Panasonic to buy Blue Yonder for $6.5 billion in biggest deal since 2011: Nikkei

DataFloq

billion), the Nikkei reported on Monday, saying it was the Japanese electronics firm's biggest acquisition since 2011. TOKYO (Reuters) - Panasonic Corp will buy U.S. software firm Blue Yonder for 700 billion yen ($6.45

Best Social Media Metrics: Conversation, Amplification, Applause, Economic Value

Occam's Razor

I am going to break one of my unspoken cardinal rules: Only write about real problems and measurement that is actually possible in the real world. I am going to break the second part of the rule. I am going to define a way for you to think about measuring social media, and you can't actually easily measure what I am going to recommend. Update: Please see update #2 below, you can now easily measure what's recommended in this post.]. So why break the rule?

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Digital Marketing and Measurement Model

Occam's Razor

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. At the end you'll also find some additional examples to inspire you.

Best Web Metrics / KPIs for a Small, Medium or Large Sized Business

Occam's Razor

We have access to more data than God wants anyone to have. Thus it is not surprising that we feel overwhelmed, and rather than being data driven we just get paralyzed. Life does not have to be that scary. In fact a data driven life is sexiest digital life you can imagine. In this blog post we are going to bring the sexyback.

Smarter Data Analysis of Google's https (not provided) change: 5 Steps

Occam's Razor

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 :).

Email Marketing: Campaign Analysis, Metrics, Best Practices

Occam's Razor

With all the sexiness oozing out of social media it might seem insane to write about email. It’s been relegated to the “OMG that cesspool of spam that no one cares about because everyone is using Google Wave and Facebook!”. Not true. Email remains an immensely credible and profitable channel, with an immense reach to boot. To not have it as an active part of your marketing portfolio is sub-optimal.

Data Analysis 101: Seven Simple Mistakes That Limit Your Salary

Occam's Razor

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. My hope is that you'll skip them if you are aware of them, and move on to making more important valuable mistakes. :).

Your Web Metrics: Super Lame or Super Awesome?

Occam's Razor

By 2011 as web experiences have become richer and more frequently and more complex I am so mad that our life is not dominated by pan-session metrics. Web Analysts are blessed with an immense amount of data, and an amazing amount of valuable, even sexy, metrics to understand business performance. Yet our heroic efforts to report the aforementioned sexy metrics lead to little business action. Sure your organization could be to blame ( org structure, bad boss , ineffectual team ).

11 Digital Marketing “Crimes Against Humanity”

Occam's Razor

" I'd postulated this rule in 2005, it is even more true in 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.

Knowledge

Occam's Razor

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? How can my company become great?" " ~ Digital Analytics: "Am I thinking right? How do I crush tough problems? Be data driven?"

Produce Actionable Insights: Mate Custom Reports With Adv Segments!

Occam's Razor

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!).

Web Analytics Career Guide: From Zero To Hero In Five Steps!

Occam's Razor

I got an email the other day with this simple question: "How do break into the world of web analytics?" " Actually I get that question almost every single day. :). The interest is not surprising. There is a ton of excitement about web analytics.

Measuring Incrementality: Controlled Experiments to the Rescue!

Occam's Razor

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?

The Difference Between Web Reporting And Web Analysis

Occam's Razor

Someone asked me this very simple question today. What's the difference between web reporting and web analysis? My instinct was to use the wry observation uttered by US Supreme Court Justice Potter Stewart in trying to define po rn: " I know it when I see it. " " That applies to what is analysis. I know it when I see it. : ). That, of course, would have been an unhelpful answer.

Excellent Analytics Tips #19: Identify Website Goal [Economic] Values

Occam's Razor

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.

I Wish I'd Known That. [Digital Analytics Edition.]

Occam's Razor

Let's start off the new year with lessons learned from a tough life on the front lines of trying to make the world a smidgen more data-driven. This post is a collection of six things I wish I knew before I started my career in decision support systems (of which web analytics is just the latest incarnation). These lessons might have made some goals easier to accomplish, some frustrations easier to avoid and some salary jumps easier to come by.

Mobile Analytics: Tracking Click-to-Call Mobile Ad Campaigns

Occam's Razor

Just when you thought you were finally getting more comfortable with website analytics and the metrics you report, here comes the massive explosion of mobile data! At one level it is the normal impressions and clicks data, but on another level we are getting new data and metrics we normally don't use. We are going to have fun doing cool stuff, learning new things.

An Incredible Analytics Experience: 5 Years of Occam’s Razor

Occam's Razor

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 ). Per my plan, I wrote two posts a week.

Web Analytics: Frequently Asked Questions And Direct Answers

Occam's Razor

After 416,350 words in posts and 845,128 words in comments on this blog, thus far, there is always more to explore, illuminate and share. Hence every once in a while I flip the tables and ask you for challenges you are facing. It is a great way to stay connected to what's most important to you (and keep the blog and its content relevant!). This past Monday I asked for your questions and you were kind enough to share some awesome questions. Thank you.

