Occam's Razor
SEPTEMBER 15, 2011
There is one difference between winners and losers when it comes to web analytics. Winners, well before they think data or tool, have a well structured Digital Marketing & Measurement Model. Losers don't. This article guides you in understanding the value of the Digital Marketing & Measurement Model (notice the repeated emphasis on Marketing, not just Measurement), and how to create one for yourself.
Edwin Chen
MARCH 13, 2011
Suppose you’re very indecisive, so whenever you want to watch a movie, you ask your friend Willow if she thinks you’ll like it. In order to answer, Willow first needs to figure out what movies you like, so you give her a bunch of movies and tell her whether you liked each one or not (i.e., you give her a labeled training set). Then, when you ask her if she thinks you’ll like movie X or not, she plays a 20 questions-like game with IMDB, asking questions like “Is X a romant
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Paul DeBeasi
JANUARY 27, 2011
Question: What do Cloud Computing, pervasive mobility, and TV white space have to do with each other? Answer: Read this blog post and find out! Virtually every customer conversation that I have these days is centered on the topic of mobility. Enterprise users want to communicate and collaborate anytime, anywhere, using any device. Most of my conversations focus on mobile device management, information security, and application architecture.
Nutanix
AUGUST 25, 2011
Gartner released a report in mid 2010 that they expected 50 million VDI desktops by 2013. Then there are the recent newsflashes surrounding VDI : Citrix buys Kaviza and RingCube.
Speaker: Timothy Chan, PhD., Head of Data Science
Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.
Occam's Razor
NOVEMBER 21, 2011
It is astonishingly common that we are asked to analyze the impossible. In perhaps a career-limiting move I'm going to try to do that today (and for a controversial topic to boot!). In this post about an important Google change, I want you to focus less on the data and focus more on the methodology. And – so important – I want you to help me with your ideas of how we can do this impossible analysis better, in the complete absence of data :).
Occam's Razor
OCTOBER 31, 2011
Data analysis is not easy. It takes years to get good at it, and once you get good at it you realize how much more there is to learn. That is part of the joy. You are always learning. You are always growing. This blogpost is a collection of tips I share with my friends who are just starting out. Each tip is a "simple" mistake that is easily avoided.
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Occam's Razor
APRIL 25, 2011
Every presentation I do is customized for the audience in the room. That means I get to spend loads and loads of time across many industry verticals, see many many campaigns, translate many many foreign websites (thanks Google Chrome for auto-translate!) and meet many many many executives and hear about their digital marketing strategies, challenges and outcomes.
Occam's Razor
AUGUST 22, 2011
Here you'll find all my blog posts categorized into a structure that will hopefully make it easy for you to discover new content, find answers to your questions, or simply wallow in some excellent analytics narratives. To assist with that process everything's organized into these sections: ~ Digital Marketing: "What is amazing out there?
Occam's Razor
AUGUST 8, 2011
99.9996253% of Web Analytics reports produced are utterly useless. Partly because of a lack of any tie to business strategy (ensure you have a Digital Marketing & Measurement Model !), partly because they are out of the box standard reports that web analytics vendors create for “average” people (and we both know that you are not average!), and partly because all they do is present data in the aggregate (a punishable criminal offence if there ever was one!).
Occam's Razor
JANUARY 24, 2011
Regular readers of this blog will recognize that I suffer from OOD. Outcomes Obsession Disorder. I am seeing a therapist for it. The way OOD manifests itself is that in every website and web business I work with I am obnoxiously persistent in helping identify the desired outcomes of the site / business before I ever log into their web analytics data.
Speaker: David Bard, Principal at VP Product Coaching
In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.
Occam's Razor
SEPTEMBER 19, 2011
With a plethora of digital media channels at our disposal and new ones on the way every day(!), how do you prioritize your efforts? How do you figure out which channels to invest in more and which to kill? How do you figure out if you are spending more money reaching the exact same current or prospective customers multiple times? How do you get over the frustration of having done attribution modeling and realizing that it is not even remotely the solution to your challenge of using multiple medi
Occam's Razor
MAY 16, 2011
My beloved little labor of love, this analytics blog, is 5 years old today. Five! I am so proud of having reached this incredible milestone, because when I started I was not sure I would make it to the first anniversary. Let me tell you how utterly improbable it seemed. My first blog post, on May 15th, 2006, was titled Traditional Web Analytics is Dead (let me emphasize the first word, traditional ).
Edwin Chen
JULY 17, 2011
Suppose you ask a bunch of users to rate a set of movies on a 0-100 scale. In classical factor analysis, you could then try to explain each movie and user in terms of a set of latent factors. For example, movies like Star Wars and Lord of the Rings might have strong associations with a latent science fiction and fantasy factor, and users who like Wall-E and Toy Story might have strong associations with a latent Pixar factor.
Edwin Chen
APRIL 26, 2011
How do you know what machine learning algorithm to choose for your classification problem? Of course, if you really care about accuracy, your best bet is to test out a couple different ones (making sure to try different parameters within each algorithm as well), and select the best one by cross-validation. But if you’re simply looking for a “good enough” algorithm for your problem, or a place to start, here are some general guidelines I’ve found to work well over the year
Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage
Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.
