. I drew out a conceptual model report, in part to demonstrate how cause-effect can be embedded into a report. Alright – so that’s a conceptual model. I believe that paid spend causes paid visits. I believe that[…]
The complexity in measurement ramps with the complexity of the channel. In this post, I’ll write a bit about an interpretation of systems thinking, and how I apply it to marketing and marketing modeling. We all seek to minimize complexity and maximize predictability. We want to minimize risk and maximize empowerment. We want to synthesize a huge amount of information and boil it down to a handful of levers. Levers cause empowerment and they enable people to make really good decisions. Systems thinking is an actual thing now. Some organizations already have models in place, and are all fairly standardized. Not every organization has them. Understanding them is pretty important. This is my approach: I write a load of variables[…]
Data Science is the mix of computer science, user experience, and statistics. The aim of data science should be: to make things better by influencing people and things to make better decisions, by making people and things more aware of better alternatives, based on better algorithms and more relevant data. Language kept intentionally vague to set up the ‘well that could be anything’ argument when it suits me later. If you do it right, nobody is really aware of the complexity of what just happened to them. The point is not to experience data. The point is to experience…an experience. And be better off for it! And, the most interesting part is that it’s not really driven by humans with[…]
How consumers are using mobile to shop IRL (In Real Life) is of paramount interest now that mobile has finally arrived. A few figures to run through. The first, below, describes what consumers report they want from mobile phone applications, for the holidays, in August 2011. A common behavior, well known to clicks-and-bricks retailers, is that consumers will research products before coming in store to buy them. This is especially true of electronics goods, but I suppose it’s conceivable they do it for home appliances, automotive purchases, and anything else that is generally of high consideration. Mobile offers the capability of researching while you’re physically in the store. And, since most stores are now ghost towns, it enables the consumer[…]
Web Analytics Wednesday is tonight at The Wellington, in downtown Toronto’s analytics alley. It’s generously supported by AT Internet. There are some 40 people – representing among the best of the best, who will be in attendance. It’s a great opportunity for web analysts, social analysts, marketing scientists, data scientists, hackers, developers, and usability professionals to come out and talk about the great ideas and opportunities we have going on in Toronto. It’s also the first get together after eMetrics New York, which was a major, and had big time Canadian attendance. These tend to be among the more interesting evenings. It has also been some three months since the last WAWTO event, so there should be quite a few[…]
Not all data is usable on its own. The vast majority of it isn’t in its raw form. Its coal. It has potential. But on its own, it has limited uses. Algorithms are the modern day equivalent to machinery. Fire (combustion) is really just statistical analysis – a violent process that generates waste in the form of heat and soot. Our modern day Watt Pump is Google. Their coal is HTML. The best coal used to be the HREF link. The algorithm that drives Google’s primary product is PageRank. It runs on a massive amount of coal. Most people aren’t aware of the complexity that goes on – and why should they. All the mine owners really cared about was[…]
The eMetrics NYC conference / data driven business week, is said to have attracted some 1600 people. The WAA Industry meeting coincided with it. It was a success. Three major takeaways that stick out in my mind. Janus Faces for Janus Audiences I’ve now seen two versions of Peter Fader. The academic Fader and the industry Fader. The one you see at an INFORMS Marketing Science conference is the academic Fader. He’s the kind of guy that’ll smile as he tells his skeptical detractors to take their perspectives and reconsider them. His insights into models and the way theories have been constructed are original. He doesn’t mess around with the bits at the edges. He’s good at the comparative method[…]
I wrote a definition of ‘Insight’ on December 29, 2010 that read: “An insight is: New information Executable Causes action Profitable Or, more detailed, an insight is: A piece of information that you didn’t know before, which – Can feasibly executed, culturally acceptable and of a scale relevant to the firm, and – Causes a decision to be made that wouldn’t have been made otherwise, and – Results in profit or a sustainable competitive advantage” If held to that standard, insights are incredibly rare. Depending on who you talk too, an insight may mean: A bullet point factoid An element that combines all that is known about a segment, compressed into the foundation what motivates and inspires them A simple[…]