I suspect I’m not alone in saying: I’ve never been a fan of the New Google Maps.
Saturday, July 12, 2014
The author’s views are entirely his or her own and may not reflect the views of Moz.
Many of us in the search industry were caught off guard by the release of Panda 4.0. It had become common knowledge that Panda was essentially "baked into" the algorithm now several times a month, so a pronounced refresh was a surprise. While the impact seemed reduced given that it coincided with other releases including a payday loans update and a potential manual penalty on Ebay, there were notable victims of the Panda 4.0 update which included major press release sites. Both Search Engine Land and Seer Interactive independently verified a profound traffic loss on major press release sites following the Panda 4.0 update. While we can't be certain that Google did not, perhaps, roll out a handful of simultaneous manual actions or perhaps these sites were impacted by the payday loans algo update, Panda remains the inference to the best explanation for their traffic losses.
So, what happened? Can we tease out why Press Release sites were seemingly singled out? Are they really that bad? And why are they particularly susceptible to the Panda algorithm? To answer this question, we must first address the main question: what is the Panda algorithm?Briefly: What is the Panda Algorithm?
The Panda algorithm was a ground-breaking shift in Google's methodology for addressing certain search quality issues. Using patented machine learning techniques, Google used real, human reviewers to determine the quality of a sample set of websites. We call this sample the "training set". Examples of the questions they were asked are below:Would you trust the information presented in this article?Is this article written by an expert or enthusiast who knows the topic well, or is it more shallow in nature?Does the site have duplicate, overlapping, or redundant articles on the same or similar topics with slightly different keyword variations?Would you be comfortable giving your credit card information to this site?Does this article have spelling, stylistic, or factual errors?Are the topics driven by genuine interests of readers of the site, or does the site generate content by attempting to guess what might rank well in search engines?Does the article provide original content or information, original reporting, original research, or original analysis?Does the page provide substantial value when compared to other pages in search results?How much quality control is done on content?Does the article describe both sides of a story?Is the site a recognized authority on its topic?Is the content mass-produced by or outsourced to a large number of creators, or spread across a large network of sites, so that individual pages or sites don't get as much attention or care?Was the article edited well, or does it appear sloppy or hastily produced?For a health related query, would you trust information from this site?Would you recognize this site as an authoritative source when mentioned by name?Does this article provide a complete or comprehensive description of the topic?Does this article contain insightful analysis or interesting information that is beyond obvious?Is this the sort of page you'd want to bookmark, share with a friend, or recommend?Does this article have an excessive amount of ads that distract from or interfere with the main content?Would you expect to see this article in a printed magazine, encyclopedia or book?Are the articles short, unsubstantial, or otherwise lacking in helpful specifics?Are the pages produced with great care and attention to detail vs. less attention to detail?Would users complain when they see pages from this site?
Once Google had these answers from real users, they built a list of variables that might potentially predict these answers, and applied their machine learning techniques to build a model of predicting low performance on these questions. For example, having an HTTPS version of your site might predict a high performance on the "trust with a credit card" question. This model could then be applied across their index as a whole, filtering out sites that would likely perform poorly on the questionnaire. This filter became known as the Panda algorithm.How do press release sites perform on these questions?
First, Moz has a great tutorial on running your own Panda questionnaire on your own website, which is useful not just for Panda but really any kind of user survey. The graphs and data in my analysis come from PandaRisk.com, though. Full disclosure, Virante, Inc., the company for which I work, owns PandaRisk. The graphs were built by averaging the results from several pages on each press release site, so they represent a sample of pages from each PR distributor.
So, let's dig in. In the interest of brevity, I have chosen to highlight just four of the major concerns that came from the surveys, question-by-question.Q1. Does this site contain insightful analysis?
Google wants to send users to web pages that are uniquely useful, not just unique and not just useful. Unfortunately, press release sites uniformly fail on this front. On average, only 50% of reviewers found that BusinessWire.com content contained insightful analysis. Compare this to Wikipedia, EDU and Government websites which, on average, score 84%, 79% and 94% respectively, and you can see why Google might choose not to favor their content.
Editor's note: Happy 4th of July! We're off observing our Independence Day, so we decided to celebrate with a non-SEO Whiteboard Friday.
From the undeniable class of a full windsor to the (all too common) mistake of letting our underwear become accidental outerwear, today's modern marketers are prone to some very easily solveable fashion faux-pas. On this Independence Day, we take a quick break from discussing the online world and bring you a whiteboard video on the lighter side. Enjoy!
For reference, here's a still of this week's whiteboard!
