Speaking at a data conference is hard. Programming data conferences is hard. It’s damn hard. It’s hard to predict who’ll show up in your audience. It’s hard to predict if what you’ve planned to say will align with your audience. It’s even harder to predict if who you’ve chosen to talk will align to who is likely to show up. Image below most certainly related. I’ve had incredibly patient mentors when it comes to this. And I’m still optimizing. And I still find it tough. Heuristics for speakers: Shilling doesn’t work You never close a sale during a presentation. Worse, you turn leads off. Putting your ad first puts the audience last. If it’s about causing awareness of your product[…]
Category: Analytics
Forrester’s own David Cooperstein wrote a tour de force in competitive strategy in the age of the consumer. If you have a subscription, it’s well worth a read. The high level summary is that the economy has evolved from competing on factory size, to competing on distribution, to competing on information, to now, competing on consumer. The way that competitive successes will be generated in the future, during this age of the consumer, is by competing on the consumer. You don’t have to buy the thesis to think about it. Here are a few points to consider: Sometimes it really is the thought that counts Identical items, packaged and marketed differently, cause difference in preference, loyalty, and retention in the[…]
A charrette is an intense, collaborative session, that enables designers to draft a solution to a very complex problem. It’s a technique first used by artists. Then designers picked it up. And then later still, urban planners. And then a few brave souls wisely invited stakeholders in on the process. Finally, this approach would evolve into software development and web development. It is very applicable to solving analytical problems. First, consider the natural law below. In analytics, the proportion of what we don’t know always grows as more knowledge is added. The more imaginative the analyst, the steeper the curve. Get three or more analysts into a room together[…]
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[…]
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.
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[…]
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[…]