Three Amazing Web Data Analyses Techniques For Analysis Ninjas

Occam's Razor

Day in and day out we stare at standard tables and rows and convert them into smaller or scarier tables and rows and through analysis we try and move the really heavy beast called the "organization" into action. It is hard. This blog post has three ideas I've learned from other smart people, ideas that help surprise the "organization" with something non-normal and get it to take action.

The Market Motive Master Certification Manifesto: Web Analytics

Occam's Razor

Many of you are aware that I am the co-Founder of Market Motive, a delightful little labor of love whose mission in life is to provide bleeding edge education via quarterly, what we call, Master Certification courses. There are seven courses in all: SEO, PPC, Social Media, Web Analytics, Conversion Optimization, Marketing Fundamentals and Online PR. Each course is taught by a world class expert who passionately loves teaching. It really is a fun group.

Introduction to Latent Dirichlet Allocation

Edwin Chen

Introduction. Suppose you have the following set of sentences: I like to eat broccoli and bananas. I ate a banana and spinach smoothie for breakfast. Chinchillas and kittens are cute. My sister adopted a kitten yesterday. Look at this cute hamster munching on a piece of broccoli. What is latent Dirichlet allocation? It’s a way of automatically discovering topics that these sentences contain. For example, given these sentences and asked for 2 topics, LDA might produce something like.

Choosing a Machine Learning Classifier

Edwin Chen

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.

Introduction to Restricted Boltzmann Machines

Edwin Chen

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.

Cloud Computing, Pervasive Mobility, and TV White Space

Paul DeBeasi

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. Rarely does someone ask me about the wireless network.

Winning the Netflix Prize: A Summary

Edwin Chen

How was the Netflix Prize won? I went through a lot of the Netflix Prize papers a couple years ago, so I’ll try to give an overview of the techniques that went into the winning solution here. Normalization of Global Effects. Suppose Alice rates Inception 4 stars. We can think of this rating as composed of several parts: A baseline rating (e.g., maybe the mean over all user-movie ratings is 3.1 stars). An Alice-specific effect (e.g.,

Layman's Introduction to Random Forests

Edwin Chen

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).

Prime Numbers and the Riemann Zeta Function

Edwin Chen

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.

IT 48

Netflix Prize Summary: Factorization Meets the Neighborhood

Edwin Chen

(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. There are two approaches to collaborative filtering: neighborhood methods and latent factor models.

Information Transmission in a Social Network: Dissecting the Spread of a Quora Post

Edwin Chen

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. How do these social designs affect which users see what? Think, for example, of what happens when your kid learns a new slang word at school.

Topic Modeling the Sarah Palin Emails

Edwin Chen

LDA-based Email Browser. Earlier this month, several thousand emails from Sarah Palin’s time as governor of Alaska were released. The emails weren’t organized in any fashion, though, so to make them easier to browse, I’ve been working on some topic modeling (in particular, using latent Dirichlet allocation) to separate the documents into different groups. I threw up a simple demo app to view the organized documents here. What is Latent Dirichlet Allocation?

Kickstarter Data Analysis: Success and Pricing

Edwin Chen

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.

IT 41

Filtering for English Tweets: Unsupervised Language Detection on Twitter

Edwin Chen

(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.

Hacker News Analysis

Edwin Chen

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. Comments. For comments, I fit a quadratic regression: Points.

Layman's Introduction to Measure Theory

Edwin Chen

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). What’s a good measure?

A Mathematical Introduction to Least Angle Regression

Edwin Chen

(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.

Introduction to Cointegration and Pairs Trading

Edwin Chen

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?

Topological Combinatorics and the Evasiveness Conjecture

Edwin Chen

The Kahn, Saks, and Sturtevant approach to the Evasiveness Conjecture (see the original paper here ) is an epic application of pure mathematics to computer science. I’ll give an overview of the approach here, and probably try to add some more information on the problem in other posts. tl;dr The KSS approach provides an algebraic-topological attack to a combinatorial hypothesis, and reduces a graph complexity problem to a problem of contractibility and (not) finding fixed points.

IT 40

Netflix Prize Summary: Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights

Edwin Chen

(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.

Counting Clusters

Edwin Chen

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. Call this sum the dispersion.

Item-to-Item Collaborative Filtering with Amazon's Recommendation System

Edwin Chen

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.

Convergence with Scott Adams

Nutanix

Converged architectures coming out of force-fit consortiums are just that — expensive toxic blobs

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VDI Series: Part 3 – Incremental Scalability

Nutanix

The Nutanix architecture, built ground-up for virtualization, is a perfect fit for the unique performance needs of VDI

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