In recent years there has been a necessary shift in the way businesses advertise themselves to consumers, thanks to the increasingly common information overload experienced by the average person.
In 1945, just after WWII, the annual total ad spend in the United States was about $2.8 billion (that's around $36.8 million before the adjustment for inflation). In 2013, it was around $140 billion.
Don't forget that this is just paid media advertising; it doesn't include the many types of earned coverage like search, social, email, supermarket displays, direct mail and so on. Alongside the growth in media spends is a growth in the sheer volume of products available, which is made possible by increasingly sophisticated technologies for sales, inventory, delivery and so on.
What does this mean? Well, simply that the strategy of 'just buy some ads and sell the benefits' isn't enough anymore: you'll be lost in the noise. How can a brand retain customers and create loyalty in an atmosphere where everyone else has a better offer? Through tapping into the psychology of social relationships.
Imagine that you are at home for Thanksgiving, and your mother has pulled out all the stops to lovingly craft the most delicious, intricate dinner ever known to man. You and your family have enjoyed a wonderful afternoon of socializing and snacking on leftovers and watching football, and now it's time to leave. As you hug your parents goodbye, you take out your wallet. "How much do I owe you for all the love and time you put into this wonderful afternoon?" you ask. "$100 for the food? here, have $50 more as a thank you for the great hospitality!" How would your mother respond to such an offer? I don't know about your mother, but my mom would be deeply offended.
New scenario: You've gone to a restaurant for Thanksgiving dinner. It's the most delicious dinner you've ever had, the atmosphere is great with the football playing in the background, and best of all, your server is attentive, warm, and maternal. You feel right at home. At the end of the meal, you give her a hug and thank her for the delicious meal before leaving. She calls the cops and has you arrested for a dine-and-dash.
And herein lies the difference between social norms and market norms.Social norms vs. market norms
The Thanksgiving dinner example is one which I've borrowed from a book by Dan Ariely, Predictably Irrational: The Hidden Forces that Shape Our Decisions. Ariely discusses two ways in which humans interact: social norms and market norms.
Social norms, as Ariely explains, "are wrapped up in our social nature and our need for community. They are usually warm and fuzzy. Instant paybacks are not required." Examples would be: helping a friend move house, babysitting your grandchild, having your parents over for dinner. There is an implied reciprocity on some level but it is not instantaneous nor is it expected that the action will be repaid on a financial level. These are the sort of relationships and interactions we expect to have with friends and family.
Market norms, on the other hand, are about the exchange of resources and in particular, money. Examples of this type of interaction would be any type of business transaction where goods or services are exchanged for money: wages, prices, rents, interest, and cost-and-benefit. These are the sort of relationships and interactions we expect to have with businesses.
I've drawn you a very rough illustration - it may not be the most aesthetically pleasing visual, but it gets the point across:
Friday, July 11, 2014
Having built an online business during the dot-com boom and bust, I’ve always been a bit skeptical about the mobile revolution. Every year since the late 90s, we’ve heard that this would be
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On June 25, 2014, Google's John Mueller made a shocking announcement: Google would be removing all author photos from Google search results. According to the MozCast Feature Graph, that task was fully accomplished by June 29.
In this post I will:Give a brief overview of how Google Authorship got to where it is today.Cover how Google Authorship now works and appears in search.Offer my take on why Author photos were removedInvestigate the oft-repeated claims of higher CTR from author photosSuggest why Google Authorship is still important, and speculate on the future of author authority in Google Search.A Brief History of Google Authorship
The Google Authorship program has been my wheelhouse (some might say "obsession") since Google first announced support for Authorship markup in June of 2011. Since I am both an SEO and a content creator, Google certainly got my attention in that announcement when they said, "...we’re looking closely at ways this markup could help us highlight authors and rank search results."
Of course, in the three years since that blog post, many search-aware marketers and content creators also jumped on the Google Authorship bandwagon. Occasional comments from prominent Google staffers that they might someday use author data as a search ranking factor, along with Bill Slawski's lucid explanations of the Google Agent Rank patent, fueled the fire of what most came to call "author rank."
Below is a video from 2011 with Matt Cutts and Othar Hansson explaining the possible significance of Authorship markup for Google at that time:
During the three years since Google announced support for rel
We all know that keywords and links alone no longer cut it as a holistic SEO strategy. But there's still plenty outside our field who try to "boil SEO down" to a naively simplistic practice - one that isn't representative of what SEOs need to do to succeed. In today's Whiteboard Friday, Rand champions the art and science of SEO and offers insight into how very broad the field really is.
For reference, here's a still of this week's whiteboard!