Edwin Chen
MARCH 13, 2011
Lots of people know that the Riemann Hypothesis has something to do with prime numbers, but most introductions fail to say what or why. I’ll try to give one angle of explanation. Layman’s Terms. Suppose you have a bunch of friends, each with an instrument that plays at a frequency equal to the imaginary part of a zero of the Riemann zeta function.
Edwin Chen
APRIL 24, 2011
Kickstarter is an online crowdfunding platform for launching creative projects. When starting a new project, project owners specify a deadline and the minimum amount of money they need to raise. They receive the money (less a transaction fee) only if they reach or exceed that minimum; otherwise, no money changes hands. What’s particularly fun about Kickstarter is that in contrast to that other microfinance site , Kickstarter projects don’t ask for loans; instead, patrons receive pre-
Edwin Chen
MARCH 13, 2011
I was playing around with the Hacker News database Ronnie Roller made (thanks!), so I thought I’d post some of the things I looked at. Activity on the Site. My first question was how activity on the site has increased over time. I looked at number of posts, points on posts, comments on posts, and number of users. Posts. This looks like a strong linear fit, with an increase of 292 posts every month.
Edwin Chen
MARCH 13, 2011
Measure theory studies ways of generalizing the notions of length/area/volume. Even in 2 dimensions, it might not be clear how to measure the area of the following fairly tame shape: much less the “area” of even weirder shapes in higher dimensions or different spaces entirely. For example, suppose you want to measure the length of a book (so that you can get a good sense of how long it takes to read).
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Edwin Chen
SEPTEMBER 6, 2011
tl;dr See this movie visualization for a case study on how a post propagates through Quora. How does information spread through a network? Much of Quora’s appeal, after all, lies in its social graph – and when you’ve got a network of users, all broadcasting their activities to their neighbors, information can cascade in multiple ways.
Edwin Chen
APRIL 30, 2011
(See a demo here.). While working on a Twitter sentiment analysis project, I ran into the problem of needing to filter out all non-English tweets. (Asking the Twitter API for English-only tweets doesn’t seem to work, as it nonetheless returns tweets in Spanish, Portuguese, Dutch, Russian, and a couple other languages.). Since I didn’t have any labeled data, I thought it would be fun to build an unsupervised language classifier.
Edwin Chen
MARCH 13, 2011
(Way back when, I went through all the Netflix prize papers. I’m now (very slowly) trying to clean up my notes and put them online. Eventually, I hope to have a more integrated tutorial, but here’s a rough draft for now.). This is a summary of Koren’s 2008 Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model.
Edwin Chen
FEBRUARY 14, 2011
Introduction. In making its product recommendations, Amazon makes heavy use of an item-to-item collaborative filtering approach. This essentially means that for each item X, Amazon builds a neighborhood of related items S(X); whenever you buy/look at an item, Amazon then recommends you items from that item’s neighborhood. That’s why when you sign in to Amazon and look at the front page, your recommendations are mostly of the form “You viewed… Customers who viewed this als
Speaker: Nicholas Zeisler, CX Strategist & Fractional CXO
The first step in a successful Customer Experience endeavor (or for that matter, any business proposition) is to find out what’s wrong. If you can’t identify it, you can’t fix it! 💡 That’s where the Voice of the Customer (VoC) comes in. Today, far too many brands do VoC simply because that’s what they think they’re supposed to do; that’s what all their competitors do.
Edwin Chen
APRIL 20, 2011
(For a layman’s introduction, see here.). Least Angle Regression (aka LARS) is a model selection method for linear regression (when you’re worried about overfitting or want your model to be easily interpretable). To motivate it, let’s consider some other model selection methods: Forward selection starts with no variables in the model, and at each step it adds to the model the variable with the most explanatory power, stopping if the explanatory power falls below some threshold.
Edwin Chen
APRIL 15, 2011
Introduction. Suppose you see two drunks (i.e., two random walks) wandering around. The drunks don’t know each other (they’re independent), so there’s no meaningful relationship between their paths. But suppose instead you have a drunk walking with her dog. This time there is a connection. What’s the nature of this connection?
Edwin Chen
MARCH 13, 2011
(Way back when, I went through all the Netflix prize papers. I’m now (very slowly) trying to clean up my notes and put them online. Eventually, I hope to have a more integrated tutorial, but here’s a rough draft for now.). This is a summary of Bell and Koren’s 2007 Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights paper. tl;dr This paper’s main innovation is deriving neighborhood weights by solving a least squares problem, instead of
Edwin Chen
MARCH 13, 2011
Given a set of datapoints, we often want to know how many clusters the datapoints form. The gap statistic and the prediction strength are two practical algorithms for choosing the number of clusters. Gap Statistic. The gap statistic algorithm works as follows: For each i from 1 up to some maximum number of clusters, Run a k-means algorithm on the original dataset to find i clusters, and sum the distance of all points from their cluster mean.
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Nutanix
OCTOBER 28, 2011
I’m privileged to be involved with disruptive technologies very early on, sometimes at a stage when it may be hard to fully see the impact.
Nutanix
OCTOBER 28, 2011
I’m privileged to be involved with disruptive technologies very early on, sometimes at a stage when it may be hard to fully see the impact.
Nutanix
SEPTEMBER 13, 2011
Back from our first vmworld on the heels of our launch on 8/16, here are some lessons we learned and a highlights video of our experience.
Nutanix
JUNE 29, 2011
Ever since we conceived Nutanix in September of 2009, we’ve been heads-down building the product and a business that have begun to hum
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