Group items tagged

They throw into relief the larger questions of privacy policy we’re currently wrestling with as a culture: Too much disclosure of the social graph can lead to friends feeling that you’re tattling on them to a corporation. The interest graph can turn your passions into a marketing campaign. The behavior graph can allow people who wish you harm to know where you are and what you’re doing. And revealing the personal graph can make it feel like an outside entity is quite literally reading your mind.

companies are actively constructing these graphs already. These products and services are in the market today, but most in existence target only one or two of these graphs. Few are pursuing all four, both given the immaturity of the space and a lack of clear targets to shoot for. This has the unintentional effect of highlighting the risks of using such services, without demonstrating their benefits. For the potential of contextual computing to be realized, these data sets must be integrated.

In an ideal contextual computing state, this graph would be complete--so gentle nudges by software and services can bring together two people who are strangers but who could get along brilliantly and are in the same place at the same time. It could be two people who share a friend and who simultaneously move to Omaha, where neither person knows a soul.

It’s easy for data to depict what you actually do instead of what you claim to do. Sensors do the job. So do, if less elegantly, self-reporting mechanisms. This data can sit in pivotal contrast to the interest graph, allowing computers to know, perhaps better than you, how likely you are to go for a jog. It would be useful, too, for a travel site that notes how you tell friends you’d like to visit China but records that you only vacation in Europe. Rather than uselessly recommending vacation deals to Beijing, a smart travel app would instead feed you deals to Paris or Berlin. The behavior graph provides the foundation, to some extent, of Google Search, Netflix recommendations, Amazon recommendations, iTunes Genius, Nike+ run tracking, FourSquare, FitBit, and the entire "quantified self" movement. When mashed against the other three graphs, there’s a potential for real insight.

Within a decade, contextual computing will be the dominant paradigm in technology.

Graph Search also enables the user to search, for instance, for "photos of my friends taken in national parks" or "photos of my friends taken before 1990".

Tom Stocky, another Google import, showed what appeared to be a market researchers' dream tool: the new feature allows users to ask, for instance, what TV shows are most liked by doctors (Grey's Anatomy, House, The Doctors), or software engineers (Big Bang Theory).

A search for music liked by those who like Mitt Romney revealed Johnny Cash. Obama-likers liked Michael Jackson.

Brian Blau, who tracks social media for the tech research firm Gartner, said the service offered a brand new way for users to experience Facebook. Confined to Facebook's eco-system, the service was not an immediate threat to Google but would gradually increase in importance, he said. "In the future, you know Facebook will figure out how to monetize this. It's going to change the way people think about search."

When people say"page load time" for a website, they usually mean one of two measurements: "document complete" time or "fully rendered" time. Think of document complete time as the time it takes a page to load before you can start clicking or entering data. All the content might not be there yet, but you can interact with the page. Think of fully rendered time as the time it takes to download and display all images, advertisements, and analytic trackers. This is all the "background stuff" you see fill in as you're scrolling through a page.

Since Google was not clear on what page load time means, we examined both the effects of both document complete and fully rendered on search rankings. However our biggest surprise came from the lack of correlation of two key metrics! We expected, if anything, these 2 metrics would clearly have an impact on search ranking. However, our data shows no clear correlation between document complete or fully rendered times with search engine rank, as you can see in the graph below:

With no correlation between search ranking and what is traditionally thought of a "page load time" we expanded our search to the Time to First Byte (TTFB). This metric captures how long it takes your browser to receive the first byte of a response from a web server when you request a particular URL. In other words, this metric encompasses the network latency of sending your request to the web server, the amount of time the web server spent processing and generating a response, and amount of time it took to send the first byte of that response back from the server to your browser.

The TTFB result was surprising in a clear correlation was identified between decreasing search rank and increasing time to first byte. Sites that have a lower TTFB respond faster and have higher search result rankings than slower sites with a higher TTFB. Of all the data we captured, the TTFB metric had the strongest correlation effect, implying a high likelihood of some level of influence on search ranking.

The surprising result here was with the the median size of each web page, in bytes, relative to the search ranking position. By "page size," we mean all of the bytes that were downloaded to fully render the page, including all the images, ads, third party widgets, and fonts. When we graphed the median page size for each search rank position, we found a counterintuitive correlation of decreasing page size to decreasing page rank, with an anomalous dip in the top 3 ranks.

Our data shows there is no correlation between "page load time" (either document complete or fully rendered) and ranking on Google's search results page. This is true not only for generic searches (one or two keywords) but also for "long tail" searches (4 or 5 keywords) as well. We did not see websites with faster page load times ranking higher than websites with slower page load times in any consistent fashion. If Page Load Time is a factor in search engine rankings, it is being lost in the noise of other factors. We had hoped to see some correlation especially for generic one- or two-word queries. Our belief was that the high competition for generic searches would make smaller factors like page speed stand out more.

our data shows there is a correlation between lower time-to-first-byte (TTFB) metrics and higher search engine rankings. Websites with servers and back-end infrastructure that could quickly deliver web content had a higher search ranking than those that were slower. This means that, despite conventional wisdom, it is back-end website performance and not front-end website performance that directly impacts a website's search engine ranking.

We suspect over time, though, that page rendering time will also factor into rankings due to the high indication of the importance of user experience.

TTFB is affected by 3 factors: The network latency between a visitor and the server. How heavily loaded the web server is. How quickly the website's back end can generate the content.

Websites can lower network latency by utilizing Content Distribution Networks (CDNs). CDNs can quickly deliver content to all visitors, often regardless of geographic location, in a greatly accelerated manner.

Do these websites rank highly because they have better back-end infrastructure than other sites? Or do they need better back-end infrastructure to handle the load of ALREADY being ranked higher? While both are possible, our conclusion is that sites with faster back ends receive a higher rank, and not the other way around.

The back-end performance of a website directly impacts search engine ranking. The back end includes the web servers, their network connections, the use of CDNs, and the back-end application and database servers. Website owners should explore ways to improve their TTFB. This includes using CDNs, optimizing your application code, optimizing database queries, and ensuring you have fast and responsive web servers.

Fast websites have more visitors, who visit more pages, for longer period of times, who come back more often, and are more likely to purchase products or click ads. In short, faster websites make users happy, and happy users promote your website through linking and sharing. All of these things contribute to improving search engine rankings.

Share stories visually with photos and videos to grab users’ attention: Posts with photos receive 50 percent more likes.

Page targeting enables page admins to publish stories into the News Feeds of audiences who are going to be most interested in the content, without inundating those who may not.

Use engaging thumbnails for link stories: Links with thumbnails received 65 percent more likes and 50 percent more comments.

Vary your post type — users don’t engage the same way with every post: Mix it up between status updates, links, polls, and photos.

Optimize your page for Graph Search and mobile: Ensure that your page description is complete and up-to-date, which will help its performance in Graph Search results, and pin posts to ensure that users see the most important stories on both desktop and mobile.