The fact that Google is looking for an alternative to 3rd Party HTTP Cookies isn’t such a surprise. The cookie retention curve has been under assault for a very long time. What is a surprise is that it made news. Google makes most of it’s money from advertising Google makes the most money from advertising. It’s a giant arbitrage play between you, your attention, and what advertisers want you to pay attention to. Google may collect a lot of data about many things, but the most important data is about you, and the versions of you expressed through browsers and operating systems. The HTTP cookie was an important source of that information for a long time. It’s been expanding it’s[…]
Author: Christopher Berry
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[…]
Someone asked, at the Digital Strategy Conference Vancouver, what the top 3 analytics tools were. Repeating the answer here: 3. The Bullet Point It’s a powerful communication device. Keep it short. Because people read short bulleted lists. 2. A statistical tool, like R or something else, that can tell you about the relationships among the variables. Understanding the relationships among variables is important for making many decisions. Most tools can’t see outside themselves; or tell you anything about the relationship among variables. R is free; others are not. 1. Your own brain. You use it to understand the world. You use it to make decisions. There is, as of yet, no substitute tool on the market for your own brain.
This is Big Data Week in Toronto. I’ll be delivering a case study on the business value of that data, but on a rather small, but beautifully complex, dataset on Monday. Big Data has now just become a marketing term. Those who have put in the effort, and read the three or four HBR articles on the subject, know more than 80% of the population. If you’ve read up on some of the applications involved, you’re ahead of 95%. If you read this, you’re ahead of 99.99% of the population. So, there’s an incentive to read on. What is Big Data? A good definition of Big Data is anything that is generally too big to fit in the memory of[…]
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[…]
We get these wonderful clues about people all the time. It’s easy to lose sight of the macroeconomic situation when you’re focused on the trees, or at least so many logs at the mill. Let’s take a look. You’re looking at consumer sentiment. It’s an index, where 1996 is set at 100. The gray bars are periods when the economy was in a technical recession. When this number is high, more consumers feel good about spending money. They feel like it’s a good time to buy a major household item. The think they’re more better off now than they were last year. They think the prospects for the next six months look good. They think things are looking up for[…]
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.[…]
The original intent of the D-LID project, the Design Lab for Interpreted Data, was to generate facts about the way different people interpreted digital analytics data. It was to be a website with a few treatments of the same dataset. Participants would be watched to see which treatments they found useful. With some help from Bayes, we’d put some hard core facts on the table about data design in the context of different audiences. We’d make the data available to members of the DAA for a year, and then open it up to the public thereafter. After it was all scoped out, the median estimated price tag was too much. In talking to partners, we got that figure down to[…]