Planning is preparation of the mind. It’s impossible to quantify every variable, every assumption, and every potential future state. Attempting to do so will simply boil the ocean and frustrate everybody around you. Analytics leaders tend to be very specific types of folk. Here are a few heuristics that might be useful for us in particular. Backcasting Backcasting is primarily an expression of preferences. The exercise almost always begins with an enunciation of a preferred, desirable, future state. Consider the following statement: “By 2016, we will be a 1 billion dollar company.” Such a statement, be it vision statements, stretch goals, or just goals, are typically not based on any sort of forecast. It’s entirely possible, and very likely, that[…]
Category: Analytics Strategy
Does it scale? That’s the number 1 question for analytics leadership in 2014. Three Trends Against Our Favour Devices are proliferating and their categorization is blurring. The neat division between desktop and cellphone has blurred into a continuum of browser based experiences and native experiences. This results in an acceleration of new interactions and instrumentation challenges. There are more people entering digital than are effectively trained in digital. Traditional areas of the economy are dying and people are following some of that money into digital. Normally this would be in our favor, but far more people are entering digital that have been, or can be, trained up in a reasonable period of time. It has been September for a long[…]
A Data Strategy is a set of choices that reinforce each other, that are difficult for competitors to replicate, and that generate a sustainable competitive advantage. A Data Strategy is set of choices about data. The benefits are the short-run outcomes. A sustained competitive advantage is a medium to long-run outcome. This post is about the alternatives and outcomes involved in a data strategy. Alternatives Alternatives can be really obvious. The most obvious ones involve fiddling with the settings on instruments that are available to you. In the face of falling intelligence, you can chose to increase the number of spreadsheets, or decrease the number of spreadsheets. And that’s really obvious. Because data, in most organizations, lives in spreadsheets. Less[…]
Are you a member of the Digital Analytics Association? Are you interested in Peer Reviewed Journals? Would you be interested in writing a review? Here’s a selection from the May Wave. Advertising and Consumers’ Communications (2013). The authors model how social media causes strategic considerations for identity brand marketers. Brand identities are tightly connected with market segments. Consumers can cause (unwanted) changes to those brand identities. It’s now long past cliche to say that web 2.0 caused brands to lose control. This paper puts forward some rigorous modelling to quantify those effects, and how the firm might respond. Economic Value of Celebrity Endorsements: Tiger Woods’ Impact on Sales of Nike Golf Balls. (2013) Evidence that celebrity endorsement increases actual sales, not just[…]
You may notice periodic sessions in the web analytics data, people visiting the website, looking at a few pages, and leaving, never to return. Or returning frequently, always checking the same pages. These are usually discount seekers, and there’s a data science niche building around them. Price interception is one of the biggest trends in big data science that we’re not telling you about. What’s driving it? Discount seekers exist in any market, but this segment grows when consumer sentiment is low. And there a whole bunch of new technologies designed to cause people feel good about bargain hunting, contributing to the growth, and likely establishment, of the sector. Many are building up an information advantage against firms, and using[…]
There are (at least) four reasons why analysts are picking python. For a decade now, I’ve relied on Python for simulation and data ETL, and I’ve depended on SPSS or R for data analysis. The reason for the two-step (and sometimes three if we include excel) is that there were no good libraries that could really replace SPSS or R completely. Scipy and numpy are excellent for operating on well formed arrays of data, but are decisively less efficient, from a user perspective, at handling data. Data frames, popularized by R, are finally available through Python through a package called PANDAS. And it’s a nice library. Scipy and numpy, two very popular libraries, are still out there in use too.[…]
Most figures I found, for the month of October 2012 (Including Mobile): Google’s search market share around 86 to 90% in the United States and 89% globally. Bing is ~7% in the US and ~5% globally. Yahoo 3% US / Baidu 3% globally (China). Search, as a design pattern requires, at minimum, a box into which you enter words or numbers, and a medium to display results, what is today called a Search Engine Results Page (SERP). (It doesn’t really have to be a page at all, which is why I use the word medium. Siri is a good example. Glass is another. That sort of thing.) The more places Google can put that box, the better it is for[…]
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[…]
Two trends, an exponential increase in data produced, and a linear increase in the number of analysts produced per quarter, continue pose a massive challenge to businesses and analytics practices alike. We need both physical technology and social technology to practice analytics at scale. There are three grouping of physical technologies: First, there’s instrumentation technology that we use to measure and record the world around us. Second, there’s analysis technology that we use to understand the data that’s coming. Third, there’s presentation technology that we use to communicate a world view, and what to do next. On the instrumentation technology side, we’ve all had a few challenges with instrumentation as of late. Specifically, the understanding of definitions, their impacts,[…]
A good twitter exchange with Evan Lapointe follows below. I’ve tried to put them into sequence, but admittedly, we were actively tweeting at one another in a cluster of ideas. I started tweeting Evan out of the blue, in part because of a podcast he was in, I initiated: CB: @evanlapointe You make good points. I’m skeptical that the person holding the ruler should also be responsible for generating the strategy EP: @cjpberry I wouldn’t say generating the strategy, necessarily, but conducting the orchestra once the music is written CB: @evanlapointe Yes. I agree. I make the distinction between measurement, convenient reasonsing, strategic analytics and marketing science. EP: @cjpberry we’re lucky if it’s only 4! CB: @EvanLapointe Measurement is straight[…]